Adela Katharine Devlin - Goldsmiths, University of Londondoc.gold.ac.uk/~mas01kd/publications/devlin_thesis.pdf · Adela Katharine Devlin. ... and, in his bid for world domination,

Perceptual fidelity for digital image display

Adela Katharine Devlin

A thesis submitted to the University of Bristol, UK in accordance with the requirements for thedegree of Doctor of Philosophy in the Faculty of Engineering, Department of Computer Science.

2004

c. 32 000 words

Abstract

Many applications require that the original version of an image will appear the same regardlessof where or how it is displayed. However, the conditions in which an image is displayed canadversely affect its appearance. Computer monitor screens not only emit light, but can also reflectextraneous light present in the viewing environment. This can cause images displayed on a monitorto appear faded by reducing their perceived contrast. Current approaches to this problem involvemeasuring this ambient illumination with specialised hardware, then altering the display device orchanging the viewing conditions. This is not only impractical, but also costly and time consuming.For a user who does not have the equipment, expertise or budget to control these facets of imagedisplay, a practical alternative is sought. This thesis presents a method whereby the display deviceitself can be used to determine the effect of ambient light on perceived contrast, thus enabling theviewers themselves to perform visually-based calibration. This method is grounded in establishedpsychophysical experimentation, and we present both an extensive procedure and an equivalentrapid procedure. Our work is extended by providing a novel method of contrast correction sothat the contrast of an image viewed in bright conditions can be corrected to appear the same asan image viewed in a darkened room. This is verified through formal psychophysical validationstudies. These methods and algorithms are easy to apply in practical settings, while accurateenough to be useful.

Declaration

The work in this thesis is original and no portion of the work referred to here has been submittedin support of an application for another degree or qualification of this or any other university orinstitution of learning.

Signed: Date:

Adela Katharine Devlin

Acknowledgements

This work was funded for the most part by EPSRC Student CASE Award 00314469 in conjunctionwith the Defence Evaluation and Research Agency.

First, thanks to Alan Chalmers for seeing this PhD through from start to finish, and for takingme to so many fantastic places along the way. There’ve been some ups and downs, but I hopewe’ve ended on an up! The whole of the infinite number of Ph.D. students in the Graphics Groupalso deserve thanks, especially Patrick, Pete and Ki without whom I would never have survivedthe final night in Saarbrucken. To those I’ve met over conference beers, cheers! (That’s got toinclude the Acknowledgement Tart, Greg Ward.) Much appreciation to those who have sharedadvice, especially Tom Troscianko. Many, many thanks indeed to Erik Reinhard who has kept upthe encouragement and advice and made conferences even more fun than usual. His knowledgeand enthusiasm has proved invaluable, and I thank him for his contributions and his friendship.

Big shout out to the lunchtime posse — the highly insecure (what was that password again?) cryptogroup and the downwardly-mobile wearable team. Alphabetically, sort of: Amoss and his luckycharms, Dan ‘cyberdolphin’ Page, Fre Vercauteren, Martijn and his antimatter flapjacks, Matt‘she’s-not-that-young’ Baldwin, Mike ‘aboot’ McCarthy, Paul ‘oooh-yeah’ Duff, Rich ‘I’m not anaggressive person’ Noad, and, in his bid for world domination, Nigel Smart. For coffee-drinkingsupport and general bitchin’, I thank you. To the cinema-goers, music-lovers, pub drinkers andindecisive diners (that’d be Barry, Eric, and Tim, ably co-ordinated by Peter ‘babe’ Flach), cheers!Indeed, much appreciation to everyone who has made my time in the department so damn enjoy-able (including Chris, Mike and Nige who had to put up with me in their office).

Sarah, Stef, Dave and Angus — there were moments when climbing (and alcohol) was all thatkept me going, and you guys were there, wielding wine bottles and holding the rope (except forAngus who was having difficulty with the 6a sitting start off of the sofa, but who more than madeup for it with the alcohol).

Special thanks to the House of Dysfunction and all who play in her: Antti, Mary, Carl, and the-best-flatmate-ever, Genevieve. Much gratitude to the Mudcatters for online and real-life music,and for putting up with my repertoire of badly-fiddled polkas. Not forgetting the gurls: Chrisand Helen — yay! Love yez babes. Also, my Northbrook buddies deserve (platonic) lurve forpacking me off to Bristol with fond memories of awful hangovers and an unsurpassable roadtripto Memphis: Whoremonger, Needledick and Nicegirlsorcha — now I’ve got time to write thatnovel.

To those who’ve been with me all the way and supported my bid to be an Eternal Student — memammy, me da, and our Louise — yous are the best family I’ve ever had.

These acknowledgements are in no order of preference, except for this: Henk Muller, my bestmate, thank you for so much, including saving my life literally and metaphorically on severaloccasions. vfw.

To Fintan, who’s always asking “what did I send you to university for?”.

Contents

List of Figures vi

List of Tables x

1 Introduction 1

1.1 Contributions of this thesis . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Thesis outline . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Application: virtual heritage .. . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3.1 Captured images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.2 Rendered representations. . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.3 Consistency in delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2 Background 19

2.1 Light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.1.1 Radiometry and photometry . . .. . . . . . . . . . . . . . . . . . . . . 20

2.1.2 Light propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2 Visual perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.1 The human eye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.2 Visual sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

i

2.2.3 Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2.4 Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2.5 The Contrast Sensitivity Function . . . . . . . . . . . . . . . . . . . . . 28

2.2.6 Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.2.7 Brightness perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2.8 Lightness and colour constancy . . . . . . . . . . . . . . . . . . . . . . 31

2.3 Digital image creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.3.1 Capturing digital images . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.3.2 Generating digital images . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.4 Display technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.4.1 Cathode Ray Tubes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4.2 Liquid Crystal Displays . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.3 Plasma Display Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.5 Controlling the display . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.5.1 Gamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.5.2 Tone reproduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.5.3 Gamut mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3 The viewing environment 57

3.1 The influences of ambient illumination. . . . . . . . . . . . . . . . . . . . . . . 57

3.2 Accounting for viewing conditions . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.2.1 Physical alterations to the hardware . . . . . . . . . . . . . . . . . . . . 59

3.2.2 Viewing environment standards . . . . . . . . . . . . . . . . . . . . . . 60

ii

3.2.3 Measurement and image correction. . . . . . . . . . . . . . . . . . . . 61

3.3 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.3.1 Ergonomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.3.2 Medical imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4 Measuring reflected ambient light 69

4.1 Conducting experiments . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.1.1 Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.1.2 Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1.3 Sample design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1.4 Pilot studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.1.5 Problems with psychophysics and statistical significance. . . . . . . . . 73

4.2 Experiment 1: contrast discrimination thresholds . . . . . . . . . . . . . . . . . 74

4.2.1 Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.2.2 Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.2.3 Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.2.4 Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.2.5 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.2.6 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.3 Experiment 2: rapid characterisation . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3.1 Alternative and pilot experiments . . . . . . . . . . . . . . . . . . . . . 84

4.3.2 Main experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.3.3 Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

iii

4.3.4 Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.3.5 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.3.6 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5 Correcting for Ambient Light 95

5.1 Contrast adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.2 Luminance remapping requirements . . . . . . . . . . . . . . . . . . . . . . . . 96

5.3 Existing remapping methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.3.1 Gamma manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.3.2 Hyperbolic functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.3.3 Histogram equalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.3.4 Spatially varying techniques . . . . . . . . . . . . . . . . . . . . . . . . 99

5.4 Schlick’s rational function as a basis for remapping . . . . . . . . . . . . . . . . 99

5.5 A new luminance remapping algorithm. . . . . . . . . . . . . . . . . . . . . . . 101

5.5.1 The range[0;L] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.5.2 The range[L;M] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.5.3 Complete remapping function . . . . . . . . . . . . . . . . . . . . . . . 103

5.5.4 Function inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.5.5 Colour space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.6 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

6 Validation of luminance remapping 111

iv

6.1 Validation experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

6.1.1 Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6.1.2 Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6.1.3 Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.1.4 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

7 Conclusions 117

7.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

7.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

7.3 Further research . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

7.4 Closing remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Bibliography 122

A Materials 137

A.1 Experimental Informed Consent Form . . . . . . . . . . . . . . . . . . . . . . . 138

A.2 Instructions for Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

A.3 Instructions for Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

B Results 143

v

vi

List of Figures

1.1 Example of a ‘washed out’ image . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Virtual heritage: system diagram. . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Experimental archaeology: physical reconstruction of fuel types.. . . . . . . . . 9

1.4 Simulations: differences in lighting . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.5 Examples of medieval pottery. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.6 Medieval house simulations . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.7 The House of the Vettii today .. . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.8 Simulation of the House of the Vettii . . .. . . . . . . . . . . . . . . . . . . . . 14

1.9 Simulation with the inclusion of furniture . . . . . . . . . . . . . . . . . . . . . 15

1.10 Cap Blanc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.1 The electromagnetic spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2 The Luminous Efficiency Curve. . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.3 Specular and diffuse reflections . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4 A schematic section through the human eye. . . . . . . . . . . . . . . . . . . . 25

2.5 Weber’s law: JND measurement. . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.6 Threshold versus intensity function . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.7 The Campbell-Robson sensitivity chart and the contrast sensitivity function . . . 29

vii

2.8 Plot of the Stevens’ Power Law . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.9 Simplified schematic diagram of a CRT . . . . . . . . . . . . . . . . . . . . . . 34

2.10 Gamma values for a CRT monitor . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.11 Example of test patterns for gamma measurement. . . . . . . . . . . . . . . . . 39

2.12 Image used for simple gamma correction . . . . . . . . . . . . . . . . . . . . . . 40

2.13 A comparative view of dynamic range.. . . . . . . . . . . . . . . . . . . . . . . 41

2.14 Ideal tone reproduction process. . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.15 Linear scaling versus tone reproduction.. . . . . . . . . . . . . . . . . . . . . . 42

2.16 Example of dynamic range extent using varying exposures.. . . . . . . . . . . . 43

2.17 Chromaticity diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.1 ICC colour management architecture . . . . . . . . . . . . . . . . . . . . . . . . 63

4.1 Example of the set up for thelight condition . . . . . . . . . . . . . . . . . . . . 76

4.2 Example stimulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.3 The staircase method . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.4 Flowchart showing procedure for Experiment 1. . . . . . . . . . . . . . . . . . . 81

4.5 Simplified measurement using a Campbell-Robson chart .. . . . . . . . . . . . 85

4.6 A type of gamma chart used to measure contrast discrimination . . .. . . . . . . 86

4.7 Grid of squares used for simplified characterisation. . . . . . . . . . . . . . . . . 88

4.8 Flowchart showing procedure for Experiment 2. . . . . . . . . . . . . . . . . . . 90

4.9 Experiment 2: average JND values . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.1 Problems with remapping by subtraction . . . . . . . . . . . . . . . . . . . . . . 96

5.2 Splitting the remapping function into two ranges. . . . . . . . . . . . . . . . . . 101

viii

5.3 Remapping functions forLR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.4 Remapping results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.5 Comparison with other techniques . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.1 Validation experiment: average JND values . . . . . . . . . . . . . . . . . . . . 114

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List of Tables

2.1 Radiometric and photometric measurements. . . . . . . . . . . . . . . . . . . . 22

2.2 Display technology comparison. . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.1 Typical lighting recommendation for offices . . . . . . . . . . . . . . . . . . . . 59

4.1 Example of RGB values used in bit-stealing. . . . . . . . . . . . . . . . . . . . 78

4.2 Experiment 1: average JND results, pedestal value = 5% grey . . . . . . . . . . . 81

4.3 Experiment 1: average JND results, pedestal value = 10% grey . . . . . . . . . . 82

4.4 Experiment 1: average JND results, pedestal value = 20% grey . . . . . . . . . . 82

5.1 Requirements for a luminance remapping function . . . . . . . . . . . . . . . . . 97

B.1 Experiment 1: average JND results, pedestal value = 5% grey . . . . . . . . . . . 144

B.2 Experiment 1: average JND results, pedestal value = 10% grey . . . . . . . . . . 144


B.4 Experiment 2: average JND results, pedestal value = 5% grey . . . . . . . . . . . 145



B.7 Validation experiment: average JND results . . . . . . . . . . . . . . . . . . . . 148

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Chapter 1

Introduction

Many applications that use electronic display devices require images to appear a certain way. In ar-

eas as diverse as medical imaging [AKK+82, BRN82, RJP87, Nat03], aviation [Fed00], visualisa-

tion [War00], photography [Hun96], and predictive lighting and realistic image synthesis [AF95],

similarity is desirable between the image as it was created and the resultant image that is viewed

by the end-user. The user must be confident that the image they are viewing is faithful to the orig-

inal — they requireperceptual fidelity. However, a given image will not always be perceived in

the same way. Problems may arise because the sequence of events from image creation to percep-

tion is open to adverse influence, which can result in an image that deviates from the way it was

intended to look. As images are often displayed on different monitors and in different locations

from where they were created (such as images displayed over a network, or on the Internet), it is

necessary to ensure that steps have been taken to ensure perceptual consistency, where any point

in an image will look the same regardless of changes in viewing location and display device. To

ensure that the scene as it was created closely resembles the scene as it is displayed, it is necessary

to be aware of any factors that might adversely influence the display medium.

A digital image goes through a sequence of processing before it is ultimately displayed on the

screen of a visual display unit (VDU). Fidelity in modelling or capturing a scene, the use of pre-

dictive lighting software (in the case of computer-generated images), the use of tone reproduction

methods and gamma correction, all go towards achieving perceptual accuracy. However, further

1

2

to the adjustments to the actual image, the processes that occurafter the luminances are displayed

on screen andbeforethey reach the retina must also be considered. This is a physical problem

with a direct perceptual impact.

For certain applications, such as trade or industry where a direct match between a displayed design

and the resulting product is essential, it is likely that a specific viewing environment exists, and full

calibration of all equipment has occurred. However, there are other fields where it is not possible

to guarantee the fidelity of a displayed image. This may be due to lack of equipment, facilities or

cost. Nonetheless, in these circumstances the user may wish to ensure that they have taken any

possible steps within their measure towards perceptual fidelity.

One example of an area where perceptual consistency between images is important is in cultural

heritage applications. In terms of cultural heritage, computer graphics has enabled the capturing

and creation of images that can be used as perceptually equivalent representations of an orig-

inal [DC01, CD02, DCB02]. Virtual reality and visualisation techniques can provide a highly

detailed model of a site or artefact. Improvements in scanning and digital photography have led

to the widespread use of this technology to preserve original text and art. For digital archiving

to be used as a technique for representation or preservation, the integrity of an image must be

vouchsafed [DCR04].

The need to exert control over image presentation has given rise to standards and guidelines con-

cerning digital display and ergonomics, such as the guidelines of the UK’s Arts and Humanities

Data Service (AHDS) [Art] and the International Organization for Standardization (ISO) [Int].

In addition, museums, libraries and archives using digital images are aware of the problems of

inconsistencies in image display. Reilly and Frey’s report to the American Library of Congress

highlighted the differences between images when viewed on different systems or monitors, with

Library staff finding it problematic ‘when discussing the quality of scans with vendors over the

telephone, because the two parties did not see the same image’ [RF96].

Some institutions do address these factors. The Bodleian Library’s online image catalogue at the

University of Oxford states:

3

Note that the apparent quality of the images as viewed on-screen is in part dependent

upon the quality of the monitor used to view them, and the apparent colour-values

are likewise dependent on whether the monitor has been correctly calibrated, and the

ambient lighting conditions of the room. [Bod].

This thesis investigates a potential influence on perceptual fidelity: the lighting in the viewing

environment, and in particular, reflections caused by the average amount of light present in a

room — theambient illumination. It argues that the presence of ambient light in the viewing

environment has an adverse effect on the user’s perception of an image, and that this effect must

be characterised and corrected in order to adhere to perceptual fidelity. It has been suggested

that ambient light can cause a reduction in the perceived contrast of an image displayed on a

CRT screen, causing an image viewed under a high level of ambient light to appear ‘washed

out’ [Gla95, Tra91, War00]. An example of this is given in Figure 1.1 where the same image

is shown as it appears when displayed in a room with no ambient light present (top), and when

displayed under illumination by a D65 light bulb (bottom).

While the presence of such illumination may have a detrimental effect on image appearance, many

working conditions require a certain level of illumination in a room, to enable note-taking, for

example. Therefore, the extraneous illumination cannot simply be removed, but rather should be

accounted for in some way. Current approaches to this problem involve measuring the ambient

illumination with specialised hardware, and altering the display device or changing the viewing

conditions. Measuring the amount of ambient illumination in an environment is possible through

the use of specialised equipment, such as a photometer, spectroradiometer or illuminance meter.

However, this method requires additional hardware — an extra expense and impractical to acquire

— and the knowledge to use this hardware. Moreover, this equipment measures the physical value

of the light present in the viewing environment rather than its perceptual impact.

In this thesis we present a method, based on an experimental framework, whereby the display

device itself can be used to determine the level of ambient illumination affecting an image. We

provide a method of contrast correction to alter the perceived contrast, so that an image viewed in

bright conditions appears the same as an image viewed in a darkened room. This work is tested

1.1 Contributions of this thesis 4

Figure 1.1: Example of a ‘washed out’ image. The presence of extraneous light in the viewingenvironment can reduce the perceived contrast of an image (bottom image) compared to an imagedisplayed in darkness (top image). For completeness, the full screen shot is shown as an inset.

through validation studies. These methods are simple, inexpensive, and require no additional

hardware. They are aimed at users who do not have appropriate equipment or facilities that ensure

accurate display, and therefore our methods are can be seen as a necessary bridge between a lack

of display quality control and a high-cost rigidly-calibrated system.

1.1 Contributions of this thesis

� We produce a wide-ranging literature review, introducing relevant terminology, discussing

pertinent aspects of visual perception, and examining issues regarding image creation and

1.2 Thesis outline 5

display.

� We present an experimental framework that assesses the effect of ambient light on image

perception, using validated psychophysical approaches.

� We hypothesise that reflected ambient illumination affects perceived contrast, and obtain

statistical evidence through our experiments to support this theory.

� We develop a form of rapid visual self-calibration to enable the measurement of ambient

light without the need for specialised equipment or external hardware.

� We present one possible algorithmic form of correction to compensate for ambient reflec-

tions. Its success is validated through a formal psychophysical user study.

1.2 Thesis outline

The remainder of this chapter gives an example application where perceptual fidelity in image dis-

play is desirable and outlines the process of image creation and viewing. The subsequent chapters

are divided as follows:

Chapter 2: Background Chapter 2 provides fundamental information on digital image display,

beginning with the terminology of light and its properties. Aspects of the human visual

system pertaining to the perception of displayed images are examined. In addition, it focuses

on the display of digital images, from their creation or capture and the technology used to

display them, through to techniques used to control the appearance of the displayed image.

Chapter 3: The viewing environment This chapter assesses lighting in workplace viewing en-

vironments. The effect of reflected ambient light on the perception of contrast is discussed.

Methods of dealing with ambient lighting are examined and approaches towards perceptual

fidelity are detailed. Finally, previous related work is described.

Chapter 4: Measuring reflected ambient light Formally-designed psychophysical studies to mea-

sure the perceptual impact of reflected ambient light are presented. A detailed first experi-

ment establishes this impact, and a quick and effective experiment is developed to measure

1.3 Application: virtual heritage 6

changes in contrast perception through visual calibration by the users themselves, without

the need for specialised equipment.

Chapter 5: Correcting for ambient light This chapter details a novel algorithm that can be used

to correct for the effect of ambient light. This is a straightforward rational function, and is

invertible, so that images created under given ambient lighting can be displayed as they

would have originally appeared. Visual examples of the algorithm’s implementation are

given.

Chapter 6: Validation The experimental validation of the algorithm described in Chapter 5 is

described and discussed. This validation follows the procedure of our shortcut experiment,

thus measuring perceived contrast in light and dark conditions, and for corrected and uncor-

rected stimuli.

Chapter 7: Conclusions This final chapter summarises the results and contributions of this the-

sis, and future work revealed during the process is suggested.

1.3 Application: virtual heritage

This section highlights an application where consistency in image perception is desirable: cultural

(and more specifically, virtual) heritage. There are two aspects of virtual heritage that require

perceptual fidelity between images as they were created and images as they are viewed. Either an

image is a captured duplicate of an original artefact or site (such as a photograph of a manuscript,

intended to record or preserve that manuscript), or it is created as a three-dimensional representa-

tion (such as a computer model of a site). It is therefore desirable that the resulting image should

be perceived in the same way by all users, regardless of where they view it, or on which system it

is displayed. Figure 1.2 provides an outline of the process from the archaeological data in its raw

form through to display of the subsequent image.


Archaeological data

3D model

rendering

original lightingstorage:

e.g. database,web server

display

ambient light

End user

image capture

Figure 1.2: Virtual heritage: system diagram showing an overview of the process from raw scenedata to final image display.

1.3.1 Captured images

Digital image archives are growing in use, and are seen as a way of not only preserving friable or

fragile material in digital form, but also of disseminating this material to a much greater audience.

This has remarkable implications for research into archives once limited in terms of physical

location and number of users. A virtual equivalent of an artefact can be examined without any

harm to the original, and can reach a global audience through a medium such as the Internet.

It is tempting to think that preserving information through image capture is as simple as taking

a photograph, but a wide range of factors need to be addressed: how the artefact will be pho-

tographed, the conditions in which this takes place, the file format that is used to store it, or what

information will be used to describe it, to name just a few examples. Underlying all this difficulty

in determining how best to capture an image is a general assumption that this image is in some

way definitive. However, not only is this image a single form of representation, but it is by no

means guaranteed that it will be displayed in a consistent manner.

1.3.2 Rendered representations

Computer graphics have been used to model archaeological sites and artefacts since the 1980s,

whereby a three dimensional representation of a site is created, then lighting and textures are

added, resulting in an image or animation that represents an original scene. Current use of com-

puter graphics in archaeology provides the public with a glimpse of the past that might otherwise

be difficult to visualise. However, these images are often chosen due to their artistic impact, and

have been manipulated to provide the most aesthetically pleasing representation of a site. To date,


the emphasis has been on using such images for presentation purposes only, with interpretative

and research purposes taking second place to the demand for visually stunning presentation. The

pervasive media of television and the Internet, and the public fascination for the past, have seen the

adoption of computer-generated representations for entertainment and education of the interested

layman, rather than as a research tool for archaeologists. For computer graphics to benefit the

archaeological community, they must offer the archaeologist the chance to extend or enhance their

analysis of a site or artefact. The accuracy of the images produced must therefore be quantifiable

— the archaeologist must be confident that what they see in the generated image is comparable to

what they would have seen in the original example [CD02].

One area of realistic simulations that is often neglected is that of the original lighting of a site or

artefact. Light cannot be captured in the archaeological record and consequently its importance is

rarely considered in interpretations of past environments. The ways in which we view, perceive

and understand objects is governed by our current lighting methods of steady, bright electric light

or large windows, but in order to understand how an environment and its contents were viewed in

the past we must consider how they were illuminated.

Standard three-dimensional modelling software tends to base the lighting conditions on daylight,

fluorescent light or filament bulbs and not the lamp and candlelight used in past. In some cases,

scenes are illuminated with lighting values that would be impossible in the real world. Realistic

lighting simulation must address both the physical interaction of light in a scene and the spectral

profile of the light source. With control over this, an accurately-lit representation of an environ-

ment can be achieved and the virtual version of an original site or artefact can be manipulated

without having to physically touch or harm the real version.

Accurate illumination

Once an archaeological site or artefact has been modelled in a three-dimensional modelling pack-

age it must berendered; that is, the colours, textures, light and shading are computed, thus pro-

ducing the final two-dimensional image from the three-dimensional geometry. In order to obtain

an approximation of the original lighting in an archaeological representation, two factors must be


Figure 1.3: Experimental archaeology: physical reconstruction of fuel types.

addressed in the rendering process. First, the spectral composition of the light — the colour of the

light given off by the burning fuel — must match that of the fuel type that would have been used in

a specific archaeological instance. Second, the distribution of this light — the path it takes around

a scene and the reflections and inter-reflections that occur — must mimic the behaviour of light in

the real world.

The only trace of light in the archaeological record are the methods used to provide it, be they

hearths, candles, lamps or windows. In pre-industrial societies, daylight was the regulating factor

of the working hours. Compared to conditions today, sunlight is now far less relevant to how we

work [MCB97]. The evidence from architecture tells us the most about lighting — a lack of glass

and a need for security often meant smaller windows, therefore dimmer interiors. Going further

back in time, the unyielding darkness of a deep cave would require some form of artificial light

for navigation purposes alone. It seems plausible that objects and environments were affected by

the limitations of lighting, and this influence may have extended into their design. By recreating

the means of illumination for a given environment and simulating it accurately, the archaeologist

may (literally) find new ways of viewing things.

The type of flames that were generally used were diffusion wick flames. A typical flame of this na-

ture consists of three parts: the inner core, the blue intermediate zone, and the outer core [GW79].

These different zones produce different emissions depending on the fuel type and environmental

conditions. Various examples of possible light sources have been physically recreated in consulta-

tion with the Department of Archaeology at the University of Bristol, (Figure 1.3). These include

tallow candles (of vegetable origin) and reeds coated in vegetable tallow, a rendered animal fat

lamp, beeswax candles (processed and unrefined) and olive oil lamps (one with olive oil only, one


with olive oil and salt, and one with olive oil and water).

Each of the above fuels produces a different colour when burnt. To obtain this unique spectral pro-

file for each fuel, detailed data was gathered using a spectroradiometer, a device that measures the

absolute value of the spectral characteristics without making physical contact with the flame. The

spectroradiometer measures the emission spectrum of the light source in the visible bandwidths

in 5nm increments, thus providing an accurate breakdown of the flame-light composition of each

fuel type. The measurements were all taken in a completely dark room, and were taken against a

diffuse white powder (Eastman Kodak Standard, 99% optically pure). An average of ten readings

was calculated for each fuel type.

The resulting spectrographic data was converted into red, green and blue (RGB) values to enable

display on a computer monitor. These RGB values provide us with the data required during the

rendering process to simulate the fuel type of the original light source. Conversion of the spectral

profile of the illuminants to RGB values for use in a computer simulation does lead to an approx-

imation of the colours present. However, at present this is the most effective method in terms of

computational time and efficiency.

The advent of ray-tracing and radiosity in computer graphics has enabled the simulation of light

interaction, providing rendering techniques that mimic the physical behaviour of light in a scene.

Despite the availability of physically-based rendering software many users prefer to produce im-

ages that are aesthetically pleasing rather than perceptually accurate [War94b]. Also, where the

use of predictive lighting software may require some specialist knowledge, access to standard

modelling software is often available in a more user-friendly form. In many cases this can lead to

problems with the validity of computer simulations where the user may — due to time or varying

areas of expertise — lack the skills desired to create a meaningful model, though be fully able to

produce an attractive picture.

The rendering package used to create the images for the case studies below is Greg Ward’sRa-

diance[War94b]. Radianceis a lighting visualisation tool kit that accurately captures luminance

and radiances, models a variety of illumination types, supports a variety of reflectance models and

supports complicated geometry [WLS97]. The values that have been measured from the original


Figure 1.4: Simulation with modern 55w lighting (left) and under tallow lighting (right).

light sources can be used inRadianceas lighting values for a computer-generated model, meaning

that a scene can be rendered under its appropriate lighting conditions.

Changes in perception

Even with the RGB approximation, significant perceptual differences related to variations in fuel

type are apparent. Figure 1.4 shows a test scene containing a MacBeth colour chart illuminated

with modern lighting and light from a tallow candle. The difference in fuel type has a discernible

effect on the appearance of the MacBeth chart. Psychophysical tests can be used to validate sim-

ulations and compare them with real scenes [MCTR98, MCTG00, CMD+01]. Given the type of

lighting that would have been used in past environments, this demonstrates the need to investi-

gate sites and artefacts under their original lighting conditions to ensure we see them as they were

intended to look.

Case studies

The following case studies demonstrate how predictive lighting can be used to benefit the ar-

chaeologist through the development and testing of new hypotheses. All three examples use the

techniques described above, with the archaeological dataset taken from, respectively, measure-

ments made by a tape measure, a scale plan, and a laser scanner. All textures were created from

photographs, with the inclusion of a colour chart for calibration.

Medieval HouseThe initial impetus for work on validated illumination was the question as to how


Figure 1.5: Examples of medieval pottery.

Figure 1.6: Medieval house simulations. Images courtesy of Patrick Ledda,c 2002.

medieval pots would have looked in their original setting [MCB97]. This case study considers the

ways in which medieval interiors were illuminated and how lighting conditions might affect the

ways in which objects were perceived and designed.

A computer-generated model of the hall of a medieval town house was created. The model is

based on the Medieval Merchant’s House museum in Southampton, a half-timbered structure ren-

ovated by English Heritage as accurately as possible to represent a 13th century dwelling of some

economic status. This model allows the examination of medieval pottery in a close approxima-

tion to its original setting (Figure 1.6). This reveals details that may bring insight into medieval

ways of living. For example, only the top half of some jugs are glazed and decorated, and this is

perhaps indicative of how they were illuminated in use, perhaps by daylight through windows or


Figure 1.7: The room in the House of the Vettii as it appears today.

from torches hung on walls, suggesting many pots would have appeared most colourful when lit

from above (Figure 1.5).

Even more crucial is the relationship between light and colour. As shown, colours will change

in appearance according to the types of light source present. The recreation of medieval light-

ing conditions is therefore seen as a vital step in comprehending attitudes to colour, shape and

decoration. If there is any symbolic meaning in the use of colour on pottery then this might be

revealed through the recreation of a medieval environment. The modelling of a realistic envi-

ronment through the application of computer graphics and psychophysics is potentially the most

far-reaching and flexible way of exploring human perceptions in the past.

Pompeii FrescoesFor highly-decorative interiors, predictive lighting can be useful in testing how

a room may have been laid out or used by the original inhabitants. The UNESCO World Heritage

site of the Archaeological Areas of Pompeii, Ercolano and Torre Annunziata contain fine examples

of Roman frescoes. The House of the Vettii in Pompeii was chosen for the study, with the work

focusing on a reception room off the colonnaded sculpture garden [Nap98]. This room is lavishly

decorated in the IV Style (Figure 1.7) and was chosen due to the rich colours, good state of

preservation, and artistic effects such astrompe l’oeil, a painting technique that deceives the eye

into viewing a two-dimensional image as having three-dimensional structure. The frescoes were

recorded photographically, with the use of a colour chart for calibration purposes and to identify

illumination levels. A three-dimensional model was generated from a scale plan. The most readily

available fuel type for this area was deemed to be olive oil, so the spectral profile of the olive oil


Figure 1.8: Simulation viewed under modern lighting (left) and under olive oil lamp (right).

lamps was used to illuminate the scene. Also, a technique for including real flame captured from

video footage and inserted in the virtual scene gave a realistic appearance to the lamps without

having to model the actual flame. Therefore, the virtual scene contained the correct illumination

levels for a scene lit by olive oil lamps, with a real flame incorporated (Figure 1.9). Full details of

this work appear in Devlin and Chalmers [DC01].

In the resulting images it is plainly demonstrable how the scenes vary depending on how they are

illuminated. Under modern lighting conditions such as we might see today, the colours are not as

vibrant as they appear under lamp light (Figure 1.8). When viewed under olive oil lamp, the red

and yellow paint of the frescoes is particularly well-emphasised. Also, thetrompe l’oeil artwork

resembling mock windows and external architecture actually takes on the appearance of a real

view to the exterior as the three-dimensional depth cues are increased.

By changing the number and the positions of the light sources in the room, various effects can

be achieved. It is possible to test how lighting may have been distributed in order to highlight

the artwork in the most effective manner. Such positioning of lighting may have determined the

arrangement of furniture in a room. Again, such manipulations are possible when working with a

virtual version of the scene.

Cave Art The prehistoric site of Cap Blanc illustrates the potential computer graphics has to offer

archaeological interpretation. The rock shelter site of Cap Blanc, overlooking the Beaune valley

in the Dordogne, contains impressive examples of Upper Palaeolithic haut-relief carving. A frieze

of horses, bison and deer — some overlaid on other images — was carved some 15 000 years ago

into the limestone as deeply as 45cms, covering 13m of the wall of the shelter. Since its discovery

in 1909 by Raymond Peyrille, several descriptions, sketches, and surveys of the frieze have been


Figure 1.9: Simulation viewed under olive oil lamp, with furniture to show shadow effects.

published, but these are variable in their detail and accuracy .

In 1999, a laser scan of was taken of part of the frieze at 20mm precision [RBCS+01], using

a low power laser to ensure there was no possibility of damage to the site. Figure 1.10 (top)

shows part of the frieze from Cap Blanc. Some 55,000 points were obtained and converted into a

triangular mesh. Using detailed photographs as textures (each with a rock art chart to enable colour

calibration) and appropriate lighting values, the model was then rendered inRadiance. Figure 1.10

(bottom left) shows the horse carving illuminated by a simulated 55W incandescent bulb (as in a

low-power floodlight), which is how visitors view the actual site today. The bottom right image

in Figure 1.10 shows the horse under the simulation of an animal fat tallow candle as it may have

been viewed 15 000 years ago. The difference between the two images is significant, with the

candle illumination giving a warmer glow to the scene, as well as increasing the shadows. The

dynamic flame, and its position in the environment may also contribute to changes in perception.

It is conceivable that the dynamic nature of flame, coupled with the careful use of three-dimensional

structure, may have been used by the prehistoric artists to create the appearance of motion, as the

carvings can seem animated under the moving shadows of a flickering flame. The legs of the

horse are not present in any detail, and this has long been believed to be due to erosion, although


Figure 1.10: Cap Blanc: part of the frieze (top); the simulation lit by 55w incandescent bulb(bottom left), and lit by animal fat lamplight (bottom right).

this does not explain why the rest of the horse is not equally eroded. The possibility exists that

the legs were deliberately not carved in any detail, thereby accentuating any motion by creating

some form of motion blur. Furthermore traces of red ochre have been found on the carvings, and

it is interesting to speculate whether the application of this at key points on the horse’s anatomy

may also have been used to enhance any motion effects. Again, lighting simulation provides an

opportunity to explore such scenarios [CGH00].

1.3.3 Consistency in delivery

A definitive explanation should never be expected in archaeology. Archaeology by its very na-

ture is dynamic, with new ideas surfacing daily. Representing an artefact or a past environment is

fraught with difficulties from the outset, so a means of validating computer-generated representa-

tions or examining virtual copies of artefacts provides an exciting opportunity to explore and test

new ideas, with computer graphics becoming as beneficial to the archaeologist as they are to the

public.

For the above applications, display factors need to be taken into consideration so that colours and


light levels are portrayed effectively, whether the final image is shown on a computer monitor, on

an audio-visual display system, or as a printed page. With such images, the interpretation of the

information hinges on the appearance of the displayed result.


Chapter 2

Background

Assessing the impact of ambient illumination in the viewing environment requires an understand-

ing of several diverse areas. This chapter provides an overview of some of the fundamental con-

cepts appropriate to the study. The first section describes the necessary concepts and terminology

concerning the physical behaviour of light. The second section examines relevant information on

visual perception. An account of the process of digital image creation is described in the third

section, and is followed by a section providing an overview of current display technologies. Tech-

niques for controlling the display of digital images are provided in the fifth section.

2.1 Light

The light that humans can see can be defined as electromagnetic radiation falling on the retina

of the eye [Pri99]. The visible range of light is only a narrow span of the entire spectrum, and

this visual band consists of electromagnetic energy with wavelengths in the range of 400 to 700

nanometres. This radiation is perceived as colour, ranging from red in the longer wavelengths, to

violet in the shorter wavelengths. Figure 2.1 illustrates the comparatively small range of visible

light in the electromagnetic spectrum.

19

2.1 Light 20

Wavelength (nanometers)

0.01 1 100 10 10 10 104 6 8 10

400 500 600 700

Visible region

Gammarays

X-rays UV

Blue Red

Infrared Radio Waves

1nm 1 micron 1 mm 1 meter

Figure 2.1: The electromagnetic spectrum. We perceive electromagnetic energy having wave-lengths in the range 400-700 nm as visible light.

2.1.1 Radiometry and photometry

The first distinction must be made betweenradiometryandphotometry. Radiometry refers to the

measurement of the whole of the optical electromagnetic spectrum (the ultraviolet, visible and in-

frared bands) and can be measured in physical quantities. Photometry refers to the measurement of

visible radiation as weighted by the photopic response of the human eye. This photopic response is

the spectral sensitivity of the human cone system to radiation, which peaks at around 555 nanome-

tres [WLS97]. Therefore, the fundamental difference between radiometry and photometry is their

units of measurement.

The radiometric quantities are explained below. Their symbols and derivation are shown in Ta-

ble 2.1.

Radiant energy The basic unit of energy.

Radiant intensity The radiant flow from a point source in a particular direction.

Radiance The energy passing through a point in a specific direction.

Radiant power or flux Radiant energy flowing through an area per unit of time.

Irradiance The integrated radiation arriving at a surface.

Human visual response varies at different light levels and from person to person. Following tests

2.1 Light 21

400nm 555nm 700nm

1.0

Lum

inou

s ef

fici

ency

Wavelength

Figure 2.2: The Luminous Efficiency Curve. The spectral response of the human eye for photopicadaptation. (After [WS00].)

with human observers, the International Commission on Illumination (Commission Internationale

de l’Eclairage, CIE) selected the wavelength 555nm, to which the eye is most sensitive, as the

reference wavelength for the lumen, the standard photometric unit of light measurement. The

lumens at all other wavelengths are scaled according to thisphotopic luminous efficiencyfunction.

Used in conjunction with a base unit, it enables the values of photometric quantities for all types

of luminous source to be precisely defined (Figure 2.2).

In 1979 the International General Conference on Weights and Measures (Conférence Génerale des

Poids et Mesures, CGPM) defined the International System of Unit’s (SI) base unit for the mea-

surement of luminous intensity as thecandela(cd). A base unit is a particular physical quantity,

defined and adopted by convention, with which other particular quantities of the same kind are

compared to express their value. The candela is the luminous intensity, in a given direction, of a

source that emits monochromatic radiation of frequency 540� 1012 hertz and that has a radiant

intensity in that direction of 1/683 watt per steradian [Nat].

From this base unit, derived units can be defined. These definitions are in the International System

as defined in British Standard 3763 [Pri99]. Their symbols and derivation from the base unit can

be viewed in Table 2.1.

2.1 Light 22

SYMBOL RADIOMETRIC PHOTOMETRIC IN SI BASE UNITS

Value Unit Value Unit

Q Radiant Energy Joule Luminous Energy Talbot

I Radiant Intensity Watt/sr Luminous Intensity candela (cd)

L Radiance Watt/m2sr Luminance nit cd/m2

Φ Radiant Power Watt Luminous Flux lumen (lm) m2� m�2

� cd = cd

E Irradiance Watt/m2 Illuminance lux (lx) m2� m�4

� cd = m�2� cd

Table 2.1: Radiometric and photometric measurements and how they are calculated in base units.

Luminous energy Radiant energy that produces a visual sensation.

Luminous intensity The quantity which describes the power of a source or surface to emit light

in a given direction.

Luminance The intensity of light emitted in a given direction per projected area of a luminous or

reflecting surface.

Luminous flux The light emitted by a source, or received by a surface.

Illuminance The luminous flux density at a point on a surface.

In this thesis, photometric measurements will be employed, as the work concerns the response of

the human visual system in relation to image perception.

2.1.2 Light propagation

When light of a single frequency strikes a surface, three types of interaction occur:absorption,

where the energy provides no further illumination;reflection, where incident light is mirrored

back into the environment; andtransmission, where incident light travels through the material

of the surface and returns to the environment. If the total energy that is received by the surface

2.1 Light 23

Figure 2.3: Specular, or smooth, materials reflect light in one direction (left), whereas diffuse, orrough, materials scatter light in all directions (right).

represents unity, then:

t + r +a= 1 (2.1)

whent is the fractional transmittance,r is the fractional reflectance anda is the fractional absorp-

tance.

The quantities that are reflected, transmitted and absorbed are weighted depending upon the ma-

terial properties of the surfaces they strike. A surface’s reflective behaviour is characterised by its

bidirectional reflectance distribution function(BRDF), which describes the quantity of incident

radiance to reflected radiance [Gla95]. Reflective and transmittive properties come in two forms:

Specular Specular materials reflect light in one direction, or transmit it without any scattering

(Figure 2.3, left).

Diffuse With diffuse materials, incident light is scattered equally in all directions (Figure 2.3,

right).

The majority of materials consist of a combination of these categories (known asmixed), and their

overall reflection will depend upon a weighted combination of diffuse and specular components.

Other attributes, such as sub-surface scattering, may also influence a material’s properties [Gla95].

2.2 Visual perception 24

2.2 Visual perception

Perception is the process that enables humans to make sense of the stimulus that surrounds them.

Visual perception deals with the information that reaches the brain through the eyes. It links the

physical environment with the physiological and psychological properties of the brain, transform-

ing sensory input into meaningful information.

In recent years visual perception has increased in importance in computer graphics, predominantly

due to the demand for realistic computer generated images [MRC+86, RWP+95, Fer03]. The

goal of perceptually-based rendering is to produce imagery that evokes the same responses as an

observer would have when viewing a real-world equivalent. To this end, work has been carried out

on exploiting the behaviour of the human visual system (HVS). For this information to measured

quantitatively, a branch of perception known aspsychophysicsis employed, where quantitative

relations between physical stimuli and psychological events can be established [SB94].

Psychophysical experiments are a way of measuring psychological responses in a quantitative way

so that they correspond to actual physical values. It is a branch of experimental psychology that

examines the relationship between the physical world and peoples’ reactions and experience of

that world. Psychophysical experiments can be used to determine responses such as sensitivity to

a stimulus. In the field of computer graphics, this information can then be used to design systems

that are finely attuned to the perceptual attributes of the visual system.

To make an assessment of the effects of reflected ambient light on the perception of electronically

displayed images, it is necessary to understand several perceptual phenomena that may play a part

in the process. The attributes of the HVS relevant to this thesis are detailed below.

2.2.1 The human eye

The human visual system receives and processes electromagnetic energy in the form of light

waves. This starts with the path of light through thepupil (Figure 2.4), which changes in size to

control the amount of light reaching the back of the eye. Light then passes through thelens, which


iris

lensretina

optic nerve

cornea

pupil

Figure 2.4: A schematic section through the human eye.

provides focusing adjustments, before reaching the photoreceptors in theretina at the back of the

eye. These receptors in the retina consist of about 120 millionrodsand 8 millioncones[SB94].

Rods are highly sensitive to light and provide low intensity vision in low light levels, but they

cannot detect colour. They are located primarily in the periphery of the visual field. In contrast

to this, high-acuity colour vision is provided through three types of cones:L, which are sensi-

tive to long wavelengths;M, which are sensitive to medium wavelengths; andS, which are short

wavelength sensitive. Finally, thephotopigmentsin the rods and cones transform this light into

electrical impulses that are passed to neuronal cells and transmitted to the brain via theoptic nerve.

2.2.2 Visual sensitivity

The way in which we perceive images depends on the amount of light available. In dark scenes our

visual acuity — the ability to resolve spatial detail — is low and colours cannot be distinguished.

This is due to photoreceptor performance, as mentioned above. It is the rods that provide us with

achromatic vision at thesescotopiclevels, functioning within a range of 10�6 to 10cd=m2, such

as starlight. The cones are active atphotopic levels of illumination, covering a range of 0.01

to 108cd=m2, such as sunlight. The overlap (themesopiclevels), when both rods and cones are

functioning, lies between 0.01 to 10cd=m2 [Fer01].


2.2.3 Contrast

The termcontrastgenerally refers to the intensity difference between given light and dark values.

If the difference is great then the contrast is said to be high; if small, then the contrast is low.

Contrast can be computed in several ways [Pel90], but one of the most common ways is the

Michelson formula. The Michelson formula is used to compute the contrast of a periodic pattern,

and is defined as

C =Lmax�Lmin

Lmax+Lmin(2.2)

whereLmax andLmin refer respectively to the maximum and minimum luminance values in the

pattern.

2.2.4 Thresholds

It is easily demonstrated that in a brightly-lit room the addition of a single candle is not obvious,

but when the room is dark, lighting a candle makes an immediate impression. Similarly, a whisper

is sufficient to be heard in a quiet environment, whereas a shout is necessary in noisy conditions.

In 1834 the German physiologist E.H. Weber observed this principle1, defining Weber’s Law:

the ratio of the increment threshold to the background intensity is a constant, denoted theWeber

fraction.

A threshold is a psychological limit to perception. Theabsolute thresholddefines the transition

between something that is undetectable and something that is detectable. Thedifference threshold

is the minimum amount by which the intensity of a stimulus must be changed before it is de-

tectable [SB94]. Weber’s Law therefore refers to the difference threshold — the minimum amount

by which the stimulus intensity must be changed before aJust Noticeable Difference(JND) is

observed. The size of this JND (∆I ) is a constant proportion of the original stimulus value.

The Weber fraction is used to determine the contrast of a target against a background through the

1Gustav Fechner, a German physicist and a contemporary of Weber, independently observed this principle andformalised Weber’s Law.


JND

Background luminance

(∆I+I)

(I)

Figure 2.5: Weber’s Law: a JND measures the contrast needed to discriminate a target from abackground.

measurement of a JND (Figure 2.5). The Weber fraction is expressed as:

∆II

= k (2.3)

whereI is the stimulus intensity (for example, a given luminance value),∆I is the increment or

decrement in intensity needed for an observer to notice a difference in the initial intensity, and

k is the value of the constant ratio. It is a relationship that shows how standard physical scales

do not represent the psychological experience [Thu59]. This thesis uses the Weber fraction as a

definition for contrast due to the nature of the psychophysical experiments employed, as detailed

in Chapter 4.

The constancy of the Weber fraction has been called into question as it does not hold at extremes

of range, i.e. it tends to increase greatly at extremely low values. However, Weber’s Law has been

shown to hold in many situations [Wan95], forming part of Legge and Foley’s contrast masking

model [LF80], for example. Plotting detection thresholds against their corresponding background

luminances results in a threshold-versus-intensity (TVI) function (Figure 2.6) that is linear over a

middle range covering 3.5 log units of background luminance, and this middle range corresponds

to Weber’s Law [Fer01].


-3

-2

-1

0

1

2

3

4

5

-6 -4 -2 0 2 4 6

cones

rods

Log

thre

shol

d lu

min

ance

(cd

/m2 )

Log background luminance (cd/m2 )

Figure 2.6: Threshold versus intensity function for the rod and cone systems. (After [Fer01].)

2.2.5 The Contrast Sensitivity Function

The ability to perceive a JND is known ascontrast sensitivity. In 1968 Campbell and Robson

presented a theory of perception showing that contrast sensitivity varies according to spatial fre-

quency [CR68].Spatial frequencyindicates the number ofgratings(pairs of bars, one black and

one white, also known as acycle) which form a retinal image at a given distance [SB94]. They

measured this variation through the use of a compound sinusoidal grating stimulus, as shown in

Figure 2.7. The use of gratings of different spatial frequencies (i.e. with different numbers of cy-

cles per degree of angle of vision) means that contrast sensitivity can be measured at each spatial

frequency. This provides a curve that describes the threshold contrast needed to detect a given

spatial frequency, and this curve is known as thecontrast sensitivity function(CSF), which is also

shown in Figure 2.7.

2.2.6 Adaptation

The visual system adjusts to the stimuli that are presented to it, resulting in changes in sensitivity

known asadaptation. This process enables the visual system to respond to large variations in

luminance, allowing it to adjust to the prevailing light level. The rods in the eye are around


Invisible

Visible

Spatial frequency (cycles/degree)

Thr

esho

ld c

ontr

ast

Sensitivity (1/threshold contrast)

Spatial frequency (cycles/mm on retina)

1.0

01

0.01

0.001

0.1 1 10 100

1 000

100

10

1

0.1 1 10 100

Figure 2.7: The Campbell-Robson sensitivity chart (left, from [CR68]). The spatial frequencyincreases logarithmically from left to right; the contrast varies logarithmically from bottom to top.The resulting curve of the threshold determines an individual’s contrast sensitivity function (right,from [SB94]).

ten times as sensitive as cones, and so provide maximum sensitivity at low light levels [Gla95].

Visual adaptation from light to dark is known asdark adaptation, and can last for tens of minutes;

for example, the length of time it takes the eye to adapt at night when the light is switched off.

Conversely,light adaptation, from dark to light, can take only seconds, such as leaving a dimly

lit room and stepping into bright sunlight. This change in sensitivity is brought about through

physiological processes. In high luminance levels the photopigment in the eye is bleached, causing

a loss of sensitivity in the photoreceptors. The photoreceptors regain their sensitivity gradually,

accounting for the temporal aspects of adaptation. Additionally, though less significantly, the

amount of light entering the pupil changes [War00].

Adaptation also influences contrast sensitivity. When the visual system has adapted to a certain

frequency, sensitivity to that, and nearby frequencies, is decreased [Gla95].

2.2.7 Brightness perception

While luminance intensity can be measured on a physical scale (Section 2.1.1), the termbrightness

actually denotes a perceptual variable, which refers to a perceived level of illumination, such as

the amount of light an area appears to emit [SB94]. In addition, the termlightnessusually refers


0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Sensation magnitude

Stimulus intensity

Brightness = 0.33

0.1

1

10

0.01 0.1 1 10 100

Log sensation magnitude

Log stimulus intensity

Simple fields under dark conditionsComplex fields under dark conditions

Figure 2.8: Plot of the Stevens’ Power Law. The exponent for brightness is known to be 0.33(left); also shown on logarithmic co-ordinates (right). The power law does not hold for complexfields (right).

to the perceived reflectance of a surface. Brightness can be estimated for unrelated stimuli (visual

stimuli presented in isolation) and related stimuli (visual stimuli presented alongside other visual

stimuli) [WS00].

The relationship between luminance intensity and perceived brightness is non-linear and can be

described by a power law function

S= kIa (2.4)

known as the Stevens’ Power Law [Ste57, Ste61], whereS is the magnitude of the sensation,k is a

scale constant, andI is the intensity of the physical stimulus raised to a powera. The exponent for

brightness was experimentally determined to be 0.33. This was established by viewing a 5Æ target

viewed in darkness. While this power law holds for simple fields viewed in darkness, experimental

work by Bartleson and Breneman showed that for complex stimuli in both dark and ambient-lit

conditions, the power function does not hold [BB67]. This is due to the contribution of the visual

field outside of the target, known as thesurround. When the surround is incorporated, by addition

of a factor representing ambient illumination, the power law no longer holds. (This can be shown

in logarithmic co-ordinates — a power function should be linear on a log-log scale, Figure 2.8.)

2.3 Digital image creation 31

2.2.8 Lightness and colour constancy

The ability to judge a surface’s reflectance properties despite any changes in illumination is known

ascolour constancy. Lightness constancyis the term used to describe the phenomena whereby a

surface appears to look the same regardless of any differences in the illumination [Pal99]. For

example, white paper with black text maintains its appearance when viewed indoors in a dark en-

vironment or outdoors in bright sunlight, even if the black ink on a page viewed outdoors actually

reflects more light than the white paper viewed indoors.Chromatic colour constancyextends this

to colour: a plant seems as green when it’s outside in the sun as it does if it’s taken indoors under

artificial light.

A number of theories have been put forward regarding constancy [Wan95, Pal99, SB94]. Early

explanations involved adaptational theories, suggesting that the visual system adjusts in sensitivity

to accommodate changes. However, this would require a longer time than is needed for lightness

constancy to occur, and adaptational mechanisms cannot account for shadow effects. Other pro-

posed theories include unconscious inference (where the visual system ‘knows’ the relationship

between reflectance and illumination and discounts it); relational theories (where perceived light-

ness depends upon the relative luminance — the contrast — between neighbouring regions); and

anchoring (where the region with the highest luminance is regarded as being white and all other

regions are scaled relative to it).

2.3 Digital image creation

Digital images can becapturedor generated. Capturing a digital image generally involves the

use of a digital camera or scanning device, whereas generating a digital image means modelling

and rendering a scene on a computer. In either case, the image is subsequently stored in some

digital format (usually 24-bit RGB). Other colour models are feasible, and more bits can be used

to increment the dynamic range, as discussed below.

2.4 Display technology 32

2.3.1 Capturing digital images

Scanners are used to sample analogue images and convert them into digital image files, and are

available in a variety of types (flatbed, film, drum and others). A digital camera samples a real-

world scene, processes it internally and then stores it in a digital form.

The majority of digital cameras and the most commonly-used flatbed and transparency scanners

usecharge-coupled device(CCD) technology. The CCD is an array of light-sensitive diodes that

convert photons (light) into electrons (electrical charge) — the brighter the light, the greater the

accumulated electrical charge. The value of the accumulated charge undergoes analogue-to-digital

conversion, storing the information in digital form.

2.3.2 Generating digital images

Computer graphics can also be used to create digital images. This is generally carried out through

the process of three-dimensional modelling, with a subsequent rendering stage where the colours,

textures, light and shading are computed, thus producing the final images. At all stages in this

process the information is in digital form.

2.4 Display technology

The two most commonly encountered visual display units (VDUs) are cathode ray tubes (CRTs)

and liquid crystal displays (LCDs), although the use of plasma display panels (PDPs) for large-

scale, multi-viewer applications, such as art galleries or museums, is becoming more popular.

This section provides an overview of the three devices. A comparative table of current VDU

specifications is given in Table??.


ATTRIBUTE CRT LCD PLASMA

Contrast Ratio* 4000+:1** 1300:1*** 3000:1****

Max Brightness 1000cd=m2 450cd=m2 700cd=m2y

Viewing Angle 180Æ 160Æ 180Æ

Fully Digital Display no yes yes

Refresh Rate n/a 10-12ms*** 8ms

Max Resolution 720p 1080i+ 1280 x 1024 1366 x 768

Weight (lbs) 60-300 20-100 50-150+

Set Depth 16” - 30” 2” 3-6”

Screen Size 20” - 40” 1” - 57”*** 30” - 80”

Power consumption High Low Medium

Table 2.2: Display technology comparison. (After [DeB04].)*Higher-end known value given.**Calculated. CRTs not generally shown with contrast ratios.***New high-definition HD2+ development****Real world tests drop this number considerably (400:1).yPlasma “real-world” measure about 100cd=m2


Colour signals

Picture tube

Electron guns

Electron beams

Screen

Glass envelope

Phosphor

Figure 2.9: Simplified schematic diagram of a CRT (after [Gla95].)

2.4.1 Cathode Ray Tubes

A colour CRT uses three electron guns (referred to as ‘red’, ‘green’ and ‘blue’ guns) which emit

an electron beam [Tra91]. When a digital image is created it is stored as an array of values that

represent an intensity of a particular part of that image. These values that are used to express colour

actually specify the voltage that will be applied to each electron gun. The values are converted

from digital to analogue, and video signals are produced, exciting the phosphors of the display

and emitting light, which results in an image on screen (Figure 2.9). Brainard, Pelli and Robson

define the light emitted by a single pixel as

C(λ) = rR(λ)+gG(λ)+bB(λ)+A(λ) (2.5)

whereλ denotes wavelength,R(λ), G(λ) andB(λ) are the maximum light emitted by the phos-

phors,r, g andb are real numbers in the range[0;1], andA(λ) is given as the ambient light emitted

or reflected by the monitor when the input voltage is zero [BPR02].

One of the advantages of a CRT display is that the luminance it produces is generally independent

of viewing angle. Therefore, measurements taken from a CRT from one viewing position apply to

a wide range of viewing positions, and this is also the main reason why we have used CRTs for

the experiments we present in this thesis. When running experiments with numerous participants,


in various locations, it is imperative to ensure that each participant has the equivalent viewpoint to

all the others. For this reason, many perceptual graphics applications use CRT technology, rather

than the now popular LCD technology described below.

2.4.2 Liquid Crystal Displays

An LCD consists of two layers of polarising material trapping a solution which has both liquid

and crystal properties; that is, the liquid crystals may be fluid, but can also retain an ordered

molecular structure. When an electrical field is applied to this solution, the crystals align so that

light cannot pass through. Therefore, two states are possible: either light passes through a cell,

or light is blocked, with each cell representing a pixel [Tra91]. Most LCD screens are backlit

with a fluorescent light which is evenly diffused to give a uniform display. LCDs have grown in

popularity in recent years due to the lower volume, weight and power consumption when compared

with the CRTs [MNK99].

2.4.3 Plasma Display Panels

Like CRTs, plasma displays are emissive and use phosphor, and like LCDs they use a grid of

electrodes as pixels. They work on the same principle as a neon sign, which emits light when

an electrical current is passed through gas. Plasma is a gas which is electrically conductive, and

as the electrons move through it they ionise the individual gas molecules. The energy gained

from ionisation is emitted as light during the decay process. Although the process is simple,

the implementation for mass production is, at present, costly and complex. Currently, a plasma

display is several times as expensive as an LCD, which is again more expensive than a CRT.

Prices of LCD and plasma screens are dropping rapidly, but unless the viewing angle dependence

of LCDs is addressed, computer graphics use will tend towards CRTs.

2.5 Controlling the display 36

2.5 Controlling the display

CRTs, LCDs and plasma screens all have different limitations in terms of how, and with what

quality, images are displayed. These limitations are not benign — display devices alter images

in various perceptually significant ways. This section details some of the corrections that may be

applied to images before they are displayed, so that their visual quality is minimally affected by the

chosen display device. In particular, in the following sections, we show the importance of gamma

correction, tone reproduction, and gamut mapping, all of which are specific image treatments that

make an image suitable for display.

2.5.1 Gamma

The mapping between input voltage values and the actual light emitted by each of the phosphors

would ideally be linear so that the input matches the output. If the intensityL is the value be-

tween 0–1 sent to the digital-to-analogue converter, and the output intensity isLd, then the ideal

relationship would be

Ld = LmaxL (2.6)

whereLmax is the maximum luminance of the display. However, for CRTs the mapping is normally

not linear as the electron gun has a non-linear response to its input voltage. This non-linearity can

be well-approximated with a power law

Ld = LmaxLγ (2.7)

whereLd is the displayed intensity,Lmax is the maximum displayable intensity,L is the input

value between 0 and 1 andγ is an approximation of the display’s non-linearity. The constant

γ, which is usually close to 2.5, also depends upon factors such as luminance and contrast set-

tings of the screen. Monitor adjustment controls labelled ‘Contrast’ and ‘Brightness’ respectively

control the luminance level and the black point of the monitor, which in turn influence what is

displayed [Poy98].


0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Out

put v

alue

Input value

g=1

g=2.5

g=1/2.5

Figure 2.10: Gamma values for a CRT monitor. Most monitors have a gamma value of around 2.5,requiring the inverse to be applied to achieve linearity.

This non-linear relationship between the input and the output is coincidentally close to the inverse

of human luminance sensitivity (perceived brightness), as described in Section 2.2.7. Since it is

desirable for the displayed output on a CRT to be linear with brightness,gamma correctioncan be

used to map luminance into a perceptually uniform domain [Wan95, Poy98] (Figure 2.10).

Different brands of computer deal with gamma correction in different ways, resulting in typical

values for Macintosh computers of 1.8 and for Silicon Graphics machines of 1.5. Personal Com-

puters (PCs) do not have gamma correction in hardware, and therefore the gamma for PCs depends

on the monitor used, with a typical value of around 2.5 [Poy98]. For LCDs and plasma screens,

the approach is more complicated. Some LCD monitors have a built-in artificial non-linearity to

mimic CRT devices. Others do not have such hardware added and may have other unknown non-

linear responses to input signals. Gamma is effectively linear for plasma display panels due to

pulse-width modulation [Poy03], which is a way of digitally controlling analogue signals where

the full power is applied for a fraction of the time.


Gamma calibration

Calibration of a system refers to attaining a predefined set-up; for example, a specified gamma,

offset and peak white luminance [Ber96]. This is achieved by characterising the properties of

the system and then making adjustments based on the desired set-up. In the case of gamma, this

can be achieved by measuring the output displayed on the screen and comparing it to the input

values. This is best accomplished by displaying a pattern on screen consisting of horizontal or

vertical stripes that step through from 0 to 255, as shown in Figure 2.11. For maximum accuracy

this should be carried out for each of the voltage guns. Stripes should be arranged to equalise the

power drain on the screen, so that a pair of adjacent stripes always totals the maximum voltage

of 255. Also, the stripes should be wide enough that they are not affected by flare from adjacent

stripes [Tra91].

The stripes of fixed voltage should be measured with a chromameter, preferably in a totally dark

environment. The resulting values should be normalised (and, if the value for a voltage of zero is

not actually zero due to a non-dark environment, this value can be subtracted from the other mea-

sured data before normalisation). A function of the formy= mx is fitted to the natural logarithm

of the data to give the value of the best gamma fit.

Shortcut gamma calibration

It is also possible to estimate the gamma of a computer system by displaying an image such as

that in Figure 2.12, which consists of a set of grey values next to an area of alternating black and

white pixels [CD01]. Seen from a distance, the black-and-white pattern fuses to appear grey and

the grey patch which matches the fused pattern best is selected. The intensityLm of this patch is

used to find the gamma response of the CRT:

0:5= Lγm (2.8)


Figure 2.11: Example of test patterns for gamma measurement.


3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6

Figure 2.12: Image used for simple gamma correction. (For printing purposes the spacing betweenthe lines has been exaggerated. An actual gamma chart would have stripes the width of one pixel.)

such that

γ =log0:5logLm

(2.9)

Gamma correction

Once the gamma for a particular set-up is known, images to be displayed may be corrected with

the following transformation which is generally known as gamma correction:

L0 = L1=γ (2.10)

2.5.2 Tone reproduction

Ideally, if a scene in the real world and an image representing that scene (be it computer generated

or photographed) are viewed under the same conditions, it is expected that the real-world scene

and the image should have the same tones, i.e. the luminance levels of both scenes match.

Physical accuracy alone of an image does not ensure that the scene in question will have a realistic

visual appearance when it is displayed. This is due to the shortcomings of standard display devices,

many of which can only reproduce a range of luminance of about 100:1 candelas per square metre

(cd=m2), as opposed to real-world luminance, which ranges from 100 000 000:1, from bright

sunlight down to starlight. The human eye can accommodate a luminance range of approximately

10 000:1 in a single view, and an observer’s adaptation to their surroundings, where their response

to a scene changes over time, also needs to be taken into account. The ratio between the maximum


100 000 000: 1

10 000: 1

100: 1

Range of luminances:

in the real-world

that the eye can accommodate in single view

displayable on a standard CRT monitor

Figure 2.13: A comparative view of dynamic range.

Real-World

Display

Scene

Tone ReproductionOperator

Display withLimited Capabilities

��

��

��

��

Observer

Perceptual Match

Observer

- - -

-

?

6

Figure 2.14: Ideal tone reproduction process

and the minimum tonal values in an image is known as thedynamic range. It is this high dynamic

range (HDR) that exists in the real world that needs to be scaled in some way to fit a display device

that is only capable of outputting a low dynamic range.

Tone reproduction (also known as tone mapping) provides a method of mapping luminance values

in the real world to a displayable range. Tone reproduction is necessary to ensure that the wide

range of light in a real-world scene is conveyed on a display with limited capabilities. In addition

to compressing the range of luminance, it can be used to mimic perceptual qualities, resulting in

an image which provokes the same responses as someone would have when viewing the scene in

the real world. For example, a tone reproduction operator may try to preserve aspects of an image

such as contrast, brightness or fine detail — aspects that might be lost through compression. In

situations where predictive imaging is required, tone reproduction is of great importance to ensure

that the conclusions drawn from a simulation are correct (Figure 2.14).

A straightforward linear scaling between the original high dynamic range data and the display is

not the best solution (see Figure 2.15) as many — if not all — details can be lost. The mapping

must be tailored in some non-linear way. Current state-of-the-art image capturing techniques


Figure 2.15: Linear scaling of HDR data will cause almost all details to be lost, as the top imageshows. Here, the light bulb is mapped to a few white pixels and the remainder of the image isblack. Tone reproduction operators attempt to solve this issue, in this case recovering detail inboth light and dark areas as well as all areas inbetween (bottom image).

allow much of the luminance values to be recorded in high dynamic range images [DM97]. This is

desirable, because in the future high dynamic range display devices will become available allowing

this data to be displayed directly [SWW03]. By capturing and storing as much of the real scene as

possible, and only reducing the data to a displayable form just before display, the image becomes

future-proof. Formats such as the SGI LogLuv TIFF, which can hold 38 orders of magnitude


Figure 2.16: The top images show the extent of the dynamic range. The bottom image istonemapped (using Radiance’spcond function) for display on a computer monitor. (Renderingof Kalabsha temple courtesy of Veronica Sundstedt,c 2003.)

in its 32-bit version, have been recommended [War98, War01] to store HDR data. Figure 2.16

demonstrates how varying levels of exposure reveal different details. By combining the various

exposure levels and tone mapping them, a better overall image can be achieved.

Although tone reproduction for HDR reduction is a separate issue from the work presented in

this thesis, it shares many common aims, not least the fact that tone mapping operators have been

developed which seek to provide the most perceptually accurate reproduction of a scene on a

computer monitor. For this reason, an account of major work to date is given in this section. Our

work can be seen as addressing a specific aspect of faithfully reproducing perceived tone on a

display device, but we are not concerned with the reduction of dynamic range (although we do

wish to exploit the dynamic range of the monitor to its fullest), nor do we seek to map a real-world

scene to a display device. Instead, we aim to preserve the original and intended appearance of

contrast in an image regardless of the location or device on which it is displayed.

A number of tone reproduction operators have been presented [DCWP02, DW00], with each gen-

erally addressing a specific aspect such as brightness preservation or contrast preservation. Some

of the operators are concerned with achieving perceptual fidelity with a real-world scene, and


mimic aspects of the human visual system. Others concentrate on producing a subjective best

image that is pleasing to the eye. Two types of tone reproduction operators can be used:spatially

uniform (also known assingle-scaleor global) andspatially varying(also known asmulti-scale

or local). Spatially uniform operators apply the same transformation to every pixel. A spatially

uniform operator may depend upon the contents of the image as a whole, but the same transfor-

mation is applied to every pixel. Conversely, spatially varying operators apply a different scale to

different parts of an image.

Spatially uniform operators

In 1984 Miller, Ngai and Miller [MNM84] were the first to use experimental data to try to match

brightness in a real scene to brightness of a displayed image of that scene, for the purpose of

determining pixel luminance for their architectural rendering system [AF95]. They used psy-

chophysical data on brightness perception from work by Stevens and Stevens [SS60]. Upstill’s

1985 PhD thesis reinforced the need for perceptual tone reproduction through the use of an ex-

plicit perceptual model [Ups85].

Tumblin and Rushmeier [TR93], also focused on preserving the viewer’s overall impression of

brightness, providing a theoretical basis for perceptual tone reproduction, again by using Stevens

and Stevens data. This model of brightness perception does not hold for complex scenes, but

was chosen by Tumblin and Rushmeier due to its low computational costs. Their aim was to

create a ‘hands-off’ method of tone reproduction in order to avoid subjective judgements. They

created observer models — mathematical models of the HVS that include light-dependent visual

effects while converting real-world luminance values to perceived brightness images. The real-

world observer corresponds to someone immersed in the environment, and the display observer

to someone viewing the display device. Their tone reproduction operator converts the real-world

luminances to the display values, which are chosen to match closely the brightness of the real-

world image and the display image. If the display luminance falls outside the range of the frame-

buffer then the frame-buffer value is clamped to fit this range.

Ward’s model [War94a] dealt with the preservation of perceived contrast rather than brightness.


Ward aimed to keep computational costs to a minimum by transforming real-world luminance

values to display values through a scaling factor, concentrating on small alterations in luminance

that are discernible to the eye. Based on a psychophysical contrast sensitivity model by Black-

well [Bla81] he exploited the fact that the consequence of adaptation can be regarded as a shift

in the absolute difference in luminance required for the viewer to notice the variation. Blackwell

produced a comprehensive model of changes in visual performance due to adaptation level. This

approach is useful for displaying scenes where visibility analysis is crucial, such as emergency

lighting, as it preserves the impression of contrast. It is also less computationally expensive than

Tumblin and Rushmeier’s operator but the use of a linear scaling factor causes very high and very

low values to be clamped and correct visibility is not maintained throughout the image [WRP97].

Ferwerda, Pattanaik, Shirley and Greenberg [FPSG96] developed a model which accounts for

changes in colour appearance, visual acuity and temporal sensitivity while preserving global vis-

ibility. This model is based on the concept of matching JNDs for a variety of adaptation levels.

It accounts for both rod and cone response and takes into consideration the aspect of adaptation

over time. Ferwerda et al. exploited the detectability of changes in background luminance in order

to remove those frequencies imperceptible when adapted to real-world illumination. Detection

threshold experiments were used as the basis of the work. By plotting the detection threshold

against the corresponding background luminance, a TVI function is produced for both the display

and the viewer. The implementation of this model is based on Ward’s 1994 operator [War94a].

Ward’s model is used without change for cone TVI data and is extended for rod TVI data. If the

level of adaptation for the real-world viewer falls in the photopic range (i.e.. above 10cd=m2) then

a photopic tone-reproduction operator is applied (making use of the cone data), and if it falls in

the scotopic range (i.e.. below 0.01cd=m2) then a scotopic tone reproduction operator is applied

(making use of the rod data). For mesopic conditions, a photopic display luminance and a scotopic

display luminance are combined appropriately.

To reproduce the loss in visual acuity, Ferwerda et al. used data from psychophysical experiments

that related the detectability of square wave gratings of different spatial frequencies to changes

in background luminance. Using this data it is possible to determine what spatial frequencies are

visible, and thereby eradicate any extraneous data in the image. Light and dark adaptation were


also considered by adding a parameter to the display luminance, the value of which changes over

time. This model is of particular importance due to the psychophysical model of adaptation that

it adopts, and proves useful for immersive display systems that cover the entire visual field so that

the viewer’s visual state is determined by the whole display [McN01].

Further work by Ward Larson, Rushmeier and Piatko [WRP97] presented a histogram adjustment

technique for reproducing perceptually accurate tone, extending earlier work by Ward [War94a]

and Ferwerda et al. [FPSG96]. The main focus of this work was object visibility and image con-

trast, with a secondary goal of recreating the viewer’s subjective response so that their impression

of the real and virtual scenes were consistent [WRP97]. This technique makes use of the fact that

the eye is sensitive to relative rather than absolute changes to luminance, so bright areas should

be displayed as bright and dim areas as dim, irrespective of the actual absolute luminance inten-

sity values. Luminance levels are not constant across an image, but appear in clusters that vary

in intensity. Also, the eye adapts rapidly to a 1Æ visual field around the fixation point. For these

reasons, the function makes adjustments on the basis of luminance adaptation levels in an image

rather than on spatial location.

The field of image processing has developed methods to adjust image contrast and visibility. One

such method is the histogram equalisation technique whereby the grey levels in an image are re-

distributed to make better use of the display device range and maximise visibility and contrast.

Ward Larson et al. exploited this idea of altering histograms and using perceptual models to guide

alteration, with their aim being to simulate, rather than maximise, visibility in an image. A log of

luminances averaged over 1Æ areas (which correspond with foveal adaptation levels for possible

points in an image) is obtained, and a histogram and cumulative distribution function is built from

this information. Cumulative distribution of the luminance histogram is used to identify clusters

of luminance levels and initially map them to the display values using a histogram adjustment

technique that is based on human contrast sensitivity. Ferwerda et al. ’s [FPSG96] threshold sensi-

tivity data is used to compress the original dynamic range to that of the display device, subject to

the contrast sensitivity limitations of the eye. Although this method is described here as spatially

uniform, spatial variation is introduced through the use of models for glare, acuity and chromatic

sensitivity to increase perceptual fidelity.


In 1999 Tumblin, Hodgkins and Guenter [THG99] produced two new tone reproduction operators

by imitating some of the HVS’s visual adaptation processes, and also revised Tumblin and Rush-

meier’s [TR93] earlier work. The first, a layering method, builds a display image from several

layers of lighting and surface properties. This is done by dividing the scene into layers and com-

pressing only the lighting layers while preserving the scene reflectances and transparencies, thus

reducing contrast while preserving image detail. Their compression function follows the work

of Schlick [Sch94b]. This method only works for synthetic images where layering information

from the rendering process can be retained. The second, a foveal method, interactively adjusts to

preserve the fine details in the region around the viewer’s gaze (which the viewer directs with a

mouse) and compresses the remainder. In this instance their final tone reproduction operator is

a revised version of the original Tumblin and Rushmeier [TR93] operator, also building on the

work of Ferwerda [FPSG96] and Ward [War94a]. Both of these operators are straightforward in

implementation and are not computationally expensive. The layering method is suited to static,

synthetic scenes (displayed or printed) and the foveal method to interactive scenes (requiring a

computer display).

Scheel, Stamminger and Seidel [SSS00] developed a method of tone reproduction for interactive

applications by representing luminances as a texture. The luminance of each vertex is coded into

texture co-ordinates, and prior to rendering these luminance co-ordinates are mapped into display

luminance values. This allows walkthroughs of large scenes where the tone reproduction can be

adjusted frame-by-frame to the current view of the user, and focuses on tone reproduction for

global illumination solutions obtained by radiosity methods. Due to interactivity, updates in tone

mapping are required to account for changes in view point and viewing direction, and new factors

need to be incorporated into the tone reproduction operator, such as computational speed and

adaptation determination. (In comparison, foveal method presented by Tumblin et al. [THG99]

was interactive to an extent, but relied on pre-computed still images where the fixation point of

the viewer could change, but an interactive walkthrough was not possible.) Spatially uniform

operators were chosen due to computational efficiency, and Scheel et al. based their work on

operators developed by Ward [War94a] and Ward Larson et al. [WRP97]. A centre-weighted

average is used to determine the probability of the user’s focus. The adaptation levels are computed

using samples obtained through ray-tracing, and the luminance of every vertex is held in texture co-


ordinates. This can then be updated frame-by-frame. This method of tone reproduction provided

a new level of interactivity, but it does not take into consideration adaptation over time.

Pattanaik, Tumblin, Yee and Greenberg [PTYG00] produced a new time-dependent tone repro-

duction operator to automatically create colour image sequences from any input scene. It followed

the perceptual models framework proposed by Tumblin and Rushmeier with the addition of an

adaptation model and appearance model to express retinal response and lightness and colour. The

adaptation model computes retina-like response signals (for rod and cone luminance and colour in-

formation) for each pixel in the scene. Using Hunt’s static model of colour vision, time-dependent

adaptation components are added to describe neural effects, pigment bleaching, regeneration and

saturation effects. The visual appearance model assumes that the real-world viewer determines a

‘reference white’ and a ‘reference black’ and judges the appearance of any visual response against

these standards. Assembling these models reproduces the appearance of scenes that evoke changes

to visual adaptation. This operator is suitable for use in real-time applications as, due to its spa-

tially uniform model of adaptation, it does not require extensive processing.

Durand and Dorsey [DD00] presented an interactive tone mapping model which made use of

visual adaptation knowledge. They also proposed extensions to the tone mapping operator by Fer-

werda et al. and incorporated it into a model for the display of global illumination solutions and

interactive walkthroughs. This model involves time-dependent tone mapping and light adaptation,

and extends the work by Ferwerda et al. by including a blue-shift for viewing night scenes and by

adding chromatic adaptation. For the interactive implementation, work by Tumblin et al. [THG99]

and Scheel et al. [SSS00] was used to take advantage of the observer’s gaze, allowing a weighted

average to be used. Photographic exposure metering used in photography is employed to better

calculate the adaptation level. Loss of visual acuity is simulated in the same manner of Ferw-

erda et al. by use of a 2D Gaussian blur filter. The scene is rendered as normal, with interactivity

introduced by tone mapping computed on the fly, accelerated by caching the function in look-up

tables.

Work by Cohen, Tchou, Hawkins and Debevec [CTHD02] addressed the problem of HDR image

display by storing and rendering high dynamic range texture maps in real time using hardware

texturing architectures. With their method, HDR texture maps are stored as two separate 8-bit


texture maps, one representing the high intensities and the other the low intensities. During display,

these two texture maps are recombined with the aid of a dynamically adjustable exposure level to

guide the overall intensity of the result.

Drago, Myszkowski, Annen and Chiba [DMAC03] presented a method for displaying high con-

trast scenes. It is based on logarithmic compression of luminance values in imitation of the visual

response to light, through the use of Perlin and Hoffert’s bias power function [PH89] and through

the manipulation of the gamma power function. The dynamic range is compressed using a linear

scaling factor after the logarithm has been applied, with this scaling factor depending on scene

content, interpolated by the bias function. The result is a perceptually-motivated function that can

be used at interactive rates.

Spatially varying operators

Work by Oppenheim, Schafer and Stockham [OSS68] on non-linear filtering in 1968 appears to

be the earliest attempt at tone reproduction in computer graphics. They describe the problem of

excessive dynamic range and suggest a method for simultaneously reducing dynamic range and

enhancing contrast using homorphic filtering. An image can be divided into two parts: the illu-

mination component (the available light) and the reflection component (the ability of objects to

reflect light). The illumination component, which contains large variations in luminance intensi-

ties, primarily consists of low frequencies, and the reflection component primarily consists of high

frequencies. Therefore, low frequency content in an image tends to be high dynamic range, and

high frequency content tends to be low dynamic range. By attenuating the low frequencies in the

Fourier domain, HDR data may be compressed while the high frequencies (the low dynamic range

detail) are preserved. Further work by Stockham [Sto72] in 1972 tied the concept of homorphic

filtering to properties of early portions of the HVS. He developed a visual model based on these

properties and used it to define a measure of image quality.

Chiu, Herf, Shirley, Swamy, Wang and Zimmerman’s [CHS+93] investigation into global opera-

tors led them to believe that the solution should be local instead, as applying the same mapping to

each pixel could produce incorrect results. With an HDR image there is no perfect compression


curve that fits every pixel in an image, so a method of incorporating local variation is desired.

They deliberately did not incorporate adaptation issues or psychophysical models into their oper-

ator; rather they experimented with a method of spatially varying image mapping. As the HVS is

more sensitive to relative as opposed to absolute changes in luminance they developed a spatially

non-uniform scaling function for high contrast images. Their basis was the argument that the eye

is more sensitive to reflectance than luminance, so that slow spatial variation in luminance may

not be greatly perceptible. The implication is that images with a wider dynamic range than the

display device can be displayed without much noticeable difference if the scaling function has a

low magnitude gradient. By blurring the image to remove high frequencies, and inverting the re-

sult, the original details can be reproduced, but reverse intensity gradients appear when very bright

and very dark areas are in close proximity [McN01]. Due to the fact that it is a local operator, this

model is also computationally demanding. It is also a ‘hands-on’ approach, based purely on ad

hoc results and therefore does not have the advantages of the more robust, theoretical basis of

other tone reproduction operators.

Schlick [Sch94b] presented practical methods of tone reproduction, concentrating on improving

computational efficiency and simplifying parameters. He used a first degree rational polynomial

function to map real-world luminances to display values, a function which worked well when ap-

plied uniformly to all pixels in an image. It is this function that forms the basis for our correction

algorithm in Chapter 5. He produced three methods of mimicking local adaptation. The first of

these, low pass filtering, was susceptible to halo artifacts, as was the method by Chiu et al. —

a problem common among spatially varying operators. The remaining two methods did not pro-

duce as satisfactory results as the uniform approach. Nonetheless, his work produced a valuable

optimisation of spatially varying techniques.

Jobson, Rahman and Woodell [JRW97] based their method on the retinex theory [LM71] of

colour vision, producing a multi-scale version to achieve simultaneous dynamic range compres-

sion, colour consistency and lightness rendition, testing it extensively on (real-world) test scenes

and over 100 images. The retinex is a computational model of lightness and colour perception

of human vision which estimates scene reflectances, and Jobson et al. modified it to perform in a

functionally similar manner to human visual perception. However, in their validation they used


24-bit RGB test images where dynamic range reduction is not an issue as it can be displayed in

a straightforward manner on a standard CRT. They expressed the need for refinement of their ap-

proach for images with greater maximum contrasts. Also, problems arose with scenes dominated

by one colour as these violated the retinex ‘grey-world’ assumption that the average reflectances

are equal in the three spectral colour bands.

Pattanaik, Ferwerda, Fairchild and Greenberg [PFFG98] developed a technique based on a multi-

scale representation of pattern, luminance, and colour processing in the HVS and addressed the

problems of high dynamic range and perception of scenes at threshold and supra-threshold levels.

They provided a computational model of adaptation and spatial vision for realistic tone reproduc-

tion. There are two main parts to this model: thevisual model, which processes an input image

to encode the perceived contrasts for the chromatic and achromatic channels in their band-pass

mechanism; and thedisplay model, which takes the encoded information and outputs a recon-

structed image. Although it is computationally demanding, the model takes chromatic adaptation

into account. However, as seen in other spatially varying operators, this method is susceptible to

strong halo effects [Tum99]. Although it was designed as a solution towards the tone reproduction

problems of wide absolute range and high dynamic range scenes, it is a general model that can be

applied across a number of areas such as image quality metrics, image compression methods and

perceptually-based image synthesis algorithms [PFFG98].

In 1999 Tumblin and Turk [TT99] produced the Low Curvature Image Simplifier (LCIS) method,

a versatile technique that can accept input from synthetic sources or real-world image maps, and

produces an output suitable for any display. Similar to Tumblin et al. ’s [THG99] layering and

foveal approaches, the LCIS separates the input scene into large features and fine details, com-

pressing the former and preserving the latter. The idea stems from art where an initial sketch

outlines the main structure of a picture, with details and shadings filled in later. The LCIS uses a

form of anisotropic diffusion to define the fine details by scene boundaries and smooth shading.

This provides a high amount of subtle detail, avoids halo artifacts, and claims moderate computa-

tional efficiency [Tum99].

Durand and Dorsey’s [DD02] 2002 method used an edge-preserving filter known as the bilateral

filter to decompose the image into two layers — an approach which builds on Tumblin and Turk’s


LCIS method [TT99], and Tumblin et al. ’s layering method [THG99] which it extends to pho-

tographs. A base layer (which consists of large-scale variations) is derived using bilateral filtering

and contrast is reduced in this layer while visibility is preserved in the detail layer. Their final

method is a faster, more robust operator that also addresses problems mentioned by Tumblin and

Turk in their LCIS method [TT99], namely halo artifacts and diffusion at discontinuities. Again,

perceptual accuracy is not the aim and their operator does not attempt to model human vision.

Fattal, Lischinski and Wermann [FLW02] presented a new computationally efficient and concep-

tually simple method based on attenuating the magnitudes of the large luminance gradients that

exist in HDR scenes, compressing large gradients and preserving fine details. The changes in

intensity are identified and the larger gradients are reduced and a low dynamic range image is

produced. They did not make an attempt at perceptual accuracy, but instead offered an effective,

fast and easy-to-use form of tone reproduction.

Reinhard, Stark, Shirley and Ferwerda’s function [RSSF02] is analogous to photographic practise,

resulting in a technique designed to suit a wide variety of images. In photography, an approach

known as the Zone System is widely used. This photographic technique divides a scene into print

zones ranging from pure black to white. A luminance reading is taken for a subjectively-defined

middle-grey tone. Readings are taken for light and dark regions and a dynamic range can be

determined, and an appropriate choice for middle-grey ensures that the maximum possible detail

is retained. Reinhard et al. take a user-specified value for middle grey. The log average luminance

of the input image is then mapped to this value by linear scaling. A spatially uniform operator

is then used to compress the high intensities in the image. Spatially varying tone reproduction

is introduced in a manner akin to “dodging and burning” in photography. This allows contrast

to be controlled locally in the image over regions bounded by large contrasts. This was based

on a centre-surround function derived from a model of brightness perception by Blommaert and

Martens [BM90]. They tested their method against existing tone reproduction operators with a

broad range of HDR images.

This operator uses a slightly different definition for dynamic range. In computer graphics, dy-

namic range is held to be the ratio of the highest to the lowest scene luminance, whereas Rein-

hard et al. adopt the photographic definition that dynamic range is the ratio of the highest and


lowest luminance regionswhere detail is visible. This results in images with ranges lower than

they would be if the computer graphics definition was used. With the standard computer graphics

definition of dynamic range it is difficult to know how successful compression of an HDR image

will be. Using the photographic definition, Reinhard et al. correlate dynamic range with difficulty

of compression, using this to predict how challenging tone reproduction for a given image will be.

This method is simple, fast and computationally efficient. As with other recent tone reproduction

operators, perceptual accuracy is not attempted. Instead they aim to produce credible results and

an image that is pleasing in appearance.

Ashikhmin [Ash02] has produced a tone mapping operator which preserves image details and also

conveys the compression of absolute brightness in a low dynamic range image using a multipass

approach. First, local adaptation luminance is estimated by determining the largest sufficiently

uniform neighbourhood for each pixel. Next, the tone mapping (using a TVI function) is applied,

using the local adaptation information to produce a locally linear mapping. Finally, local contrast

is estimated, thus preserving detail throughout the image. This approach is simple in implementa-

tion and moderate in computational expense.

Related effects

Replication of visual effects that are related to the area of tone reproduction include the modelling

of glare. Spencer, Shirley, Zimmerman and Greenberg [SSZG95] developed a method for repli-

cating glare effects. The idea of adding glare effects was previously recognised by Nakamae et

al [NKON90], although their algorithm did not account for the visual masking effects of glare.

Spencer et al. produced psychophysically-based algorithms for adding glare to digital images,

simulating the flare and bloom seen around very bright objects, and carried out a psychophysical

test to demonstrate that these effects increased the apparent brightness of a light source in an

image. While highly effective, glare simulation is computationally expensive.


Tone reproduction choices

With a large number of operators available, and validation of tone reproduction operators in its

infancy, the choice of tone reproduction operator is currently a matter of deciding on the best

tool for the job. Currently, there are no defined criteria for selecting the best tone reproduction

operator for a specific task. Initial validation studies have been undertaken [DMMS02, LWC02]

which have led to the development of new operators based on this assessment [DMMC03], but as

yet no formal framework for comparison has been established.

2.5.3 Gamut mapping

While the previous section deals with the range of image intensities that can be displayed, display

devices are also limited in the range of colours that may be displayed. The termgamutis used to

indicate the range of colours that the human visual system can detect, or that display devices can

display. Our work does not deal with issues regarding colour appearance (a well-established field

in its own right), but this section is included to give an overview of factors peripheral to those that

we address in this thesis.

Even with 24-bit colour, sometimes indicated as ‘millions of colours’ or ‘true colour’, there are

many colours within the visible spectrum that monitors cannot reproduce. To show the extent

of this limitation for particular display devices, chromaticity diagrams are often used. Here, the

Yxy colour space is used, where Y is a luminance channel (which ranges from black to white via

all greys), and x and y are two chromatic channels representing all colours. Figure 2.17 shows a

chromaticity diagram indicating the gamut of colours visible to humans, and two restricted gamuts,

one for a typical monitor and one for a printing device. Given that the triangle indicating monitor

capability is completely contained with the shape of all visible colours, there are many visible

colours that cannot be reproduced on a monitor.

Assuming that the some of the colours to be displayed in an image are outside a monitor’s gamut,

the image’s colours may be remapped to bring all its colours within displayable range. This

process is referred to as gamut mapping [BF99, GWA90]. A simple mapping would only map out-

2.6 Summary 55

1

2

3

Green

Yellow

White

Blue-green

Cyan

Blue

Violet

ReddishPurple

Red

y

x

Figure 2.17: Chromaticity diagram showing the range of colours that humans can detect (1), aswell as the ranges of colours displayable on a monitor (2) and printable on a printer (3). The x andy axes show values for the x and y chromaticity coordinates respectively. (After [War00].)

of-range colours directly inward towards the monitor’s triangular gamut. Such a ‘colorimetric’

correction produces visible artefacts. A better solution is to re-map the whole gamut of an image

to the monitor’s gamut, thus remapping all colours in an image. This ‘perceptual’ or ‘photometric’

correction may avoid the above artefacts, but conversely there are many different ways in which

such remapping may be accomplished. As such, there is no standard way to map one gamut into

another more constrained gamut.

2.6 Summary

This chapter has reviewed the background information pertaining to the work presented in this

thesis. The information on light helps to provide an understanding of the following chapter. The

information on visual psychophysics is employed in the design of all of the experiments that we

present. The information on displays forms the basis for our work described in Chapter 5.

2.6 Summary 56

Chapter 3

The viewing environment

This chapter examines ambient lighting in the viewing environment, and details current measures

which could be used to strive for perceptual fidelity. Related work in the fields of ergonomics and

medical imaging display is described.

3.1 The influences of ambient illumination

VDUs are open to influences from the environment in which they are located. A major factor

is that the screen of a display device may reflect any light present in the viewing environment.

The average amount of light present in a room is known as theambient illuminationand it is the

reflection of this off the screen of a monitor that affects the perceived contrast of displayed images.

A computer monitor does not fill the whole of the visual field, and as a result, visual adaptation

is partly determined by the ambient illumination present. It is estimated that under normal office

conditions, between 15% and 40% of illumination reaching the eye via the monitor will indirectly

come from the reflection of ambient light [War00].

Ambient light may be assumed to be uniformly distributed over a screen. This is true in many

working conditions with overhead lighting, but not for an environment where a spotlight (such as

a table or desk lamp) is aimed towards the screen. As with light propagation, described in Sec-

57

3.1 The influences of ambient illumination 58

tion 2.1.2, reflections may be specular or diffuse. Specular reflections occur when light emitted or

reflected by objects is reflected in one direction. Diffuse reflections cause an increase in luminance

in all directions [RJP87]. CRT technology is particularly prone to specular reflection on the screen.

These can often be overcome by adjusting the angle of the screen relative to the light source and

the viewer. LCDs also suffer, but as they are more mobile they can often be easily moved, whereas

a CRT’s bulk means it cannot [SFP99]. Unfortunately, adjusting the viewing angle of an LCD

screen may corrupt the appearance of colours due to the viewing angle dependency.

Reflected ambient illumination may produce a form of glare. This is caused when vision suffers

due to too much brightness. Either the user suffers visual discomfort (discomfort glare), or cannot

see well enough to perform a task (disability glare). A formula for disability glare (defined by a

reduction in contrast) was produced through a series of experiments by Holladay [Hol26], deter-

mined by the position of the source with respect to the user and by the amount of light entering

the user’s eye. Thus:

Contrast reduction=kEθ2 (3.1)

whereE is the illuminance from the glare source reaching the eye,θ is the angle between the line

of sight and the direction of the view gaze, andk is a constant depending on the age of the observer

(given that age causes changes in the consistency of fluids in the eyeball) [Obo95].

For discomfort glare, the international formula for measurement is the CIE’sglare index, also

called aUnified Glare Rating(UGR). This is calculated by:

Discomfort glare=L1:6

s ω0:8

LBP1:6 (3.2)

whereLs is the luminance of the source,LB is the average luminance of the background,ω is the

angular size of the source andP is the position index (indicating the effect of the source’s position

on glare) [Pri99]. As a rough guide, a UGR of less than 10 is rated as ‘barely perceptible’, while

a value of 28 or more is considered ‘intolerable’ [Obo95].

In office environments where computers are widely used, lighting design must consider the effect

3.2 Accounting for viewing conditions 59

LOCATION ILLUMINANCE (LX ) LIMITING UGR

General offices 500 19

Computer workstations 300–500 19

Drawing boards 750 16

Table 3.1: Typical lighting recommendation for offices. The Limiting UGR refers to the CIE’sUnified Glare Rating classification of discomfort glare, calculated through knowledge of thesource luminance, background luminance, and position. (After [Cha].)

of reflected light. Table 3.1 gives the recommended lighting values and glare rating for offices.

Two conflicting areas exist: the aforementioned reduction in contrast (a form of disability glare)

caused by ambient light, and the requirement of an appropriate level of ambient lighting to carry

out other visual tasks [Pri99].

3.2 Accounting for viewing conditions

Correcting for reflections off computer monitors typically follows one of three approaches: the

display device can be physically altered to reduce reflections; the environment can be adjusted,

thereby controlling the ambient light, or the environment can be characterised and the effects of

the ambient light can be taken into account when an image is displayed by applying some form of

algorithmic correction.

3.2.1 Physical alterations to the hardware

To physically alter the display device, anti-glare screens may be fitted to reduce reflections. While

this changes the amount of light reflected off a screen, it does not eliminate the problem — it

merely changes it in an uncalibrated manner as the amount of light reflected still depends on the

amount of light present in the environment. As this ambient quantity is typically unknown, anti-

glare screens may be viewed as a quick fix rather than a principled approach. Also, although


screen reflections may be reduced, this can be at the expense of reduced screen brightness and

resolution [Obo95].

Although monitors have controls labelled ‘Contrast’ and ‘Brightness’, these specify the luminance

level and the black point of the monitor, respectively. The black point should be set to true black,

while the contrast level setting depends on preference. However, setting this excessively high can

produce problems such as sensitivity to flicker, reduced contrast due to light scatter and defocus-

ing of the electron beam [Poy03]. It is therefore recommended that these controls are not used

to reduce the effect of ambient light, and should instead be set only once, and left unchanged

thereafter.

3.2.2 Viewing environment standards

The viewing environment may be controlled to conform to known standards. The International

Standards Organization (ISO) has specified a controlled viewing environment [ISO00], listing a

wide range of prerequisites that should be fulfilled to achieve the best possible viewing conditions

when working with images displayed on screen, thus reducing inconsistencies in image percep-

tion. For many applications, adhering to this standard is impractical as it includes designing the

environment to minimise interference with the visual task, baffling extraneous light, ensuring no

strongly coloured surfaces (including the observer’s clothing) are present within the immediate

environment, and ensuring that walls, ceiling, floors, clothes and other surfaces in the field of view

are coloured a neutral matt grey with a reflectance of 60% or less.

While such guidelines are a step towards a controlled viewing environment, such specific condi-

tions are not always available, or indeed feasible. Much work is carried out in non-specialised

office space, and this must conform to a different set of standards: legislation on workspace con-

ditions. The UK’s Health and Safety at Work Act 1974 [Her74] states that employers should

provide lighting appropriate to the work space and its activities. Further to this, specifications of

the European Commission directive 89/654/EEC [Eur89] regarding minimum safety and health

requirements for the workplace, are met with a level of illumination not less than 200 lx in all con-

tinuously occupied work areas. A further directive, 90/270/EEC [Eur90], specifies requirements


for work with display screen equipment. The minimum requirements state that ‘the screen shall

be free of reflective glare and reflections liable to cause discomfort to the user’. In terms of the

viewing environment, the directive is more specific:

(b) Lighting

Room lighting and/or spot lighting (work lamps) shall ensure satisfactory lighting

conditions and an appropriate contrast between the screen and the background envi-

ronment, taking into account the type of work and the user’s vision requirements.

Possible disturbing glare and reflections on the screen or other equipment shall be

prevented by coordinating workplace and workstation layout with the positioning and

technical characteristics of the artificial light sources.

(c) Reflections and glare

Workstations shall be so designed that sources of light, such as windows and other

openings, transparent or translucid walls, and brightly coloured fixtures or walls cause

no direct glare and, as far as possible, no reflections on the screen.

Windows shall be fitted with a suitable system of adjustable covering to attenuate the

daylight that falls on the workstation. [Eur90]

These directives are established to ensure optimum conditions for office workers, but adherence to

these, including additional requirements, such as the provision of natural light, mean that the ISO’s

controlled viewing environment, described above, is far more difficult to achieve. For this reason,

control of the viewing environment is not a practical or easy approach to controlling ambient light,

and is therefore not widely adopted.

3.2.3 Measurement and image correction

To characterise and correct for the reflective properties of display devices, the amount of reflected

light needs to be measured. Currently, this requires expensive and specialised equipment such as

photometers, illuminance meters or spectroradiometer. Although no changes to the physical envi-

ronment need to be made for this approach, the cost of characterising display reflections is simply


too high to be practical for many applications. This appears to be a major reason why it is not

standard practice to routinely correct for reflections off display devices. Additionally, the ability

of hardware devices to measure the effect of ambient lighting is still lacking, as accurate measures

can require more luminance data than is practical to collect [TV95], or cannot be incorporated in

physical measurements [BS98].

The following sections describe existing approaches that can be used to correct images subject to

ambient illumination.

Gamma adjustment

Ware [War00] has identified the effect of light reflections on the appearance of images. He ex-

presses the presence of ambient illumination by adding a constant after gamma correction

Ld = LmaxLγ +A (3.3)

whereLd is the luminance output,L is the luminance input to the monitor andA is the ambient

illumination reflected from the screen. He suggests that a possible solution would be to apply

gamma correction with a lower value of gamma than the display device itself would dictate. A

value of aroundγ = 1:5 is proposed.

ICC profiles

As shown in Section 2.5.3, an image’s colours may be gamut mapped to bring all its colours

within displayable range. In addition to this procedure, it is desirable to ensure that colours appear

consistent regardless of the display medium. A colour management system can be employed

to ensure colour fidelity across various platforms. In 1983 the International Color Consortium

(ICC) was established to create a specification for cross-platform fidelity through the use of a

standard, reference colour space, independent of viewing environment. ICC profiles are normally

used to convert images for reproduction on different display devices such that the perception of


Application

GraphicsLibrary

Imaging Library

Default Colour

ManagementModule

3rd PartyColour

ManagementModule

3rd PartyColour

ManagementModule

ProfilesColour management Framework

Interface

Figure 3.1: ICC colour management architecture. (After [ICC03].)

the displayed material is minimally affected [ICC03]. Each piece of hardware has anICC profile

which conforms to ICC specification and may be correctly interpreted by other users as they refer

to a standard colour space (Figure 3.1). Thus, a profile for a camera, monitor display and printer

allows these devices to produce consistent colour.

Colour appearance models

Ambient illumination may cause displayed colours to appear desaturated, which adds to the ‘washed

out’ appearance of images [LV82]. To counter this, colour appearance models attempt to predict

how colours are perceived in particular environments, taking into account the ambient light re-

flected off the screen, and any changes in the state of adaptation of the viewer [Fai98]. They are

useful and important tools because they aid in the preservation of colour appearance across display

environments where the monitor does not fill the whole of the field of view.

Many colour appearance models are related to each other [Hun96, Fai97, HL97, ZW97, Fai98,

FJ02, LLH+02, MFH+02] and are logical revisions and extensions of previous versions, and all

aim to predict the appearance of a coloured patch under specific viewing conditions. This is


achieved by computing appearance correlates such as brightness, lightness, colourfulness, chroma,

saturation and hue from relative tristimulus valuesXYZ, the adapting field luminanceLA, the

relative tristimulus values of the white pointXWYWZW, the relative luminance of the background

Yb and the degree of adaptationD [MFH+02]. Their use is therefore advocated alongside this

work. However, colour appearance models do not address the specific issue of monitor reflections.

Contrast manipulation

Images are made up of contrast variations (i.e. differing levels of luminance), so the ability to alter

what is seen by manipulating these variations is useful.Linear contrast stretchingis a method of

manipulation that linearly scales the pixel values in an image, thereby ‘stretching’ the range of

intensity values to span a larger range. In its simplest form, the desired range of the luminance

values is determined (for example,[0;255]) and the actual range of luminance values present in

the image (the input range) is found. This input range is then mapped to the display range. While

this is a simple procedure, a drawback is that the presence of an outlying high or low-value pixel

may produce an unrepresentative scaling [SHB99].

A form of contrast manipulation that does not suffer from outliers ishistogram equalisation. A

histogram of an image describes the frequency of occurrence of each pixel luminance value in that

image. An image with high contrast has a histogram with a broad spread of values. Histogram

equalisation employs a non-linear function to remap the luminance values so that the range of grey

levels is expanded, and the output image therefore contains a uniform distribution of intensities.

While histogram equalisation produces a uniform intensity distribution,histogram specification

can be used to enhance certain luminance values in an image. This is achieved by mapping a

luminance range into a desired distribution range using histogram equalisation as an intermediate

step.

Contrast manipulation can be viewed as a form of tonemapping, but does not claim any perceptual

attributes, and so the perceptual fidelity of the resulting image cannot be guaranteed.

3.3 Related work 65

3.3 Related work

Previous work into the effect of reflected ambient light on perception has tended to be related to

colour appearance, as described above, or in the area of medical imaging. Additionally, work has

been carried out in the field of office ergonomics. This section provides an overview of research

in these areas of ergonomics and medical imaging.

3.3.1 Ergonomics

In addition to the standards described in Section 3.2.2, guidelines also exist pertaining to how

an environment affects the user. This is an area known asergonomics(sometimes referred to

as human factors) and concerns human interaction of technological and work situations. The

objective is to enhance efficiency (by increasing productivity and reducing errors, for example)

and to improve working conditions [SE87]. Vision is of particular interest in this discipline. The

Ergonomics Society reports that:

Vision is usually the primary channel for information, yet systems are often so poorly

designed that the user is unable to see the work area clearly. Many workers using

computers cannot see their screens because of glare or reflections. Others have insuf-

ficient lighting and suffer eyestrain and reduced output as a result. [Soc]

Computer use can be problematic in terms of office ergonomics. The monitor itself is a source

of light, yet often office work requires viewing on-screen and paper documents at the same time.

Poor or inadequate lighting can lead to eye strain, causing headaches, and users may adopt poor

posture when trying to read something in low light levels. By contrast, too much illumination can

cause glare, leading to eye irritation, and poor posture due to the user moving to avoid glare. Eye

discomfort may even be caused by a lack of colour in the surroundings. The legislation on working

conditions described previously make use of ergonomic principles to ensure the wellbeing of office

workers.

3.3 Related work 66

Work by Schenkman, Fukuda and Persson [SFP99] evaluated the visual effect of monitor glare

through the measurement of subjective scales and eye movements. They presented participants

with an image and a piece of text under four conditions: non-glare, specular, diffuse and com-

bined specular/diffuse reflections. Eye tracking was used to record participants’ eye movements,

measuring percent viewing time, average velocity of eye movements, average viewing duration,

maximum eye movement direction and secondary eye movement direction. In addition to this,

participants were asked to provide their subjective responses on scales by assigning a value of 1

(lowest) to 7 (highest) to a number of categories. These included picture quality, irritation, total

impression and legibility. Analysis of the eye movement responses showed no significant effect on

participant responses due to reflection conditions. However, the subjective scaling was significant

for all scales, and showed that participants rated the combined specular/diffuse glare as being the

most disturbing, followed by the specular reflections, then the diffuse reflections. This led them

to conclude that lighting designers should avoid creating brightness fields that lead to specular

reflections. This lends support to the European Directives described above.

3.3.2 Medical imaging

Medical imaging, and in particular, radiology, requires the interpretation of images on either film

or soft-copy. In both cases (film displayed on a lightbox, or on-screen soft-copy) contrast dis-

crimination is important to ensure that the radiologist detects any relevant information on the

radiograph. For this reason, ambient light needs to be kept low, but cannot be completely absent

as enough illumination for paperwork may still be required.

In 1982, Alter, Kargas, Kargas, Cameron and McDermott investigated the influence of ambient

light on visual detection of low-contrast targets in a radiograph [AKK+82]. They carried out their

experiments under two different ambient lighting conditions — fluorescent room lights on, and

room lights off. In addition to this, they varied the lightboxes from a single illuminated viewing

area through to all viewing areas illuminated, resulting in total of 14 conditions. In general, they

found that the visual detection rate was higher when the ambient lighting was lower, and this was

particularly due to extraneous light from surrounding lightboxes.

3.3 Related work 67

In the same year, Baxter, Ravindra and Normann examined changes in lesion detectability in film

radiographs [BRN82]. This work specifically focused on physiological mechanisms in the retina

that can affect contrast perception. Their psychophysical experiments showed that light adaptation

effects can influence the detectability of low-contrast patches, and that extraneous peripheral light

affects visual sensitivity.

Work by Rogers, Johnston and Pizer in 1987 used luminance-discrimination threshold measure-

ment to determine the effect of ambient light on electronically displayed medical images [RJP87].

They were motivated by the fact that images are viewed on multiple displays, and are discussed

with colleagues in different locations, so the information needs to remain constant, without alter-

ation by display device or viewing environment. They investigated this effect using three ambient

light levels typical to radiology reading rooms — 4, 40 and 148 lx (the first two values correspond

to electronic display reading rooms, and the latter represents a typical low-end value for a lightbox

reading room). We have adopted their type of experimental procedure (a two-alternative forced

choice procedure) for our first experiment, so a detailed description of this is given in Section 4.2.

Rogers et al. measured JND detection under different ambient light levels for 4 stimuli. Their

first experiment held the stimuli constant, so that the same stimuli were viewed under each light

condition. Therefore, any changes in discrimination would be attributable to either the ambient

light, or adaptation in the participant. A second experiment varied the stimuli in accordance with

the ambient light, so that it appeared constant regardless of viewing conditions, so that any chances

in discrimination would therefore be due to changes in the visual sensitivity (i.e. the adaptation

level) of the user. Their results for the two experiments indicated that there was no significant

change in adaptation levels for each ambient light condition, but that the ambient light did produce

a significant change in the appearance of the displayed image.

Further to the above work, recent years have seen a move to digital radiology in the USA, where

it has almost entirely replaced hardcopy film. This has resulted in the establishment of the Digital

Imaging and Communications in Medicine (DICOM) standard in 1993. The DICOM Standards

Committee aims to achieve compatibility between imaging systems, and is widely supported by

medical professionals and vendors. Among its current activities, DICOM provides standards on

diagnostic displays, with the goal of visual constancy of images delivered across a network. Their

3.4 Summary 68

‘Greyscale Standard Display Function’ [Nat03] proposes that every sensor quantisation level maps

to at least one JND on the display device. Their function is derived from Barten’s model of

human contrast sensitivity [Bar92] to meet the objective of a perceptual linearisation of the display

device. This perceptual linearisation ensures efficient utilisation of the input luminance levels; if

luminance levels are indistinguishable, they are wasted, and if they are too far apart, the observer

may see contours.

Annex E of the DICOM greyscale standards [Nat03] is entitled ‘Realizable JND range of a dis-

play under ambient light’, and it describes how the dynamic range of an image may be affected

by veiling glare, by noise or by quantisation, in that the theoretically achievable JNDs may not

match the realised JNDs that are ultimately perceived. These standards assume that the emissive

luminance from the monitor and the ambient light are both measured using a photometer.

3.4 Summary

The factors described above that contribute to viewing conditions are pervasive in all decisions

taken in the work that we present. While specialised fields may cater for perceptual fidelity through

the provision of a purpose-built environment in which to work, many users will not have this

facility and must pursue a pragmatic alternative. They will have to seek a practical approach that

involves a trade off between ease-of-use and perceptual loss. In the following chapters we offer

a realistic and serviceable way of measuring and correcting for reduced contrast due to ambient

reflections.

Chapter 4

Measuring reflected ambient light

This chapter constitutes the main contribution of this thesis — a method of measuring the effect of

ambient light without the need for specialised equipment, using the viewer’s perceptual response

to the environment in which they are located. We use this method in the next chapter to enable us

to correct for the effect of ambient light. In this chapter, we discuss the theory behind the experi-

mental framework and detail the decision-making process used in designing the experiments. Our

two measurement experiments are then described. The first experiment was designed to measure

contrast discrimination under various levels of reflected ambient light, indicating that there is a re-

duction in perceived contrast due to extraneous illumination. The second experiment was designed

to be a concise, rapid version of the first that can be undertaken in a more practical manner. Also

included are accounts of pilot studies and our alternative experimental ideas, which were carried

out to decide upon the optimal experimental process.

4.1 Conducting experiments

This section provides an outline of the considerations required when conducting psychophysical

experiments. In this, we detail the planning behind all the experiments undertaken for this thesis.

69

4.1 Conducting experiments 70

4.1.1 Hypotheses

Our experimental framework pursues a certain course — one that is commonly undertaken in

psychological research. Psychophysical experiments follow a set process from their inception to

their conclusion. An initial observation or idea is developed through background research into

a testable hypothesis. This testable, orresearch hypothesis(H1), puts forward a relationship be-

tween data. The research hypothesis may benon-directional, where a difference between groups

is expected but the direction of this difference is not specified (for example, in this thesis, the

hypothesis could be that reflected ambient light affects the perception of perceived contrast); or it

may bedirectional, where the direction of the difference is specified (for example, an increase of

reflected ambient light causes a decrease in perceived contrast). A common practice is to test this

experimental hypothesis against anull hypothesis(H0), which maintains that there is no difference

between conditions. The null hypothesis is an implied hypothesis and is accepted as true in the

absence of any other information. It provides a benchmark, defining a range within which chance

may be a factor [EKR99, Sal00].

A hypothesis suggests an effect on thevariablesin a condition. A condition has adependent vari-

able that may be influenced by changes in one or moreindependent variables. For the hypotheses

proposed in this thesis, the dependent variable is contrast discrimination, while the independent

variable is ambient light. Thus, manipulation of the independent variable may lead to changes in

the dependent variable [Fie00].

The purpose of determining hypotheses for an experiment is to permitsignificance testingthrough

the use of statistical analysis. Any results obtained from experimentation may feasibly have been

caused by chance alone. A level of statistical significance is therefore given to show that this

is not the case. An estimate of the probability,p, represents how much of the result is down to

chance. Thus, a largep value indicates that chance played a large part in the results, and a small

p value suggests that the results are due to an effect, rather than statistical accident. A:05 level

of significance is conventionally used when reporting experimental results. Ifp is less than this

value, the null hypotheses can be rejected, as an observed effect has a low probability of being

caused merely by chance.


4.1.2 Ethics

Our experiments were carried out using human observers: participants from the University of Bris-

tol. A number of ethical issues need to be considered when conducting experiments with human

participants. The British Psychological Society has a published Code of Conduct to promote good

practice [Bri02]. The importance of obtaining consent from the participants is emphasised, ensur-

ing that the participants are aware of the nature of the experiment and any consequences it might

have. Where disclosure of the experimental procedure might introducebias in the responses, it

is sufficient to provide full information about the aims and outcomes retrospectively in adebrief-

ing. For the following experiments, all participants received a full debriefing, including results, by

e-mail.

Participants should be made aware that they are free to withdraw from the experiment at any time.

This is particularly important where the participant sample is drawn from a population who are

required to take part in an experiment, such as students receiving credit for participating as part of

a course [EKR99]. Of the three main experiments in this thesis, Experiment 2 used participants

who were receiving course credit for their participation.

When a participant gives their informed consent, they should also be assured that confidentiality

is upheld, and none of their information will be revealed without their permission. For the experi-

ments detailed in this thesis, participants were provided with information about the experiment on

a consent form (Appendix A.1). Their information pertaining to the experiment was then stored

under a randomly allocated identifier.

4.1.3 Sample design

In the following experiments, the participants are either student, research or staff members of

the Department of Computer Science at the University of Bristol. The main purpose of running

experiments is to be able to generalise from a smaller subset of a population. This population

may be a specific group, such as computer users, or people under a certain age; or it may refer to

human behaviour as a whole. In any case, since the population of a given group may be numerous,


experiments should be conducted on a subset orsample(denoted byX) that is representative of

that whole population. Approaches must be taken to avoidsampling biaswhere the subset chosen

differs from the norm [Coo99].

The selection of the type and quantity of participants depends upon the purpose of the study.

The experiments developed in this thesis measure low-level visual phenomena, meaning that there

should be little variation in participants reactions. As cognitive processes are not involved, the

background of the participant is also not an issue [KHI+03].

This thesis uses arepeated measuresdesign for all experiments contained herein. In a repeated

measures design the same participants carry out the same task for each condition, providing a

consistent set of results across all conditions. Ideally, if the participants are identical in each

condition, and all other variables are controlled, then any effects that arise should be due to changes

in the independent variable [Coo99].

4.1.4 Pilot studies

Much of the work involved in running psychophysical experiments takes place in the preparation

phase, where the experimental framework is designed and a pre-study, orpilot study, is carried

out to check feasibility and highlight any problems before the actual collection of experimental

data begins [Coo99]. A pilot study tends to be a scaled-down version of the intended experimental

process, with any feedback gained during the pilot version being used to prepare a more robust

and refined full version of the experiment.

Both Experiments 1 and 2 had initial pilot studies to allow the set up to be checked and to determine

the anticipated responses of the participants. The pilot version also enabled the testing of the

written instructions, ensuring that the participants fully understood the process of the experiment

and their required actions, without any additional input. Additionally, the pilot versions allowed

the experiments to be timed, thus determining an optimal experimental process.

Separate pilot studies were also required for each experiment due to the fact that the same equip-

ment, locations and participants were not always available at the same time. These changes in


set-up restricted direct comparisons between experimental results, but the experimental design

was such that direct comparisons were not essential, or indeed desirable, and the statistical analy-

sis of each experiment could therefore stand alone and be compared on that basis.

4.1.5 Problems with psychophysics and statistical significance

As mentioned above, psychophysical experiments may not require a large sample size, and low-

level visual studies of the type carried out for this thesis may involve single-figure numbers of

participants [BRN82, RJP87, SFP99]. However, although this is acceptable and adequate, prob-

lems arise with statistical analysis. When comparing means of results for significance testing, it

is important the data is normally distributed. With small sample sizes, this is difficult (or indeed

impossible) to prove. There is no simple solution to this problem. Large-scale projects may have

the time and resources to use enough participants — or enough repeated measures — to ensure

a normal distribution of results. In the case of this thesis, however, this option was not possible.

Participants were volunteers, and there was no means of financing their participation or offering

renumeration, so the time available was limited to what they gave freely. Further constraints were

imposed due to pressure on resources. Decisions had to be made as to the best use of time and

people, and the experiments were designed with this in mind. These restrictions do not affect

the validity of the results, but they do make it harder to analyse those results. This problem is

by no means limited to our work in this thesis — it is common to many psychophysical experi-

ments [WH01].

One approach that can be used to counter the effect of small sample size is to use a non-parametric

test in addition to means analysis. When these two types of tests are used in conjunction, and both

give a statistically significant result, it can remove the doubt cast on the validity of small sample

means testing.

4.2 Experiment 1: contrast discrimination thresholds 74

4.2 Experiment 1: contrast discrimination thresholds

In order to establish the quantity of light reflected off a computer monitor in commonly-encountered

viewing environments, and to establish how this influences the perception of contrast, a psy-

chophysical user study was undertaken, with images displayed on cathode ray tube (CRT) mon-

itors. As described in Section 2.4, Liquid Crystal Display (LCD) monitors are growing in pop-

ularity, but the image quality is affected by the viewing angle. This is particularly important if

the user is outside the optimal viewing position, or if there are multiple users. Also, where the

non-linearity of a CRT can be described by a power law, this does not hold well for describing

LCDs [BPR02]. Hence, we assume that for applications where perceptual fidelity is of crucial im-

portance, current LCD technology will not be used. However, had there been a way of ensuring a

consistent viewing experience for LCDs throughout our experiments, we would have incorporated

the use of these screens.

The image that reaches the eye of the observer is a combination of emitted light and reflected light.

The surface of a CRT screen is typically made of glass, and so the reflections on the glass are

specular. A full characterisation of these reflections would accordingly be viewpoint dependent.

However, since glass is predominantly translucent, most light passes through the glass and lights

the layer below, resulting in diffuse reflections. In addition, for most viewing conditions direct

specular reflections may be minimised with appropriate lighting design [Rea00]. For this thesis,

it is therefore assumed that the environment causes a uniform increase of luminance across the

CRT screen. Further, it is assumed that the environment is lit by white light, i.e. colour appearance

issues are not addressed. (However, the work does not preclude the application of a suitable colour

appearance model.)

The initial experiment followed that of Rogers et al. [RJP87] who measured contrast discrimi-

nation thresholds for computer generated images under three ambient light levels (Section 3.3.2).

Their work was concerned with the effect of ambient light on radiographic images, and this is re-

flected in the low ambient light levels they chose to examine. This experiment uses ambient light

values that reflect common workplace conditions.


4.2.1 Hypotheses

Based on the work of Rogers et al. , and on existing psychophysical knowledge (that of Weber’s

Law, Section 2.2.4), we predicted that the presence of reflected ambient light in the viewing en-

vironment would affect the perceived contrast of an image displayed on a CRT monitor. The

research hypothesis was that there exists a significant difference between JND perception in the

dark condition, JND perception in themediumcondition, and JND perception in thelight condi-

tion, H1 : Xdark 6= Xmedium 6= Xlight .

4.2.2 Participants

Six individuals (three male, three female) participated in this experiment. All had normal or

corrected-to-normal vision. All participants were fully adapted to the prevailing illumination con-

ditions before beginning their task. All participants took part in all conditions and the order of

their participation was randomised.

4.2.3 Conditions

Three light conditions were chosen for this study. In order to act as a ground truth for the ex-

periments, one condition had no ambient light present. The two other conditions were based on

common viewing environments observed in the workplace. The first condition (dark, 0 lux) —

the ground truth — contained no ambient light and consisted of a room painted entirely with matt

black paint. The tabletop was draped with black fabric. The only light came from the monitor

on which the experimental targets were displayed. The second condition (medium, 45 lux ) was

an office with white walls. No natural light was present. The sole illuminant was an angle-poise

desk lamp with a 60 watt incandescent tungsten bulb with tracing paper used to diffuse the light.

The third condition (light, 506 lux) was the same white-walled office as before, but with overhead

fluorescent reflector lights used instead of the desk lamp (Figure 4.1).

The ambient illumination values for each condition were verified using a Minolta CL-200 180Æ


Figure 4.1: Example of the set-up for thelight condition.

chromameter. This was mounted on a tripod and placed in a position equivalent to the participants’

viewpoints, 70–90cms from the screen. The CRT was a 19 inch Dell Trinitron monitor with

gamma correction applied to the displayed images, placed parallel to the light source to avoid

specular reflections.

4.2.4 Stimuli

The stimuli used in this experiment were noise images with af�2 power spectrum, equivalent to

a 1= f amplitude spectrum. They were created by randomising the amplitudeA and phase spectra

P in the Fourier domain [RST01]:

A(x;y) = r1 f�α=2

P(x;y) = r2A(x;y) (4.1)


Figure 4.2: Example stimulus consisting of noise with a 1= f amplitude spectrum.

with r1 and r2 uniformly distributed random variables,α the desired spectral slope andf =px2+y2 the frequency. An inverse Fourier transform was then applied to create a grayscale

image. This closely conforms to natural images [BM87, Fie87]. In addition, images with this

particular power spectrum are scale-invariant, which means that the power spectrum of the image

as it is formed on the observer’s retina does not change with distance. This permits an experi-

ment whereby the distance of the observer does not have to be as rigidly controlled as would be

the case with other stimuli. As previously mentioned in Section 2.2.5, observers are not equally

sensitive to contrasts at all frequencies, as evidenced by the Campbell-Robson contrast sensitivity

curves [CR68]. For stimuli other than those with power spectral slopes of�2, the exact spectral

composition of the stimulus would confound the results of the experiments as well as the use-

fulness of the approach. These targets have only one background luminance value (thepedestal

value), and a foreground luminance value which differs slightly from the background. To create a

two-tone image, the noise images were thresholded (Figure 4.2).

The experiment was concerned with finding the smallest observable difference between pedestal

and foreground under different lighting conditions. Targets had a pedestal value of either 5%, 10%

or 20% grey. The pedestal value of 20% grey is close the reference ‘mid-grey’, a commonly-used

photographic reference point. To maximise the luminance range, a technique known asbit-stealing

was employed, whereby 1786 levels of grey can be encoded in a 24-bit colour image through a

form of dithering that makes use of imperceptible changes in hue [TCL+92, Tyl97]. This increased


R G B99 99 9999 99 100100 99 99100 99 10099 100 9999 100 100100 100 100100 100 100

Table 4.1: Example of RGB values used in bit-stealing. Instead of two RGB values for grey(99,99,99 and 100,100,100), six intermediate values are created, thus increasing the luminanceresolution.

the luminance resolution, providing more accuracy for JND measurement. The resulting superfine

greyscale is achieved by exploiting the fact that RGB values consist of different luminances. Thus,

instead of stepping between the two grey levels (for example, RGB values of 99, 99, 99 and 100,

100, 100), six intermediate grey levels can be created, as shown in Table 4.1.

While bit-stealing provides the opportunity for higher accuracy in threshold detection, it can be

problematic as the saturation is greater at low levels, so bit-stealing to provide a pseudo-grey may

result in the undesirable appearance of colour. However, a pilot study showed that this effect was

not noticeable at the grey-levels used in the experiment.

4.2.5 Procedure

The main experiment took the form of a signal-detection task consisting of 120 trials. Atwo-

alternative forced choice(2afc) procedure, using two random interleaving staircases, was em-

ployed. This is a process whereby the participant’s answer can be verified, and the magnitude of

the stimulus is then modified dependent on that answer. The 2afc procedure presents the partici-

pant with a stimulus in one of two randomly selected intervals, and the participant must identify

in which interval the stimulus was present. The drawback of this technique is that, with only two

choices, even if the participant guesses the answer they will still make the correct choice half of

the time. Because of this, a large number of trials should be used (Farell and Pelli recommend

at least 60 trials [FP98]) to obtain a good threshold estimate. However, combining this procedure


with another method, as described next, can limit the probability of error.

The use of thestaircase method, in conjunction with the 2afc mentioned above, provides a more

efficient method of threshold detection, concentrating trials in a range close to threshold. The

staircase method is a variant on themethod of limits. The method of limits is one of three methods

proposed by Fechner to measure thresholds (the other two being themethod of constant stimuliand

themethod of adjustment) [SB94]. Using the method of limits, the stimulus is changed gradually

on each trial until the participant’s response changes. The intensity of the stimulus when this

response change occurs signifies the threshold.

Cornsweet adapted this method through the use of two random interleaving staircases [Cor72].

With this, an experiment begins with an intensity that a participant can definitely see, and de-

creases it until it can no longer be seen. As soon as the participant can no longer identify the

stimulus, its intensity is increased. This point of change is known as acontrast reversal. The

intensity of the stimulus continues to increase until the participant can once again see the it, fol-

lowing which the intensity begins to decrease again. The trials continue until a specific number

of contrast reversals have occurred, and the stimulus intensities at these contrast reversals are then

averaged to estimate the threshold. Figure 4.3 shows how most of the stimulus values are therefore

concentrated close to the threshold, making the procedure more efficient [SB94]. By interleaving

two or more concurrent staircases arbitrarily, randomness is introduced to ensure the participant

does not become aware of the process.

A pilot study, following the main procedure outlined below, was undertaken to observe the re-

quirements of the experiment and decide on the number of trials necessary to provide sufficient

data. The optimal length of the experiment was found to be around 120 trials per pedestal value,

which allowed for at least 4 contrast reversals to occur.

The main trial of Experiment 1 took the form of two 0.5 second intervals, separated by 0.5 seconds

of the pedestal grey value and followed by 4 seconds of grey before the beginning of the next trial.

The first interval was marked by a beep, and the second by a double beep. During one of the

intervals, a target was shown. Participants had to choose whether this target appeared in the first

or the second interval. The instructions shown to the participants are given in Appendix A.2.


y

y

n

y

n

y

n

n

n

n

y

n

y

n

y = correct repsonse

n = incorrect response

Trial

Stim

ulus

inte

nsity

1 2 3 4 5 6 7 8 9 10 11 12 13

Threshold value

Figure 4.3: Example of results using two interleaving staircases. Use of the staircase methodmeans that stimulus values are concentrated in the threshold region.

Following five correct selections, the contrast of the target was decreased towards the value of

the pedestal grey. Following an incorrect selection, the contrast of the target was set further from

the pedestal grey. With these increases and decreases, the stimulus level approaches the point

where the probability of a correct result is 87%, i.e. 0:50:2, where 0.5 is the 50% chance of

the participant giving the correct answer. This resulted in the collection of threshold values for

each participant, for each given pedestal value, under each of the ambient light conditions. The

experimental program flow is shown in Figure 4.4.

4.2.6 Results and discussion

The research hypothesis stated that JND detection would differ depending on the amount of re-

flected ambient light. Participants’ JND responses were measured to determine their sensitivity

to contrast perception under the three conditions (dark, mediumand light). The average JND

measurements are given in Tables 4.2–4.4 and in Appendix B.

Taking Participant D as an example, the JND value observed by this participant in thedark condi-

tion was 0.004962 for a pedestal of 5% grey. In themediumcondition, when some ambient light

was present in the viewing environment, this JND value increased to 0.005472 — more contrast

was required to discriminate the target from the background. When the ambient lighting was in-


display target in 1 of 2 intervals

decreasecontrast

increasecontrast

End

Start

correct detection

trials = 120 ?

yes no

yes

no

Figure 4.4: Flowchart showing procedure for Experiment 1.

DARK, MEDIUM , LIGHT,PARTICIPANT PEDESTAL 5% GREY PEDESTAL5% GREY PEDESTAL5% GREY

A 0.005534 0.005307 0.006338B 0.004980 0.005167 0.006771C 0.005460 0.005530 0.005955D 0.004962 0.005472 0.005759E 0.005964 0.005688 0.009213F 0.006305 0.005699 0.005855

Table 4.2: Experiment 1: average JND results for each participant, for each condition, pedestalvalue = 5% grey.

creased further in thelight condition, the JND value observed by Participant D also increased,

resulting in a measurement of 0.005759.



A 0.009335 0.009250 0.009415B 0.008307 0.007464 0.010490C 0.008153 0.008384 0.008597D 0.009786 0.008213 0.010583E 0.007852 0.009602 0.012601F 0.006976 0.008490 0.009529



A 0.007740 0.009428 0.008280B 0.007586 0.007854 0.008774C 0.007002 0.007874 0.010476D 0.006343 0.009005 0.009404E 0.007272 0.009241 0.010531F 0.008031 0.008764 0.010011



Due to the small sample size both means testing and a non-parametric statistical test were deemed

appropriate. It was not satisfactory to rely solely on means testing using Analysis of Variance

(ANOVA), as ANOVA generally requires 30+ participants so that a normal distribution of data

can be assumed. A non-parametric Friedman test, which does not require an assumption of nor-

mal distribution, was therefore also conducted to determine whether participants had performed

differently in detecting JNDs (contrast thresholds) under the three ambient lighting conditions.

ANOVA results

A repeated measures ANOVA compares three or more groups for variability, comparing the vari-

ance in the sample means between each group with the variance occurring within each group.

In this experiment, these three groups correspond to the three different lighting conditions. A

repeated measures ANOVA indicated that overall there was a significant difference in contrast

discrimination depending on the presence of reflected ambient light (F(2;10) = 13:21; p = :002).

Estimated marginal means showed that the mean JND size increased as the amount of ambient light

increased. Specific significant differences were: for a pedestal of 5% grey,F(2;10) = 4:636; p =

:038; for a pedestal of 10% grey,F(2;10) = 5:484; p = :025; and for a pedestal of 20% grey,

F(2;10) = 12:234; p = :002.

Friedman test results

When using a pedestal of 5% grey, the difference in JND perception between the three conditions

was significant (χ2(2)6:333; p = :042), as was the case for a pedestal of 10% grey (χ2

(2)9:00; p =

:011), and 20% grey (χ2(2)10:333; p = :006). These results again indicate that ambient lighting

has a significant effect on contrast discrimination when carried out on a CRT monitor under the

aforementioned conditions.

4.3 Experiment 2: rapid characterisation 84

Comparison with Rogers et al.

Our experiment followed the two-alternative forced choice procedure that was used by Rogers et al. .

However, we used different stimuli, and also incorporated a random interleaving staircases method

to improve on efficiency and accuracy. The results of our experiment support the findings by

Rogers et al. that the appearance of an image on-screen is altered by the presence of reflected am-

bient illumination. Further, the results indicate that the apparent reduction in perceived contrast is

increased as the ambient lighting is increased.

4.3 Experiment 2: rapid characterisation

The experiment described above highlights the significance of the contribution of reflected light

to the perception of contrast in complex images, and provides a JND measurement of contrast

perception for the tested level of illumination and display intensity. However, the method is of

little use in a practical setting due to the lengthy procedure (over one-and-a-half hours per person,

excluding periods of rest). Compromise was therefore sought between accurate measurement of

screen reflections, and practical use, with the aim of developing a rapid technique that did not

require any specialised equipment, using only the display device itself to gather information about

the viewing environment. The research hypothesis remained the same as that in Experiment 1:

that the presence of reflected ambient light in the viewing environment would affect the perceived

contrast of an image displayed on a CRT monitor.

4.3.1 Alternative and pilot experiments

In a manner analogous to simplified determination of the gamma value for a display device, as

described in Section 2.5.1, we sought a simple method for measuring contrast perception. We

considered a number of ideas in deciding on a concise method of Experiment 1. Pilot studies

were carried out for each of these in order to determine the most promising method of measuring

contrast discrimination in a quick and effective manner. In all cases, the core idea involved the


Figure 4.5: Simplified measurement using a Campbell-Robson chart. A curve denoting the line ofgrating visibility is drawn in dark conditions (left) and in the desired light condition (right).

user’s response to the display of a stimulus on screen, which could then used to determine the

amount of reflected light.

Using a Campbell-Robson chart

An initial thought was to display a Campbell-Robson contrast sensitivity chart on-screen and ask

the user to draw the line of visibility (denoting their contrast sensitivity) on it. This could be

carried out first in dark conditions, and then in the presence of ambient illumination (Figure 4.5).

The resulting two contrast sensitivity curves could then be compared. More sensitivity would be

expected when a line was drawn in the dark condition.

While this method is theoretically sound, it was quickly discarded, as immediate difficulties were

apparent. First, if a difference in contrast sensitivity was evident, it was not easily quantifiable.

Second, asking the user to draw a line on the image was not a controlled enough way of measuring

discrimination as inaccuracies could arise from the actual drawing process (with the use of the

mouse to draw the line, for example). Third, unlike the 2afc procedure used in Experiment 1, this

method was too subjective — there was no way of determining if the participant’s judgement was

correct or incorrect. Given that the image did not consist of discrete regions, a pilot study showed

that it was difficult for participants to pinpoint where the line should be drawn on the gratings.

It was decided, therefore, that a method should be devised such that the participant should be


Figure 4.6: A type of gamma chart used to measure contrast discrimination. (It should be notedthat scaling and printing of the image remove the appearance of fusing between the stripes and thelined area.)

presented with a distinct choice of options when determining what they could or could not see.

Using a form of a gamma chart

Given that the desired concise method was inspired by the simplified form of short-cut gamma

calibration, we devised an experiment using a type of gamma chart similar to that described in

Section 2.5.1. Gamma charts of the type shown in Figure 4.6 were created with different levels

of black (either 25%, 50% or 75%) in the lower half of the image. The participants viewed these

charts under the different lighting conditions, selecting the stripe which appeared to fuse best with

the black and white pixels. This resulted in a measurement of the display luminance in the dark

condition, and a corresponding measurement of display luminance plus an ambient term in the

illuminated conditions.

Problems were immediately evident with the user interaction. The concept of simplified gamma


charts had to be explained to participants unfamiliar with the process, and the appearance of fusing

between the top and bottom halves of the chart had to be described in great detail. Participants

also complained that blurring their vision by squinting their eyes (an action that facilitates the

appearance of fusing) began to hurt them after several trials. Also, the pilot study of this procedure

did not suggest a significant difference between conditions. As we were aware that significant

differences should exist, due to the findings of Experiment 1, we decided not to continue with a

full experiment of this type. Instead, a more sensitive method of measurement was sought.

Using a tableau of stimuli

Our third idea for the experimental method was to present the participant with a tableaux consisting

of stimuli similar to those used in Experiment 1. These could be presented in a two dimensional

matrix of stimuli with varying levels of contrast.

Two pilot studies were undertaken using this method. The first used randomly distributed stimuli,

and the second used ordered stimuli that increased in contrast from the top left to the bottom right

of the screen. Participants found the second of these to be more intuitive, as it was explained to

them that they should pick the square on the grid where they could just see the stimulus appear,

and that anything prior to this square should appear blank, and anything following it should be

more easy to distinguish.

Preliminary results from this second method suggested that a significant difference could be mea-

sured by this means. The use of discrete stimuli seemed to indicate that users had more confidence

in their responses (unlike the method which used the Campbell-Robson chart). It was therefore

decided that this was the most suitable method for Experiment 2.

4.3.2 Main experiment

The results from the aforementioned pilot studies led us to adopt the following procedure. It is

almost as straightforward as reading a value off a chart, and constitutes a sensible compromise


Figure 4.7: Grid of squares used for simplified characterisation.

between accuracy and speed. Using a tableau of stimuli under similar conditions to Experiment

1, the participants were shown a 10� 10 grid of squares each containing targets with increasing

contrast from the top left to the bottom right of the grid (Figure 4.7). For practical purposes,

the targets consisted of random noise images with a power spectral slope of�2, as detailed in

Section 4.2.4.

4.3.3 Participants

Twenty-one individuals participated in this experiment. All had normal or corrected-to-normal

vision. All participants were fully adapted to the prevailing light conditions before beginning

their task. All participants took part in all conditions and the order of their participation was

randomised.

4.3.4 Conditions

Three light conditions were chosen for this study, similar to those in Experiment 1, above:dark, 0

lux; medium, 80 lux; andlight, 410 lux. The ambient illumination values were verified as before.

Two 17 inch Sun Microsystems CRTs were used, fully calibrated with the appropriate gamma

correction applied to the displayed images.


4.3.5 Procedure

Again, the experiment constituted a signal-detection task. A tableau of images displayed in a 10

� 10 grid was shown. The pedestal value was set to either 5%, 10% or 20% grey. A target of

randomly generated 1= f noise was displayed in each square of the grid, with the contrast increas-

ing linearly in each square from the top left to the bottom right of the grid. The minimal contrast

value (0) increased to a pedestal-dependent maximum contrast value (0.004–0.006, determined

through the results of the Experiment 1, described above). The participants were given instruc-

tions which asked them to click once on the square where they couldjust noticesome noise on

the grey background, and it was explained that ‘Just noticeablemeans that it is the square closest

to appearing blank: the other squares contain either no noise or more noise’ (full instructions are

given in Appendix A.3).

By clicking on their chosen square, another tableau was displayed, this time with the contrast

increasing by a power of 2, effectively showing more squares closer to the threshold region. With

each choice made by the participant, the power increased, until the participant could only see

contrast in the high part of the curve, whereupon the power was decreased. For each pedestal

value, under each ambient light condition, the participant made 5 choices indicating in which part

of the tableau they perceived the minimal contrast. These values were then averaged to give an

average JND value for each individual, for each pedestal value, under each condition. This process

is outlined in Figure 4.8

4.3.6 Results and discussion

The research hypothesis stated that JND detection would differ depending on the amount of re-

flected ambient light, as with Experiment 1, above. Participants’ JND responses were measured

to determine their sensitivity to contrast perception under the three conditions (dark, medium and

light). An average threshold (JND value) was calculated for each participant under each condition.

These values are shown in Figure 4.9. Tables of results are given in Appendix B.

As with Experiment 1, Experiment 2 consisted of a repeated measures design with three or more


for each pedestal value

alter tableaurange

End

Start

tableauxper pedestal

=5 ?

yes

yes

no

display tableau

user selects JND

selected for all

pedestals?

no

Figure 4.8: Flowchart showing procedure for Experiment 2.

levels to the independent variable (the three lighting conditions). For this reason, ANOVA was

used to calculate these interactions with the dependent variable [Fie00].


0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

5 10 15 20

Avg JND value

Participant

Average JND values, dark condition, pedestal = 5% grey

Average JND values, medium condition, pedestal = 5% grey

Average JND values, light condition, pedestal = 5% grey

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

5 10 15 20

Avg JND value

Participant




0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

5 10 15 20

Avg JND value

Participant




Figure 4.9: Graphs to show the average JND values measured in Experiment 2 for pedestal valuesof 5% grey (top), 10% grey (middle) and 20% grey (bottom).

ANOVA results

A repeated measures ANOVA revealed an overall significant difference in threshold detection be-

tween the three lighting conditions,F(2;40) = 58:9; p < :001. These results indicate that when

measured by this rapid method, it can be shown that ambient lighting has a significant effect

4.4 Summary 92

on contrast discrimination. Specific significant differences were: for a pedestal of 5% grey,

F(2;40) = 65:770; p < :001; for a pedestal of 10% grey,F(2;40) = 37:414; p < :001; and for

a pedestal of 20% grey,F(2;40) = 35:761; p< :001.

Friedman test results

The difference in JND perception between the three conditions for a pedestal of 5% grey was

significant (χ2(2)34:048; p < :001), as was the case for a pedestal of 10% grey (χ2

(2)24:795; p <

:001), and 20% grey (χ2(2)25:810; p < :001). These results correspond to the ANOVA results,

confirming the rejection of the null hypothesis.

Comparison with Experiment 1

Although direct comparison cannot be made between the results of Experiment 1 and Experiment

2, both experiments have shown a significant difference between contrast perception under three

different levels of reflected ambient light. Both the ANOVA and the Friedman test results for

Experiment 2 produce a smallerp value than the results of Experiment 1. However, this cannot

be taken to mean that Experiment 2 is somehow more accurate in measuring contrast perception.

This smallerp value may indeed be a result of the larger sample size used in Experiment 2.

Nonetheless, it has been revealed that Experiment 2 is as valid a method of measuring changes in

contrast perception as Experiment 1, yet takes only a fraction of the time (generally no more than

2 minutes in total per person).

4.4 Summary

This chapter has described a method of measuring the effect of reflected ambient light on contrast

perception. A direct correlation was revealed between the amount of reflected ambient light and

the reduction in perceived contrast. Significant differences were revealed between JND discrim-

ination in three different lighting conditions. This relationship was also evident in our second

4.4 Summary 93

experiment, which was designed to perform the same task in much less time. For this second

experiment, we designed and implemented a rapid form of visual calibration, which takes only

minutes to complete, making it around 50 times quicker to carry out than Experiment 1. Thus,

Experiment 2 constitutes the main contribution to this thesis — a method of determining the effect

of ambient light in a quick and effective manner, with the user needing no equipment other than

the computer monitor itself.

In the following chapter, we extend our work to include a novel form of contrast manipulation that

can use the results from Experiment 2 to produce a method of luminance remapping, resulting in

an image displayed under ambient light appearing as it would look when displayed in darkness.

In a subsequent chapter, the success of this algorithm will then be validated through a similar

psychophysical method to Experiment 2.

4.4 Summary 94

Chapter 5

Correcting for Ambient Light

The amount of light reflected by a computer monitor may be indirectly measured with one of the

experiments described in the previous chapter. It is envisaged that the viewer establishes a JND

(∆Ld) in darkness, and a second JND (∆Lb) with normal office lights switched on. In both cases

some desired pedestal valueL will be used (such as 20% of the maximum display intensity, for

example).

The light that travels from the monitor to the eye is thenL in the dark condition, andL+LR in the

light condition. The termLR represents the amount of light diffusely reflected by the monitor and

constitutes the unknown value required for adjustment purposes. Using Weber’s Law,LR can be

computed with the following equations:

∆Ld

L=

∆Lb

L+LR

LR = L

�∆Lb

∆Ld�1

�(5.1)

95

5.1 Contrast adjustment 96

0

m

0 m

Out

put v

alue

Input value

L

L - LR

loss of dynamic range

negative voltage

Figure 5.1: Problems with remapping by subtraction. Dark pixels would produce a negative volt-age, and the dynamic range would be reduced.

5.1 Contrast adjustment

Under the assumption thatLd < Lb, and hence thatLR > 0, we should ideally subtractLR from

each pixel to undo the effect of reflected light. The reflections off the monitor would then add

on this same amount, thus producing the desired percept. Remapping luminance by subtraction

would also yield a function with a derivative of 1 over its range. Any other slope would result

in changes in contrast that may affect image perception. However, there are two problems with

this approach. First, dark pixels will become negative and are therefore impossible to display.

Negative pixels could be clamped to zero, but that would reduce the dynamic range, which for

typical display devices is already limited (Section 2.5.2). The second problem is that subtraction

of LR leads to under-use of the available dynamic range at the upper end of the scale. These

problems are illustrated in Figure 5.1.

5.2 Luminance remapping requirements

With the ability to measureLR through the measurement of JNDs, we seek a function that remaps

intensities such that the amount of contrast perceived around the pedestal valueL is the initial∆Ld,

5.3 Existing remapping methods 97

REQUIREMENTS PURPOSE

f (0) = 0 minimum input maps to minimum output

f (m) = m maximum input maps to maximum output

f (L) = L�LR input luminance (pedestal value) minus the ambient term

f 0(L) = 1 slope of one at pedestal value to maintain contrast ratios

f 0(x)� 0 monotonically increasing to avoid contrast reversals

Table 5.1: The characteristics required of a luminance remapping functionf : [0;m]! [0;m].

thereby adequately correcting for theLR term. Also, the full dynamic range of the display device

should be employed.

It can be observed that subtracting the ambient termLR from pixels with a luminance value of

L will produce the required behaviour aroundL. Furthermore, it is required that the derivative

of our remapping function is 1 atL so that contrast ratios are unaffected. For values much

smaller and much larger thanL, a remapping is desired that is closer to linear to fully exploit

the dynamic range of the display device. The function should also be monotonically increasing to

avoid contrast reversals. In summary, Table 5.1 describes the characteristics required of a function

f : [0;m]! [0;m].

5.3 Existing remapping methods

This section specifies methods that have been previously proposed to manipulate contrast in an

image. These methods were not specifically developed to compensate for reflected ambient light

— to date there has been no method designed for this purpose.

5.3 Existing remapping methods 98

5.3.1 Gamma manipulation

One alternative form of remapping may be to apply gamma correction (see Section 2.5.1 and 3.2.3)

in an attempt to correct for the additive termLR [War00]. By reducing the gamma value applied

to the image, the result may become perceptually closer to linear. However, while a value for

gamma correction may be chosen such that the pedestal valueL is mapped toL�LR, the slope of

this function atL will not be 1 and the perceived contrast around the chosen pedestal value will

therefore still not be the desired∆Ld. In particular, for a gamma functionf (x) = x1=γ, thenγ =

logL= log(L�LR) would be required to achieve the desired reduction in intensity. The derivative

of f would have a slope of logL(L�LR)(L�LR)=L atL, which will only be 1 if no light is reflected

off the screen, i.e.LR= 0. To ensure perceptually accurate display, a function that mapsL to L�LR

is necessary at the very least, while at the same time forcing the derivative atL to 1.

5.3.2 Hyperbolic functions

Hyperbolic functions have been proposed to manipulate image contrast [LH94]:

f (x) =tanh(ax�b)+ tanh(b)tanh(a�b)+ tanh(b)

(5.2)

The parametersa andb control the slope of the function at 0 and 1. Although this function may be

used to adjust contrast, it is not suitable for the control of the slope for some intermediary value

such as pedestal valueL.

5.3.3 Histogram equalisation

Histogram equalisation is a well-known method for manipulating contrast [Wee96], and is de-

scribed in Section 3.2.3. Based on the histogram of an image, a function is constructed which

remaps the input luminances such that in the output each luminance value is equally likely to oc-

cur. Therefore, the remapping function will be different for each image. Although it maximises

contrast, this approach does not allow control over the value and slope of the mapping function at

5.4 Schlick’s rational function as a basis for remapping 99

specific control points and is therefore not suitable for this application.

5.3.4 Spatially varying techniques

Finally, several techniques have been developed which are spatially variant, i.e. a pixel’s luminance

is adjusted based on its value as well as the values of neighbouring pixels. These methods are prone

to contrast reversals which is generally undesirable. It is therefore not advocated to use spatially

variant mechanisms such as multi-scale representations [LHW94], genetic algorithms [ML99] and

level-set based approaches [CLMS99].

5.4 Schlick’s rational function as a basis for remapping

As none of the commonly-used techniques to adjust contrast are suitable to correct for reflections

off computer screens, a new remapping function is required. Although power-laws such as gamma

correction can not be parameterized to satisfy all the above function requirements, a rational func-

tion proposed by Schlick [Sch94b] may be used as a basis. This function was originally proposed

as a tone reproduction operator (Section 2.5.2), and a variation was published as a fast replacement

for Perlin and Hoffert’s [PH89] gain function [Sch94a]. The basic function is given by:

f (x) =px

(p�1)x+1(5.3)

wherex is an input luminance value in the range[0;1], and p is a scaling constant in the range

[1;∞].

Schlick proposed this algorithm to quantise and map high dynamic range data to a lower dynamic

range display, as an alternative to linear or logarithmic mapping, and as a simpler method than the

Tumblin and Rushmeier brightness preservation function. Interestingly, he devised a method of

automatically generating the scaling parameterp by asking the viewer to select the darkest patch

they could see on a black background. The intensity of this patch provides the valueM — the

darkest grey level that can be clearly distinguished from black (an absolute threshold). Schlick

5.4 Schlick’s rational function as a basis for remapping 100

reasoned that since the parameters controlling a tone reproduction function (contrast, brightness,

viewing conditions, observation distance, etc.) are difficult to define or measure , thenM can be

used in place of these measurements, on the basis that it is this value that noticeably changes when

the aforementioned parameters change. Schlick’s premise was that rendering programs contain

an ‘epsilon’ value that generates the smallest non-zero value (ε) of the image, beneath which

computed intensities are considered to be negligible. This value can be mapped toM thus:

p=Mm�MεNε�Mε

'MmNε

(becausem� ε andN�M) (5.4)

wherem is the maximum display value andN is any value in the display range[0;N�1]. For our

algorithm, we do not need to setp as we can solve for it given that we will know the values ofL

andLR.

While Schlick reports that this automatic parameter generation method provides satisfactory re-

sults without the need for guesswork, he also acknowledges that this method does not work with

logarithmic or power law tone reproduction. Schlick’s use of measuring a threshold to determine

a value forM shares a common idea with the work we present in this thesis, whereby the display

device itself is used to infer something about the viewing conditions. However, the aim of this

method differs, with Schlick using this information to set a general parameter for the purpose of

dynamic range reduction, whereas we wish to specifically measure JNDs in order to remap lumi-

nance to maintain the appearance of contrast, regardless of dynamic range. Schlick did not provide

any quantitative evidence supporting the success of this parameter generation, whereas our method

for determining JNDs is supported by statistical analysis of psychophysical experimentation. Also,

we provide a form of JND measurement to determine difference thresholds, rather than absolute

thresholds. Nonetheless, our work can be used alongside existing tone reproduction operators, and

would be particularly useful in conjunction with those that aim to mimic perceptual qualities.

5.5 A new luminance remapping algorithm 101

0 m0

m

[0,L]

[L,m]

L

L-LR

0 m

0 m

0

m

0

m

Figure 5.2: An example of how the remapping function is split into two ranges,[0;L] and[L;m]

5.5 A new luminance remapping algorithm

The list of requirements given in Table 5.1 may be satisfied by splitting the function into two

ranges, namely[0;L] and[L;m]. An example of this split is shown in Figure 5.2.

The algorithm presented below maintains the original contrast for a selected input luminance

value, L, and approximately correct perceived contrast for all other input values. As ambient

light reduces the perceived dynamic range, the problem is similar to that of tonemapping for the

purpose of dynamic range reduction, so a tailored mapping based around a specific input value is

feasible.

Using Equation 5.3, the appropriate substitutions are made forx. As we already know the values

for L andLR, we can solve for the free parameterp. In particular, the inputx and the outputf (x)

is scaled before solving forp.


5.5.1 The range[0;L]

For the range[0;L] we substitutex! x=L in Equation 5.3, thereby normalising it to that range,

and the output is then scaled byL�LR:

f[0;L](x) = (L�LR)px

L

(p�1) xL +1

=(L�LR)pxx(p�1)+L

(5.5)

This equation satisfies the requirements thatf[0;L](0) = 0 and f[0;L](L) = L�LR. As the slope of

f[0;L] is known to be 1 atL, the following equation can be solved forp:

f 0[0;L](x) =p(L�LR)

L((p�1)x=L+1)�

(p�1)px(L�LR)

L2((p�1)x=L+1)2

=p(L�LR)L(xp�x+L)2

= 1 (5.6)

By substitutingx= L then

p=(L�LR)

L(5.7)

5.5.2 The range[L;M]

For the range[L;m] we substitutex! (x�L)=(m�L) in Equation 5.3, and scale the output by

m�L+LR andL�LR is added to the result:

f[L;m](x) =p

x�Lm�L

(m�L+LR)

(p�1)x�Lm�L

+1+L�LR (5.8)


The above equation satisfies the requirements thatf[L;m](L) = L� LR and f[L;m](m) = m. The

derivative of this function is:

f 0[L;m](x) =p(m�L+LR)

(m�L)

�(p�1)(x�L)

m�L+1

� � (p�1)p(x�L)(m�L+LR)

(m�L)2�(p�1)(x�L)

m�L+1

�2 (5.9)

Again, p is solved by requiringf 0[L;m](L) to be 1, resulting in

p=(m�L)

(m�L+LR)(5.10)

5.5.3 Complete remapping function

By making the appropriate substitutions ofp and simplifying the equation, the function that

remaps luminance to correct for the loss of contrast due to screen reflectionsLR is given by:

f (x) =

8>>>>>>><>>>>>>>:

(L�LR)2x

L2�LRxif 0 � x� L

x�L

1�LR(x�L)

(m�L+LR)(m�L)

+L�LR if L� x�m

(5.11)

For a pedestal valueL of one third the maximum valuem= 255, a set of curves is plotted in

Figure 5.3. The different curves were created by varying the amount of lightLR reflected off the

monitor.

5.5.4 Function inversion

Our forward algorithm presented above is suitable to display images that were created under op-

timal viewing conditions. However, in many practical cases images are created using specific

displays located in uncalibrated viewing environments. Assuming that such images are optimal

for the viewing environment in which they were created, it may be useful to convert them for dis-


0 50 100 150 200 2500

50

100

150

200

250Contrast correction

L = 12.75L = 25.50L = 38.25L = 51.00

f(x)

x

L = 76.5m = 255

R

R

R

R

Figure 5.3: Remapping functions forLR set to 5%, 10%, 15% and 20% of the maximum displayvaluem. The pedestal value was set toL = 0:3m for demonstration purposes. In practise, a baseluminance value ofL = 0:2m is appropriate.

play in a different viewing environment. An effective way to accomplish this is by transforming

the image into a standard space that is independent of the viewing environment. This is analogous

to the Profile Connection Space used in ICC profiles [ICC03]. ICC profiles are normally used to

convert images for reproduction on different display devices such that the perception of the dis-

played material is least affected. It can be envisaged that the methodology and algorithm described

in this thesis could become part of the ICC file format since it would address device dependent

issues not covered by ICC profiles to date.

A simple method of inverse correction would be to addLR to every pixel value, but this requires

a file format with a pixel range of[LR: : :m+LR]. This is impractical given that many file format

standards only support a specific number of bits (e.g. 8). Therefore, we can instead invert the

function described above to create an inverse correction which does not alter the dynamic range.

The first step in converting between the viewing environment that was used to create an image (the

source environment) and some other display environment would be to undo the effect of the source

environment. Hence, it is desirable to convert such images to a hypothetical viewing environment

in which the screen does not reflect light. This may be achieved by measuring∆Ld and∆Lb for


the source environment, computingLR and then applying the inverse transformation to the image:

finv(x) =

8>>>>><>>>>>:

xL2

(L�LR)2+xLR0� x� L�LR

x(m�L)2+mLR(x+m+Lr�2L)(m�L)2+LR(x+m+Lr�2L)

L�LR� x�m

(5.12)

For the destination environmentf (x) may then be applied prior to display. One limitation of this

approach is that for both forward and inverse transformations the same pedestal valueL needs

to be used. However, it would not be unreasonable to standardise by fixingL to 0:2m such that

middle grey is always displayed correctly.

5.5.5 Colour space

Most images are given in a device-dependent colour space. While it is possible to apply the

remapping function to the individual colour channels, this is not recommended. Non-linear scaling

functions such as the one described above will alter the colour ratios for individual pixels, leading

to changes in chromatic appearance. This would be an undesirable side-effect of the algorithm,

which is easily avoided by applying the equation to the luminance channel only.

It is therefore necessary to convert to a different colour space which has a separate luminance

channel such as XYZ or Lab. These conversions require knowledge of the image’s white point,

which more often than not is unknown. If the white point is known, an appropriate conversion

matrix may be constructed [Poy03]. In many cases it may be reasonable to make the grey-world

assumption, i.e. the average reflective colour of a scene is grey. If the average pixel value of

the image deviates from grey, this may be attributed to the illuminant. Under the grey-world

assumption the average pixel value is a good estimate of the scene’s white point. Otherwise, one

can resort to white-point estimation techniques [CFB99], or simply estimate that the white point

is always D65. This will be true to a first approximation for outdoor photographs.

5.6 Results and discussion 106

As a convenience, the conversion from RGB to XYZ for a D65 white point and back is: [ITU90]:

26664

X

Y

Z

37775 =

26664

0:412453 0:357580 0:180423

0:212671 0:715160 0:072169

0:019334 0:119193 0:950227

37775

26664

R

G

B

37775 (5.13)

and the reverse case is given by inverting the matrix above:

26664

R

G

B

37775 =

26664

3:240479 �1:537150 �0:498535

�0:969256 1:875992 0:041556

0:055648 �0:204043 1:057311

37775

26664

X

Y

Z

37775 (5.14)

5.6 Results and discussion

Figure 5.4 shows the success of the remapping function applied to images under different ambi-

ent light values (LR). Given the limited dynamic range of most current display devices, it is not

possible to adjust the contrast for all bright, dark and intermediate areas of an image. However,

the above remapping function provides a sensible trade-off between loss of detail in the bright-

est and darkest areas of the image, while at the same time allowing the flexibility to choose for

which pedestal value ofL the remapping produces accurate contrast perception. While a value of

L = 0:2m will be appropriate for many practical applications, the function is easily adjusted for

different values ofL. Only the two JNDs need to be re-measured, after whichLR may be computed

and inserted into the above equation. A further advantage of this function over other contrast ad-

justment methods is that the data does not need to be scaled between 0 and 1, since the maximum

valuem is given as a parameter.

A comparison with the aforementioned existing remapping methods, in particular a reduced gamma

value and histogram equalisation, is given in Figure 5.5. The reference image is an aerial photo-


Original photograph LR = 0.08m

LR = 0.11m LR = 0.14m

LR = 0.17m LR = 0.20m

Figure 5.4: Uncorrected photograph followed by a progression of corrected images. In each case,L is set to= 0:2m andm= 255.

graph of an archaeological excavation, showing ditches, trenches and features uncovered during

the excavation process. Photographs of this nature are used to gain an understanding of the site as

a whole, so it is important that any detail is adequately preserved. The top two images show the

original photograph as it appears when viewed in darkness (left) and when viewed in the presence

of ambient illumination (right). The valueLR is 0.13m. The reduction in perceived contrast due

to the ambient term is notable, with shadow information and fine detail being lost due to reflected

ambient light. The middle two images are the result of applying existing remapping techniques to

the uncorrected (top right) image. The image on the middle left has been corrected using a reduced

gamma value. This has darkened the image as a whole — an undesirable effect. The middle right

image has been corrected using histogram equalisation. This has led to considerable darkening in


Figure 5.5: Comparison with other techniques. Top: original image (left) and original uncorrectedimage under ambient illumination,LR = 0.13m (right). Middle: correction using a reduced gammavalue (left) and using histogram equalisation (right). Bottom: correction using our algorithm,L = 0:2. (Photograph courtesy of Fintan Walsh,c 2003.)

some areas, and undesirable lightening in others. It does not preserve the contrast appearance of

the original (top left) image. The bottom image has been corrected using our luminance remap-

ping algorithm, using a value ofL = 0:2m. The contrast ratios have been preserved and the overall

appearance is closest to that of the original.

5.7 Summary 109

5.7 Summary

This chapter has described our method for correcting images where the contrast appears reduced

due to the presence of ambient light. We based our algorithm on Schlick’s rational function,

splitting it into two ranges to meet the requirements for our luminance remapping. We show how

this function can be inverted for images created in an uncalibrated environment. Photographic

results of this algorithm are given. In the following chapter, this algorithm is validated using a

specifically-designed formal psychophysical user study.

5.7 Summary 110

Chapter 6

Validation of luminance remapping

This chapter describes a psychophysical experiment that was developed to validate the success of

the algorithm detailed in the previous chapter. The validation experiment needed to show that an

image displayed in the presence of ambient illumination could be corrected so that its appearance

matched that of the same image viewed when no ambient light was present.

6.1 Validation experiment

The validation of the algorithm follows the form of Experiment 2 — the rapid measurement pro-

cedure described in Section 4.3. This method of validation was chosen because the success of

Experiment 2 had already been established, and it was logical to adapt such an experiment to

allow for validation. Whereas Experiment 2 measured JND discrimination under three different

lighting conditions, the validation experiment required JND measurement under two conditions:

light and dark. This could then establish the effect of the light condition on contrast perception. A

third iteration of the validation experiment could then be carried out under the same light condi-

tions, but with our luminance remapping algorithm applied to the stimuli.

111

6.1 Validation experiment 112

6.1.1 Hypotheses

The research hypotheses were as follows:

� there exists a significant difference between JND perception in thedark condition and JND

perception in thelight condition,H1 : Xdark 6= Xlight ;

� that there exists a significant difference between JND perception for the uncorrected stimuli

shown in thelight condition and for corrected stimuli shown in thelight condition, H2 :

Xlight (uncorrected)6= Xlight (corrected).

A third expectation was that there exists no significant difference between JND perception for the

uncorrected stimuli in thedark condition and JND perception for the corrected stimuli in thelight

condition. This is a form of null hypotheses, and as such cannot be used to indicate a similarity

between two variables [Abe02]. Significance testing is used to determine if it is unlikely that the

null hypothesis is true. It does not allow for the likelihood that a null hypothesis is true. It is a

misconception that failing to reject the null hypothesis means that it must be true. Failure to reject

the null hypothesis actually implies that there is insufficient evidence for its rejection [Nic00].

6.1.2 Participants

As in Experiment 2 (Section 4.3), twenty-one individuals participated in this experiment. How-

ever, due to differences and restrictions in time and location, these were not the same participants

from Experiment 2, nor were the conditions identical, so no direct comparison of results with

those of previous experiments was anticipated. All had normal or corrected-to-normal vision. All

participants were fully adapted to the prevailing lighting conditions before beginning their task.

All participants took part in all conditions and the order of their participation was randomised.


6.1.3 Conditions

Participants carried out this procedure under two conditions — dark (0 lux) and light (255 lux).

The ambient illumination values were verified as before. A 17 inch Sun Microsystems CRT was

used, fully calibrated with the appropriate gamma correction applied to the displayed images.

6.1.4 Procedure

The procedure was initially identical to that in Experiment 2, using a pedestal value of 20% grey,

as this was the default pedestal value for the algorithm, described in Section 5.2. Participants

proceeded with the experiment under the dark and the light conditions, choosing the squares where

they could just notice the target.

The results from these dark and light conditions, corresponding with the values∆Ld and ∆Lb

(Section 5.1), were then used to determineLR. With these values, our contrast correction algorithm

was applied to the experiment stimulus, and participants repeated the JND selection procedure

once more in the light condition, using this corrected version of the stimulus. Thus, JNDs were

measured under three conditions:dark, uncorrected stimulus (our ground truth);light, uncorrected

stimulus; andlight, corrected stimulus.

6.1.5 Results

A significant difference was expected between the JND values for the two lighting conditions.

Further, a significant difference was anticipated between the JND values detected in thelight con-

dition and the JND values detected using corrected stimuli in thelight condition. The independent

variables had two levels for each hypotheses:dark andlight; and original stimulus and corrected

stimulus. At-test was used to compare the two sets of means for each hypothesis. This statistical

test is used to compare mean values of the same type of measurement made under two different

conditions.


0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

5 10 15 20

JND value

Participant

Average JND values for the validation experiment

Average JND values, dark condition

Average JND values, light condition (uncorrected)

Average JND values, light condition (corrected)

Figure 6.1: Graph to show the average JND values measured in the validation experiment for apedestal value of 20% grey.

t-Test results

A dependent meanst-test is used when an experiment follows a repeated measures design. Each

participant carried out the same task under each condition, and each hypothesis compared two

sets of means: mean JND values indark andlight (uncorrected)conditions for Hypothesis 1, and

mean JND values inlight (uncorrected)andlight (corrected)conditions for Hypothesis 2.

A dependent meanst-test indicated a significant difference between JND values measured in the

dark condition and uncorrected stimulus JND values measured in thelight condition, t(20) =

�5:034; p < :001. In addition, there was a significant difference between JND values measured

using corrected and uncorrected stimuli in thelight condition,t(20) = 2:69; p = :014. Figure 6.1

shows the average JND measurements of each participant for an image shown in thedarkandlight

conditions, and a corrected version of that image shown inlight conditions. Tables of these results

are also given in Appendix B. Additionally, as might be anticipated, no significant difference was

found between JND values measured in thedark condition and JND values measured using the

corrected stimulus in thelight condition. However, as mentioned above, this represents a null

hypothesis and therefore cannot be directly tested, nor (technically) be accepted [Abe02].

6.2 Summary 115

Wilcoxon Signed Ranks Test results

A Wilcoxon Signed Ranks test (the non-parametric equivalent of a pairedt-test) revealed that JND

values were significantly higher in thelight (uncorrected)condition than in thedark condition,

z= �3:736; p < :001. As predicted, the JND values were also significantly higher in thelight

(uncorrected)condition than in thelight (corrected)condition,z=�2:450; p= :014. These values

match those of the abovet-test).

6.2 Summary

In this chapter we presented a formal method of psychophysical validation for our luminance

remapping algorithm. This validation experiment followed the procedure of Experiment 2, altering

it to permit comparison of uncorrected and corrected stimuli in thelight condition. The statistical

results indicate that when applied to an image that is perceived differently under different levels

of illumination, the algorithm described in Chapter 5 can restore the original contrast appearance,

producing an image that appears unaltered by reflected ambient light. The reuse of the procedure

from Experiment 2 again confirms the ability of that experiment to measure changes in contrast

perception under reflected ambient light.

6.2 Summary 116

Chapter 7

Conclusions

From the outset, this thesis assumes that an image displayed on a computer monitor should appear

the same no matter where, or on which system, it is displayed. This is particularly true for certain

areas of work where the creator of the image must be certain that others who view it see a percep-

tual equivalent of the original. This research focused on a particular aspect, namely the effect that

ambient light has on on-screen images. Working environments tend to require a level of ambient

light sufficient to allow for paperwork alongside computer use. However, when this ambient light

is reflected off the monitor screen, it causes a reduction in perceived contrast, altering the appear-

ance of the image. While this is known in theory, it is seldom corrected due to the expense, skill

and/or knowledge involved. Additionally, while equipment such as photometers or illuminance

meters can physically measure the quantity of ambient light present, the perceptual impact lies

in a human response to that light. Thus, our goal was to devise a rapid method of measuring the

effect of the reflected ambient light, without the use of any extra equipment, instead relying on

the user’s perception of the image. In doing so, we hoped to provide a more amenable approach

to correcting for the effect of reflected ambient light, in the hope that this would aid the quest for

perceptual fidelity in image display.

We have presented a quick and effective way to determine the reduction in perceived contrast

caused by reflected ambient light. The framework is based on established psychophysical knowl-

edge, and is derived from attested psychophysical experimentation. The feasibility of the method

117

118

is evidenced by its comparison with a prior full-length and detailed experiment. Our rapid method

(Experiment 2) produces significant results, but takes only a fraction of the time needed for the

full-length experiment (Experiment 1).

Experiments 1 and 2 output average JND values, i.e. difference threshold values that correspond to

the change in contrast needed for a difference between a target and a background to be perceived.

Since the contrast of an image is reduced when ambient light is reflected off the screen, this

difference threshold value is greater when there is more ambient light present. As expected, this

corresponds with Weber’s Law.

While our method of assessing the effect of reflected ambient light has been shown to be suc-

cessful, it can also be used in a practical way. The values obtained from the experiments provide

information about changes in contrast, and can therefore be employed to manipulate the contrast

in an image to undo the perceived contrast reduction caused by the ambient term. We therefore

extended our work to incorporate a method of contrast correction, producing an algorithm that

permits luminance remapping in order to restore the original perceived contrast of an image, while

maintaining the overall appearance. This produces correct perception of contrast for one input

luminance value, and approximately correct perception of contrast for all other input values. This

algorithm not only works for an image created where no ambient light was present, but is also

invertible, so that an image created in certain ambient-lit conditions can be displayed as it would

have looked in the environment in which it was created.

Visual results of our algorithm were provided, including comparison with other existing remap-

ping methods. However, a better indication of the success of our algorithm is the psychophysical

validation study. This employed the procedure of Experiment 2, thereby measuring JNDs for orig-

inal stimuli under dark and light conditions, and measuring JNDs for stimuli corrected with our

algorithm under the same light conditions. This validation experiment confirmed the ability of our

algorithm to restore the original perceived contrast.

In summary, we have confirmed that light reflected off a monitor significantly alters contrast per-

ception. We have devised a rapid calibration technique to estimate by how much the appearance of

contrast is altered. By specifying a simple task that every viewer can carry out in a short amount of

7.1 Advantages 119

time, we avoid using expensive equipment such as photometers or spectroradiometers. A straight-

forward rational function is then used to adjust the contrast of images based on these measurements

made by each viewer. The effectiveness of this algorithm is shown through a formal user study.

7.1 Advantages

For applications such as visualisation, photography and our intended application of virtual her-

itage, our procedure and algorithm provides a significantly simplified alternative to gain control

over the perception of displayed material. It fits alongside existing correction steps such as gamma

correction and colour appearance models and addresses and solves a significant problem in im-

age display. The visual self-calibration procedure lends itself well to use on the Internet where

perceptual consistency may be desirable amongst online images that are viewed worldwide on a

variety of display devices. This approach may also see use in ICC colour profiles where it not only

allows images to be exchanged between different devices, but between devices located in specific

viewing environments.

With current improvements in rendering and display algorithms, especially those that mimic per-

ceptual traits, it is no longer immediately obvious that one algorithm performs better than another

just by looking at the images they produce. Currently, we must rely more and more on user studies

to decide which algorithm is best for a specific task. Providing easy-to-use, quick and effective

psychophysical methods will enable the progression of perceptual realism in computer graphics.

Our method has shown that this is a feasible and worthwhile approach to graphics research.

7.2 Disadvantages

The methods that we present are not intended to provide full and accurate calibration and correc-

tion for ambient lighting. We realise that for some specific applications, there is no substitute for

extensive and methodical calibration of equipment and provision of a specialised viewing environ-

ment. This includes areas such as fabric dyeing, or pre-press advertising, where perceptual fidelity

7.3 Further research 120

is imperativeand the means to obtain this are achievable; that is, the time, money and expertise are

available to eliminate ambient lighting, thereby making our methods redundant. However, despite

this, we feel that there is still an audience for our work. Gamma correction, in its shortcut form, is

widely used by digital photographers, especially by amateur photographers who do not have the

specialised equipment needed to calibrate their monitors1. In the same way that gamma correction

via a chart is an estimate, and not a full monitor calibration, we have presented a shortcut method

that is similarly an estimate, and not a full calibration. Our work can be seen as an intermediate

step, between a complete lack of calibration and fully-compliant specification. There is a neces-

sary trade-off between accuracy and cost. Therefore, our work, like shortcut gamma correction, is

a usable approach for people concerned about the effect of ambient lighting, yet unable to meet

rigid specifications.

Our methods necessarily assume several factors: that the reflected light is uniform, that the display

device meets certain standards (for example, uniform luminance output, monitor brightness and

contrast controls have been set), and that a new measurement is made if the ambient lighting

changes. While these are reasonable assumptions, it would be interesting to investigate how much

variation is tolerable without invalidating the method.

7.3 Further research

Several interesting areas of research have been revealed during the course of this work. One line

of enquiry is to further increase the accuracy of the rapid calibration procedure by introducing

random grey patches in the tableaux. This would have the effect of making the procedure closer

to that of Experiment 1 by providing a form of 2afc procedure, whereby the participant’s response

can be validated — choosing one of these grey squares would indicate that they were guessing

where the just-noticeable noise appeared.

A second line of enquiry would be to make the ambient light component part of tone mapping

algorithms. Unless tone mapping operators are designed specifically for darkness only, ambient

1A recent Internet search on “gamma chart” using Google produced some 141 000 results:http://www.google.com/search?hl=en&lr=&ie=UTF-8&oe=UTF-8&q=gamma+chart&btnG=Google+Search

7.4 Closing remarks 121

light will play a factor in their delivery and should therefore be accounted for in the operator.

Third, colour appearance should be taken into account. The experiment could be redesigned in

order to calibrate for both colour and luminance. Existing colour appearance models require ex-

tensive testing and our method could provide a fast and efficient alternative.

Finally, a question that should be investigated is that of the role of visual constancy, to examine

how much the human visual system compensates for inadequacies in displays. This is already a

major field of research in its own right (such as the way in which colour constancy affects colour

appearance models), and the work in this thesis could be considered alongside it.

7.4 Closing remarks

The study of human perception is inseparable from the presentation of images in computer graph-

ics. Both at low-level visual responses and higher cognitive processing, the attributes of the human

visual system play an important role in how we perceive all that we are presented with on-screen.

Research into this is still at an exploratory stage — there is a wealth of information still to be

investigated, and much to learn from related fields, such as vision, psychology and neuroscience.

With every advance in computer graphics techniques, new challenges are thrown open, and we find

ourselves looking beyond the traditional boundaries of the field. This thesis demonstrates how we

can learn from other disciplines and use established knowledge to further our studies. Rather than

reinventing research, perhaps without the necessary skill sets of other disciplines, we can glean

information from existing proven sources and employ it to further computer graphics as a whole.

7.4 Closing remarks 122

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136

Appendix A

Materials

137

A.1 Experimental Informed Consent Form

Investigators

Kate Devlin, University of Bristol.

Purpose

Specific hypotheses and predictions cannot be divulged until after the experiment because such

knowledge could affect the results. After the experiment is completed, a detailed debriefing will

be provided.

Selection of Participants

Participants are volunteers who have agreed to take part.

Confidentiality

Your data are recorded with no identifier other than the ID number that is assigned randomly.

Procedure

Procedures vary from study to study. You will be given detailed instructions.

Benefits

Participants will receive no direct benefit from this research.

Risks

The procedures used in the experiments are harmless and have been used in prior research.

Compensation

Participants will be entered into a prize draw. When the study is completed all participants will

receive a summary of the results via e-mail.

Withdrawal

Participation in this research is voluntary. Volunteers are under no obligation to complete the study

and can cease participation at any time.

Further Questions

If you have any questions regarding the purpose, procedure, or other aspects of the experiment,

please feel free to send an e-mail message to the investigator at [email protected]

138

=========================================================================

Name: .....................................................................

Address: ...................................................................

...................................................................................

...................................................................................

...................................................................................

Declaration:

I have been informed about the aims and procedures involved in the experiment. I reserve the right

to withdraw at any stage in the proceedings, and information that I provide as part of the study

will be destroyed or my identity removed unless I agree otherwise.

Signed: ....................................................................

Date: ....................................

139

A.2 Instructions for Experiment 1

For this experiment you will be seated in front of a CRT monitor. Please relax and sit as you would

normally sit at a desk, i.e. without leaning too far forward or too far back. The chair is aligned

with black tape on the floor — please do not move it away from this position.

During the experiment randomly generated noise stimuli (an example is shown here) are displayed

on screen, on top of a fixed grey background. A stimulus will appear in one of two intervals. The

intervals are signalled by a beep; one beep sounds to indicate the beginning of the first interval,

and two beeps sound to indicate the beginning of the second interval.

You must determine whether the stimulus appeared in the first interval or the second interval.

There will be a gap of 4 seconds following the second interval in which you can make your

selection.

If you think the stimulus appeared in the first interval then press the “1” key.

If you think the stimulus appeared in the second interval then press the “2” key.

120 of these stimuli, chosen at random, will be shown. If you make a mistake, do not worry,

simply carry on. You will have a chance to practise before the actual experiment starts.

You are free to withdraw from this experiment at any time. If you do not want to complete the

experiment then you can end it prematurely by pressing the ESC key in the top left corner of the

keyboard.

140

A.3 Instructions for Experiment 2

For this experiment you will be seated in front of a CRT monitor. Please relax and sit as you would

normally sit at a desk, i.e. without leaning too far forward or too far back.

During the experiment a grid of squares is displayed on screen. Some squares may appear blank

and others may have some noise displayed in them (there is an example picture above showing a

square containing noise). The amount of noise that a square contains increases from the top left

of the screen to the bottom right of the screen. You must choose the square where you canjust

noticesome noise on the grey background.“Just noticeable” means that it is the square closest

to appearing blank: the other squares contain either no noise or more noise.

When you have decided which square contains the noise that is just noticeable then click on it

once with the left mouse button. A new table will then appear (please wait for a few seconds until

it fully loads). Follow the same procedure again. The experiment will end automatically after 15

iterations.

If you make a mistake, do not worry, simply carry on. You will have a chance to practise before

the actual experiment starts. There is no time limit.

You are free to withdraw from this experiment at any time. If you do not want to complete the

experiment then you can end it prematurely.

141

142

Appendix B

Results

143


A 0.005534 0.005307 0.006338B 0.004980 0.005167 0.006771C 0.005460 0.005530 0.005955F 0.004962 0.005472 0.005759G 0.005964 0.005688 0.009213H 0.006305 0.005699 0.005855

Table B.1: Experiment 1: average JND results for each participant, for each condition; pedestalvalue = 5% grey.


A 0.009335 0.009250 0.009415B 0.008307 0.007464 0.010490C 0.008153 0.008384 0.008597D 0.009786 0.008213 0.010583E 0.007852 0.009602 0.012601F 0.006976 0.008490 0.009529



A 0.007740 0.009428 0.008280B 0.007586 0.007854 0.008774C 0.007002 0.007874 0.010476D 0.006343 0.009005 0.009404E 0.007272 0.009241 0.010531F 0.008031 0.008764 0.010011


144


A 0.001147 0.002329 0.005741B 0.00294 0.003125 0.00552C 0.002296 0.001081 0.002214D 0.001163 0.001837 0.003882E 0.000997 0.001691 0.003519F 0.00294 0.003064 0.005103G 0.001212 0.0021 0.005535H 0.002192 0.00294 0.003077I 0.00294 0.003036 0.004488J 0.001584 0.003603 0.003603K 0.0021 0.002429 0.00316L 0.000913 0.000936 0.003155M 0.00161 0.00294 0.004824N 0.002814 0.003031 0.004622O 0.002856 0.002854 0.003302P 0.001876 0.00294 0.004205Q 0.002338 0.00296 0.004461R 0.001079 0.001416 0.002958S 0.000937 0.001166 0.002591T 0.001293 0.001583 0.003607U 0.00149 0.002315 0.004438


145




146




147

PARTICIPANT DARK LIGHT, UNCORRECTED LIGHT, CORRECTED


Table B.7: Validation experiment: average JND results for each participant, for each condition.

148

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