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Tone Reproduction
Assignments
• Checkpoint 6 – Due Monday
• Checkpoint 7– To be given Monday
• RenderMan – Due Nov 3rd
Projects
• Approx 22 projects• Listing of projects now on Web• Presentation schedule
– Haven’t scheduled? Please do so!
Logistics
• Final Report– Introduction – Approach Taken– Implementation Details– Results– Appendix/Code
• All project material due Friday, Nov 19th
– No late submission• else I can’t get your grades in!
This and Next Week
• Tone Reproduction Week!– Today
• Intro to tone Reproduction– Erik Reinhard (U of Central Fla)
• Colloquium – Monday, Nov 1st / 1-2 pm /70-1400 (auditorium)
• Guest Lecture (Tone Reproduction)– CGII Class
– Joe Geigel• Colloquium
– Thursday, Nov 4th / 1-2pm / 70-3000 (CS conf room)
Computer Graphics as Virtual Photography
camera (captures light)
synthetic image
camera model
(focuses simulated lighting)
processing
photo processing
tone reproduction
real scene
3D models
Photography:
Computer Graphics:
Photographic print
2
Tone/Color Reproduction
• Where are we?– Described our scene during modeling– Simulated light transport during rendering– Captured and projected light from the scene
onto a 2D plane during capture– Now we must convert this simulated light
capture into an image for display
Tone Reproduction
• Luminance levels
Sky = 12400 nits
Trees = 64 nits
Traditional Photography
camera
processing
photo processing
real scene
Photographic printPhotography:
Reinterpretation of scene optimized for viewing
Digital Photography
camera
processing
Processing performed by camera
real scene Digital image
Photography:
Reinterpretation of scene optimized for viewing
(24 bit RGB)
Digital Photography
• Issues–Tone Reproduction is “hard coded”
into camera–Color Management Issues
• Which RGB?• Optimized for what display?
Image Synthesis in CG
camera
synthetic image
camera model
processing
photo processing
tone reproduction
real scene
3D models
Photographic printPhotography:
Computer Graphics:
Reinterpretation of scene optimized for viewing
(24 bit RGB)
Scene luminance
3
High Dynamic Range (HDR) Imaging
• high dynamic range imaging is a set of techniques that allow a far greater dynamic range of exposures than normal digital imaging techniques.
• The intention is to accurately represent the wide range of intensity levels found in real scenes, ranging from direct sunlight to the deepest shadows.
Wikipedia
HDR in Computer Graphics
[Ward 2001]
HDR in Computer Graphics
[Debevec 2001]
HDR in Computer Graphics
[Debevec 2004]
4
What if we ignore tone Reproduction?
• Simple Linear tone reproduction
Light source = firefly Light source = Searchlight
[Tumblin93]
Tone Reproduction
Definition: Compressing the dynamic range of a scene’s luminances/radiances so that it can be displayed on a given device in such a way that minimizes the perceptual difference between viewing the scene and viewing the rendering of the scene.
Tone Reproduction - Definition
• Dealing with luminances / radiances• Rendering will be displayed on a given
device• Minimize perceptual difference between
real and created.
Tone Reproduction
• Radiance / Luminance– Flux arriving at or
leaving from a given point or surface in a given direction.
– Radiance measured in W / m2 /sr
– Luminance measured in cd/m2 (nit)
dA
5
Tone Reproduction
• Using 0 – 1 to indicate light intensity– What does 1 mean?
• CG tends to use intensity space of output device
• Images optimized for a given output device.
Why Tone Reproduction?
• Human response to light is neither simple nor linear.
• Most display devices are not linear• Incorrect response modeling results in
incorrect perception of results.
The Tone Reproduction Problem• What operator will create a close match between real-
world and display brightness sensation?
[Tumblin93]
Tone Reproduction in CG
[Ferwerda 1998]
Tone / Color Reproduction
• Response / Observer– How does a system (like the human visual
system or photography) respond to the collected light?
• Display– How do we translate that response using a
particular output device (like a CRT or printer)?
Response Models
• Applying observer/response model will result in the luminances as seen by your display observer.– i.e., will be in luminance range of your output
device.• Observer/Response Models
– Human Visual System (today)– Photographic Systems (Monday)
6
Response Models
• Image Characteristics– Spectral response - how system responds to
different wavelengths of light– Intensity response - how system responds to
different intensities of light– Acuity - the sharpness of the image produced by
the system– Noise – inherent noise in the image produced
by the system
Human Visual Response
Human Visual Response• Pupil
– Regulates the amount of light that gets to the retina
• Photoreceptors– Rods
• 75 - 150 million• sensitive to 10-6 to 102 cd/m2 (low light levels)• Achromatic (detects “brightness”)
– Cones • 6 - 7 million• sensitive to 0.01 to 108 cd/m2 (high light levels)• Responsible for color vision
Human Visual Response
• Levels of Brightness Response– Scotopic (Primarily rods)
• 10-6 to 102 cd/m2
– Photopic (Primarily cones)• 0.01 to 108 cd/m2
– Mesopic (overlap!)• 0.01 to 102 cd/m2
• Both rods and cones• Little known -- active area of research
Human Visual Response
• Spectral response– Human Visual System is sensitive to light in the
wavelength range of approx. 350 - 700 nm.– Sensitivity changes dependent on illumination
level
Human Visual Response• Changes in Spectral Sensitivity
Scotopic Mesotopic Photopic
[Ferwerda96]
7
Human Visual System
• Acuity– Ability to
resolve spatial detail
• Snellen Chart– View from 20 ft away– Line 8 subtends 1 min
of visual angle– People who can read
this is said to have 20/20 vision
[Ferwerda96]
Human Visual System• Acuity also changes dependent on luminance
level
[Ferwerda96]
Human Visual System• Response at different illumination levels
[Ferwerda96]
Human Visual System
• Adaptation– Our vision system has the ability to adapt to a
given luminance level– Light Adaptation - from darkness to light– Dark Adaptation - from brightness to dark– Adaptation is gradual, not immediate (and is
subject to age! )
Human Visual System
• Threshold Studies– determine the threshold at which a person can
notice the change between a light sample given a certain background luminance.
Human Visual System
• Time course for light adaptation
For rods For cones
[Ferwerda96]
8
Human Visual System• Time course of light adaptation
[Ferwerda96]
Human Visual System
• Time course of dark adaptation
[Ferwerda96]
Human Visual System• Time course of dark adaptation
[Ferwerda96]
Human Visual System
• Ferwerda’s model– Scales luminances as to preserve perceived
contrast using psychophysical data as a guide. • Lw = mLd
– Different models for scotopic and photopic vision with slider to blend the two to simulate mesopic vision.
• m will vary dependent upon whether scene is in scotopic, photopic, or mesopic range.
• Greg Ward offers a simpler approach in Graphics Gems, IV
Ward Tone Reproduction Original Tumblin-Rushmeier operator
• Based on “brightness”, a perceptual measure of how bright humans perceive light.
9
“Normal” Linear Mapping
[Graphics Gems, IV]
Tumblin-Rushmeier Operator
[Graphics Gems, IV]
Ward Operator Results
[Graphics Gems, IV]
Human Visual System
• A good overview of CG tone reproduction operators is available from– “Tone Reproduction and Physically Based Spectral Rendering” by
Devlin et al., State of the Art Report, EUROGRAPHICS 2002.
• Note that Tone Reproduction operators are now starting to run in real time using GPU.
• Questions? Break.
Photographic Response• Print photography process
Camera Film Process
Process
Negative
PrintPaperPrinter
[Geigel97]
Optics Photographic Material
Processed Photographic
Material
Photographic Materials
• Comprised of microscopic grains of silver halide in a gelatin (emulsion)
• Latent image formed when exposed to light• Silver halide converted to metallic silver
during processing.• Converted silver results in opacity
10
Photographic Response
• Illumination Response - high level response of an emulsion to light
• Spectral Sensitivity - Response of a material to different wavelengths of light
• Acuity - Level at which material can reproduce spatial details
• Graininess - Observed variation due to grain distribution
Photographic Response
• Sensitometry– The science of measuring the sensitivity of
photographic materials– Each characteristic has its own unique
sensitometric measure.
Photographic Response
• A typical brightness response / characteristic curve
Log Exposure
Den
sity
I
II
III
IV
I - toeII - straight line
sectionIII - shoulderIV - area of
solarization
γ - gamma
γ
[Geigel97]
Photographic Responsegamma - slope of region II
gives contrast rangespeed - indicates sensitivity to light
S0
1
2
3
-2 0 2
γ
0
1
2
3
-2 0 2
Photographic Response Effects of film Speed
Original 100 Speed Film
400 Speed Film 800 Speed Film
[Geigel97]
Photographic Response - Gamma
Original Low Contrast
Medium Contrast High Contrast
[Geigel97]
11
Photographic ResponseSpectral Response for Three Types of Film
0
100
panchromatic
0
100
orthochromatic
0
100
300 400 500 600
blue sensitive
(Entire visible spectrum)
(Blue/Green sensitive)
(Untreated- blue/ultraviolet)
[Geigel97]
Photographic ResponseEffects of Spectral Sensitivity
Original Panchromatic Blue Sensitive
[Geigel97]
Photographic Response - Grain
∆Di = deviation of sample i from the mean
rms deviation:
A = area of scanning aperture
Selwyn Granularity:
G = (2A) σ
Indication of sample uniformity Measure of granularity
σ 1NΣ(∆Di)2 =
2
Photographic Response - Grain
[Geigel97]
Photographic Response – Acuity (Resolution)
modulation transfer function
point spread function
0
20
40
60
80
100
0 40 80 120
spatial freq. (cycles/mm)
(%)
Photographic Response - Acuity
Without MTF With MTF With MTF & Grain
[Geigel97]
12
Photographic Response
• Observer model can mimic response of photographic systems– Reinhard (Monday) – model based on
photographic response and photographic techniques
– Geigel (Thursday) – general model on simulating response of media to light.
• Questions?
Display Models
• Need to determine the control values (RGB) needed to produce luminances calculated by observer models
Display Models
• Two Problems to be addressed by display models– Gamma
• Luminances from observer model are on a linear scale. Most display devices are non linear
– Gamut• Chromaticities calculated by observer model may
not be reproducible on a given device due to a limited color gamut.
Display Models
– Luminances from observer model are based on a linear scale.
– Most display devices are non linear.
Display Models• CRTs respond non-linearly to voltage• This non-linearity is described by gamma
• where– Ld is the actual display luminance– Ldmax is the maximum display luminance– V is the voltage [0,1]
γ)( maxVLL dd =
Display Models
• CRTs are non-linear
Sample input to monitor Graph of input
Output from monitor Graph of output
13
Display Models
• Gamma correction
Sample input to monitor Graph of input
Gamma correction Graph of Gamma correction
Output from monitor Graph of output
Display Models
• Most displays/video cards now have gamma control as part of their OS.– If we can correct so that gamma is 1.0 then, getting
using Ldmax from specs, the voltage V is given by
maxd
d
LLV =
1/γ
Display Models
• Gamut– Range of chromaticities reproducible by a
device
Display Models
• Different Devices have different gamuts
Display Models
• Perceptual color spaces– CIELAB– Distances between color values corresponds to
difference in perception– Computed from X,Y,Z values and X,Y,Z of a
reference white.
Display Models
Handling out of gamut colors
14
Display Models
• Display Models must address– Gamma / non-linearity of device– Gamut
• Usually dealt with by Color Management Systems.
Tone Reproduction
• A final word on Tone Reproduction– Recall that viewing conditions also affect
perception– TR Operator should also make modifications if
viewing conditions of world observer does not match that of display observer
– Generally included in color management systems but not Tone Reproduction operators.
Tone Reproduction
• Summary– Means of compressing dynamic range of scene to fit
that of display– Observer / Response Model
• Human Visual System• Photographic Systems
– Device Model
• Questions?