Batch6 Final Review

8/2/2019 Batch6 Final Review

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SUBJECTIVE IMAGE QUALITY TRADEOFF BETWEEN

SPATIAL RESOLUTION AND QUANTIZATION NOISE

Under the Esteemed Guidance ofMr.K.Vasu Babu

Assistant professor

TEAM MEMBERS:

Ch.V.Krishna Mohan

B.Sravani

B.J.V.P.Gowtham Kumar

K.Sindhura

G.Anudeep Varma


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CONTENTS

ABSTRACT

Lossless and Lossy Compression

Overview of JPEG Lossy Compression Comparison between JPEG and JPEG 2000


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ABSTRACT

In general quality metrics compare the original image to a distorted

image at the same resolution assuming a fixed viewing condition.

However, in many applications, such as video streaming, due to the

diversity of channel capacities and display devices, the viewing distance

and the spatiotemporal resolution of the displayed signal may be adapted

in order to optimize the perceived signal quality.

For example, at low bit rate coding applications an observer may prefer

to reduce the resolution or increase the viewing distance to reduce the

visibility of the compression artifacts.


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The tradeoff between resolution/viewing conditions and visibility of

compression artifacts requires new approaches for the evaluation of image

quality that account for both image distortions and image size.

In order to better understand such tradeoffs, we conducted subjective tests

using two representative still image coders, JPEG and JPEG 2000.

Our results indicate that an observer would indeed prefer a lower spatial

resolution (at a fixed viewing distance) in order to reduce the visibility of

the compression artifacts, but not all the way to the point where the artifacts

are completely invisible.


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Lossless and Lossy Compression

Lossless compression

There is no information loss, and the image can be

reconstructed exactly the same as the original

Applications: Medical imagery, Archiving

Lossy compression

Information loss is tolerable

Many-to-1 mapping in compression eg. quantization

Applications: commercial distribution (DVD) and rateconstrained environment where lossless methods can

not provide enough compression ratio


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Why Lossy?

o In most applications related to consumerelectronics, lossless compression is not necessary

o What we care is the subjective quality of the decodedimage, not those intensity values

o With the relaxation, it is possible to achieve ahigher compression ratio (CR)

o For photographic images, CR is usually below 2 forlossless, but can reach over 10 for lossy


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Lossy Image Compression and JPEG Coding

Standard

Why lossy for images?

Tradeoff between Rate and Distortion

Transform basics

Unitary transform

Quantization basics

Uniform Quantization

JPEG=T+Q+C T: DCT, Q: Uniform Quantization, C: Run-length and

Huffman coding


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Overview of JPEG Lossy Compression

Flow-chart diagram of DCT-based coding algorithm specified by

Joint Photographic Expert Group (JPEG)


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Original


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JPEG

27:1


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JPEG2000

27:1


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JPEG Compression Example

Original image

512 x 512 x 8 bits

= 2,097,152 bits

JPEG

27:1 reduction

=77,673 bits


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JPEG

JPEG is a lossy compression technique used

for full-color or gray-scale images, by

exploiting the fact that the human eye will not

notice small color changes.

JPEG 2000 is an initiative that will provide an

image coding system using compression

techniques based on the use of wavelettechnology.


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1MB Before JPEG COMPRESSION JPEG COMPRESSED IMAGE 18.4kb


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Comparison between JPEG and JPEG 2000

JPEG 2000 offers numerous advantages over the oldJPEG standard.

One main advantage is that JPEG 2000 offers both lossy and

lossless compression in the same file stream.

while JPEG usually only utilizes lossy compression.

The JPEG 2000 files can also handle up to 256 channels of

information as compared to the current JPEG standard.

Another advantage of JPEG 2000 over JPEG is that JPEG

2000 is able to offer higher compression ratios for lossycompression.

For lossy compression, data has shown that JPEG 2000 can

typically compress images from 20%-200% more than JPEG


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Compression efficiency for lossy compression istypically measured using the peak signal to noise ratio, or

PSNR, and the root mean square error (RMSE)


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Table: Comparison of PSNR compression efficiencies

(in dB) for two images at various bit rates


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OUTPUT

INPUT IMAGE

JPEG COMPRESSED IMAGE

RESOLUTION MODIFIED IMAGE


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REFERENCES

[1] T. N. Pappas, R. J. Safranek, and J.Chen, .Perceptual criteria for image

quality evaluation,. inHandbook of

Image and Video Processing, 2nd ed.,

A. C. Bovik, Ed. Academic Press,

2005, pp. 939.959.

[2] J. H. D. M. Westerink and J. A. J.

Roufs, .Subjective image quality as a

function of viewing distance,

resolution, and picture size,. SMPTE

Journal, vol. 98, pp. 113.119, Feb.

1989. [3] P. G. J. Barten, .The SQRI method:

A new method for the evaluation of

visible resolution on a display,. in

Proc. Society for Information Display,

vol. 28, 1987, pp. 253.262.

[4] .The effects of picture size anddefinition on perceived image quality,.

inIEEE Trans. Electron Devices, vol.

36, 9, Sept. 1989, pp. 1865.1869.

[5] Subjective image quality of high-

definition television images,. in Proc.

Society for Information Display, vol.

31, 1990, pp. 239.243.

[6] C. Kuhmunch, G. Kuhne, C.

Schremmer, and T. Haenselmann, .A

video-scaling algorithm based on

human perception for spatio-temporalstimuli,. inMultimedia Computing and

Networking, W. chi Feng andM. G.

Kienzle, Eds., Proc. SPIE, Vol. 4312,

San Jose, CA, Jan. 2001, pp. 13.24.


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CONCLUSION

This paper has highlighted the need for a fundamentalchange in our understanding of image qualityassessment, both subjective and objective.

The results of our subjective tests are expected to beapplicable in the development of image fidelitymeasures that predict image quality over multipleresolutions and viewing conditions, and take intoaccount both the visibility of the compression

artifacts and the image size, i.e., the visibility of thesignal itself. Such measures will be invaluable forscalable image and video compression applications.


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Batch6 Final Review