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- Acharya T.

Acharya T. - John Wiley & Sons, 2000. - 292 p.
ISBN 0-471-48422-9
Download (direct link): standardforImagecompressioncon2000.pdf
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Some people in the industry have a misconception that LZ coding techniques are applied in text compression only and they do not work for compressing any other multimedia data type. In lossless compression mode, the LZ coding techniques have been found to be effective to compress different kinds of images. For example, the popular image-compression algorithm GIF (Graphical Interchange Format) is an implementation of the LZW algorithm and very similar to the Unix compress utility. GIF is effective to compress computer-generated graphical images and pseudocolor or color-mapped images. TIFF (Tag Image File Format) is another industry standard. Some of the modes in TIFF have been developed based on LZ coding. This is useful for compressing dithered binary images, which simulate grayscale images
20 INTRODUCTION TO DATA COMPRESSION
through a variation in the density of black dots. The TIFF Revision 6.0 was released in 1992 and supports numerous data compression schemes such as LZW, CCITT Group 3 and Group 4, and JPEG.
1.9 SUMMARY
In this chapter, we introduced readers with the fundamentals of data and image compression. We discussed why data compression is important and how it became an integrated part of today’s multimedia computing and communications systems. We discussed some fundamentals including information theory such as discrete memoryless model, entropy, noiseless source coding theorem, unique decipherability, etc., in order to aid the readers to understand the principles behind data compression. We discussed the concepts of classification of compression techniques, performance measures, etc. We also presented brief introduction of various international standards for digital compression techniques of various multimedia data types—image, video, text, audio, data, etc. Different source coding algorithms for data compression, the principles of image compression techniques, and details of JPEG and JPEG2000 image compression standards will be discussed in the following chapters.
REFERENCES
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2. C. E. Shannon, “Certain Results in Coding Theory for Noisy Channels,” Information Control, Vol. 1, No. 1, pp. 6-25, September 1957.
3. C. E. Shannon, “Coding Theorems for a Discrete Source with a Fidelity Criterion,” IRE National Convention Record, Part 4, pp. 142-163, 1959.
4. B. McMillan, “The Basic Theorems of Information Theory,” Ann. Math. Statist., Vol. 24, pp. 196-219, 1953.
5. D. S. Hirschberg and D. A. Lelewer, “Efficient Decoding of Prefix Codes,” Comm, of the ACM, Vol. 33, No. 4, pp. 449-459, April 1990.
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7. D. Huffman, “A Method for the Construction of Minimum Redundancy codes,” Proc. IRE, Vol. 40, pp. 1098-1101, 1952.
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REFERENCES 21
9. I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic Coding for Data Compression,” Communications of the ACM, Vol. 30, No. 6, June 1987.
10. A. N. Netravali and B. G. Haskell, Digital Pictures. Plenum Press, New York, 1988.
11. ISO/IEC 11172 (MPEG-1), “Information Technology—Coding of Moving Pictures and Associated Audio for Digital Storage Media at Up to About
1.5 Mbit/s.”
12. ISO/IEC 13818 (MPEG-2), “Information Technology—Generic Coding of Moving Pictures and Associated Audio Information.”
13. ISO/IEC 10918 (JPEG), “Information Technology—Digital Compression and Coding of Continuous-Tone Still Images.”
14. R. Koenen, F. Pereira, and L. Chiariglione, “MPEG-4: Context and Objectives,” Image Communication Journal, Vol. 9, No. 4, 1997.
15. MPEG-7: ISO/IEC JTC1/SC20/WG211, N2207, “Context and Objectives,” March 1998.
16. G. Wallace, W. B. Pennebaker, and J. L. Mitchell, “Draft Proposal for the Joint Photographic Expert Group (JPEG),” JPEG-8-R6, June 24, 1990.
17. ISO/IEC WD15444 (V3.0), “JPEG2000 Lossless and Lossy Compression of Continuous-Tone and Bi-level Still Images,” 1999.
18. ITU-T, “Draft ITU-T Recommendation H.263: Video Coding for Low Bit Rate Communication,” December 1995.
19. ITU-T Recommendation G.729, “Coding of Speech at 8 Kbit/s Using Conjugate Structure Algebraic Code Excited Linear Prediction (CS-ACELP),” March 1996.
20. “Mandatory Speech Codec Speech Processing Functions—AMR Speech Codec; General Description (3GPP),” 3G TS 26.071 v3.0.0, June 1999.
21. United States Advanced Television Systems Committee (ATSC), Audio Specialist Group (T3/S7) Doc. A/52, “Digital Audio Compression Standard (AC-3),” November 1994.
22. A. Hoogendoorn, “Digital Compact Cassette,” Proc. IEEE, Vol. 82, pp. 554-563, April 1994.
23. J. Ziv and A. Lempel, “A Universal Algorithm for Sequential Data Compression,” IEEE Trans, on Info. Theory, IT-23, Vol. 3, pp. 337-343, May 1977.
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