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Image Compression

  • David Salomon

Modern computers employ graphics extensively. Window-based operating systems display the disk’s file directory graphically. The progress of many system operations, such as downloading a file, may also be displayed graphically. Many applications provide a graphical user interface (GUI), which makes it easier to use the program and to interpret displayed results. Computer graphics is used in many areas in everyday life to convert many types of complex information to images. Images are thus important, but they tend to be big! Since modern hardware can display many colors, it is common to have a pixel represented internally as a 24-bit number, where the precentages of red, green and blue occupy 8 bits each. Such a 24-bit pixel can specify one of 224 ≈ 16.78 million colors. An image at a resolution of 512×512 that consists of such pixels thus occupies 786,432 bytes. At a resolution of 1024 × 1024 it gets four times as big, requiring 3,145,728 bytes. Movies are also commonly used with computers, making for even bigger images. This is why image compression is so important. An important feature of image compression is that it can be lossy. An image, after all, exists for people to look at, so, when it is compressed, it is okay to lose image features for which the human eye is not sensitive. This is one of the main ideas behind JPEG and other lossy image compression methods described in this chapter.

Keywords

Discrete Cosine Transform Image Compression Data Unit Iterate Function System Discrete Cosine Transform Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliography

  1. Blinn, J. F. (1993) “What’s the Deal with the DCT,” IEEE Computer Graphics and Applications pp. 78–83, July.Google Scholar
  2. Pennebaker, William B., and Joan L. Mitchell (1992) JPEG Still Image Data Compression Standard, Van Nostrand Reinhold.Google Scholar
  3. Rao, K. R., and P. Yip (1990) Discrete Cosine Transform — Algorithms, Advantages, Applications, London, Academic Press.zbMATHGoogle Scholar
  4. Wallace, Gregory K. (1991) “The JPEG Still Image Compression Standard,” Communications of the ACM 34(4):30–44, April.CrossRefGoogle Scholar
  5. Zhang, Manyun (1990) The JPEG and Image Data Compression Algorithms (Dissertation).Google Scholar
  6. Pennebaker, W. B., and J. L. Mitchell (1988) “Probability Estimation for the Q-coder,” IBM Journal of Research and Development 32(6):717–726.CrossRefGoogle Scholar
  7. Pennebaker, W. B., et al. (1988) “An Overview of the Basic Principles of the Q-coder Adaptive Binary Arithmetic Coder,” IBM J. of Research and Development 32(6):737–752.CrossRefGoogle Scholar
  8. Langdon, G., and J. Rissanen (1981) “Compression of Black White Images with Arithmetic Coding,” IEEE Transactions on Communications COM-29(6):858–867, June.CrossRefGoogle Scholar
  9. Moffat, A. (1991) “Two-Level Context Based Compression of Binary Images,” in Proceedings of the 1991 Data Compression Conference, J. Storer Ed., Los Alamitos, CA, IEEE Computer Society Press, pp. 382–391.Google Scholar
  10. Howard, P. G. and J. S. Vitter, (1993) “Fast and Efficient Lossless Image Compression,” in Proceedings of the 1993 Data Compression Conference, J. Storer Ed., Los Alamitos, CA, IEEE Computer Society Press, pp. 351–360.Google Scholar
  11. Howard, Paul G., and J. S. Vitter (1992a), “New Methods for Lossless Image Compression Using Arithmetic Coding,” Information Processing and Management, 28(6):765–779.CrossRefGoogle Scholar
  12. Howard, Paul G., and J. S. Vitter (1992b), “Error Modeling for Hierarchical Lossless Image Compression,” in Proceedings of the 1992 Data Compression Conference, J. Storer ed., Los Alamitos, CA, IEEE Computer Society Press, pp. 269–278.CrossRefGoogle Scholar
  13. Wu, Xiaolin (1995), “Context Selection and Quantization for Lossless Image Coding,” in Storer, James A., and Martin Cohn (eds.), DCC ‘95, Data Compression Conference, Los Alamitos, CA, IEEE Computer Society Press, p. 453.Google Scholar
  14. Wu, Xiaolin (1996), “An Algorithmic Study on Lossless Image Compression,” in Storer, James A. (ed.), DCC ‘96, Data Compression Conference, Los Alamitos, CA, IEEE Computer Society Press.Google Scholar
  15. Sayood, K., and K. Robinson (1992) “A Differential Lossless Image Compression Scheme,” IEEE Transactions on Signal Processing 40(1):236–241, January.CrossRefGoogle Scholar
  16. Prusinkiewicz, P., and A. Lindenmayer (1990) The Algorithmic Beauty of Plants, New York, Springer Verlag.zbMATHCrossRefGoogle Scholar
  17. Prusinkiewicz, P., A. Lindenmayer, and F. D. Pracchia (1991) “Synthesis of Space-Filling Curves on the Square Grid,” in Fractals in the Fundamental and Applied Sciences, edited by Peitgen, H.-O. et al., Amsterdam, Elsevier Science Publishers, pp. 341–366.Google Scholar
  18. Sagan, Hans (1994) Space-Filling Curves, New York, Springer Verlag.zbMATHCrossRefGoogle Scholar
  19. Culik, Karel II, and V. Valenta(1996), “Finite Automata Based Compression of Bi-Level Images,” in Storer, James A. (ed.), DCC ‘96, Data Compression Conference, Los Alamitos, CA, IEEE Computer Society Press, pp. 280–289.CrossRefGoogle Scholar
  20. Barnsley, F., and Sloan, A. D. (1988) “A Better Way to Compress Images,” Byte magazine pp. 215–222 January.Google Scholar
  21. Barnsley, M. (1988) Fractals Everywhere, New York, Academic Press.zbMATHGoogle Scholar
  22. Demko, S., L. Hodges, and B. Naylor (1985) “Construction of Fractal Objects with Iterated Function Systems,” Computer Graphics 19(3):271–278, July.Google Scholar
  23. Feder, Jens (1988) Fractals, New York, Plenum Press.zbMATHGoogle Scholar
  24. Fisher, Yuval (ed.), (1995) Fractal Image Compression: Theory and Application, New York, Springer-Verlag.Google Scholar
  25. Mandelbrot, B., (1982) The Fractal Geometry of Nature, San Francisco, CA, W. H. Freeman.zbMATHGoogle Scholar
  26. Peitgen, H. O., et al. (eds.) (1982) The Beauty of Fractals, Berlin, Springer-Verlag.Google Scholar
  27. Peitgen, H.O., and Dietmar Saupe (1985) The Science of Fractal Images, Berlin, Springer-Verlag.Google Scholar
  28. Reghbati, H. K. (1981) “An Overview of Data Compression Techniques,” IEEE Computer 14(4):71–76.CrossRefGoogle Scholar
  29. DeVore R. et al. (1992) “Image Compression Through Wavelet Transform Coding,” IEEE Transactions on Information Theory 38(2):719–746, March.MathSciNetzbMATHCrossRefGoogle Scholar
  30. Stollnitz, E. J., T. D. DeRose, and D. H. Salesin (1996) Wavelets for Computer Graphics, San Francisco, CA, Morgan Kaufmann.Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • David Salomon
    • 1
  1. 1.Department of Computer ScienceCalifornia State UniversityNorthridgeUSA

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