Skip to main content

Rate Distortion Theory and Data Compression

  • Chapter
Advances in Source Coding

Part of the book series: International Centre for Mechanical Sciences ((CISM,volume 166))

Abstract

In this introductory lecture we present the rudiments of rate distortion theory, the branch of information theory that treats data compression problems. The rate distortion function is defined and a powerful iterative algorithm for calculating it is described. Shannon’s source coding theorems are stated and heuristically discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. SHANNON, C.E., (1948). “A Mathematical Theory of Communication”, BSTJ, 27, 379–423, 623–656.

    Google Scholar 

  2. SHANNON, C.E., (1959) “Coding Theorems for a Discrete Source with a Fidelity Criterion”, IRE Nat’l. Cony. Rec., Part 4, 142–163.

    Google Scholar 

  3. BLAHUT, R.E., (1972) “Computation of Channel Capacity and Rate-distortion Functions”, Trans. IEEE, IT-18, 460–473.

    MathSciNet  Google Scholar 

  4. PINKSTER, M.S., (1963) “Sources of Messages”, Problemy Peredecii Informatsii. 14, 5–20.

    Google Scholar 

  5. GAL LAGER, R.G., (1968) “Information Theory and Reliable Communication”, Wiley, New York.

    MATH  Google Scholar 

  6. BERGER, T., (1968) “Rate Distortion Theory for Sources with Abstract Alphabets and Memory”, Information and Control, 13, 254–273.

    Article  MATH  MathSciNet  Google Scholar 

  7. GOBLICK, T.J., Jr. (1969) “A Coding Theorem for Time-Discrete Analog Data Sources”, Trans. IEEE, IT-15, 401–407.

    MathSciNet  Google Scholar 

  8. BERGER, T., (1971) “Rate Distortion Theory. A Mathematical Basis for Data Compression”, Prentice-Hall, Englewood Cliffs, N.Y.

    Google Scholar 

  9. GRAY, R.M., and L.D. DAVISSON (1973) “Source Coding Without Ergodicity”, Presented at 1973 IEEE Intern. Symp. on Inform. Theory, Ashkelon, Israel.

    Google Scholar 

  10. GOBLICK, T.J., Jr. (1962) “Coding for a Discrete Information Source with a Distortion Measure”, Ph.D.Dissertation, Elec. Eng. Dept. M. I. T. Cambridge, Mass.

    Google Scholar 

  11. KOLMOGOROV, A.N., (1956) “On the Shannon Theory of Information Transmission in the Case of Continuous Signals”, Trans. IEEE, IT-2, 102–108.

    Google Scholar 

  12. HAMMING, R.W., (1950) “Error Detecting and Error Correcting Codes”, BSTJ, 29, 147–160.

    MathSciNet  Google Scholar 

  13. BERLEKAMP, E.R., (1968) “Algebraic Coding Theory”, McGraw-Hill, N.Y.

    Google Scholar 

  14. BERGER, T., and J.A. VAN DER HORST (1973) “BCH Source Codes”, Submitted to IEEE Trans. on Information Theory.

    Google Scholar 

  15. POSNER, E.C., (1968) In Man H.B.“Error Correcting Codes”, Wiley, N.Y. Chapter 2.

    Google Scholar 

  16. JELINEK, F., (1969) “Tree Encoding of Memoryless Time-Discrete Sources with a Fidelity Criterion”, Trans. IEEE, IT-15, 584–590.

    MathSciNet  Google Scholar 

  17. JELINEK, F., and J.B. ANDERSON (1971) “Instrumentable Tree Encoding and Information Sources”, Trans. IEEE, IT-17, 118–119.

    Google Scholar 

  18. ANDERSON, J.B., and F. JELINEK (1973) “A Two-Cycle Algorithm for Source Coding with a Fidelity Criterion”, Trans. IEEE, IT-19, 77–92.

    MathSciNet  Google Scholar 

  19. GALLAGER, R.G., (1973) “Tree Encoding for Symmetric Sources with a Distortion Measure”, Presented at 1973 IEEE Int’l. Symp. on Information Theory, Ashkelon, Israel.

    Google Scholar 

  20. VITERBI, A.J., and J.K. OMURA (1974) “Trellis Encoding of Memoryless Discrete-Time Sources with a Fidelity Criterion”, Trans. IEEE, IT-20, 325–332.

    MathSciNet  Google Scholar 

  21. BERGER, T., R.J. DICK and F. JELINEK (1974) “Tree Encoding of Gaussian Sources”, Trans. IEEE, IT-20, 332–336.

    MathSciNet  Google Scholar 

  22. BERGER, T., F. JELINEK and J.K. WOLF (1972) “Permutation Codes for Sources”, Trans. IEEE, IT-18, 160–169

    Google Scholar 

  23. SLEPIAN, D., (1965) “Permutation Modulation”, Proc. IEEE, 53, 228–236.

    Article  Google Scholar 

  24. BERGER, T., (1972) “Optimum Quantizers and Permutation Codes”, Trans. IEEE, IT-18, 759–765.

    Google Scholar 

  25. BERGER, T., (1973) “Information - Singular Random Processes”, Presented at Third International Symposium on Information Theory, Tallinn, Estonia, USSR.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 1975 Springer-Verlag Wien

About this chapter

Cite this chapter

Berger, T. (1975). Rate Distortion Theory and Data Compression. In: Advances in Source Coding. International Centre for Mechanical Sciences, vol 166. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2928-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-2928-9_1

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-81302-7

  • Online ISBN: 978-3-7091-2928-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics