Fast and Space-Efficient Adaptive Arithmetic Coding⋆

  • Boris Ryabko
  • Andrei Fionov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1746)


We consider the problem of constructing an adaptive arithmetic code in the case when the source alphabet is large. A method is suggested whose coding time is less in order of magnitude than that for known methods. We also suggest an implementation of the method by using a data structure called “imaginary sliding window”, which allows to significantly reduce the memory size of the encoder and decoder.


Memory Size Arithmetic Code Alphabet Size Sliding Window Adaptive Code 
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  1. 1.
    Jelinek, F.: Probabilistic Information Theory. New York: McGraw-Hill (1968) 476–489zbMATHGoogle Scholar
  2. 2.
    Rissanen, J.J.: Generalized Kraft inequality and arithmetic coding. IBM J. Res. Dev. 20 (1976) 198–203zbMATHMathSciNetGoogle Scholar
  3. 3.
    Pasco, R.: Source coding algorithm for fast data compression. Ph. D. thesis, Dept. Elect. Eng., Stanford Univ., Stanford, CA (1976)Google Scholar
  4. 4.
    Rubin, F.: Arithmetic stream coding using fixed precision registers. IEEE Trans. Inform. Theory 25,6 (1979) 672–675zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Rissanen, J.J., Langdon, G.G.: Arithmetic coding. IBM J. Res. Dev. 23,2 (1979) 149–162zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Guazzo, M.: A general minimum-redundancy source-coding algorithm. IEEE Trans. Inform. Theory 26,1 (1980) 15–25zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Witten, I.H., Neal, R., Cleary, J.G.: Arithmetic coding for data compression. Comm. ACM 30,6 (1987) 520–540CrossRefGoogle Scholar
  8. 8.
    Ryabko, B.Y., Fionov, A.N.: Homophonic coding with logarithmic memory size. Algorithms and Computation. Berlin: Springer (1997) 253–262 (Lecture notes in comput. sci.: Vol. 1350)Google Scholar
  9. 9.
    Rissanen J., Langdon G.G.: Universal modeling and coding. IEEE Trans. Inform. Theory 27, 1 (1981) 12–23zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Cleary, J.G., Witten, I.H.: Data compression using adaptive coding and partial string matching. IEEE Trans. Commun. 32, 4 (1984) 396–402CrossRefGoogle Scholar
  11. 11.
    Moffat, A.: A note on the PPM data compression algorithm. Res. Rep. 88/7, Dep. Comput. Sci., Univ. of Melbourne, Australia, 1988.Google Scholar
  12. 12.
    Willems, F.M.J., Shtarkov, Y.M., Tjalkens, T.J.: The context-tree weighting method: Basic properties. IEEE Trans. Inform. Theory 41, 3 (1995) 653–664zbMATHCrossRefGoogle Scholar
  13. 13.
    Ryabko, B.Y.: The imaginary sliding window. IEEE Int. Symp. on Information Theory. Ulm (1997) 63Google Scholar
  14. 14.
    Krichevsky, R.: Universal Compression and Retrieval. Dordrecht: Kluwer Academic Publishers (1994)zbMATHGoogle Scholar
  15. 15.
    Feller, W.: An Introduction to Probability Theory and Its Applications. New York: Wiley & Sons (1970)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Boris Ryabko
    • 1
  • Andrei Fionov
    • 1
  1. 1.Siberian State University of Telecommunications and Information SciencesNovosibirskRussia

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