On families of new adaptive compression algorithms suitable for time-varying source data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3261, Issue , 2004, Pages 234-244

  Rueda, Luis ^a   John Oommen, B ^b  

^a UNIVERSITY OF WINDSOR   (Canada)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ENCODING (SYMBOLS); STOCHASTIC SYSTEMS; INFORMATION SYSTEMS; PROBABILITY; SIGNAL ENCODING;

ADAPTIVE CODING; ADAPTIVE COMPRESSION; COMPRESSION ALGORITHMS; ENCODING ALGORITHMS; ENCODING AND DECODING; ESTIMATION TECHNIQUES; NEW APPROACHES; SOURCE DATA; STOCHASTIC LEARNING; TIME VARYING; ADAPTIVE SCHEME; ARITHMETIC CODING; NON-STATIONARITIES;

SIGNAL ENCODING; CODES (SYMBOLS);

EID: 35048874203     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30198-1_24     Document Type: Article

References (14)

1. T. Bell, J. Cleary, and I. Witten. Text Compression. Prentice-Hall, 1990.
2. G. Cormack and R. Horspool. Algorithms for Adaptive Huffman Codes. Information Processing Letters, pages 169-165, 1984.
3. M. Effros, K. Visweswariah, S. Kulkarni, and Sergio Verdú. Universal Lossless Source Coding With the Burrows Wheeler Transform. IEEE Transactions on Information Theory, 48(5):1061-1081, 2002.
4. R. Gallager. Variations on a Theme by Huffman. IEEE Transactions on Information Theory, 24(6):668-674, 1978.
5. D. Hankerson, G. Harris, and P. Johnson Jr. Introduction to Information Theory and Data Compression. CRC Press, 1998.
6. P. Jacquet, W. Szpankovvski, and I. Apostol. A Universal Predictor Based on Pattern Matching. IEEE Transactions on Information Theory, 48(6): 1462-1472, 2002.
7. J. C. Kieffer and E. Yang. Grammar-Bassed Codes: A new Class of Universal Lossless Source Codes. IEEE Transactions on Information Theory, 46(3):737-754, 2000.
8. D. Knuth. Dynamic Huffman Coding. Journal of Algorithms, 6:163-180, 1985.
9. J. Muramatsu. On the Performance of Recency Rank and Block Sorting Universal Lossless Data Compression Algorithms. IEEE Transactions on Information Theory, 48 (9):2621-2625, 2002.
10. K. Narendra and M. Thathachar. Learning Automata. An Introduction. Prentice Hall, 1989.
11. B.J. Oommen and L. Rueda. Stochastic Learning-based Weak Estimation of Multinomial Random Variables and Its Applications to Non-stationary Environments. (Submitted for Publication).
12. L. Rueda and B. J. Oommen. Greedy Adaptive Fano Coding. In Proceedings of the 2002 IEEE Aerospace Conference, BigSky, MT, USA. Track 10.0407, March 2002.
13. L. Rueda and B.J. Oommen. Weak Estimator-based Novel Adaptive Compression Techniques Applicable for Files with Non-Stationary Distributions. (Submitted for Publication).
14. M. J. Weinberger and E. Ordentlich. On Delayed Prediction of Individual Sequences. IEEE Transactions on Information Theory, 48(7): 1959-1976, 2002.