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Source coding algorithms for fast data compression
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The article was published on 1976-01-01 and is currently open access. It has received 224 citations till now. The article focuses on the topics: Data compression & Data compression ratio.read more
Citations
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Journal ArticleDOI
Arithmetic coding for data compression
TL;DR: The state of the art in data compression is arithmetic coding, not the better-known Huffman method, which gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.
Book
Introduction to data compression
TL;DR: The author explains the development of the Huffman Coding Algorithm and some of the techniques used in its implementation, as well as some of its applications, including Image Compression, which is based on the JBIG standard.
Journal ArticleDOI
Data Compression Using Adaptive Coding and Partial String Matching
John G. Cleary,Ian H. Witten +1 more
TL;DR: This paper describes how the conflict can be resolved with partial string matching, and reports experimental results which show that mixed-case English text can be coded in as little as 2.2 bits/ character with no prior knowledge of the source.
Journal ArticleDOI
The context-tree weighting method: basic properties
TL;DR: The authors derive a natural upper bound on the cumulative redundancy of the method for individual sequences that shows that the proposed context-tree weighting procedure is optimal in the sense that it achieves the Rissanen (1984) lower bound.
Journal ArticleDOI
The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression
Ian H. Witten,Tim Bell +1 more
TL;DR: The authors propose the application of a Poisson process model of novelty, which ability to predict novel tokens is evaluated, and it consistently outperforms existing methods and offers a small improvement in the coding efficiency of text compression over the best method previously known.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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An Introduction to Probability Theory and Its Applications
David A. Freedman,William Feller +1 more
Book
Information Theory and Reliable Communication
TL;DR: This chapter discusses Coding for Discrete Sources, Techniques for Coding and Decoding, and Source Coding with a Fidelity Criterion.