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January 1, 1984IEEE Transactions on Communications1,259 citations

Data Compression Using Adaptive Coding and Partial String Matching

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JCJohn J. ClearyIWIan H. Witten

Key Points

  • To resolve the trade-off between using high-order Markov models and achieving rapid initial model formation in adaptive data compression.
  • Combined arithmetic coding with dynamically updated Markov source models constructed by both encoder and decoder during transmission.
  • Employed partial string matching to blend statistics across varying context orders without requiring explicit transmission of model statistics.
  • Compressed mixed-case English text to as little as 2.2 bits per character with zero prior knowledge of the source text.
  • Demonstrated lower coding overhead with dynamic adaptive modeling compared to explicit statistical transmission.

Abstract

The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during the course of the transmission, and has been shown to incur a smaller coding overhead than explicit transmission of the model's statistics. But there is a basic conflict between the desire to use high-order Markov models and the need to have them formed quickly as the initial part of the message is sent. 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.

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Cite This Study

Cleary et al. (1984) studied this question.

synapsesocial.com/papers/6a03b1fd1506208190f016c7https://doi.org/10.1109/tcom.1984.1096090
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