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1990•IEEE Transactions on CommunicationsRequires access

Implementing the PPM data compression scheme

Alistair Moffat

Open publisher page 423 citations

Abstract

The prediction by partial matching (PPM) data compression algorithm developed by J. Cleary and I. Witten (1984) is capable of very high compression rates, encoding English text in as little as 2.2 b/character. It is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a variant is described that encodes and decodes at over 4 kB/s on a small workstation and operates within a few hundred kilobytes of data space, but still obtains compression of about 2.4 b/character for English text.>

About this research paper

What this paper is about

The prediction by partial matching (PPM) data compression algorithm developed by J. Cleary and I. Witten (1984) is capable of very high compression rates, encoding English text in as little as 2.2 b/character. It is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a variant is described that encodes and decodes at over 4 kB/s on a small workstation and operates within a few hundred kilobytes of data space, but still obtains compression of about 2.4 b/character for English text.>

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OpenAlex reports 423 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The prediction by partial matching (PPM) data compression algorithm developed by J. Cleary and I. Witten (1984) is capable of very high compression rates, encoding English text in as little as 2.2 b/character. It is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a variant is described that encodes and decodes at over 4 kB/s on a small workstation and operates within a few hundred kilobytes of data space, but still obtains compression of about 2.4 b/character for English text.>

Key concepts: Decodes, Data compression, Compression (physics), Scheme (mathematics), Character (mathematics), Computer science, Encoding (memory), Matching (statistics)

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