Adding state merging to the DMC data compression algorithm

Dynamic Markov Compression (DMC) generates Markov models for data compression by starting with an initial model then expanding it one state at a time using a cloning operation. Typically, expansion continues until memory is full then the model is discarded and restarted. We present an alternative me...

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Bibliographic Details
Published inInformation processing letters Vol. 70; no. 5; pp. 223 - 228
Main Author Young-Lai, Matthew
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 21.06.1999
Elsevier Science
Elsevier Sequoia S.A
Subjects
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science; control theory; systems
Data compression
Data processing. List processing. Character string processing
Data structures
Dynamic Markov Compression
Exact sciences and technology
Information processing
Memory organisation. Data processing
Software
Studies
Theoretical computing
Data structures
Dynamic Markov Compression
Data processing
Markov model
Data structure
Merging
Algorithm
Data compression
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ISSN0020-0190
1872-6119
DOI10.1016/S0020-0190(99)00066-6

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Summary:Dynamic Markov Compression (DMC) generates Markov models for data compression by starting with an initial model then expanding it one state at a time using a cloning operation. Typically, expansion continues until memory is full then the model is discarded and restarted. We present an alternative method that retracts the model based on selective merging of similar states. This requires extra time, but allows DMC to attain its best compression using much less memory.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0020-0190
1872-6119
DOI:10.1016/S0020-0190(99)00066-6