Context Modeling for Text Compression
Adaptive context modeling has emerged as one of the most promising new approaches to compressing text. A finite-context model is a probabilistic model that uses the context in which input symbols occur (generally a few preceding characters) to determine the number of bits used to code these symbols. We provide an introduction to context modeling and recent research results that incorporate the concept of context modeling into practical data compression algorithms.
KeywordsMemory Requirement Hash Table Data Compression Context Model Internal Memory
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