Streamlining Context Models for Data Compression
نویسندگان
چکیده
Context modeling has emerged as the most promising new approach to compressing text. While context-modeling algorithms provide very good compression, they su er from the disadvantages of being slow and requiring large amounts of main memory in which to execute. We describe a context-model-based algorithm that runs signi cantly faster, uses much less space, and provides compression ratios close to those of earlier context modeling algorithms. We achieve these improvements through the use of self-organizing lists.
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