Titlebook: Image and Text Compression; James A. Storer Book 1992 Kluwer Academic Publishers 1992 Hardware.algorithms.coding.coding theory.communicati

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Image and Text Compression978-1-4615-3596-6Series ISSN 0893-3405

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Practical Implementations of Arithmetic Codingistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-efficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic, probability estimation scheme.

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Context Modeling for Text Compressionext 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.

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Massively Parallel Systolic Algorithms for Real-Time Dictionary-Based Text Compressionionary of strings. They are often called . or “LZ” methods after the important work of Lempel and Ziv. With many applications, high speed hardware that can perform compression or decompression in real time is essential. We present massively parallel approaches for real-time textual substitution.

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Fractal Image Compression Using Iterated TransformsThis article presents background, theory, and specific implementation notes for an image compression scheme based on fractal transforms. Results from various implementations are presented and compared to standard image compression techniques.

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Variations on a Theme by GallagerUpper and lower bounds on the redundancy of Huffman codes, in terms of the most likely and of the least likely letter probability are discussed. Whenever it is possible the general situation of codes with arbitrary alphabet size is considered.