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Lossless Compression Algorithm Based on Context Tree

Published: 25 February 2020 Publication History

Abstract

In order to deal with the context dilution problem introduced in the lossless compression of M-ary sources, a lossless compression algorithm based on a context tree model is proposed. By making use of the principle that conditioning reduces entropy, the algorithm constructs a context tree model to make use of the correlation among adjacent image pixels. Meanwhile, the M-ary tree is transformed into a binary tree to analyze the statistical information of the source in more details. In addition, the escape symbol is introduced to deal with the zero-frequency symbol problem when the model is used by an arithmetic encoder. The increment of the description length is introduced for the merging of tree nodes. The experimental results show that the proposed algorithm can achieve better compression results.

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    cover image ACM Other conferences
    ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing
    December 2019
    270 pages
    ISBN:9781450376822
    DOI:10.1145/3376067
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Shanghai Jiao Tong University: Shanghai Jiao Tong University
    • Xidian University
    • TU: Tianjin University

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    Published: 25 February 2020

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    Author Tags

    1. context modeling
    2. description length
    3. entropy coding
    4. escape symbol
    5. lossless compression

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