Context quantization by minimum adaptive code length

Søren Forchhammer; Xiaolin Wu

doi:10.1109/ISIT.2007.4557234

Context quantization by minimum adaptive code length

Søren Forchhammer, Xiaolin Wu

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

Context quantization is a technique to deal with the issue of context dilution in high-order conditional entropy coding. We investigate the problem of context quantizer design under the criterion of minimum adaptive code length. A property of such context quantizers is derived for binary symbols. A fast context quantizer design algorithm for conditioning binary symbols is presented and its complexity analyzed. It is conjectured that this algorithm is optimal. The context quantization is performed in what may be perceived as a probability simplex space rather than in the space of context instances.

Original language	English
Title of host publication	IEEE International Symposium on Information Theory, 2007. ISIT 2007.
Number of pages	2975
Place of Publication	Nice, France
Publisher	IEEE
Publication date	2007
Pages	246-250
ISBN (Print)	14-24-41397-3
DOIs	https://doi.org/10.1109/ISIT.2007.4557234
Publication status	Published - 2007
Event	IEEE International Symposium on Information Theory, 2007. - Nice, France Duration: 1 Jan 2007 → …

Conference

Conference	IEEE International Symposium on Information Theory, 2007.
City	Nice, France
Period	01/01/2007 → …

Bibliographical note

Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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abstract = "Context quantization is a technique to deal with the issue of context dilution in high-order conditional entropy coding. We investigate the problem of context quantizer design under the criterion of minimum adaptive code length. A property of such context quantizers is derived for binary symbols. A fast context quantizer design algorithm for conditioning binary symbols is presented and its complexity analyzed. It is conjectured that this algorithm is optimal. The context quantization is performed in what may be perceived as a probability simplex space rather than in the space of context instances.",

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