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A mean-removed variation of weighted universal vector quantization for image coding

Andrews, Barry D. and Effros, Michelle and Chou, Philip A. and Gray, Robert M. (1993) A mean-removed variation of weighted universal vector quantization for image coding. In: Data Compression Conference (3rd : 1993 : Snowbird, Utah). IEEE , Los Alamitos, CA, pp. 302-309. ISBN 0-8186-3392-1.

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Weighted universal vector quantization uses traditional codeword design techniques to design locally optimal multi-codebook systems. Application of this technique to a sequence of medical images produces a 10.3 dB improvement over standard full search vector quantization followed by entropy coding at the cost of increased complexity. In this proposed variation each codebook in the system is given a mean or 'prediction' value which is subtracted from all supervectors that map to the given codebook. The chosen codebook's codewords are then used to encode the resulting residuals. Application of the mean-removed system to the medical data set achieves up to 0.5 dB improvement at no rate expense.

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Additional Information:© 1993 IEEE. Reprinted with permission. This material is based upon work supported under a Natural Sciences and Engineering Research Council of Canada Scholarship and a Sony Corporation Fellowship, a National Science Foundation Graduate Fellowship, and National Science Foundation Grant MIP-9016974.
Subject Keywords:source code design; source coding theory; universal source coding
Record Number:CaltechAUTHORS:ANDdcc93
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2955
Deposited By: Archive Administrator
Deposited On:08 May 2006
Last Modified:02 Oct 2019 22:58

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