Published 1993
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A mean-removed variation of weighted universal vector quantization for image coding
Abstract
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.
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.Files
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