A Mean-Removed Variation of Weighted Universal Vector Quantization for Image Coding

Approaching a multi-codebook system as a codebook of codebooks, weighted universal vector quantization (WUVQ) 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 paper we propose a mean-removed variation of WUVQ. 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 .5 dB improvement over WUVQ at no rate expense. ‘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. 302 0-8186-3392-1193 $3.00 8 1993 IIjIiE