IMAGE VECTOR QUANTIZATION USING ORDERED CODEBOOKS - PROPERTIES AND APPLICATIONS

We consider the application of self-organizing feature maps (SOFMs) to the problem of image vector quantization. We show that, using a SOFM- based codebook, vector quantizers which are better than conventionally designed ones can be obtained if their property of ordering is suitab ly exploited. In fact, there are at least three possible applications, which are described in the paper, where they give better results at n o extra cost. We first introduce a measure of disorder, which is used for evaluating the quality of the ordering of SOFM-based codebooks. Th en, we exploit the ordering of these codebooks for two purposes: a dif ferential coding technique which relies on the correlation between suc cessive addresses related to adjacent blocks, and a fast search algori thm based on a 'first guess' of the winning codeword. We also show tha t an ordered codebook is robust with respect to channel errors. Simula tion results are presented, which confirm the useful properties of SOF M-based vector quantizers.