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Classified vector quantizer based on minimum-distance partitioning

In this paper, we describe a new classified vector quantization (CVQ) technique employing the minimum-distance classifier to reduce the encoding complexity required in the full search vector quantization (VQ). The determination of the optimal subcodebook sizes for each class is an important task in the CVQ. However, we propose a CVQ technique, which, with an equal subcodebook size, suboptimally satisfies the optimal CVQ condition described in [4]. In addition, a cluster modifying algorithm, which alleviates the local minimum problem in the clustering algorithm, is proposed to ensure the optimal CVQ condition. The proposed CVQ is a kind of the partial search VQ because it requires a search process through each subcodebook only. However, simulation result reveal that the performance of the proposed CVQ is almost comparable to that of the full search VQ, while the encoding complexity is only 6.5 % of that required in the full search VQ.