Optimization of code book in vector quantization

In this paper, we present a novel approach to design a code book for vector quantization using standard deviation. The proposed algorithm optimizes the partitioning space to explore the search space for a set of equally viable and equivalent partitions. Essentially the partition space is partitioned into perceptive clusters, so that the code book is optimized. The proposed algorithm is proved better than the widely used quantization algorithm in applications.