On the structure of vector quantizers

Abstract

Vector quantization is intrinsically superior to predictive coding, transform coding, and other suboptimal and {\em ad hoc} procedures since it achieves optimal rate distortion performance subject only to a constraint on memory or block length of the observable signal segment being encoded. The key limitation of existing techniques is the very large randomly generated code books which must be stored, and the computational complexity of the associated encoding procedures. The quantization operation is decomposed into its rudimentary structural components. This leads to a simple and elegant approach to derive analytical properties of optimal quantizers. Some useful properties of quantizers and algorithmic approaches are given, which are relevant to the complexity of both storage and processing in the encoding operation. Highly disordered quantizers, which have been designed using a clustering algorithm, are considered. Finally, lattice quantizers are examined which circumvent the need for a code book by using a highly structured code based on lattices. The code vectors are algorithmically generated in a simple manner rather than stored in a code book, and fast algorithms perform the encoding algorithm with negligible complexity.