A construction of optimum vector quantizers by simulated annealing algorithm

Abstract

Vector quantization is known widely as a highly efficient coding for image and speech signals. The algorithm proposed by Linde, Buzo, and Gray (the LEG algorithm) is the most fundamental design procedure for this kind of quantizer. However, the LBG algorithm usually gives only the local minimum of the distortion measure based on the quantization error. With such a background, this paper introduces the simulated annealing procedure into the design of the vector quantizer based on the LBG algorithm, which is known as the effective method for the NP-complete probabilistic combinational optimization problem. The widely employed mean-square error is adopted as the distortion measure. Various approaches are considered to derive the global minimum of the measure. In other words, this paper is based on the LBG algorithm and presents a design procedure for the vector quantizer (more generally, the vector quantizer considering the channel error) by introducing simulated annealing. By this approach, a vector quantizer with a better performance than the traditional design as well as a more ideal highly efficient coding is realized. A simulated experiment is executed for the standard image data (SIDBA), and it is demonstrated that the simulated annealing is useful in the design of the vector quantizer.