HIGH-SPEED CLOSEST CODEWORD SEARCH ALGORITHMS FOR VECTOR QUANTIZATION

Abstract

One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through a codebook in both codebook design and encoding phases. In this paper, based on the assumption that the distortion is measured by the squared Euclidean distance, two high-speed search methods will be proposed to speed up the search process. The first one uses the difference between the mean values of two vectors to reduce the search space. The second is to find the Karhunen-Loeve transform (KLT) for the distribution of the set of training vectors and then applies the partial distortion elimination method to the transformed vectors. Experimental results show that the proposed methods can reduce lots of mathematical operations.