A fast LBG codebook training algorithm for vector quantization

Abstract

A fast codebook training algorithm based on the Linde, Buzo and Gray (1980) LBG algorithm is proposed. The fundamental goal of this method is to reduce the computation cost in the codebook training process. In this method, a kind of mean-sorted partial codebook search algorithm is applied to the closest codeword search. At the same time, a generalized integral projection model is developed for the generation of test conditions, which are used to speed up the search process in finding the closest codeword for each training vector. With this proposed method, a significant time reduction can be achieved by avoiding the computation of unnecessary codewords. Our simulation results show that a significant reduction in computation cost is obtained with this proposed method. Besides, this method provides a flexible way of selecting the test conditions to accommodate the different image training sets.