A fast VQ codebook design algorithm for a large number of data

Abstract

The authors point out that the LBG algorithm (see Y. Linde et al., (1980)) requires a lot of computation as the training vectors increase, and proposes a fast VQ (vector quantization) algorithm for a large amount of training data. This algorithm consists of three steps: first, divide training vectors into small groups; second, quantize each group into a few codewords by the LBG algorithm; finally, construct a codebook by clustering these codewords using the LBG algorithm again. The authors also report they can reduce the distortion error of the algorithm by adapting an effective data-dividing method. In experiments of quantizing 17500 training vectors into 512 codewords, this algorithm requires only 1/6 computation time compared with the conventional algorithm, while the increase of distortion is only 0.5 dB.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>