Abstract

Vector quantization is an efficient compression technique for which many variants are known. Product code vector quantizers use multiple codebooks for coding separately features of a vector. In shape-gain and mean-shapegain vector quantizers, the bottleneck in the encoder is a nearest neighbor search on a hypersphere. We define an angular constraint for speeding up the search in shapegain and mean-shape-gain vector quantizers, based on a spherical triangular inequality. This constraint may be combined with other known techniques to give significant acceleration ratios. Experimental results are provided, showing the number of codewords compared and the total search time for three different methods. 1 INTRODUCTION A standard vector quantizer may be defined as a mapping M : R k ! C, where C is a finite set of vectors in R k called the codebook. Each representative vector in C is called a codeword. Vector quantization is a good way of vector data compression used for example in di...

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