Codewords Distribution-Based Optimal Combination of Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Fast Search Algorithm for Vector Quantization Encoding.

Abstract

Vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the encoding procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.