An Improved Fast Encoding Algorithm for Vector Quantization Using 2-Pixel-Merging Sum Pyramid and Manhattan-Distance-First Check

Vector quantization (VQ) features a very heavy encoding process. In previous work [3], an efficient encoding algorithm using mean pyramid has been developed. To improve it further, a fast search algorithm is proposed in this letter. Specifically speaking, four major modifications are made. First, to rearrange the original codebook directly along the sorted real sums to reduce the search scope and then update the lower and upper bound dynamically. Second, to use sum instead of the mean that includes roundoff error to thoroughly avoid a possible mismatched winner. Third, to construct a sum pyramid using 2-pixel-merging other than 4-pixel-merging way to generate more in-between levels. Fourth, to introduce the Cauchy-Schwarz inequality to bridge Euclidean and Manhattan distance together so that the difference check between 2 vectors can be pre-conducted only by much lighter Manhattan distance computation. Experimental results show that the proposed algorithm is more search-efficient.