Fast vector quantization by mean value predictive algorithm

Yung-Gi Wu; Kuo-Lun Fan

doi:10.1117/12.515227

1 May 2003 Fast vector quantization by mean value predictive algorithm

Yung-Gi Wu, Kuo-Lun Fan

Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.515227
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States

Abstract

Vector Quantization (VQ), is an efficient technique for signal compression. In traditional VQ, the major computation is on searching the nearest codeword of the codebook for every input vector. This paper presents an efficient search method to speed up the encoding process. The search algorithm is based on partial distance Elimination (PDE) and binary search is used to determine first search point. We sort the codebook by the mean value in pre-processing before all the practical compression. The first search point is the closest mean value between the input vector and the codewords in the codebook. Then, find the best match codeword by PDE to reduce the search time. The proposed algorithm demonstrates outstanding performance in terms of the time saving and arithmetic operations. Compared to full search algorithms, it saves more than 95% search time.

Citation Download Citation

Yung-Gi Wu and Kuo-Lun Fan "Fast vector quantization by mean value predictive algorithm", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); https://doi.org/10.1117/12.515227

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