Two fast nearest neighbor searching algorithms for image vector quantization

Shen-Chuan Tai, C. C. Lai, Yu-Cheng Lin

研究成果: Article同行評審

50 引文 斯高帕斯(Scopus)

摘要

In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy on transform domain and the geometrical relations between the input vector and every codevector to eliminate those codevectors that have no chance to be the closest codeword of the input vector. It achieves a full search equivalent performance. As compared with other fast methods of the same kind, this algorithm requires the fewest multiplications and the least total times of distortion measurements. Then, a suboptimal searching method, which sacrifices the reconstructed signal quality to speed up the search of nearest neighbor, is presented. This algorithm performs the search process on predefined small subcodebooks instead of the whole codebook for the closest codevector. Experimental results show that this method not only needs less CPU time to encode an image but also encounter less loss of reconstructed signal quality than tree-structured VQ does.

原文English
頁(從 - 到)1623-1628
頁數6
期刊IEEE Transactions on Communications
44
發行號12
DOIs
出版狀態Published - 1996 十二月

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

指紋 深入研究「Two fast nearest neighbor searching algorithms for image vector quantization」主題。共同形成了獨特的指紋。

引用此