Two fast nearest neighbor searching algorithms for image vector quantization

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

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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 treestructured VQ does.

Original languageEnglish
Pages (from-to)16231628
Number of pages1
JournalIEEE Transactions on Communications
Issue number12
Publication statusPublished - 1996

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Two fast nearest neighbor searching algorithms for image vector quantization'. Together they form a unique fingerprint.

Cite this