Fast vector quantization algorithms based on nearest partition set search

• Published in: IEEE transactions on image processing 2006-08, Vol.15 (8), p.2422-2430
• Main Author: QIAN, Shen-En
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Coding, codes; Computation; Computational complexity; Data compression; Data structures; Exact sciences and technology; fast codevector search; hyperspectral imagery; Hyperspectral imaging; Image coding; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information Storage and Retrieval - methods; Information, signal and communications theory; Mathematical analysis; nearest partition set; Numerical Analysis, Computer-Assisted; Partitioning algorithms; Partitions; Sampling, quantization; Search methods; Searching; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Studies; Telecommunications and information theory; Testing; Training; Vector quantization; Vectors (mathematics)
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.875217

A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for the MDP for a training vector only in this subset of partitions plus the single previous MDP. As the size of this subset is much smaller than the total number of codevectors, the search process is speeded up significantly. The creation of the subset is essential, as it has a direct effect on the improvement in computation time of the proposed method. The schemes that create the subset efficiently have been proposed. The proposed method generates a codebook identical to that generated using the GLA. It is simple and requires only minor modification of the GLA and a modest amount of additional memory. The experimental results show that the computation time of codebook training was improved by factors from 6.6 to 50.7 and from 5.8 to 70.4 for two test data sets when codebooks of sizes from N=16 to 2048 were trained. The proposed method was also combined with an earlier published method to further improve the computation time.