Vector quantizer design by constrained global optimization

Author

Wu, Xiaolin

Author_Institution

Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada

fYear

1992

fDate

24-27 March 1992

Firstpage

132

Lastpage

141

Abstract

Central to vector quantization is the design of optimal code book. The construction of a globally optimal code book has been shown to be NP-complete. However, if the partition halfplanes are restricted to be orthogonal to the principal direction of the training vectors, then the globally optimal K-partition of a set of N D-dimensional data points can be computed in O((N+KM/sup 2/)D) time by dynamic programming, where M is the intensity resolution. This constrained optimization strategy improves the performance of vector quantizer over the classic LBG algorithm and the popular methods of tree-structured recursive greedy bipartition of the training data set.<>

Keywords

dynamic programming; encoding; vector quantisation; NP-complete; constrained global optimization; dynamic programming; intensity resolution; optimal code book; partition halfplanes; training vectors; vector quantization; vector quantizer design; Algorithm design and analysis; Books; Clustering algorithms; Computer science; Constraint optimization; Design optimization; Distortion measurement; Dynamic programming; Training data; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Compression Conference, 1992. DCC '92.

Conference_Location

Snowbird, UT, USA

Print_ISBN

0-8186-2717-4

Type

conf

DOI

10.1109/DCC.1992.227468

Filename

227468