A neural vector quantizer

Author

Wang, Zhicheng ; Hanson, John

Author_Institution

Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada

Volume

1

fYear

1992

fDate

10-13 May 1992

Firstpage

351

Abstract

A technique of vector quantization based on neural networks called neural vector quantization is investigated. A neural vector quantizer has been developed for data compression and is capable of faster parallel quantization than conventional vector quantizers. The architecture, dynamics, and training strategies are presented. Neural vector quantizers are designed and simulated for Gauss-Markov source data, and the resulting performance, computation, and storage requirements are compared to those for neural vector quantizers of different sizes for the same task and data sources

Keywords

learning (artificial intelligence); neural nets; parallel algorithms; parallel architectures; vector quantisation; Gauss-Markov source data; architecture; data compression; neural networks; neural vector quantizer; parallel quantization; signal processing; storage requirements; vector quantization; Arithmetic; Computational modeling; Computer architecture; Data compression; Decoding; Encoding; Gaussian processes; Neural networks; Partitioning algorithms; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.229941

Filename

229941