Adaptive vector quantizer for image compression using self-organization approach

Oscal T.C. Chen, Bing J. Sheu, Wai-Chi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A self-organization neural network architecture is used to implement the vector quantizer for image compression. A modified self-organization algorithm, which is based on the frequency upper-threshold and centroid learning rule, is utilized for constructing the codebooks. The performances of the self-organization network and the conventional algorithm for vector quantization are compared. This algorithm yields near-optimal results and is computationally efficient. The self-organization network approach is suitable for adaptive vector quantizers. The self-organization network approach uses massively parallel computing structures and is very promising for VLSI implementation.

Original languageEnglish
Title of host publicationICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages385-388
Number of pages4
ISBN (Electronic)0780305329
DOIs
StatePublished - 1 Jan 1992
Event1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States
Duration: 23 Mar 199226 Mar 1992

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Conference

Conference1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
CountryUnited States
CitySan Francisco
Period23/03/9226/03/92

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