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
    Country/TerritoryUnited States
    CitySan Francisco
    Period23/03/9226/03/92

    Fingerprint

    Dive into the research topics of 'Adaptive vector quantizer for image compression using self-organization approach'. Together they form a unique fingerprint.

    Cite this