Adaptive vector quantizer for image compression using self-organization approach

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

    研究成果: Conference contribution同行評審

    3 引文 斯高帕斯(Scopus)

    摘要

    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.

    原文English
    主出版物標題ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
    發行者Institute of Electrical and Electronics Engineers Inc.
    頁面385-388
    頁數4
    ISBN(電子)0780305329
    DOIs
    出版狀態Published - 1 一月 1992
    事件1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States
    持續時間: 23 三月 199226 三月 1992

    出版系列

    名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    2
    ISSN(列印)1520-6149

    Conference

    Conference1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
    國家/地區United States
    城市San Francisco
    期間23/03/9226/03/92

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