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

指紋

深入研究「Adaptive vector quantizer for image compression using self-organization approach」主題。共同形成了獨特的指紋。

引用此