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Proceedings Paper

Neural-network-based image compression using AMT DAP 610
Author(s): Kwang-Shik Min; Hisook L. Min
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Paper Abstract

An error-less image compression of complex images has been achieved using a massively parallel computer. The algorithm involves utilization of a multi-level hierarchical structure of Kohonen type self-organizing learning vector quantization. The compression ratio increases greatly if a small amount of error is tolerated by limiting the number of templates employed. Utilization of DAP 610 enables the processing of compression and reconstruction in very short time. A few cases of error-less compression of several images, as well as some examples which achieved higher compression ratios by allowing a reasonable amount of error, are shown and compared.

Paper Details

Date Published: 16 September 1992
PDF: 8 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140016
Show Author Affiliations
Kwang-Shik Min, East Texas State Univ. (United States)
Hisook L. Min, East Texas State Univ. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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