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Proceedings Paper

Adaptive image compression based on backpropagation neural networks
Author(s): Defu Cai; Ming Zhou
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Paper Abstract

Increasingly huge amounts of digital data from a wide range of sources such as B-ISDN services, satellite transmission of photographs, and police database of human face images are being transmitted and stored. Therefore, both transmission channel capacity and disk space are limited. For some advanced techniques, such as multi-media terminal and HDTV etc., the problems are even more apparent. Based on this it is important that efficient image compression algorithms are used in order to reduce the transmission capacity and storage space. In this paper, a scheme of image data compression with an adaptive BP neural network is presented. The data compression property of mapping original image to a feature space of reduced dimensionality is utilized. Images are divided as a set of 8 X 8 sub-image blocks which apply to a three layer BP neural network as inputs. It is shown from computer simulation that the results are better than Sonehara, et al.

Paper Details

Date Published: 16 December 1992
PDF: 6 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130873
Show Author Affiliations
Defu Cai, Institute of Electronics (China)
Ming Zhou, Institute of Electronics (China)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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