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Proceedings Paper

Study on image data compression by using neural network
Author(s): Zhong Zheng; Masayuki Nakajima; Takeshi Agui
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Paper Abstract

Properties of the neural networks employed in image data compression are studied, and a method for increasing the compression capability is proposed. Since the multiple gray level image have a large quantity of data, the poor mapping capacity of the neural network is the main problem causing the poor data compression capability. In order to increase the compression capability, in the proposed method, first an image is divided into subimages, that is blocks. Then these blocks are divided into several classes. Several independent neural networks are assigned adaptively to these blocks according to their classes. Since the mapping capacity is proportional to the number of the neural networks, and no data quantity increases, the compression capability is increased efficiently by our method. The computer simulation results show that the signal to noise ratio (SNR) of the reconstructed images was increased by about 1 approximately 2 (dB) by our method. Especially the visual image quality has increased.

Paper Details

Date Published: 1 November 1992
PDF: 9 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131414
Show Author Affiliations
Zhong Zheng, Tokyo Institute of Technology (Japan)
Masayuki Nakajima, Tokyo Institute of Technology (Japan)
Takeshi Agui, Tokyo Institute of Technology (Japan)

Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

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