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Proceedings Paper

Image compression using a neural network with learning capability of variable function of a neural unit
Author(s): Ryuji Kohno; Mitsuru Arai; Hideki Imai
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Paper Abstract

STRACT This paper proposes image compression using an advanced neural network in which a variable input-output function of a neural unit can be learnt as well as a weight coefficient of a neural connection corresponding to information source and application. Since the neural network has the improved learning capability for local nonlinearity of information source its application to compression of nonlinear information such as image is investigated. A learning algorithm and adaptive controlling schemes of input-output functions are derived. Simulation results show that tire neural network can achieve higher SNR and shorter learning time than a conventional network having only variable wdghts.

Paper Details

Date Published: 1 September 1990
PDF: 7 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.24107
Show Author Affiliations
Ryuji Kohno, Yokohama National Univ. (Japan)
Mitsuru Arai, Yokohama National Univ. (Japan)
Hideki Imai, Yokohama National Univ. (Japan)


Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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