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Proceedings Paper

Adaptive constructive neural networks using Hermite polynomials for compression of still and moving images
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Paper Abstract

Compression of digital images has been a very important subject of research for several decades, and a vast number of techniques have been proposed. In particular, the possibility of image compression using Neural Networks (Nns) has been considered by many researchers in recent years, and several Feed-forward Neural Networks (FNNs) have been proposed with reported promising experimental results. Constructive One-Hidden-Layer Feedforward Neural Network (OHL-FNN) is one such architecture. At previous SPIE conferences, we have proposed a new constructive OHL-FNN using Hermite polynomials for regression and recognition problems, and good experimental results were demonstrated. In this paper, we first modify and then apply our proposed OHL-FNN to compress still and moving images and investigated its performance in terms of both training and generalization capabilities. Extensive experimental results for still images (Lena, Lake, and Girl) and moving images (football game) are presented. It is revealed that the performance of the constructive OHL-FNN using Hermite polynomials is quite good for both still and moving image compression.

Paper Details

Date Published: 11 March 2002
PDF: 12 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458703
Show Author Affiliations
Liying Ma, Concordia Univ. (Canada)
Khashayar Khorasani, Concordia Univ. (Canada)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 4739:
Applications and Science of Computational Intelligence V
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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