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Proceedings Paper

Neural networks for image coding: a survey
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Paper Abstract

Neural networks are highly parallel architectures, which have been used successfully in pattern matching, clustering, and image coding applications. In this paper, we review neural network based techniques that have been used in image coding applications. The neural networks covered in this paper include multilayer perceptron (MLP), competitive neural network (CNN), frequency sensitive competitive neural network (FS-CNN), and self-organizing feature map network (SOFM). All of the above mentioned neural networks except MLP are trained using competitive learning and used for designing the vector quantizer codebook. The major problem with the competitive learning is that some of the neurons may get a little or no chance at all to win the competition. This may lead to a codebook containing several untrained codevectors or the codevectors that have not been trained enough. There are several possible ways to solve this problem, FS-CNN and SOFM offer solution to under-utilization of neurons. We present design algorithms for above mentioned neural networks as well as evaluate and compare their performance on several standard monochrome images.

Paper Details

Date Published: 9 March 1999
PDF: 12 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341123
Show Author Affiliations
Syed A. Rizvi, CUNY/College of Staten Island (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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