Neural networks for image coding: a survey
Syed A. Rizvi, Nasser M. Nasrabadi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
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.