Digital Pictures: Representation, Compression, and Standards
Digital Pictures: Representation, Compression, and Standards
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
Haar image compression using a neural network
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
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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.We have previously proposed a new constructive OHLFNN 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 images and investigate its performance in terms of both training and generalization capabilities. Extensive experimental results for still images (Lena, Lake, and Girl) are presented. It is revealed that the performance of the constructive OHL-FNN using Hermite polynomials is quite good.