Applications Of Wavelet Transform And Hybrid Neural Network To Image Compression

Artificial neural networks technology research has gone a long way in the past decade. The specialists at home and abroad in the image processing field have paid high attention and been engaged in the advantages of Neural Network techniques such as the abilities of parallel computing, nonlinear mapping and self-adaptiveness, and applied a variety of Neural Network models into the image processing field.According to the practical combination of the mixed neural networks technology and imge compressing technology, this thesis researches into new algorithms in detail.Outcomes of this paper are as follows:Firstly, this paper discusses basic theories and methods correlated with subject research in thesis, including basic theories and methods about image compression, the theories and methods of Wavelet Transform,the theories and methods of artificial neural network ,the using mechanism and advantage of neural network in image compression etc.And then, the main content of the article is analysed in detail , giving an overview on the basic theories and algorithm of SOFM(Self-Organizing Feature Map) neural network,and the basic theories and algorithm of PCA(Principle Components Analysis) neural network.Again, the article has gived an overview on PCA/SOFM mixed neural network algorithm and its application analysis in image compression in detail,also the article has gived an summary about the advantages of the mixed code algorithm based on PCA/SOFM network over the traditional SOFM algorithm, the mixed code algorithm is introduced into the wavelet field, having already used wavelet good compressing characteristics in frequency field, good space compressing characteristics and multi-dimensions similarity, also having used the Self-Organizing characteristics of PCA/SOFM network and the method of four-forks trees of wavelet, putting forward a kind of new picture about image...