Applications Of Neural Networks To Image Compression

Digital image processing techniques have been being used more and more widely in many fields such as multimedia, the Internet, television and fax, etc. Image compression is one of the most important key techniques in image processing. Traditional compression methods include prediction coding, transform coding and vector quantization(VQ). In past twenty years, modern techniques based on neural networks, fractal theory and wavelet transform have been successfully used to image compression.This dissertation focuses on the applications of neural networks to static image compression. It has three main parts. First, the Counterpropagation Network (CPN) is used to VQ image compression. A quantizer based on the standard CPN is proposed and then modified. A new codebook designing algorithm, referred to as the Fast Competitive Learning and Error Correction Algorithm (FCLECA), and a model of fast vector quantizer based on the modified CPN, are presented. Secondly, based on the optimization functions of the continuous Hopfield neural networks (CHNN), a competitive CHNN model for codebook designing is presented. The corresponding energy function, the dynamic equations for neurons, and the codebook algorithm based on the model are also designed. Finally, the dissertation discusses the applications of neural networks to KL transform coding. A neural network model for solving the symmetric eigenvalue problem, referred to as the SEVNN, is proposed. And the learning rule of the SEVNN is designed and then used to KL transform coding image compression.The results of simulating experiments show that the three above-mentioned algorithms based on neural networks have many remarkable advantages. They have higher learning speed, are able to obtain higher-quality compressed images and have great robustness in image compression.