【作者】 戴彦群；

【导师】 胥泽银； 卢玉蓉；

【作者基本信息】 成都理工大学 ， 应用数学， 2004， 硕士

【摘要】 数字图像处理技术在多媒体、因特网、电视、传真等领域的应用越来越广泛。图像压缩是数字图像处理中最重要的关键技术之一，传统的图像压缩方法有预测编码、变换编码和矢量量化等。近二十年来，基于神经网络、分形理论、小波变换的现代压缩方法已成功地用于图像压缩。 论文研究神经网络在静止图像压缩中的应用，主要内容包括三个部分，一是将对向传播神经网络(CPN)用于矢量量化图像压缩，并对标准CPN矢量量化器模型进行改进，提出一种新的快速码书设计算法FCLECA和一个基于改进CPN的快速矢量量化器模型；二是在研究连续Hopfield神经网络(CHNN)优化功能的基础上，提出一个实现码书设计的竞争性CHNN模型，设计了相应的能量函数和神经元动力学方程，并给出基于该模型的码书设计算法；三是讨论神经网络在KL变换编码中的应用，提出一个求图像矢量协方差矩阵(对称矩阵)全部特征值及特征矢量的神经网络模型SEVNN，设计了相应的学习规则，并使用基于SEVNN的KL变换算法来实现图像压缩。 仿真试验结果表明，以上三种基于神经网络的图像压缩算法在训练速度、压缩质量、健壮性等方面具有明显的优势。更多还原

【Abstract】 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.更多还原