基于自组织特征映射神经网络的矢量量化

近年来，许多学者已经成功地将Ｋｏｈｏｎｅｎ的自组织特征映射（ＳＯＦＭ）神经网络应用于矢量量化（ＶＱ）图象压缩编码，相对于传统的ＫＬＢＧ算法，基于的ＳＯＦＭ算法的两个主要缺点是计算量大和生成的码书性能较差因此为了改善码书性能，对基本的ＳＯＦＭ算法的权值调整方法作了一些改进，同时为了降低计算量，又在决定获得胜神经元的过程中，采用快速搜索算法，在将改进的算法用于矢量量化码书设计后，并把生成的码书用于图象

Vector Quantization Based on Self-Organizing Feature Map Neural Network

In recent years, many scholars have successfully applied the Kohonen s self-organizing feature map (SOFM) neural networks to vector quantization image compression encoding. The two main shortcomings of the basic SOFM method are its high computation complexity and its poor codebook quality compared to the conventional LBG algorithm. In order to improve the codebook performance, some modification is made in the weight factor adjustment of the basic SOFM algorithm in this paper. In order to reduce the computation complexity of the basic SOFM algorithm, some fast search methods are used in SOFM iterations during the search for the winning neuron. The proposed algorithm is used to generate vector quantization codebook and the generated codebooks are used for image compression encoding in this paper. Simulation shows that the reduction of computation is substantial and the codebook performance is improved. Compared to the basic SOFM algorithm, the reduction of computation is about 75%. For not only image in the training set but also the image outside the training set, the encoding quality can be improved by 0.80dB~0.90dB compared to the basic SOFM algorithm.