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投稿时间：2003-10-27
投稿时间：2003-10-27
中文摘要: 对图像和语音等信息的压缩、传输及保存,压缩是极为重要的一环,矢量量化的压缩算法是广泛应用的一种方法.介绍了基于自组织特征映射神经网络的K-means矢量量化前置法和后置法两种复合算法,并将其应用于图像压缩的问题.从数值仿真的结果看,K-means前置法较其他算法表现出良好的效果.
Abstract:This paper proposes two compound methods, namely, K-means after NG nad NG after Kmeans.Image processing problems are discussed.From the numerical simulation of image compression, the method of K-means after NG shows better performance compared to other ones.
文章编号：20030403 中图分类号： 文献标志码：
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引用文本：
马立新,王仁峰.基于自组织特征映射神经网络的矢量量化及其在图像压缩中的应用[J].上海电力大学学报,2003,19(4):9-13.
MA Li-xin,WANG Ren-feng.Self-organizing Neural Network for Vector Quantization and its Application in Image Compression[J].Journal of Shanghai University of Electric Power,2003,19(4):9-13.
马立新,王仁峰.基于自组织特征映射神经网络的矢量量化及其在图像压缩中的应用[J].上海电力大学学报,2003,19(4):9-13.
MA Li-xin,WANG Ren-feng.Self-organizing Neural Network for Vector Quantization and its Application in Image Compression[J].Journal of Shanghai University of Electric Power,2003,19(4):9-13.