图像边缘保持矢量量化及神经网络的实现

Edge preserving vector quantization using neural network

矢量量化（ＶＱ）作为一种有效的图像数据压缩技术，越来越受到人们的重视，但研究表明：目前矢量量化技术存在的主要问题之一是图像边缘失真严重．本文讨论了一种应用神经网络的图像边缘保持矢量量化方法，它以Ｋｏｈｏｎｅｎ的自组织特征映射算法（ＳＯＦＭ）为基础，根据人的视觉系统对图像边缘的敏感性，在图像编码前，先对整幅图像的边缘提取，再将每一图像子块的边缘特性用一“活跃因子”表示．在矢量量化过程中，根据不同训练矢量（图像子块）的活跃因子，自适应地调整ＳＯＦＭ的学习参数．实验结果表明，和单纯用神经网络直接进行矢量量化相比较，应用这种技术的图像编码在同一压缩比下译码图像的边缘质量有明显的提高．

Recently the vector quantization(VQ) has received considerable interests as a powerful image data compression technique.However,studies of image coding with VQ have revealed that VQ for image compression suffers from edge degradation in the reproduced images.In this paper,we describe an adaptive learning method of the edge preserving VQ based on kohonen′s self orfganizing feature map neural network.The learning procedure is performed by extracting the edge of the whole image,then adaptively adjusting the learning rate that are determined according to the subimage block 'activity factor',which represent the sensitivity of the block feature to the human visual system.Compared with direct image VQ coding,the experiment results show the reproduced images quality are well improved,at the same compression ratio.