面向图象压缩的图象分类及压缩结果预测

图象数据存在冗余使图象压缩成为可能 ,而不同图象的数据冗余度特别是空间冗余度相差很大 .对被压缩图象的空间冗余度这一图象的本质属性进行研究、减少图象压缩及方法选择时的盲目性是非常必要的 .为此提出了面向图象压缩的图象分类这一新概念以及具体分类算法 .该算法利用图象小波高频系数的分布特点 ,采用图象边缘度作为图象空间冗余度的度量 ,将不同内容的图象按边缘度大小分类 .分类的结果可对不同图象的压缩结果进行预测 .实验结果表明 ,图象分类结果和对压缩结果的预测是有意义的 ,并与人的视觉相吻合 .该分类思想对其他图象处理算法的选择和优化也有参考价值 .

Image Classification for Image Compression and Compression Result Forecast

There is a considerable amount of redundant information in image data which makes image compression possible. Redundancy of data, spatial redundancy in particular varies with different images. It is necessary to study the spatial redundancy of compressed images and reduce the random selection of image compression methods. In this paper, a novel idea of image classification for image compression is proposed and its algorithm is presented too. The distribution of wavelet high frequency coefficients in images is considered while edge active measure (EAM) is defined to describe the nature of images in this algorithm. By EAM images can be classified and compression result can be forecasted .The experiments have shown that the image classification and result forecast implemented in this paper make sense and correspond to human visual understanding. The idea suggested in this paper has been of great value to the election and optimization of algorithms for different purposes.