支持向量回归方法在图像压缩中的应用

Application of support vector regression method in image compression

在把图像划分为若干个子块的基础上,以广义欧氏距离为评判标准对图像子块进行分类,然后采用支持向量回归方法建立各类型图像子块的模型,得到对应各类型图像子块的支持向量回归模型参数的集合,从而仅用图像子块编号及其类型号和相应的支持向量回归模型参数来描述整个图像,达到图像压缩的目的。实验表明,该方法压缩比高、误码率低、信噪比高、图像恢复质量良好。此外,该方法可以通过改变图像分块的大小或阈值调整压缩比,还可通过改变支持向量回归模型的逼近误差控制图像的恢复质量。

A novel image compression method based on Support Vector Regression（SVR） method is proposed in this paper.On the basis of dividing the image into some sub-blocks,the general Euclid distance is employed to classify these sub-blocks.Then the SVR is employed to establish the model of each class of image sub-block in order to obtain the SVR model＇s parameters sets of each class.Thus an image can be described by the sub-block class number,position feature of each block in an image and a group parameters of SVR model.The results of experiment show that this method has high compression ratio,high signal-to-noise ratio and fine resuming effect on subject.Additionally,the compression ratio of this method can be adjusted by changing the dimension of block or threshold.The resuming effect on image can be adjusted by changing the approaching error while establishing the SVR model.