通过回顾图像质量评价的发展过程，对现有的各种图像质量评价方法进行分类。针对各种失真图像如压缩图像、噪声图像等，比较一些有代表性的评价方法的预测值与主观评价分数之间的 Pearson 相关系数和 Spearman 相关系数。结果发现不同评价方法对不同失真的反映灵敏度是不同的，即使是同一种方法在不同相关系数上的表现也不一致。采用多元线性回归分析对选定的图像质量评价方法进行拟合，综合不同方法的优点，提出了一种基于多元线性回归分析的图像质量评价方法。该方法对各种类型和各种程度的失真的灵敏度都比较好，性能也比较稳定。将新方法与选定的各种具有代表性的图像质量评价方法进行比较，实验结果表明，新方法在各个方面都具有较好的鲁棒性。

We describe the development of the objective quality estimation. For some typical methods, we classified them into several groups. Spearman correlation coefficient and Pearson correlation coefficient of given image quality methods of different kind of distortion such as compression, noise and so on are analyzed. It turns out that different methods have different simulations to different distorted images. After that multilinear regression analysis is used to integrate the advantages of different methods and then we get a new image quality estimation which can simulate smartly all kind of distortions. In our experiment, by analyzing the correlation coefficient we prove that our new quality estimation is robust.