将灰度图像的局部方差分布(QLS)作为表征图像结构信息的一个重要特征，对局部方差分布矩阵进行奇异值分解，计算得到相应的奇异值特征向量；通过计算降质图像与原参考图像局部方差矩阵奇异值特征向量的夹角大小度量两图像的结构相似度，实现了对降质图像的质量评价。实验结果表明：局部方差分布更能突出图像的结构特征,评价结果优于传统的均方误差(MSE)、峰值信噪比(PSNR)、结构相似度(SSIM)以及直接评价图像像素分布的奇异值分析(SVD)等方法,与人眼视觉感知效果的一致性较好。

The local variance distribution of a gray level image is taken as an important characteristic to express image structural information, and the Singular Value Decomposition(SVD) is performed on a local variance distribution matrix. The angle between the singular vectors of the reference image and distorted image is used to measure the structural similarity of the two images, and then the image quality assessment is achieved. Experimental result shows that the local variance distribution can emphasize the structural information. It is better consistent with human visual perception characteristics and the assessment results are superior to those from Mean Square Error(MSE), Peak Signal to Noise(PSNR), Structure Similarity(SSIM) and SVD methods based on pixel value distribution.