Compression Image Quality Assessment Based on Human Visual Weight and Singular Value Decomposition
基于视觉加权的奇异值分解压缩图像质量评价测度

The traditional image quality assessment methods based on pixels have their own limitations, such as the lack of consideration of the image structure, or the need of a complete reference image. To avoid these problems, this paper presented a new image quality assessment method based on Block Weighted Singular Value Decomposition (BWSVD). First, the images are divided into blocks at size 8×8, then, the singular value vector difference and the mean bias between the original image blocks and the distorted image blocks are considered to evaluate the distortion degree. Beside this, the Human Visual Sensibility (HVS) is considered to determine the weight of each block. Many tests are conducted to evaluate the performance; the 227 testing images are coming from the Live Image Quality Assessment Database, Release 2005. Compared with the PSNR, RMSE, UQI, MSSIM, MSVD algorithms, the presented method shows a great improvement in both the consistency with the DMOS (Differential Mean Opinion Score, DMOS) and the stability when it is applied to different compression rates.