A new image quality assessment algorithm based on SSIM and multiple regressions

作者：Wang, Zhengyou^*; Li, Liying; Wu, Shuang; Xia, Yanhui; Wan, Zheng; Cai, Cong

来源：International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8(11): 221-230.

DOI：10.14257/ijsip.2015.8.11.20

• Image processing algorithm
• Image quality assessment
• Image quality assessment (IQA)
• Multiple linear regressions
• Objective quality assessment
• Spearman correlation coefficients
• SSIM
• Structural similarity indices (SSIM)

Image quality assessment (IQA) is crucial in image processing algorithms. In the state-of-the-art IQA index, the structural similarity (SSIM) index has been proved to be better objective quality assessment metric. However, the accuracy of SSIM is relatively lacking when used to access blurred images. And the component weights of structural similarity (SSIM) index are fixed in some past environments. So an improved assessment algorithm incorporating multiple linear regressions and SSIM index was proposed in this paper. We use regression analysis to adjust the component weight of SSIM index. So the improved algorithm is more accuracy on different distortion types’quality assessment. Experimental results show that the improved SSIM algorithm is better than traditional methods in nonlinear regression correlation coefficient, Spearman correlation coefficient and out ratio.

• 出版日期2015
• 单位石家庄铁道大学