A perceptually relevant MSE-based image quality metric.
Image quality metrics (IQMs), such as the mean squared error (MSE) and the structural similarity index (SSIM), are quantitative measures to approximate perceived visual quality. In this paper, through analyzing the relationship between the MSE and the SSIM under an additive noise distortion model, we propose a perceptually relevant MSE-based IQM, MSE-SSIM, which is expressed in terms of the variance of the source image and the MSE between the source and distorted images. Evaluations on three publicly available databases (LIVE, CSIQ, and TID2008) show that the proposed metric, despite requiring less computation, compares favourably in performance to several existing IQMs. In addition, due to its simplicity, MSE-SSIM is amenable for the use in a wide range of image and video tasks that involve solving an optimization problem. As an example, MSE-SSIM is used as the objective function in designing a Wiener filter that aims at optimizing the perceptual visual quality of the output. Experimental results show that the images filtered with a MSE-SSIM-optimal Wiener filter have better visual quality than those filtered with a MSE-optimal Wiener filter.
Hui Li
HL
Tan
Zhengguo
Z
Li
Yih Han
YH
Tan
Susanto
S
Rahardja
Chuohuo
C
Yeo
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D014796:Visual Perception