A Perceptually Relevant MSE-Based Image Quality Metric

• Published in: IEEE transactions on image processing Vol. 22; no. 11; pp. 4447 - 4459
• Main Authors: Hui Li Tan, Zhengguo Li, Yih Han Tan, Rahardja, S., Chuohuo Yeo
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Approximation methods; Artificial Intelligence; Biomimetics - methods; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image quality metric; Indexes; Information, signal and communications theory; mean squared error (MSE); Measurement; Optimization; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal, noise; Signal-To-Noise Ratio; structural similarity index (SSIM); Telecommunications and information theory; Transform coding; Visual Perception; Visualization; Wiener filter; Additive noise; Performance evaluation; Optimal filter; Similarity; mean squared error (MSE); Image quality metric; Optimization; Mean square error; Image quality; Wiener filter; Quality control; structural similarity index (SSIM); Database; Metric; Objective function; Signal distortion; Image evaluation
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2013.2273671

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.