Full-Reference Image Quality Metrics Performance Evaluation Over Image Quality Databases

A quantitative predictive performance evaluation of 18 well-known and commonly used full-reference image quality assessment metrics has been conducted in the present work. The process has been run over six publicly available and subjectively rated image quality databases for four degradation types namely JPEG and JPEG2000 compression, noise and Gaussian blur. Results show that the existing predictive performance evaluation tools of the different full-reference image quality metrics are significantly impacted by the choice of the image quality database. Three of them, namely Toyama, LIVE and TID, have been found to give different assessment results. The visual information fidelity (VIF) quality metric has been found to have superior predictive capabilities to its counterparts. MS-SSIM (multi-scale structural similarity index), MSSIM (modified SSIM) and VIFP (pixel-based VIF) have also closer performances in terms of their correlation to the subjective human ratings, accuracy and monotonicity to the VIF model.