Analysis and comparison of quality metrics with reference based on uniform colour spaces

Image quality assessment is becoming increasingly used in many applications. In most of the existing image quality assessment approaches, the main objective is to develop measures that are consistent with the subjective evaluation. Therefore, the performance of a given image quality metric is evaluated against the MOS determined from a series of subjective tests performed on a database. A plethora of image quality metrics has been developed. However, a few studies have been reported on the analysis and comparison of these metrics. This study attempts to provide a new framework for analysing and comparing some of the most common image quality metrics. Three quality representative metrics of the most known approaches have been chosen for this study. The Peak Signal to Noise Ratio (PSNR), the Visible Differences Predictor (VDP) and the Mean Structural SIMilarity index (SSIM). The two latter are found to be consistent with Weber's law. However, subjective testing in literature and computations derived from uniform color spaces such as CIE-L*a*b* suggest a different photometric invariance law. In this paper, we establish this photometric invariance law and show through numerical simulations how to check whether a given quality metric is compliant with this law.