Image quality metric based on multidimensional contrast perception models

Abstract

The procedure to compute the subjective difference between two input images should be equivalent to a straightforward difference between their perceived versions, hence reliable subjective difference metrics must be founded on a proper perception model. For image distortion evaluation purposes, perception can be considered as a set of signal transforms that successively map the original image in the spatial domain into a feature and a response space. The properties of the spatial pattern analyzers involved in these transforms determine the geometry of these different signal representation domains. In this work the general relations between the sensitivity of the human visual system and the perceptual geometry of the different representation spaces are presented. This general formalism is particularized through a novel physiological model of response summation of cortical cells that reproduce the psychophysical data of contrast incremental thresholds. In this way, a procedure to compute subjective distances between images in any representation domain is obtained. The reliability of the proposed scheme is tested in two different contexts. On the one hand, it reproduces the results of suprathreshold contrast matching experiences and subjective contrast scales (Georgeson and Shackleton, Vision Res. 34 (1994) 1061–1075; Swanson et al., Vision Res. 24 (1985) 63–75; Cannon, Vision Res. 19 (1979) 1045–1052; Biondini and Mattiello, Vision Res. 25 (1985) 1–9), and on the other hand, it provides a theoretical background that generalizes our previous perceptual difference model (Malo et al., Im. Vis. Comp. 15 (1997) 535–548) whose outputs are linearly related to experimental subjective assessment of distortion.