Reduced-reference SSIM estimation

Abstract

The structural similarity (SSIM) index has been shown to be a good perceptual image quality predictor. In many real-world applications such as network visual communications, however, SSIM is not applicable because its computation requires full access to the original image. Here we propose a reduced-reference approach that estimates SSIM with only partial information about the original image. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy analogous to that in the construction of SSIM. We found an interesting linear relationship between our reduced-reference SSIM estimate and full-reference SSIM when the image distortion type is fixed. A regression-by-discretization method is then applied to normalize our measure between image distortion types. We use the LIVE database to test the proposed distortion measure, which shows strong correlations with both SSIM and subjective evaluations. We also demonstrate how our reduced-reference features may be employed to partially repair a distorted image.