Calibrating MS-SSIM for compression distortions using MLDS

Christophe Charrier, Kenneth Knoblauch, Laurence T. Maloney, Alan C. Bovik

Abstract: In this paper, we describe a recently developed method for assessing perceived image quality, Maximum Likelihood Difference Scaling (MLDS), and use it to assess the performance of MS-SSIM on compression distored images. MLDS allows us to quantify supra-threshold perceptual differences between pairs of images and to examine how perceived image quality, estimated through MLDS, changes as the compression rate is increased. We show how the data collected by MLDS allows us to recalibrate MS-SSIM to improve its performance.