Screen Content Image Quality Assessment with Edge Features in Gradient Domain

Abstract

Objective visual quality assessment specific for screen content images (SCIs) has been increasingly investigated over the years. In this paper, an effective full-reference quality evaluation model for SCIs is proposed, in which edge features in gradient domain (EFGD) are extracted for better visual perceptual representation. Unlike traditional edge feature extraction directly in the image pixel domain, all edge features in the proposed EFGD model are extracted based on the gradient map of input SCIs, including edge sharpness, edge brightness/contrast, and edge chrominance. Specifically, the gradient profile model that can well represent the spatial layout of edges is adopted to measure the edge sharpness degree. A novel computation way is reported to measure the edge brightness and contrast change between the reference and distorted SCIs, while color moments are used to account for the color chrominance variation. In addition, an adaptive weighting strategy is designed to adjust the effects of these three kinds of edge features, according to the statistical distributions of the input SCIs. Moreover, the maximum value of edge sharpness features is extracted from the test SCIs as the pooling weight to get the final image quality assessment (IQA) score. The experimental results on two commonly used SCIs databases have verified the superiorities of the EFGD model and show that the EFGD model is in more conformity with the subjective assessment results than most of the existing IQA models.