Full-Reference Quality Assessment for Screen Content Images Based on the Concept of Global-Guidance and Local-Adjustment

Benefiting from the development of multimedia communication terminals, the visual content presented to people on mobile devices is no longer a single form, but contains text, natural images, and other computer-generated graphics, which is called screen content images (SCIs). Inspired by the different visual stimuli that text and images bring to human eyes and the concept of global-guidance and local-adjustment, we design a novel full-reference image quality assessment (IQA) model using the structural features of the text, the perceptual features of pictures, and a score integration model (SPSIM) to evaluate SCIs quality. Firstly, we split the SCIs into textual and pictorial regions through a fully convolutional network (FCN) to conduct separate analyses. For textual regions, we take advantage of narrow edge extensions and high edge steps as structural features to compute the textual score. For pictorial regions, we extract the just noticeable difference (JND) features, which measure the human eye's ability to detect distortion as perceptual features to calculate the pictorial score. Finally, an innovative score integration method based on the global-guidance and local-adjustment is designed to better analyze the relationship between the above regional scores and the whole global SCIs score. Abundant experiments in SCIs databases have shown that the SPSIM model can achieve better consistency with the human eyes system (HVS) in predicting the visual quality of SCIs.

Automated summary

The article introduces a novel full-reference image quality assessment model that evaluates the quality of SCIs using textual structural features and pictorial perceptual features, and better analyzes the relationship between regional scores and the overall score through a score integration method. Experiments demonstrate that the model can predict visual quality consistently with the human eye system.