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Quality Measurement of Screen Images via Foreground Perception and Background Suppression

The widespread screen sharing poses a great challenge in the visual quality control of industrial applications, as there is a significant difference between the visual quality perception of natural content (NC) and screen content (SC) images (or videos) during acquisition, compression, distribution, and display. Apart from the foreground distortion, artifacts appeared in the smooth background of SC images also play a key role in its perceived quality. In this article, a robust approach is designed for quality measurement of SC images, which embodies both foreground perception and background suppression (FPBS) in a satisfying way. Specifically, the extraction of foreground perceptual features is investigated by considering both the local contrast variation and the local edge response features, while for the first time, a suppression strategy is devised to eliminate the influences of artifacts appeared in the simple and homogeneous background. Experimental results show that, based on only two simple features, the proposed scheme achieves the state-of-the-art results on two latest benchmark databases. We believe that the feature engineering method can further improve the performance of the FPBS framework on the quality measurement of screen images.