Subjective quality assessment of Screen Content Images
Research on Screen Content Images (SCIs) becomes important as they are increasingly used in multi-device communication applications. In this paper, we present a study of subjective quality assessment for distorted SCIs, and investigate which part (text or picture) contributes more to the overall visual quality. We construct a large-scale Screen Image Quality Assessment Database (SIQAD) consisting of 20 source and 980 distorted SCIs. The 11-category Absolute Category Rating (ACR) is employed to obtain three subjective quality scores corresponding to the entire image, textual and pictorial regions respectively. Based on the subjective data, we investigate the applicability of 12 state-of-the-art Image Quality Assessment (IQA) methods for objectively assessing the quality of SCIs. The results indicate that existing IQA methods are limited in predicting human quality judgement of SCIs. Moreover, we propose a prediction model to account for the correlation between the subjective scores of textual and pictorial regions and the entire image. The current results make an initial move towards objective quality assessment of SCIs.