Toward a Blind Quality Predictor for Screen Content Images

Blind quality assessment of screen content images (SCIs) is much challenging than traditional natural images. In this paper, we propose a blind quality predictor for SCIs to explore the issue from the perspective of sparse representation. Specifically, we conduct local sparse representation for the textual and pictorial regions, respectively, and conduct global sparse representation for the global SCIs. Subsequently, the underlying relationship between the feature and quality vectors is bridged in a universal sparse representation framework. The quality pooling is comparatively simply that only need to estimate the local and global quality scores and combine them to a total one. Our experimental results show that the proposed predictor can achieve better prediction performance to be in line with subjective assessment.