Blind quality assessment for screen content images by combining local and global features

Recently, several no-reference image quality assessment (NR-IQA) metrics have been developed for the quality evaluation of screen content images (SCIs). While, most of them are opinion-aware methods, which are limited by the subjective opinion scores of training data. Hence, in this paper, we propose a novel opinion-unaware method to predict the quality of SCIs without any prior information. Firstly, an union feature is proposed by considering the local and global visual characteristics of human visual system simultaneously. Specifically, a local structural feature is extracted from the rough and smooth regions of SCIs by leveraging a sparse representation model. As a supplement, a global feature is obtained by combining the luminance statistical feature and local binary pattern (LBP) feature of entire SCIs. Secondly, to get rid of the limitation of subjective opinion scores, a new large-scale training dataset contained 80,000 distorted SCIs is constructed, and the quality labels of those distorted SCIs are derived by an advanced full-reference IQA metric. Thirdly, a regression model between image features and image quality labels is learned from the training dataset by employing a learning-based framework. And then, the quality scores of test SCIs can be predicted by the pre-trained regression model. The experimental results on two largest SCI-oriented databases show that the proposed method is superior to the state-of-the-art NR-IQA metrics.