A highly efficient method for blind image quality assessment

Blind image quality assessment (BIQA) has attracted a great deal of attention due to the increasing demand in industry and the promising recent progress in academia. To bridge the gap between academic research accomplishment and industrial needs, high efficiency BIQA approaches that allow for real-time computation are highly desirable. In this paper, we propose a novel BIQA method by selecting statistical features extracted from binary patterns of local image structures. This allows us to largely reduce the feature space to eventually one dimension. Somewhat surprisingly, such a single feature, faster-than-real-time approach named local pattern statistics index (LPSI) exhibits impressive generalization ability across different distortion types and achieves competitive quality prediction performance in comparison with state-of-the-art approaches on public databases such as LIVE II and TID2008.