A novel structural variation detection strategy for image quality assessment
Yibing Zhan, Rong Zhang · 2016
Structural information is critical in image quality assessment (IQA). Although existing objective IQA methods have achieved high consistency with subjective perception, detecting structural variation remains a difficult task. In this paper, we propose a novel structural variation detection strategy that is based on binary logic and inspired by the bag-of-words model. The proposed strategy detects structural variation by comparing the occurrences of structural features within the original and distorted images. In order to show the effectiveness of this strategy, this paper also proposes a novel and simple IQA method based on this strategy. The proposed method evaluates the image quality from two aspects: the structure distortion and the luminance distortion. The experimental results from four public databases show that the proposed method is highly congruous with subjective evaluation. The results also prove that the detection strategy is useful.