Open AccessGSW: A High Performance IQA Index Based on Global and Saliency Window Similarity
Author(s) -
Yibo Fan,
Zihao Meng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012061
Subject(s) - robustness (evolution) , image quality , computer science , artificial intelligence , similarity (geometry) , window (computing) , pattern recognition (psychology) , consistency (knowledge bases) , feature (linguistics) , quality score , image (mathematics) , perception , structural similarity , index (typography) , computer vision , metric (unit) , psychology , engineering , biochemistry , chemistry , linguistics , philosophy , operations management , neuroscience , world wide web , gene , operating system
Perceptual image quality assessment (IQA) attracts significant attention in recent years. It is proved that both global score and an image’s visual saliency (VS) are consistent with subjective evaluation. The global quality score reflects the consistency of the overall structure between two images, and VS map contributes complementarily in evaluating perceptual quality. This paper presents an effective IQA index based on global and saliency window similarity, namely GSW. It chooses VS map as an image feature and uses a special strategy to draw the saliency window with largest significancy. Meanwhile, the background area is taken into account to guarantee the robustness of the quality score. Experimental results on four most widely used databases verify that, compared with state-of-the-art IQA methods, GSW performs consistently well can provide more accurate quality prediction with a low computational complexity.