A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Image Corrected by Retinex Method Based on S-CIELAB and Gazing Information
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Evaluation of image quality metrics for color prints
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Full-Reference Image Quality Metrics: Classification and Evaluation
Foundations and Trends® in Computer Graphics and Vision
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
GAFFE: A Gaze-Attentive Fixation Finding Engine
IEEE Transactions on Image Processing
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Many objective image quality assessment algorithms firstly apply quality metrics in local regions that results in a quality map, and then pool the quality values in the quality map into a single quality score. The simplest pooling method is the average of quality values, which assumes that all the quality values are independent and equally important. However, visual perception is so complex that the assumption underlying average pooling might be too strict. There is an agreement that some regions in the images might be more perceptually significant, which leads to more advanced spatial pooling methods. In this work we evaluate existing spatial pooling methods for five important quality attributes, which are proposed to reduce the complexity of image quality assessment. The results show that: (1) more advanced spatial pooling methods are generally better than simple average; (2) spatial pooling depends on both image quality metrics and the attributes of the image.