Abstract:
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
