Perceptually significant spatial pooling techniques for image quality assessment

Spatial pooling strategies used in recent Image Quality Assessment (IQA) algorithms have generally been that of simply averaging the values of the obtained scores across the image. Given that certain regions in an image are perceptually more important than others, it is not unreasonable to suspect that gains can be achieved by using an appropriate pooling strategy. In this paper, we explore two hypothesis that explore spatial pooling strategies for the popular SSIM metrics. The first is visual attention and gaze direction ‘where’ a human looks. The second is that humans tend to perceive ‘poor’ regions in an image with more severity than the ‘good’ ones and hence penalize images with even a small number of ‘poor’ regions more heavily. The improvements in correlation between the objective metrics’ score and human perception is demonstrated by evaluating the performance of these pooling strategies on the LIVE database of images.