HALF OF AN IMAGE IS ENOUGH FOR QUALITY ASSESSMENT

Deep networks show promising performance in image quality assessment (IQA), whereas few studies have investigated how a deep model works. In this work, a positional masked transformer for IQA is first developed, based on which we observe that half of an image might contribute trivially to image quality, whereas the other half is crucial. Such observation is generalized to that half of the image regions can dominate image quality in several CNN-based IQA models. Motivated by this observation, three semantic measures (saliency, frequency, objectness) are then derived, showing high accordance with importance degree of image regions in IQA.