Encoding Distortions for Multi-task Full-Reference Image Quality Assessment

Most existing image quality assessment models focus on evaluating the image quality score, however, the quality score alone is not enough to characterize the degeneration. In this paper, we propose a full reference framework named Mask Gated Convolutional Network (MGCN) for evaluating the image quality score and identifying distortions simultaneously. Observing the fact that the reference images are distorted by various distortions in pixel space, we design an encoder module to capture the transformation between reference images and distorted images as low level features. Further higher level features are extracted from the low level features and shared by both the regression and the classification tasks. Instead of simply cropping patches to augment data, we mask the high level feature map in the spatial domain to randomly sample patches from the image and learn to assign the image quality score to the patch set. The proposed method achieves the state-of-the-art performance on LIVE2, TID2008 and TID2013 datasets.