Authentically Distorted Image Quality Assessment by Learning From Empirical Score Distributions

Most existing works on image quality assessment (IQA) focus on predicting a scalar quality score (SQS) based on the assumption that people can reach a consensus on the judgment of image quality. However, assigning a single scalar fails to reveal the subjective diversity that an image will probably receive divergent opinion scores from different subjects. This is particularly true for real-world authentically distorted images which usually involve composite mixtures of multiple distortions. To characterize such an property, this letter proposes to use a more informative vectorized label called empirical score distribution (ESD) to build an ESD-aided deep neural network (DNN) for authentically distorted image quality prediction. Our proposed network contains two streams: ESD prediction stream and SQS prediction stream. The whole DNN is optimized end-to-end with a combined loss so that both of the supervision information from ESD and SQS can be fully utilized in the training process. Experiments on two public authentically distorted image databases verify the superiority of our method.