Blind Image Quality Assessment Based on Separate Representations and Adaptive Interaction of Content and Distortion

The visual quality of an image mainly relies on its content and its distortions. However, the adaptability between their contributions to the image quality has not be well investigated yet. Besides, albeit of many promising efforts, lacking sufficient labeled data still hinders the robust representation of quality-related information. In this work, we first design a self-supervised architecture, named collaborative autoencoder (COAE), to separately represent the content and the distortion information, and then develop a Self-Adaptive Weighting based quAlity predictoR (SAWAR) to balance the individual representations of the content and the distortions in the prediction of image quality. Specifically, the COAE is trained with large-scale unlabeled data, consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work collaboratively and individually. While the CAE is a standard autoencoder for the content representation, the design of the DAE is unique. We introduce the CAE-encoded content representation as an extra input to the decoder of the DAE to learn to reconstruct distorted images, thus effectively forcing it to extract the distortion representation. The SAWAR, whose parameter number is much smaller than that of the COAE, is trained with labeled data in existing IQA datasets. It takes advantage of the interaction between the image content and the distortions to adaptively balance their contributions. Extensive experiments show that the COAE effectively extracts quality-related representations and the SAWAR achieves the state-of-the-art performance.