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Blind image quality assessment (BIQA) is a challenging problem with importantreal-world applications. Recent efforts attempting to exploit powerfulrepresentations by deep neural networks (DNN) are hindered by the lack ofsubjectively annotated data. This paper presents a novel BIQA method whichovercomes this fundamental obstacle. Specifically, we design a pair ofcollaborative autoencoders (COAE) consisting of a content autoencoder (CAE) anda distortion autoencoder (DAE) that work together to extract content anddistortion representations, which are shown to be highly descriptive of imagequality. While the CAE follows a standard codec procedure, we introduce theCAE-encoded feature as an extra input to the DAE's decoder for reconstructingdistorted images, thus effectively forcing DAE's encoder to extract distortionrepresentations. The self-supervised learning framework allows the COAEincluding two feature extractors to be trained by almost unlimited amount ofdata, thus leaving limited samples with annotations to finetune a BIQA model.We will show that the proposed BIQA method achieves state-of-the-artperformance and has superior generalization capability over other learningbased models. The codes are available at:https://github.com/Macro-Zhou/NRIQA-VISOR/.

Collaborative Auto-encoding for Blind Image Quality Assessment