Collaborative Auto-encoding for Blind Image Quality Assessment
Zehong Zhou Fei Zhou Guoping Qiu
Blind image quality assessment (BIQA) is a challenging problem with important
real-world applications. Recent efforts attempting to exploit powerful
representations by deep neural networks (DNN) are hindered by the lack of
subjectively annotated data. This paper presents a novel BIQA method which
overcomes this fundamental obstacle. Specifically, we design a pair of
collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and
a distortion autoencoder (DAE) that work together to extract content and
distortion representations, which are shown to be highly descriptive of image
quality. While the CAE follows a standard codec procedure, we introduce the
CAE-encoded feature as an extra input to the DAE's decoder for reconstructing
distorted images, thus effectively forcing DAE's encoder to extract distortion
representations. The self-supervised learning framework allows the COAE
including two feature extractors to be trained by almost unlimited amount of
data, thus leaving limited samples with annotations to finetune a BIQA model.
We will show that the proposed BIQA method achieves state-of-the-art
performance and has superior generalization capability over other learning
based models. The codes are available at:
https://github.com/Macro-Zhou/NRIQA-VISOR/.