  Blind image quality assessment (BIQA) aims to automatically evaluate the
perceived quality of a single image, whose performance has been improved by
deep learning-based methods in recent years. However, the paucity of labeled
data somewhat restrains deep learning-based BIQA methods from unleashing their
full potential. In this paper, we propose to solve the problem by a pretext
task customized for BIQA in a self-supervised learning manner, which enables
learning representations from orders of magnitude more data. To constrain the
learning process, we propose a quality-aware contrastive loss based on a simple
assumption: the quality of patches from a distorted image should be similar,
but vary from patches from the same image with different degradations and
patches from different images. Further, we improve the existing degradation
process and form a degradation space with the size of roughly $2\times10^7$.
After pre-trained on ImageNet using our method, models are more sensitive to
image quality and perform significantly better on downstream BIQA tasks.
Experimental results show that our method obtains remarkable improvements on
popular BIQA datasets.
