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.

翻译：无参考图像质量评估旨在自动评价单张图像的感知质量，近年来基于深度学习的方法已显著提升了其性能。然而，标注数据的匮乏在一定程度上制约了深度学习型无参考图像质量评估方法充分发挥潜力。本文提出通过为无参考图像质量评估定制的预文本任务，以自监督学习方式解决该问题，从而能够从数量级更大的数据中学习表征。为约束学习过程，我们基于一个简单假设提出质量感知对比损失：同一失真图像中图像块的质量应具有相似性，但该质量应与来自同一图像但经历不同退化处理及来自不同图像的图像块质量存在差异。此外，我们改进现有退化流程，构建了一个规模约$2\times10^7$的退化空间。采用所提方法在ImageNet上进行预训练后，模型对图像质量更为敏感，并在下游无参考图像质量评估任务中表现显著提升。实验结果表明，该方法在主流无参考图像质量评估数据集上取得了显著改进。