Quality-aware Pre-trained Models for Blind Image Quality Assessment

TL;DR

This paper introduces a self-supervised pre-training approach for blind image quality assessment, leveraging a quality-aware contrastive loss and an extensive degradation space to improve model sensitivity and performance.

Contribution

It proposes a novel self-supervised pretext task and a quality-aware contrastive loss tailored for BIQA, enabling learning from much larger unlabeled datasets.

Findings

Significant performance improvements on BIQA datasets.

Pre-trained models are more sensitive to image quality variations.

Degradation space size increased to approximately 20 million.

Abstract

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.