No Reference Opinion Unaware Quality Assessment of Authentically Distorted Images

Nithin C. Babu, Vignesh Kannan, Rajiv Soundararajan; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 2459-2468

The quality assessment (QA) of camera captured authentically distorted images is important on account of its ubiquitous applications and challenging due to the lack of a reference. While there exists a plethora of supervised no reference (NR) image QA (IQA) algorithms, there is a need to study unsupervised or opinion unaware algorithms on account of their superior generalization performance. We explore self-supervised learning (SSL) for the feature design on authentically distorted images to predict quality without training on human labels. While SSL on synthetic distortions has recently shown promise, there is a need to enrich the feature learning on authentic distortions. The key challenge in achieving this is in the learning of quality sensitive features with mitigated content dependence. We design a self-supervised contrastive learning approach which only requires positives and introduce a content separation loss by estimating a bound on the mutual information between the features learnt and the content information. We show on multiple authentically distorted datasets that our self-supervised features can predict image quality by comparing with a corpus of pristine images and achieve state-of-the-art performance.