  No-Reference Image Quality Assessment (NR-IQA) aims to develop methods to
measure image quality in alignment with human perception without the need for a
high-quality reference image. In this work, we propose a self-supervised
approach named ARNIQA (leArning distoRtion maNifold for Image Quality
Assessment) for modeling the image distortion manifold to obtain quality
representations in an intrinsic manner. First, we introduce an image
degradation model that randomly composes ordered sequences of consecutively
applied distortions. In this way, we can synthetically degrade images with a
large variety of degradation patterns. Second, we propose to train our model by
maximizing the similarity between the representations of patches of different
images distorted equally, despite varying content. Therefore, images degraded
in the same manner correspond to neighboring positions within the distortion
manifold. Finally, we map the image representations to the quality scores with
a simple linear regressor, thus without fine-tuning the encoder weights. The
experiments show that our approach achieves state-of-the-art performance on
several datasets. In addition, ARNIQA demonstrates improved data efficiency,
generalization capabilities, and robustness compared to competing methods. The
code and the model are publicly available at
https://github.com/miccunifi/ARNIQA.
