ARNIQA: Learning Distortion Manifold for Image Quality Assessment

TL;DR

ARNIQA introduces a self-supervised method that models the distortion manifold for no-reference image quality assessment, achieving state-of-the-art results without requiring high-quality reference images.

Contribution

The paper presents a novel self-supervised approach that models the distortion manifold for NR-IQA, improving data efficiency and generalization over existing methods.

Findings

Achieves state-of-the-art performance on multiple datasets.

Demonstrates improved data efficiency and robustness.

Models the distortion manifold effectively for quality assessment.

Abstract

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.