We consider the problem of obtaining image quality representations in a
self-supervised manner. We use prediction of distortion type and degree as an
auxiliary task to learn features from an unlabeled image dataset containing a
mixture of synthetic and realistic distortions. We then train a deep
Convolutional Neural Network (CNN) using a contrastive pairwise objective to
solve the auxiliary problem. We refer to the proposed training framework and
resulting deep IQA model as the CONTRastive Image QUality Evaluator
(CONTRIQUE). During evaluation, the CNN weights are frozen and a linear
regressor maps the learned representations to quality scores in a No-Reference
(NR) setting. We show through extensive experiments that CONTRIQUE achieves
competitive performance when compared to state-of-the-art NR image quality
models, even without any additional fine-tuning of the CNN backbone. The
learned representations are highly robust and generalize well across images
afflicted by either synthetic or authentic distortions. Our results suggest
that powerful quality representations with perceptual relevance can be obtained
without requiring large labeled subjective image quality datasets. The
implementations used in this paper are available at
https://github.com/pavancm/CONTRIQUE.

本文提出了一种名为 CONTRIQUE 的训练框架和深度 IQA 模型，使用自监督学习的方法从标有多种不同程度和类型扭曲度的无标签图像数据集中学习特征，通过冻结 CNN 权重和使用线性回归器将学习到的表示映射到质量得分，获得了很强的性能表现。该深度学习模型的训练无需大量标记数据。