The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming, especially for authentic images. Training on synthetic data is expected to be beneficial, but synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that introducing more distortion types in the synthetic dataset may not improve or even be harmful to generalizing authentic image quality assessment. To solve this challenge, we propose distortion-guided unsupervised domain adaptation for BIQA (DGQA), a novel framework that leverages adaptive multi-domain selection via prior knowledge from distortion to match the data distribution between the source domains and the target domain, thereby reducing negative transfer from the outlier source domains. Extensive experiments on two cross-domain settings (synthetic distortion to authentic distortion and synthetic distortion to algorithmic distortion) have demonstrated the effectiveness of our proposed DGQA. Besides, DGQA is orthogonal to existing model-based BIQA methods, and can be used in combination with such models to improve performance with less training data.

翻译：盲图像质量评估（BIQA）的人工标注费时费力，尤其是对真实图像而言。利用合成数据进行训练虽有望带来益处，但由于域间差异，合成训练模型在真实域中往往泛化性能不佳。本研究发现关键现象：在合成数据集中引入更多失真类型，可能并不会改善甚至反而损害真实图像质量评估的泛化能力。为解决该挑战，我们提出面向BIQA的失真引导无监督域自适应框架（DGQA），该框架通过利用失真先验知识的自适应多域选择，实现源域与目标域的数据分布匹配，从而减轻离群源域的负迁移效应。在两个跨域场景（合成失真→真实失真、合成失真→算法失真）上的大量实验证明了DGQA的有效性。此外，DGQA与现有基于模型的BIQA方法正交，可与其结合使用，在更少训练数据条件下提升性能。