Cross-Domain AI-Generated Image Quality Assessment via Content-Distortion Awareness

Abstract

With the expanding use of artificial intelligence generated images (AGIs) in scenarios such as gaming, art, and film production, evaluating their quality is essential to ensure their practical utility. To guarantee effective quality measurement, both content and distortion must be considered. However, existing image quality assessment (IQA) methods fail to sufficiently combine the two aspects in their assessments. Meanwhile, the AGIs annotated with credible content and distortion labels are lack, thus, employing cross-domain methods to utilize typically public image datasets is meaningful. Based on the above conclusions, this paper proposed a cross-domain AI-generated IQA via content-distortion awareness (CDAQA). Firstly, the large language model has been utilized to obtain content and distortion expressions of images. Secondly, a dual-stream contrastive language-image pre-training module (DCLIP) has been designed to gain both quality-aware features in images and the corresponding expressions that related to content and distortion, respectively. Thirdly, a graph knowledge transferring module (GKT) that constructs graphs via inter-domain content relevance has been proposed. GKT can update distortion information in the source domain according to the target domain representations. Finally, the cross-domain framework has been designed to update the existing IQA model for AGIs. And, the contrastive loss of DCLIP, feature update loss of GKT, and quality prediction loss are coordinated to jointly update different modules, enabling sufficient fusion of target domain knowledge. Experimental results demonstrate that CDAQA achieves higher accuracy and stability in cross-domain AGIs tasks.