A Two-Stage AIGC Image Quality Assessment with T2I Correspondence and Visual Perception

Image quality assessment (IQA) of artificial intelligence-generated content (AIGC) has recently attracted significant research attention. Unlike general-purpose IQA, which primarily focuses on evaluating image content, AIGCIQA often requires addressing both the Text-to-Image (T2I) correspondence and the perceptual quality of images. To address this requirement, this paper proposes a novel two-stage AIGCIQA method. The first stage evaluates the alignment of the AI-generated images (AIGIs) with their corresponding descriptions, serving as an indicator of overall image quality. Specifically, positive and negative prompts are constructed to describe the T2I correspondence degree, and then a CLIP model is employed to predict the degree based on these image-prompt pairs. The second stage refines the perceptual quality assessment by integrating both global and local degradation features of AIGIs. Importantly, the contribution of local features is measured according to their correlation with the overall image, ensuring key regions are adequately represented in the quality prediction. Experimental results on AGIQA-1K, AGIQA-3K, and AIGCIQA2023 demonstrate the superior performance of the proposed method.