AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity

Recently, AI-generated images (AIGIs), synthesized based on initial textual prompts, have attracted widespread attention. However, due to limitations in current generation techniques, these images often exhibit degraded perceptual quality and semantic misalignment with the guiding prompts. Therefore, evaluating both perceptual quality and text-to-image alignment is essential for optimizing the performance of generative models. Existing methods design textual prompts solely based on the initial prompt for both perceptual and alignment quality tasks, and compute only coarse-grained similarity between the designed prompt and the generated image. However, such task-agnostic prompts overlook the distinctions between the perceptual and alignment quality tasks, and coarse-level similarity fails to capture semantic details, leading to suboptimal evaluation performance. To address these challenges, we propose a novel AIGI quality assessment framework, termed TPMS, which incorporates task-specific prompt and multi-granularity similarity computation. The task-specific prompt constructs dedicated prompts for perceptual and alignment quality respectively, allowing the model to capture distinct quality cues tailored to each evaluation task. Multi-granularity similarity measures the coarse-level similarity between the generated image and task-specific prompts to capture global quality characteristics, and the fine-level similarity between the generated image and the initial prompt to enhance semantic detail awareness. By integrating these two complementary similarities, TPMS enables precise and robust quality prediction. Extensive experiments on four widely-used AIGI quality benchmarks validate the effectiveness and superiority of the proposed framework.