PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality

This paper introduces PromptIQA, a novel framework for no-reference image quality assessment (NR-IQA) that addresses the challenge of adapting to diverse assessment requirements without time-consuming fine-tuning. Unlike typical NR-IQA models, PromptIQA utilizes Image-Score Pairs (ISPs) as prompts to guide its predictions, significantly reducing reliance on extensive datasets for new requirements. To enhance the model's ability to comprehend and learn from these prompts, the authors propose two data augmentation strategies: random scaling and random flipping. Experiments demonstrate that PromptIQA, trained on mixed datasets, achieves superior performance and generalization compared to existing state-of-the-art methods, proving its effectiveness in various IQA tasks.