PromptIQA: Boosting the Performance and Generalization for No-Reference
  Image Quality Assessment via Prompts
Zewen Chen Haina Qin Juan Wang Chunfeng Yuan Bing Li Weiming Hu Liang Wang
Due to the diversity of assessment requirements in various application
scenarios for the IQA task, existing IQA methods struggle to directly adapt to
these varied requirements after training. Thus, when facing new requirements, a
typical approach is fine-tuning these models on datasets specifically created
for those requirements. However, it is time-consuming to establish IQA
datasets. In this work, we propose a Prompt-based IQA (PromptIQA) that can
directly adapt to new requirements without fine-tuning after training. On one
hand, it utilizes a short sequence of Image-Score Pairs (ISP) as prompts for
targeted predictions, which significantly reduces the dependency on the data
requirements. On the other hand, PromptIQA is trained on a mixed dataset with
two proposed data augmentation strategies to learn diverse requirements, thus
enabling it to effectively adapt to new requirements. Experiments indicate that
the PromptIQA outperforms SOTA methods with higher performance and better
generalization. The code will be available.