In no-reference image quality assessment (NR-IQA), the challenge of limited
dataset sizes hampers the development of robust and generalizable models.
Conventional methods address this issue by utilizing large datasets to extract
rich representations for IQA. Also, some approaches propose vision language
models (VLM) based IQA, but the domain gap between generic VLM and IQA
constrains their scalability. In this work, we propose a novel pretraining
framework that constructs a generalizable representation for IQA by selectively
extracting quality-related knowledge from VLM and leveraging the scalability of
large datasets. Specifically, we carefully select optimal text prompts for five
representative image quality attributes and use VLM to generate pseudo-labels.
Numerous attribute-aware pseudo-labels can be generated with large image
datasets, allowing our IQA model to learn rich representations about image
quality. Our approach achieves state-of-the-art performance on multiple IQA
datasets and exhibits remarkable generalization capabilities. Leveraging these
strengths, we propose several applications, such as evaluating image generation
models and training image enhancement models, demonstrating our model's
real-world applicability. We will make the code available for access.

提出了一种新颖的预训练框架，通过从通用视觉语言模型中选择性提取与图像质量相关的知识，并利用大型数据集的可扩展性，构建了一种适用于图像质量评估的通用表示。同时我们的方法在多个数据集上取得了最先进的性能，并展现了显著的泛化能力。