Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SEe and Assess ROIs quality with GUidance from a Large vision-Language model. SEAGULL incorporates a vision-language model (VLM), masks generated by Segment Anything Model (SAM) to specify ROIs, and a meticulously designed Mask-based Feature Extractor (MFE) to extract global and local tokens for specified ROIs, enabling accurate fine-grained IQA for ROIs. Moreover, this paper constructs two ROI-based IQA datasets, SEAGULL-100w and SEAGULL-3k, for training and evaluating ROI-based IQA. SEAGULL-100w comprises about 100w synthetic distortion images with 33 million ROIs for pre-training to improve the model's ability of regional quality perception, and SEAGULL-3k contains about 3k authentic distortion ROIs to enhance the model's ability to perceive real world distortions. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, SEAGULL shows remarkable performance on fine-grained ROI quality assessment. Code and datasets are publicly available at the https://github.com/chencn2020/Seagull.

翻译：现有的图像质量评估方法在分析整体图像质量方面取得了显著成功，但很少有工作探索针对兴趣区域的质量分析。兴趣区域的质量分析可为图像质量提升提供细粒度指导，对于关注区域级质量的场景至关重要。本文提出了一种新颖的网络SEAGULL，它能够借助大型视觉-语言模型的指导来观察和评估兴趣区域的质量。SEAGULL整合了视觉-语言模型、通过Segment Anything Model生成的掩码以指定兴趣区域，以及精心设计的基于掩码的特征提取器，用于提取指定兴趣区域的全局和局部标记，从而实现对兴趣区域的精确细粒度图像质量评估。此外，本文构建了两个基于兴趣区域的图像质量评估数据集SEAGULL-100w和SEAGULL-3k，用于训练和评估基于兴趣区域的图像质量评估。SEAGULL-100w包含约100万张合成失真图像及3300万个兴趣区域，用于预训练以提升模型的区域质量感知能力；SEAGULL-3k包含约3000个真实失真兴趣区域，用于增强模型对现实世界失真的感知能力。在SEAGULL-100w上预训练并在SEAGULL-3k上微调后，SEAGULL在细粒度兴趣区域质量评估方面展现出卓越性能。代码和数据集已在https://github.com/chencn2020/Seagull公开提供。