Image Quality Assessment (IQA) is a challenging task that requires training on massive datasets to achieve accurate predictions. However, due to the lack of IQA data, deep learning-based IQA methods typically rely on pre-trained networks trained on massive datasets as feature extractors to enhance their generalization ability, such as the ResNet network trained on ImageNet. In this paper, we utilize the encoder of Segment Anything, a recently proposed segmentation model trained on a massive dataset, for high-level semantic feature extraction. Most IQA methods are limited to extracting spatial-domain features, while frequency-domain features have been shown to better represent noise and blur. Therefore, we leverage both spatial-domain and frequency-domain features by applying Fourier and standard convolutions on the extracted features, respectively. Extensive experiments are conducted to demonstrate the effectiveness of all the proposed components, and results show that our approach outperforms the state-of-the-art (SOTA) in four representative datasets, both qualitatively and quantitatively. Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks. Code: https://github.com/Hedlen/SAM-IQA

本文研究了基于深度学习的图像质量评估（Image Quality Assessment，IQA）方法，利用Segment Anything模型的编码器进行高级语义特征提取，并结合空域和频域特征来提高预测准确性。实验证明，该方法在四个代表性数据集上优于现有方法，验证了Segment Anything模型的强大特征提取能力，以及在IQA任务中结合空域和频域特征的价值。

SAM-IQA：分割任何东西能提升图像质量评估吗？