<p>No-reference image quality assessment (NR-IQA) aims to quantitatively predict the perceptual quality of an image in the absence of a reference. Most existing methods model perceptual quality primarily in the spatial domain, overlooking the potential of frequency domain information. In this paper, we propose a Frequency Domain Information Aware Network (FANet) that leverages frequency domain features to capture complementary information across domains. In FANet we propose a Frequency Encoding and Aggregation Module (FEAM) to effectively learn the relationship between spatial and frequency representations. Then, a Dynamic Swintransformer Fusion Module (DSFM) is designed to further enhance the aggregated features. Additionally, a Color Histogram Module (CHM) is incorporated into the feature extraction process to enable the model to better learn color distribution characteristics. Experimental results on multiple IQA datasets with both synthetic and authentic distortions demonstrate the effectiveness of the proposed method. Notably, our approach exhibits strong generalization capabilities, achieving competitive performance in cross-dataset evaluations.</p>