HAR-HFNet: Hybrid-Attention Refinement and Hierarchical Fusion Network for No-Reference Image Quality Assessment

No-Reference Image Quality Assessment (NR-IQA) is an important task in the field of computer vision. Most methods utilize the pre-trained features with information irrelevant to image quality. In addition, some methods directly regress the pre-trained features without interaction or simply concatenate all features for score regression. They ignore the differences between local and global features. These issues lead to the limited IQA performance. To address this, we propose a Hybrid-Attention Refinement and Hierarchical Fusion Network (HAR-HFNet) for NR-IQA, which consists of a feature extraction module, a hybrid attention refinement module, a hierarchical dilated-attention fusion module and a quality prediction module. Firstly, the hybrid attention refinement module filters and refines the multi-stage features extracted by the pre-trained Swin Transformer, which enhances distortion-related information. Secondly, the hierarchical dilated-attention fusion module fuses the deep global feature with local features. It enables effective hierarchical integration of global semantics and local details. Finally, the quality prediction module predicts the score through weighted feature aggregation. Experiments on six public IQA datasets demonstrate that the HAR-HFNet outperforms some baseline NR-IQA methods in prediction accuracy and generalization ability.