FsPN: Blind Image Quality Assessment Based on Feature-Selected Pyramid Network

Blind image quality assessment (BIQA) is crucial for user satisfaction and the performance of various image processing applications. Most BIQA methods directly use the pre-trained model to extract features and then perform feature fusion. However, the features extracted by pre-trained models may contain irrelevant information to BIQA. Although some methodspre-train the feature extraction network from scratch, these approaches raise computational costs and resource demands. In this letter, a Feature-selected Pyramid Network(FsPN) is proposed to address this issue from a different perspective. First, a spatial selection module selects useful information from the features extracted by the pre-trained model. Additionally, a pyramid network based on skip connections is utilized to fuse the selected multi-scale features. The proposed method is verified in six public datasets, where it consistently outperformed existing state-of-the-art methods, affirming its effectiveness and adaptability.