FsPN: Blind Image Quality Assessment Based on Feature-Selected Pyramid Network
Long Tang, Yongming Han, Liang Yuan, Guangtao Zhai
Abstract
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.