No-Reference Image Quality Assessment via Semantic-Guided Multi-Scale Feature Extraction

Image quality assessment is crucial in the development of digital technology. No-reference image quality assessment aims to predict image quality accurately without depending on reference images. In this paper, we propose a semantic-guided multi-scale feature extraction network for no-reference image quality assessment. The network begins with a scale-wise attention module to capture both global and local features. Subsequently, we design a layer-wise feature guidance block that leverages high-level semantic information to guide low-level feature learning for effective feature fusion. Finally, it predicts quality scores through quality regression using the Kolmogorov–Arnold network. Experimental results with 19 existing methods on six public IQA datasets—LIVE, CSIQ, TID2013, KADID-10k, LIVEC and KonIQ-10k—demonstrate that the proposed method can effectively simulate human perceptions of image quality and is highly adaptable to different distortion types.