Learning Decoupled Features With Perceptual Distillation for Blind Image Quality Assessment

Existing Blind Image Quality Assessment (BIQA) approaches typically employ subjective scores as optimization targets to train the model, aiming for results consistent with human judgments. Such judgments are derived from a comprehensive analysis of complex distortions and diverse semantics from images, whereas subjective scores represent the overall quality. This poses a significant challenge for a single model to learn diverse perceptual cues under weak supervision. To address this, we propose a Decoupled Feature Learning (DFL) framework that learns compact global content-aware and local distortion-aware features in a disentangled modeling for BIQA. Our key insight is to leverage global-local input pairs to decompose content-aware and distortion-aware cues entangled in distorted images, and aggregate decoupled perceptual features into a single network. We design a perceptual knowledge distillation strategy that progressively guides the student from fragmented representations to build local-to-global correspondences by distilling self-supervised semantic knowledge, while incorporating the Just-Noticeable-Difference (JND) model to highlight the transfer of perceptually sensitive content features. Finally, we introduce a local distortion-guided attention module to model synergistic effects of different perceptual features from the student for quality evaluation. Extensive experiments on eight benchmark datasets demonstrate the superior performance of the proposed model over the state-of-the-arts. In addition, the DFL framework is flexibly used to improve the perception ability of other Transformer variants. The code is released at https://github.com/JianjunXiang/DFT.