Rethinking Knowledge Transfer in Image Quality Assessment: A Perceptual Preference Structure Alignment Perspective

As imaging scenarios diversify rapidly, Image Quality Assessment (IQA) faces a key challenge: how to effectively transfer perceptual knowledge from existing annotated datasets to ensure reliable quality prediction in new scenarios. However, current IQA models struggle to generalize: direct transfer often leads to severe performance degradation, while multi-dataset joint training rarely yields stable gains and can even harm target performance. In this work, we identify perceptual preference structure mismatch as an important and underexplored factor behind transfer failure in IQA. Models trained on different source datasets may rely on different perceptual cues, leading to discrepancies in the conditional distribution P (Y |X) that hinder effective transfer. To address this, we propose Perceptual Preference Representation (PPR), which characterizes dataset-specific perceptual preference structures by analyzing correlations between visual features and quality scores. Based on PPR, we define Perceptual Preference Consistency (PPC), a training-free and interpretable measure of relative preference compatibility across datasets. Building on this, we develop Preference-Structure-Aligned Transfer (PreSTA), which selects source samples better aligned with the target domain in terms of perceptual preference structure. Extensive experiments show that PreSTA achieves superior performance across cross-domain, within-domain, and targeted joint transfer settings while using only a small fraction of source data. These results highlight that aligning perceptual preference structures, rather than simply increasing dataset size, is crucial for effective and dataefficient knowledge transfer in IQA. Code will be publicly available at https://github.com/Li-aobo/PreSTA.