HIFC-IQA: Train-Free Cross-Domain Image Quality Assessment via Dual-Process Cognition

The discrepancy between controlled synthetic distortions and complex real-world degradations poses a significant domain shift challenge for No-Reference Image Quality Assessment (NR-IQA). While Unsupervised Domain Adaptation (UDA) methods aim to bridge this gap, they heavily suffer from retraining overhead and error propagation during iterative pseudo-labeling. Inspired by the dual-process theory of human cognition, we propose Hierarchical Intuitive and Fuzzy Consensus (HIFC), a completely train-free cross-domain IQA framework. HIFC employs an asymmetric perceptual decomposition to disentangle semantic and distortion features. It derives the final quality score through a synergy of two branches: a memory consensus branch that retrieves a stable Fuzzy Quality Centroid from a synthetic reference gallery, and an intuitive branch that captures aesthetic polarization via zero-shot vision-language alignment. Extensive experiments demonstrate that, without any parameter updates or target-domain adaptation, HIFC achieves highly competitive results against state-of-the-art UDA methods. Most notably, it exhibits exceptional robustness and generalization in the challenging synthetic-to-authentic cross-domain setting. The code will be available upon acceptance.