AI RESEARCH
Preference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment
arXiv CS.CV
•
ArXi:2603.20086v1 Announce Type: new Current no-reference image quality assessment (NR-IQA) models for enhanced images often struggle to generalize, as they tend to overfit to the distinct patterns of specific enhancement algorithms rather than evaluating genuine perceptual quality. To address this issue, we propose a preference-guided debiasing framework for no-reference enhancement image quality assessment