AI RESEARCH
iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment
arXiv CS.CV
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ArXi:2605.19522v1 Announce Type: new Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, but also to provide convincing and image-grounded reasoning. In the NTIRE 2026 RAIM challenge, this requirement is further emphasized by jointly evaluating preference prediction and rationale generation. To address this task, we propose iDiff, an Interpretable Difference-aware framework for pairwise image quality assessment.