iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment

TL;DR

iDiff is an interpretable framework for pairwise image quality assessment that combines preference prediction with rationale generation, achieving top results in the NTIRE 2026 RAIM challenge.

Contribution

The paper introduces a dual-branch model that jointly predicts preferences and generates explanations, enhancing robustness and interpretability in image quality assessment.

Findings

Achieved first place in NTIRE 2026 RAIM challenge.

Effectively models discriminative decision making and structured explanations.

Improves both accuracy and reasoning quality in IQA.

Abstract

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. Our method adopts a dual-branch design consisting of an Answer Model and a Thinking Model. The Answer Model performs robust preference prediction by explicitly decomposing each sample into left/right global and local views, followed by content-aware specialization for person and scene images and ensemble-based aggregation across backbones. The Thinking Model focuses on rationale generation and is progressively enhanced with expert-style templates, multi-source quality features, and answer-aware supervision conditioned on the Answer Model prediction. In this way, iDiff jointly models discriminative decision making and structured explanation, improving both robustness and interpretability. Extensive experiments demonstrate the effectiveness of the proposed framework on both accuracy and reasoning-quality metrics. Our method achieved first place in the NTIRE 2026 RAIM challenge, showing the effectiveness of integrating explicit difference modeling with structured multimodal reasoning for pairwise IQA.