Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing

• URL: http://arxiv.org/abs/2601.15356v2
• Date: Tue, 27 Jan 2026 03:25:24 GMT
• Title: Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing
• Authors: Xiang Li, XueHeng Li, Yu Wang, XuanHua He, ZhangChi Hu, WeiWei Yu, ChengJun Xie,
• Abstract summary: "Thinking with Images" paradigms induce spurious "cropping-implies-degradation" biases and misinterprets natural depth-of-field as artifacts.<n>We propose Q-Probe, the first agentic IQA framework designed to scale IQA to high resolution via context-aware probing.<n>Q-Probe achieves state-of-the-art performance in high-resolution settings while maintaining superior efficacy across resolution scales.
• Score: 10.754977682331855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Quality Assessment (IQA). However, existing RL-based IQA models typically rely on coarse-grained global views, failing to capture subtle local degradations in high-resolution scenarios. While emerging "Thinking with Images" paradigms enable multi-scale visual perception via zoom-in mechanisms, their direct adaptation to IQA induces spurious "cropping-implies-degradation" biases and misinterprets natural depth-of-field as artifacts. To address these challenges, we propose Q-Probe, the first agentic IQA framework designed to scale IQA to high resolution via context-aware probing. First, we construct Vista-Bench, a pioneering benchmark tailored for fine-grained local degradation analysis in high-resolution IQA settings. Furthermore, we propose a three-stage training paradigm that progressively aligns the model with human preferences, while simultaneously eliminating causal bias through a novel context-aware cropping strategy. Extensive experiments demonstrate that Q-Probe achieves state-of-the-art performance in high-resolution settings while maintaining superior efficacy across resolution scales.

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