[2601.15356] Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2601.15356 (eess) [Submitted on 21 Jan 2026 (v1), last revised 8 Apr 2026 (this version, v3)] 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 View a PDF of the paper titled Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing, by Xiang Li and 6 other authors View PDF HTML (experimental) 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 progressivel...