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

Q-Probe introduces a novel agentic framework for scaling image quality assessment to high-resolution images by addressing limitations in existing reinforcement learning approaches. The research presents Vista-Bench, a new benchmark for fine-grained degradation analysis, and demonstrates state-of-the-art performance across multiple resolution scales through context-aware probing mechanisms.

Q-Probe represents a meaningful advancement in image quality assessment technology by tackling a genuine limitation in current multimodal large language models. Existing RL-based IQA systems struggle with high-resolution images because they rely on global visual processing that misses subtle local degradations, a constraint that becomes increasingly problematic as image resolution grows in professional and creative applications.

The core innovation addresses a specific bias problem: when existing zoom-in approaches crop images for detailed analysis, the model misinterprets the cropping itself as image degradation rather than contextual examination. Q-Probe's context-aware cropping strategy eliminates this causal bias while also preventing misidentification of natural depth-of-field effects as artifacts. The three-stage training paradigm progressively aligns the model with human preferences, suggesting iterative refinement rather than single-pass learning.

Vista-Bench serves as a critical contribution, providing researchers with benchmark data explicitly designed for high-resolution scenarios with fine-grained local degradations. This addresses the current scarcity of appropriate evaluation datasets for advanced IQA tasks. The framework demonstrates practical utility across resolution scales, indicating robust generalization capabilities.

For developers and researchers in computer vision, this work enables more sophisticated quality assessment pipelines for high-resolution content creation, professional imaging, and automated quality control systems. The agentic approach also reflects broader trends toward autonomous reasoning in multimodal AI systems. However, real-world deployment would require validation across diverse image types and degradation patterns beyond what benchmarks typically capture.

• →Q-Probe eliminates spurious 'cropping-implies-degradation' biases through context-aware probing, solving a key limitation in zoom-in IQA approaches.
• →Vista-Bench provides the first benchmark specifically designed for fine-grained local degradation analysis in high-resolution image quality assessment.
• →The three-stage training paradigm progressively aligns model predictions with human preferences while mitigating causal bias.
• →State-of-the-art performance is maintained across multiple resolution scales, demonstrating robust generalization capabilities.
• →The framework enables practical applications in professional imaging, content creation, and automated quality control systems.