PrismIQA: Capability-Factorized Multi-Agent Image Quality Assessment with Falsification and Region-to-Global Attribution

Reasoning-capable vision–language models have substantially advanced image quality assessment (IQA), including high-resolution assessment through active crop-and-zoom operations. Nevertheless, most existing systems assign global scoring, defect localization, degradation discrimination, and perceptual-impact estimation to one general-purpose policy. Recent agentic systems alleviate part of this burden through workflow-level roles such as planner–executor–summarizer, but the planner still commonly performs broad perception and task allocation. We instead study capability-level factorization: each agent has a distinct objective, evidence space, and action set. Our framework, PrismIQA, comprises four stages: a Global Assessor estimates a calibrated holistic prior; a high-recall Region Proposer identifies candidate defect regions; a Falsification Agent compares each crop with full-image context and rejects pseudo-degradations such as intentional depth-of-field and natural texture; and an Impact Assessor estimates the severity and semantic importance of verified defects and attributes their residual effect on the global score. We analyze this architecture as a policy over semantic interaction trajectories. Through Lipschitz–transport analysis and a KL-chain diagonal constraint, we prove that the factorized four-agent system is superior to a shared model: four independently optimized models yield a strictly smaller deviation upper bound than one shared model. On seven IQA datasets, PrismIQA achieves SOTA performance and reaches 0.854 SRCC and 0.892 PLCC on Vista-Bench.