Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning
Guoqiang Liang, Jianyi Wang, Zhonghua Wu, Shangchen Zhou

TL;DR
Zoom-IQA is a novel vision language model for image quality assessment that emphasizes reliable region-aware reasoning, uncertainty awareness, and iterative refinement to improve robustness, explainability, and generalization.
Contribution
It introduces a two-stage training pipeline with supervised fine-tuning and reinforcement learning, enhancing reasoning reliability and scoring accuracy in IQA.
Findings
Achieves improved robustness and explainability in IQA
Demonstrates better generalization across datasets
Effective in downstream tasks like image restoration
Abstract
Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores without explanation or providing low-level descriptions lacking precise scores. Recent reasoning-based vision language models (VLMs) have shown strong potential for IQA by jointly generating quality descriptions and scores. However, existing VLM-based IQA methods often suffer from unreliable reasoning due to their limited capability of integrating visual and textual cues. In this work, we introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors: uncertainty awareness, region reasoning, and iterative refinement. Specifically, we present a two-stage training pipeline: 1) supervised fine-tuning (SFT) on our Grounded-Rationale-IQA (GR-IQA) dataset to teach the model to ground its assessments in key regions, and 2)…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI) · Generative Adversarial Networks and Image Synthesis
