Papers › VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank

VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank

20 May 2025arXiv:2505.14460archive 2025-07-28

Tianhe Wu, Jian Zou, Jie Liang, Lei Zhang, Kede Ma

DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-induced computational modeling has not been thoroughly explored in the context of image quality assessment (IQA), a task critically dependent on visual reasoning. In this paper, we introduce VisualQuality-R1, a reasoning-induced no-reference IQA (NR-IQA) model, and we train it with reinforcement learning to rank, a learning algorithm tailored to the intrinsically relative nature of visual quality. Specifically, for a pair of images, we employ group relative policy optimization to generate multiple quality scores for each image. These estimates are then used to compute comparative probabilities of one image having higher quality than the other under the Thurstone model. Rewards for each quality estimate are defined using continuous fidelity measures rather than discretized binary labels. Extensive experiments show that the proposed VisualQuality-R1 consistently outperforms discriminative deep learning-based NR-IQA models as well as a recent reasoning-induced quality regression method. Moreover, VisualQuality-R1 is capable of generating contextually rich, human-aligned quality descriptions, and supports multi-dataset training without requiring perceptual scale realignment. These features make VisualQuality-R1 especially well-suited for reliably measuring progress in a wide range of image processing tasks like super-resolution and image generation.

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get_image_data_url tianhewu/visualquality-r1/src/open-r1-multimodal/local_scripts/create_vision_cot_data.py official repository ran · fixture could not drive it Apache-2.0 (permissive) · 3f31b48fd862935d · report
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format_reward tianhewu/visualquality-r1/src/open-r1-multimodal/src/open_r1/grpo.py official repository unverified Apache-2.0 (permissive) · e89f2eec0a18d4c2 · report
generate_temp_json_data tianhewu/visualquality-r1/datasets/make_data.py official repository unverified Apache-2.0 (permissive) · cdda70c471530e2d · report
get_vlm_module tianhewu/visualquality-r1/src/open-r1-multimodal/src/open_r1/grpo_rec.py official repository unverified Apache-2.0 (permissive) · a34f33d5ef7c45a9 · report
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scoring_images tianhewu/visualquality-r1/datasets/make_data.py official repository unverified Apache-2.0 (permissive) · 5b01b590f99f82db · report

Tasks

Image GenerationImage Quality AssessmentLearning-To-RankNR-IQASuper-ResolutionVisual Reasoning

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