title: VisualQuality-R1: Reasoning-Induced Image Quality Assessment via
    Reinforcement Learning to Rank
summary: 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.
paper_authors._id: 682d54b0396c1e613eaac5ef
paper_authors.name: Tianhe Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:26.204Z
paper_authors._id: 655de51982afda0fc479fb91
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/655de51982afda0fc479fb91/-t9RLNEBAESO0niQGHoss.png
paper_authors.fullname: Tianhe Wu
paper_authors.type: user
paper_authors.user: TianheWu
paper_authors._id: 682d54b0396c1e613eaac5f0
paper_authors.name: Jian Zou
paper_authors._id: 682d54b0396c1e613eaac5f1
paper_authors.name: Jie Liang
paper_authors._id: 682d54b0396c1e613eaac5f2
paper_authors.name: Lei Zhang
paper_authors._id: 682d54b0396c1e613eaac5f3
paper_authors.name: Kede Ma
paper_title: VisualQuality-R1: Reasoning-Induced Image Quality Assessment via
    Reinforcement Learning to Rank
paper_summary: 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.
paper_ai_summary: VisualQuality-R1, a reasoning-induced no-reference IQA model trained via reinforcement learning, outperforms discriminative models in visual quality assessment by generating human-aligned quality descriptions and supporting multi-dataset training.
paper_ai_keywords: reinforcement learning reasoning-induced no-reference IQA (NR-IQA) group relative policy optimization Thurstone model continuous fidelity measures super-resolution image generation