VisualQuality-R1: Reasoning-Induced Image Quality Assessment via
  Reinforcement Learning to Rank
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.
682d54b0396c1e613eaac5ef
Tianhe Wu
claimed_verified
2025-05-21T08:40:26.204Z
655de51982afda0fc479fb91
https://cdn-avatars.huggingface.co/v1/production/uploads/655de51982afda0fc479fb91/-t9RLNEBAESO0niQGHoss.png
Tianhe Wu
user
TianheWu
682d54b0396c1e613eaac5f0
Jian Zou
682d54b0396c1e613eaac5f1
Jie Liang
682d54b0396c1e613eaac5f2
Lei Zhang
682d54b0396c1e613eaac5f3
Kede Ma
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via
  Reinforcement Learning to Rank
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.
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.
reinforcement learning reasoning-induced no-reference IQA (NR-IQA) group relative policy optimization Thurstone model continuous fidelity measures super-resolution image generation