title: Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
summary: With the rapid evolution of the Text-to-Image (T2I) model in recent years,
  their unsatisfactory generation result has become a challenge. However,
  uniformly refining AI-Generated Images (AIGIs) of different qualities not only
  limited optimization capabilities for low-quality AIGIs but also brought
  negative optimization to high-quality AIGIs. To address this issue, a
  quality-award refiner named Q-Refine is proposed. Based on the preference of
  the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA)
  metric to guide the refining process for the first time, and modify images of
  different qualities through three adaptive pipelines. Experimental shows that
  for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs
  of different qualities. It can be a general refiner to optimize AIGIs from both
  fidelity and aesthetic quality levels, thus expanding the application of the
  T2I generation models.
paper_authors._id: 6594c5441adf6d577e59ec3a
paper_authors.name: Chunyi Li
paper_authors._id: 6594c5441adf6d577e59ec3b
paper_authors.name: Haoning Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-03T12:33:49.200Z
paper_authors._id: 63047ed2412a1b9d381b09c9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63047ed2412a1b9d381b09c9/2Ill5G0uSMyGstrawgmIb.jpeg
paper_authors.fullname: Haoning Wu, Teo
paper_authors.type: user
paper_authors.user: teowu
paper_authors._id: 6594c5441adf6d577e59ec3c
paper_authors.name: Zicheng Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T07:02:57.789Z
paper_authors._id: 6526cc6bab4f5d98382f5603
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6526cc6bab4f5d98382f5603/NekYYb61I5nt4au6gXsVK.jpeg
paper_authors.fullname: Zicheng Zhang
paper_authors.type: user
paper_authors.user: zhangzicheng
paper_authors._id: 6594c5441adf6d577e59ec3d
paper_authors.name: Hongkun Hao
paper_authors._id: 6594c5441adf6d577e59ec3e
paper_authors.name: Kaiwei Zhang
paper_authors._id: 6594c5441adf6d577e59ec3f
paper_authors.name: Lei Bai
paper_authors._id: 6594c5441adf6d577e59ec40
paper_authors.name: Xiaohong Liu
paper_authors._id: 6594c5441adf6d577e59ec41
paper_authors.name: Xiongkuo Min
paper_authors._id: 6594c5441adf6d577e59ec42
paper_authors.name: Weisi Lin
paper_authors._id: 6594c5441adf6d577e59ec43
paper_authors.name: Guangtao Zhai
paper_title: Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
paper_summary: With the rapid evolution of the Text-to-Image (T2I) model in recent years,
  their unsatisfactory generation result has become a challenge. However,
  uniformly refining AI-Generated Images (AIGIs) of different qualities not only
  limited optimization capabilities for low-quality AIGIs but also brought
  negative optimization to high-quality AIGIs. To address this issue, a
  quality-award refiner named Q-Refine is proposed. Based on the preference of
  the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA)
  metric to guide the refining process for the first time, and modify images of
  different qualities through three adaptive pipelines. Experimental shows that
  for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs
  of different qualities. It can be a general refiner to optimize AIGIs from both
  fidelity and aesthetic quality levels, thus expanding the application of the
  T2I generation models.
paper_ai_summary: Q-Refine, a quality-award refiner guided by IQA metrics, effectively optimizes AI-generated images across different qualities and models, enhancing both fidelity and aesthetic quality.
paper_ai_keywords: Text-to-Image AI-Generated Images quality-award refiner Human Visual System Image Quality Assessment adaptive pipelines