Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
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.
6594c5441adf6d577e59ec3a
Chunyi Li
6594c5441adf6d577e59ec3b
Haoning Wu
claimed_verified
2024-01-03T12:33:49.200Z
63047ed2412a1b9d381b09c9
https://cdn-avatars.huggingface.co/v1/production/uploads/63047ed2412a1b9d381b09c9/2Ill5G0uSMyGstrawgmIb.jpeg
Haoning Wu, Teo
user
teowu
6594c5441adf6d577e59ec3c
Zicheng Zhang
claimed_verified
2025-04-08T07:02:57.789Z
6526cc6bab4f5d98382f5603
https://cdn-avatars.huggingface.co/v1/production/uploads/6526cc6bab4f5d98382f5603/NekYYb61I5nt4au6gXsVK.jpeg
Zicheng Zhang
user
zhangzicheng
6594c5441adf6d577e59ec3d
Hongkun Hao
6594c5441adf6d577e59ec3e
Kaiwei Zhang
6594c5441adf6d577e59ec3f
Lei Bai
6594c5441adf6d577e59ec40
Xiaohong Liu
6594c5441adf6d577e59ec41
Xiongkuo Min
6594c5441adf6d577e59ec42
Weisi Lin
6594c5441adf6d577e59ec43
Guangtao Zhai
Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
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.
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.
Text-to-Image AI-Generated Images quality-award refiner Human Visual System Image Quality Assessment adaptive pipelines