Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
This addresses the challenge of improving the perceptual quality of AI-generated images for users of text-to-image models, though it appears incremental as it builds on existing IQA methods.
The paper tackles the problem of uniformly refining AI-generated images (AIGIs) of varying qualities, which limits optimization for low-quality images and causes negative effects on high-quality ones, by proposing Q-Refine, a quality-aware refiner that uses Image Quality Assessment (IQA) metrics to guide adaptive pipelines, resulting in effective optimization for different qualities from mainstream text-to-image models.
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.