Q-Refine：用于AI生成图像的感知质量优化器
Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
January 2, 2024
作者: Chunyi Li, Haoning Wu, Zicheng Zhang, Hongkun Hao, Kaiwei Zhang, Lei Bai, Xiaohong Liu, Xiongkuo Min, Weisi Lin, Guangtao Zhai
cs.AI
摘要
近年来，随着文本到图像（T2I）模型的快速发展，它们生成的结果仍然存在不尽人意的问题。然而，统一优化不同质量的人工智能生成图像（AIGIs）不仅限制了对低质量AIGIs的优化能力，还给高质量AIGIs带来了负面优化。为了解决这一问题，提出了一种名为Q-Refine的质量奖励精化器。基于人类视觉系统（HVS）的偏好，Q-Refine首次利用图像质量评估（IQA）指标指导精化过程，并通过三个自适应流程修改不同质量的图像。实验证明，对于主流T2I模型，Q-Refine能够有效优化不同质量的AIGIs。它可以作为通用精化器，从保真度和美学质量两个层面优化AIGIs，从而拓展了T2I生成模型的应用。
English
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.