AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
Chunyi Li Tengchuan Kou Yixuan Gao Yuqin Cao Wei Sun Zicheng Zhang Yingjie Zhou Zhichao Zhang Weixia Zhang Haoning Wu Xiaohong Liu Xiongkuo Min Guangtao Zhai
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated
Images (AIGIs) have been widely applied in entertainment, education, and social
media. However, due to the significant variance in quality among different
AIGIs, there is an urgent need for models that consistently match human
subjective ratings. To address this issue, we organized a challenge towards
AIGC quality assessment on NTIRE 2024 that extensively considers 15 popular
generative models, utilizing dynamic hyper-parameters (including
classifier-free guidance, iteration epochs, and output image resolution), and
gather subjective scores that consider perceptual quality and text-to-image
alignment altogether comprehensively involving 21 subjects. This approach
culminates in the creation of the largest fine-grained AIGI subjective quality
database to date with 20,000 AIGIs and 420,000 subjective ratings, known as
AIGIQA-20K. Furthermore, we conduct benchmark experiments on this database to
assess the correspondence between 16 mainstream AIGI quality models and human
perception. We anticipate that this large-scale quality database will inspire
robust quality indicators for AIGIs and propel the evolution of AIGC for
vision. The database is released on
https://www.modelscope.cn/datasets/lcysyzxdxc/AIGCQA-30K-Image.