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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6327-6336

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 20000 AIGIs and 420000 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.