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
this https URL

通过考虑 15 个流行生成模型和动态超参数，本研究组织了一项挑战，全面评估人类主观评分、感知质量和文本图像对齐等方面，以创建迄今最大的细粒度 AIGI 主观质量数据库 AIGIQA-20K，并通过基准实验评估 16 个主流 AIGI 质量模型与人类感知之间的相应关系。预期该数据库将推动 AIGC 在视觉领域的进展。