As image generation technology advances, AI-based image generation has been applied in various fields and Artificial Intelligence Generated Content (AIGC) has garnered widespread attention. However, the development of AI-based image generative models also brings new problems and challenges. A significant challenge is that AI-generated images (AIGI) may exhibit unique distortions compared to natural images, and not all generated images meet the requirements of the real world. Therefore, it is of great significance to evaluate AIGIs more comprehensively. Although previous work has established several human perception-based AIGC image quality assessment (AIGCIQA) databases for text-generated images, the AI image generation technology includes scenarios like text-to-image and image-to-image, and assessing only the images generated by text-to-image models is insufficient. To address this issue, we establish a human perception-based image-to-image AIGCIQA database, named PKU-I2IQA. We conduct a well-organized subjective experiment to collect quality labels for AIGIs and then conduct a comprehensive analysis of the PKU-I2IQA database. Furthermore, we have proposed two benchmark models: NR-AIGCIQA based on the no-reference image quality assessment method and FR-AIGCIQA based on the full-reference image quality assessment method. Finally, leveraging this database, we conduct benchmark experiments and compare the performance of the proposed benchmark models. The PKU-I2IQA database and benchmarks will be released to facilitate future research on \url{https://github.com/jiquan123/I2IQA}.

翻译：随着图像生成技术的进步，基于AI的图像生成已应用于多个领域，人工智能生成内容（AIGC）也引起了广泛关注。然而，AI图像生成模型的发展也带来了新的问题和挑战。一个显著的挑战是，与自然图像相比，AI生成图像（AIGI）可能表现出独特的失真，且并非所有生成的图像都满足真实世界的要求。因此，更全面地评估AIGI具有重要意义。虽然以往的研究已建立了若干基于人类感知的文本生成图像AIGC图像质量评估（AIGCIQA）数据库，但AI图像生成技术包含文生图和图生图等场景，仅评估文生图模型的输出是不够的。为解决这一问题，我们建立了一个基于人类感知的图生图AIGCIQA数据库，命名为PKU-I2IQA。我们通过精心组织的主观实验收集了AIGI的质量标签，并对PKU-I2IQA数据库进行了全面分析。此外，我们提出了两个基准模型：基于无参考图像质量评估方法的NR-AIGCIQA和基于全参考图像质量评估方法的FR-AIGCIQA。最后，利用该数据库，我们进行了基准实验，并比较了所提基准模型的性能。PKU-I2IQA数据库及基准将公开发布以促进未来研究，详见\url{https://github.com/jiquan123/I2IQA}。