In recent years, image generation technology has rapidly advanced, resulting in the creation of a vast array of AI-generated images (AIGIs). However, the quality of these AIGIs is highly inconsistent, with low-quality AIGIs severely impairing the visual experience of users. Due to the widespread application of AIGIs, the AI-generated image quality assessment (AIGIQA), aimed at evaluating the quality of AIGIs from the perspective of human perception, has garnered increasing interest among scholars. Nonetheless, current research has not yet fully explored this field. We have observed that existing databases are limited to images generated from single scenario settings. Databases such as AGIQA-1K, AGIQA-3K, and AIGCIQA2023, for example, only include images generated by text-to-image generative models. This oversight highlights a critical gap in the current research landscape, underscoring the need for dedicated databases catering to image-to-image scenarios, as well as more comprehensive databases that encompass a broader range of AI-generated image scenarios. Addressing these issues, we have established a large scale perceptual quality assessment database for both text-to-image and image-to-image AIGIs, named PKU-AIGIQA-4K. We then conduct a well-organized subjective experiment to collect quality labels for AIGIs and perform a comprehensive analysis of the PKU-AIGIQA-4K database. Regarding the use of image prompts during the training process, we propose three image quality assessment (IQA) methods based on pre-trained models that include a no-reference method NR-AIGCIQA, a full-reference method FR-AIGCIQA, and a partial-reference method PR-AIGCIQA. Finally, leveraging the PKU-AIGIQA-4K database, we conduct extensive benchmark experiments and compare the performance of the proposed methods and the current IQA methods.

近年来，图像生成技术迅速发展，产生了大量的人工智能生成图像（AIGIs），然而，这些AIGIs的质量不一致，并严重影响了用户的视觉体验。因此，基于人类感知角度评估AIGIs质量的AI生成图像质量评估（AIGIQA）引起了学者们的广泛关注，然而，现有研究尚未完全探索该领域。本研究通过构建名为PKU-AIGIQA-4K的大规模感知质量评估数据库，旨在填补现有研究领域中的关键空白，该数据库兼顾文本到图像和图像到图像的AIGIs，提出了基于预训练模型的三种图像质量评估方法：无参考方法NR-AIGCIQA，全参考方法FR-AIGCIQA和部分参考方法PR-AIGCIQA，并利用PKU-AIGIQA-4K数据库进行了广泛的基准实验，与当前的图像质量评估方法进行了比较。

PKU-AIGIQA-4K：一种用于文本转图像和图像转图像AI生成图像的感知质量评估数据库