  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.
