PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images.

With the development of image generation technology, AI-based image generation has been applied in various fields. However, the development of AIGC image generative models also brings new problems and challenges. A significant challenge is that AI-generated images (AIGI) compared to natural images may have some unique distortions, and not all generated images meet the requirements of the real world, so it is of great significance to evaluate AI-generated images more comprehensively. Although previous work has established some human perception-based AIGC image quality assessment 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 have established a human perception-based image-to-image AIGC image quality assessment database, named PKU-I2IQA. We conducted a comprehensive analysis of the PKU-I2IQA database. Furthermore, we introduced two benchmark models: NR-AIGCIQA based on no-reference image quality assessment and FR-AIGCIQA based on full-reference image quality assessment.Finally, leveraging this database, we conducted benchmark experiments and compared the performance of the proposed benchmark models. The PKU-I2IQA database and benchmarks will be released to facilitate future research on https://github.com/jiquan123/I2IQA. Keywords: AIGC, image-to-image generation, image quality assessment, NR-AIGCIQA, FR-AIGCIQA