AIGCIQA2023

This is the official repo of the paper AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence:

@misc{wang2023aigciqa2023,
      title={AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence}, 
      author={Jiarui Wang and Huiyu Duan and Jing Liu and Shi Chen and Xiongkuo Min and Guangtao Zhai},
      year={2023},
      eprint={2307.00211},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Abstract: In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a well-organized subjective experiment is conducted to assess the human visual preferences for each image from three perspectives including quality, authenticity and correspondence. Finally, based on this large-scale database, we conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.

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Database

The constructed AIGCIQA2023 database can be accessed using the links below. Download AIGCIQA2023 database:[百度网盘 (提取码：q9dt)], [Terabox]

The mapping relationship between MOS points and filenames are as follows:

mosz1: Quality

mosz2: Authenticity

mosz3: Correspondence

Contact

If you have any question, please contact wangjiarui@sjtu.edu.cn