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

• URL: http://arxiv.org/abs/2307.00211v2
• Date: Sat, 15 Jul 2023 11:05:04 GMT
• Title: AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence
• Authors: Jiarui Wang, Huiyu Duan, Jing Liu, Shi Chen, Xiongkuo Min, Guangtao Zhai
• Abstract summary: 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 subjective experiment is conducted to assess the human visual preferences for each image from three perspectives. We conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.
• Score: 42.85549933048976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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