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AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence (2307.00211v2)
Published 1 Jul 2023 in cs.CV and eess.IV
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.
- Jiarui Wang (33 papers)
- Huiyu Duan (38 papers)
- Jing Liu (526 papers)
- Shi Chen (87 papers)
- Xiongkuo Min (139 papers)
- Guangtao Zhai (231 papers)