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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.

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Authors (6)
  1. Jiarui Wang (33 papers)
  2. Huiyu Duan (38 papers)
  3. Jing Liu (526 papers)
  4. Shi Chen (87 papers)
  5. Xiongkuo Min (139 papers)
  6. Guangtao Zhai (231 papers)
Citations (39)