TY - GEN
T1 - AIGCOIQA2024
T2 - 31st IEEE International Conference on Image Processing, ICIP 2024
AU - Yang, Liu
AU - Duan, Huiyu
AU - Teng, Long
AU - Zhu, Yucheng
AU - Liu, Xiaohong
AU - Hu, Menghan
AU - Min, Xiongkuo
AU - Zhai, Guangtao
AU - Le Callet, Patrick
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - [?] In recent years, the rapid advancement of Artificial Intelligence Generated Content (AIGC) has attracted widespread attention. Among the AIGC, AI generated omnidirectional images hold significant potential for Virtual Reality (VR) and Augmented Reality (AR) applications, hence omnidirectional AIGC techniques have also been widely studied. AI-generated omnidirectional images exhibit unique distortions compared to natural omnidirectional images, however, there is no dedicated Image Quality Assessment (IQA) criteria for assessing them. This study addresses this gap by establishing a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024 and constructing a comprehensive benchmark. We first generate 300 omnidirectional images based on 5 AIGC models utilizing 25 text prompts. A subjective IQA experiment is conducted subsequently to assess human visual preferences from three perspectives including quality, comfortability, and correspondence. Finally, we conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database. The AIGCOIQA2024 database is released to facilitate future research on https://github.com/IntMeGroup/AIGCOIQA.
AB - [?] In recent years, the rapid advancement of Artificial Intelligence Generated Content (AIGC) has attracted widespread attention. Among the AIGC, AI generated omnidirectional images hold significant potential for Virtual Reality (VR) and Augmented Reality (AR) applications, hence omnidirectional AIGC techniques have also been widely studied. AI-generated omnidirectional images exhibit unique distortions compared to natural omnidirectional images, however, there is no dedicated Image Quality Assessment (IQA) criteria for assessing them. This study addresses this gap by establishing a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024 and constructing a comprehensive benchmark. We first generate 300 omnidirectional images based on 5 AIGC models utilizing 25 text prompts. A subjective IQA experiment is conducted subsequently to assess human visual preferences from three perspectives including quality, comfortability, and correspondence. Finally, we conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database. The AIGCOIQA2024 database is released to facilitate future research on https://github.com/IntMeGroup/AIGCOIQA.
KW - AI generated content (AIGC)
KW - image quality assessment
KW - omnidirectional images
KW - text-to-image generation
UR - https://www.scopus.com/pages/publications/85215960535
U2 - 10.1109/ICIP51287.2024.10647885
DO - 10.1109/ICIP51287.2024.10647885
M3 - 会议稿件
AN - SCOPUS:85215960535
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1239
EP - 1245
BT - 2024 IEEE International Conference on Image Processing, ICIP 2024 - Proceedings
PB - IEEE Computer Society
Y2 - 27 October 2024 through 30 October 2024
ER -