AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866913293691518976 |
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| author | Yang, Liu Duan, Huiyu Teng, Long Zhu, Yucheng Liu, Xiaohong Hu, Menghan Min, Xiongkuo Zhai, Guangtao Callet, Patrick Le |
| author_facet | Yang, Liu Duan, Huiyu Teng, Long Zhu, Yucheng Liu, Xiaohong Hu, Menghan Min, Xiongkuo Zhai, Guangtao Callet, Patrick Le |
| contents | 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 database will be released to facilitate future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images Yang, Liu Duan, Huiyu Teng, Long Zhu, Yucheng Liu, Xiaohong Hu, Menghan Min, Xiongkuo Zhai, Guangtao Callet, Patrick Le Computer Vision and Pattern Recognition Image and Video Processing 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 database will be released to facilitate future research. |
| title | AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.01024 |