AIGCOIQA2024: Perceptual Quality Assessment of AI Generated
  Omnidirectional Images
Liu Yang Huiyu Duan Long Teng Yucheng Zhu Xiaohong Liu Menghan Hu Xiongkuo Min Guangtao Zhai Patrick Le Callet
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.