April 2, 2024, 7:48 p.m. | Liu Yang, Huiyu Duan, Long Teng, Yucheng Zhu, Xiaohong Liu, Menghan Hu, Xiongkuo Min, Guangtao Zhai, Patrick Le Callet

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.01024v1 Announce Type: new
Abstract: 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 …

abstract advancement aigc ai generated applications artificial artificial intelligence arxiv assessment attention augmented reality cs.cv eess.iv generated images intelligence quality reality type virtual virtual reality

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