NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, Xiaopeng Sun, Weiqi Li, Zhenyu Zhang, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Wanwan Cui, Tianyu Xu et al. (34 additional authors not shown)

Abstract: This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets.