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, Xuhan Sheng, Bin Chen, Haoyu Ma, Ming Cheng, Shijie Zhao, Huaibo Huang, Xiaoqiang Zhou, Yuang AiRan He, Renlong Wu, Yi Yang, Zhilu Zhang, Shuohao Zhang, Junyi Li, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Hao Hsiang Yang, Yi Chung Chen, Zhi Kai Huang, Wei Ting Chen, Yuan Chun Chiang, Hua En Chang, I. Hsiang Chen, Chia Hsuan Hsieh, Sy Yen Kuo, Zebin Zhang, Jiaqi Zhang, Yuhui Wang, Shuhao Cui, Junshi Huang, Li Zhu, Shuman Tian, Wei Yu, Bingchun Luo, Wanwan Cui, Tianyu Xu, Chunyang Li, Long Bao, Heng Sun, Zhenyu Zhang, Qian Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
PublisherIEEE Computer Society
Pages1731-1745
Number of pages15
ISBN (Electronic)9798350302493
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada
Duration: 18 06 202322 06 2023

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2023-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Country/TerritoryCanada
CityVancouver
Period18/06/2322/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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