AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-...

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Main Authors: Nah, Seungjun, Son, Sanghyun, Timofte, Radu, Lee, Kyoung Mu, Siyao, Li, Pan, Ze, Xu, Xiangyu, Sun, Wenxiu, Choi, Myungsub, Kim, Heewon, Han, Bohyung, Xu, Ning, Park, Bumjun, Yu, Songhyun, Kim, Sangmin, Jeong, Jechang, Shen, Wang, Bao, Wenbo, Zhai, Guangtao, Chen, Li, Gaon, Zhiyong, Chen, Guannan, Lu, Yunhua, Duan, Ran, Liu, Tong, Zhang, Lijie, Park, Woonsung, Kim, Munchurl, Pisha, George, Naor, Eyal, Aloni, Lior
Format: Conference Proceeding
Language:English
Subjects:
Adaptive optics
Benchmark testing
Cameras
frame interpolation
Interpolation
Kernel
Training
video frame interpolation
Video Temporal Super Resolution
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Summary:Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-frame-rate (60 fps) video sequences by estimating temporally intermediate frames. We employ the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. The competition had 62 registered participants, and a total of 8 teams competed in the final testing phase. The challenge winning methods achieve the state-of-the-art in video temporal super-resolution.
ISSN:2473-9944
DOI:10.1109/ICCVW.2019.00421