AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and
Results
- URL: http://arxiv.org/abs/2005.01233v1
- Date: Mon, 4 May 2020 01:51:23 GMT
- Title: AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and
Results
- Authors: Seungjun Nah, Sanghyun Son, Radu Timofte and Kyoung Mu Lee
- Abstract summary: This paper reviews the first AIM challenge on video temporal super-resolution (frame)
From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-framerate (60 fps) video sequences.
We employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
- Score: 129.15554076593762
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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-framerate (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 superresolution.
Related papers
- NTIRE 2023 Quality Assessment of Video Enhancement Challenge [97.809937484099]
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge.
The challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos.
The challenge has a total of 167 registered participants.
arXiv Detail & Related papers (2023-07-19T02:33:42Z) - Learning Trajectory-Aware Transformer for Video Super-Resolution [50.49396123016185]
Video super-resolution aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts.
Existing approaches usually align and aggregate video frames from limited adjacent frames.
We propose a novel Transformer for Video Super-Resolution (TTVSR)
arXiv Detail & Related papers (2022-04-08T03:37:39Z) - NTIRE 2021 Challenge on Video Super-Resolution [103.59395980541574]
Super-Resolution (SR) is a computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.
This paper reviews the NTIRE Challenge on Video Super-Resolution.
arXiv Detail & Related papers (2021-04-30T09:12:19Z) - AIM 2020 Challenge on Video Temporal Super-Resolution [118.46127362093135]
Second AIM challenge on Video Temporal Super-Resolution (VTSR)
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR)
arXiv Detail & Related papers (2020-09-28T00:10:29Z) - NTIRE 2020 Challenge on Image and Video Deblurring [129.15554076593762]
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.
In each competition, there were 163, 135, and 102 registered participants.
The winning methods demonstrate the state-ofthe-art performance on image and video deblurring tasks.
arXiv Detail & Related papers (2020-05-04T03:17:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.