Abstract
A novel video interpolation network to improve the temporal resolutions of video sequences is proposed in this work. We develop a multi-scale warping module to interpolate intermediate frames robustly for both small and large motions. Specifically, the proposed multi-scale warping module deals with large motions between two consecutive frames using coarse-scale features, while estimating detailed local motions by exploring fine-scale features. To this end, it takes multi-scale features from the encoder and estimates kernel weights and offset vectors for each scale. Finally, it synthesizes multi-scale warping frames and combines them to obtain an intermediate frame. Extensive experimental results demonstrate that the proposed algorithm outperforms state-of-the-art video interpolation algorithms on various benchmark datasets.
Original language | English |
---|---|
Pages (from-to) | 150470-150479 |
Number of pages | 10 |
Journal | IEEE Access |
Volume | 9 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- adaptive convolution
- convolutional neural network
- deformable convolution
- kernel-based approach
- multi-scale feature
- Video frame interpolation
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering