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Multi-Scale Warping for Video Frame Interpolation

Authors
Choi, WhanKoh, Yeong JunKim, Chang-Su
Issue Date
2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Interpolation; Kernel; Feature extraction; Convolution; Adaptive optics; Streaming media; Optical imaging; Video frame interpolation; convolutional neural network; multi-scale feature; kernel-based approach; deformable convolution; adaptive convolution
Citation
IEEE ACCESS, v.9, pp.150470 - 150479
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
9
Start Page
150470
End Page
150479
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138658
DOI
10.1109/ACCESS.2021.3126593
ISSN
2169-3536
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.
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