Multi-Scale Warping for Video Frame Interpolation

被引:1
|
作者
Choi, Whan [1 ]
Koh, Yeong Jun [2 ]
Kim, Chang-Su [1 ]
机构
[1] Korea Univ, Sch Elect Engn, Seoul 136701, South Korea
[2] Chungnam Natl Univ, Dept Comp Sci & Engn, Daejeon 34134, South Korea
基金
新加坡国家研究基金会;
关键词
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; MOTION ESTIMATION;
D O I
10.1109/ACCESS.2021.3126593
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:150470 / 150479
页数:10
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