DRVI: Dual Refinement for Video Interpolation

The quality of a video clip is considered to be poor if the resolution or the frame rate is low. Video interpolation is thus introduced to enhance video quality and provide a better viewing experience to users. However, there are still some challenges, like the blur caused by motion changes. In this...

Full description

Bibliographic Details
Main Authors: Xuanyi Wu, Zhenkun Zhou, Anup Basu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9513293/
id doaj-97d2e373ee2f4666965fa8d547dea819
record_format Article
spelling doaj-97d2e373ee2f4666965fa8d547dea8192021-08-19T23:00:26ZengIEEEIEEE Access2169-35362021-01-01911356611357610.1109/ACCESS.2021.31045269513293DRVI: Dual Refinement for Video InterpolationXuanyi Wu0https://orcid.org/0000-0001-9817-070XZhenkun Zhou1Anup Basu2https://orcid.org/0000-0002-7695-4148Department of Computing Science, University of Alberta, Edmonton, AB, CanadaHuawei Fields Laboratory, Hangzhou, ChinaDepartment of Computing Science, University of Alberta, Edmonton, AB, CanadaThe quality of a video clip is considered to be poor if the resolution or the frame rate is low. Video interpolation is thus introduced to enhance video quality and provide a better viewing experience to users. However, there are still some challenges, like the blur caused by motion changes. In this paper, we introduce a dual refinement technique for video interpolation (DRVI). It has three main steps, namely flow refinement, frame synthesis, and Haar refinement. The flow refinement can generate accurate bi-directional flows, which are more suitable for frame interpolation tasks. The Haar refinement uses the Discrete Wavelet Transform (DWT). It can preserve information in different frequency domains and also speed up the learning process. We also add an arbitrary time approximation module to allow multi-frame generation. The number of learnable parameters in our model is much less than existing methods; still, it has excellent performance. Our method is trained on Vimeo90K (Xue <italic>et al.</italic>, 2019) and tested on three well-known datasets to demonstrate its effectiveness.https://ieeexplore.ieee.org/document/9513293/Video interpolationframe interpolationoptical flowvideo quality
collection DOAJ
language English
format Article
sources DOAJ
author Xuanyi Wu
Zhenkun Zhou
Anup Basu
spellingShingle Xuanyi Wu
Zhenkun Zhou
Anup Basu
DRVI: Dual Refinement for Video Interpolation
IEEE Access
Video interpolation
frame interpolation
optical flow
video quality
author_facet Xuanyi Wu
Zhenkun Zhou
Anup Basu
author_sort Xuanyi Wu
title DRVI: Dual Refinement for Video Interpolation
title_short DRVI: Dual Refinement for Video Interpolation
title_full DRVI: Dual Refinement for Video Interpolation
title_fullStr DRVI: Dual Refinement for Video Interpolation
title_full_unstemmed DRVI: Dual Refinement for Video Interpolation
title_sort drvi: dual refinement for video interpolation
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description The quality of a video clip is considered to be poor if the resolution or the frame rate is low. Video interpolation is thus introduced to enhance video quality and provide a better viewing experience to users. However, there are still some challenges, like the blur caused by motion changes. In this paper, we introduce a dual refinement technique for video interpolation (DRVI). It has three main steps, namely flow refinement, frame synthesis, and Haar refinement. The flow refinement can generate accurate bi-directional flows, which are more suitable for frame interpolation tasks. The Haar refinement uses the Discrete Wavelet Transform (DWT). It can preserve information in different frequency domains and also speed up the learning process. We also add an arbitrary time approximation module to allow multi-frame generation. The number of learnable parameters in our model is much less than existing methods; still, it has excellent performance. Our method is trained on Vimeo90K (Xue <italic>et al.</italic>, 2019) and tested on three well-known datasets to demonstrate its effectiveness.
topic Video interpolation
frame interpolation
optical flow
video quality
url https://ieeexplore.ieee.org/document/9513293/
work_keys_str_mv AT xuanyiwu drvidualrefinementforvideointerpolation
AT zhenkunzhou drvidualrefinementforvideointerpolation
AT anupbasu drvidualrefinementforvideointerpolation
_version_ 1721201892771495936