Forward Warping-Based Video Frame Interpolation Using a Motion Selective Network Article Swipe
Jeonghwan Heo
,
Jechang Jeong
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11162553
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11162553
Recently, deep neural networks have shown surprising results in solving most of the traditional image processing problems. However, the video frame interpolation field does not show relatively good performance because the receptive field requires a vast spatio-temporal range. To reduce the computational complexity, in most frame interpolation studies, motion is first calculated with the optical flow, then interpolated frames are generated through backward warping. However, while the backward warping process is simple to implement, the interpolated image contains mixed motion and ghosting defects. Therefore, we propose a new network that does not use the backward warping method through the proposed max-min warping. Since max-min warping generates a clear warping image in advance according to the size of the motion and the network is configured to select the warping result according to the warped layer, using the proposed method, it is possible to optimize the computational complexity while selecting a contextually appropriate image. The video interpolation method using the proposed method showed 34.847 PSNR in the Vimeo90k dataset and 0.13 PSNR improvement compared to the Quadratic Video Interpolation method, showing that it is an efficient frame interpolation self-supervised learning.
Related Topics
Concepts
Image warping
Artificial intelligence
Computer vision
Computer science
Interpolation (computer graphics)
Motion interpolation
Image scaling
Motion estimation
Frame (networking)
Image processing
Motion (physics)
Image (mathematics)
Block-matching algorithm
Video processing
Video tracking
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics11162553
- https://www.mdpi.com/2079-9292/11/16/2553/pdf?version=1661243402
- OA Status
- gold
- Cited By
- 1
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4291743125
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4291743125Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics11162553Digital Object Identifier
- Title
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Forward Warping-Based Video Frame Interpolation Using a Motion Selective NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-15Full publication date if available
- Authors
-
Jeonghwan Heo, Jechang JeongList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics11162553Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/11/16/2553/pdf?version=1661243402Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/11/16/2553/pdf?version=1661243402Direct OA link when available
- Concepts
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Image warping, Artificial intelligence, Computer vision, Computer science, Interpolation (computer graphics), Motion interpolation, Image scaling, Motion estimation, Frame (networking), Image processing, Motion (physics), Image (mathematics), Block-matching algorithm, Video processing, Video tracking, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.an | 182 |
| abstract_inverted_index.in | 8, 43, 110, 163 |
| abstract_inverted_index.is | 49, 70, 122, 139, 181 |
| abstract_inverted_index.it | 138, 180 |
| abstract_inverted_index.of | 11, 116 |
| abstract_inverted_index.to | 72, 113, 124, 130, 141, 172 |
| abstract_inverted_index.we | 84 |
| abstract_inverted_index.The | 152 |
| abstract_inverted_index.and | 80, 119, 167 |
| abstract_inverted_index.are | 59 |
| abstract_inverted_index.new | 87 |
| abstract_inverted_index.not | 24, 91 |
| abstract_inverted_index.the | 12, 18, 30, 40, 53, 66, 74, 93, 98, 114, 117, 120, 126, 131, 135, 143, 157, 164, 173 |
| abstract_inverted_index.use | 92 |
| abstract_inverted_index.0.13 | 168 |
| abstract_inverted_index.PSNR | 162, 169 |
| abstract_inverted_index.deep | 1 |
| abstract_inverted_index.does | 23, 90 |
| abstract_inverted_index.good | 27 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.most | 10, 44 |
| abstract_inverted_index.show | 25 |
| abstract_inverted_index.size | 115 |
| abstract_inverted_index.that | 89, 179 |
| abstract_inverted_index.then | 56 |
| abstract_inverted_index.vast | 35 |
| abstract_inverted_index.with | 52 |
| abstract_inverted_index.Since | 102 |
| abstract_inverted_index.Video | 175 |
| abstract_inverted_index.clear | 107 |
| abstract_inverted_index.field | 22, 32 |
| abstract_inverted_index.first | 50 |
| abstract_inverted_index.flow, | 55 |
| abstract_inverted_index.frame | 20, 45, 184 |
| abstract_inverted_index.image | 14, 76, 109 |
| abstract_inverted_index.mixed | 78 |
| abstract_inverted_index.shown | 5 |
| abstract_inverted_index.using | 134, 156 |
| abstract_inverted_index.video | 19, 153 |
| abstract_inverted_index.while | 65, 146 |
| abstract_inverted_index.34.847 | 161 |
| abstract_inverted_index.frames | 58 |
| abstract_inverted_index.image. | 151 |
| abstract_inverted_index.layer, | 133 |
| abstract_inverted_index.method | 96, 155, 159 |
| abstract_inverted_index.motion | 48, 79, 118 |
| abstract_inverted_index.neural | 2 |
| abstract_inverted_index.range. | 37 |
| abstract_inverted_index.reduce | 39 |
| abstract_inverted_index.result | 128 |
| abstract_inverted_index.select | 125 |
| abstract_inverted_index.showed | 160 |
| abstract_inverted_index.simple | 71 |
| abstract_inverted_index.warped | 132 |
| abstract_inverted_index.advance | 111 |
| abstract_inverted_index.because | 29 |
| abstract_inverted_index.dataset | 166 |
| abstract_inverted_index.max-min | 100, 103 |
| abstract_inverted_index.method, | 137, 177 |
| abstract_inverted_index.network | 88, 121 |
| abstract_inverted_index.optical | 54 |
| abstract_inverted_index.process | 69 |
| abstract_inverted_index.propose | 85 |
| abstract_inverted_index.results | 7 |
| abstract_inverted_index.showing | 178 |
| abstract_inverted_index.solving | 9 |
| abstract_inverted_index.through | 61, 97 |
| abstract_inverted_index.warping | 68, 95, 104, 108, 127 |
| abstract_inverted_index.However, | 17, 64 |
| abstract_inverted_index.Vimeo90k | 165 |
| abstract_inverted_index.backward | 62, 67, 94 |
| abstract_inverted_index.compared | 171 |
| abstract_inverted_index.contains | 77 |
| abstract_inverted_index.defects. | 82 |
| abstract_inverted_index.ghosting | 81 |
| abstract_inverted_index.networks | 3 |
| abstract_inverted_index.optimize | 142 |
| abstract_inverted_index.possible | 140 |
| abstract_inverted_index.proposed | 99, 136, 158 |
| abstract_inverted_index.requires | 33 |
| abstract_inverted_index.studies, | 47 |
| abstract_inverted_index.warping. | 63, 101 |
| abstract_inverted_index.Quadratic | 174 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.according | 112, 129 |
| abstract_inverted_index.efficient | 183 |
| abstract_inverted_index.generated | 60 |
| abstract_inverted_index.generates | 105 |
| abstract_inverted_index.learning. | 187 |
| abstract_inverted_index.problems. | 16 |
| abstract_inverted_index.receptive | 31 |
| abstract_inverted_index.selecting | 147 |
| abstract_inverted_index.Therefore, | 83 |
| abstract_inverted_index.calculated | 51 |
| abstract_inverted_index.complexity | 145 |
| abstract_inverted_index.configured | 123 |
| abstract_inverted_index.implement, | 73 |
| abstract_inverted_index.processing | 15 |
| abstract_inverted_index.relatively | 26 |
| abstract_inverted_index.surprising | 6 |
| abstract_inverted_index.appropriate | 150 |
| abstract_inverted_index.complexity, | 42 |
| abstract_inverted_index.improvement | 170 |
| abstract_inverted_index.performance | 28 |
| abstract_inverted_index.traditional | 13 |
| abstract_inverted_index.contextually | 149 |
| abstract_inverted_index.interpolated | 57, 75 |
| abstract_inverted_index.Interpolation | 176 |
| abstract_inverted_index.computational | 41, 144 |
| abstract_inverted_index.interpolation | 21, 46, 154, 185 |
| abstract_inverted_index.self-supervised | 186 |
| abstract_inverted_index.spatio-temporal | 36 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5018642097 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4575257 |
| citation_normalized_percentile.value | 0.3974332 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |