Abstract
High-quality video frame interpolation often necessitates accurate motion estimation, which can be obtained using modern optical flow methods. In this paper, we use the recently proposed Local All-Pass (LAP) algorithm to compute the optical flow between two consecutive frames. The resulting flow field is used to perform interpolation using cubic splines. We compare the interpolation results against a well-known optical flow estimation algorithm as well as against a recent con-volutional neural network scheme for video frame interpolation. Qualitative and quantitative results show that the LAP algorithm performs fast, high-quality video frame interpolation, and perceptually outperforms the neural network and the Lucas-Kanade method on a variety of test sequences.
| Original language | English |
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| Title of host publication | 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings |
| Publisher | IEEE Computer Society Press |
| Pages | 4195-4199 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538662496 |
| DOIs | |
| Publication status | Published - Sept 2019 |
| Event | 26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China Duration: 22 Sept 2019 → 25 Sept 2019 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
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| Volume | 2019-September |
| ISSN (Print) | 1522-4880 |
Conference
| Conference | 26th IEEE International Conference on Image Processing, ICIP 2019 |
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| Country/Territory | Taiwan, Province of China |
| City | Taipei |
| Period | 22/09/19 → 25/09/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Convolutional neural network
- Lucas-Kanade algorithm
- Optical flow
- Splines
- Video interpolation
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing