The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Video Compression Through Image Interpolation
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 2×
“Our deep video codec outperforms all deep learning baselines, MPEG-4 Part 2, and H.261 in both compression rate and visual quality measured by MS-SSIM [29] and PSNR.”
MethodOverview of the Scalable Video Coding Extension of the H.264/AVC Standard
Schwarz, H., Marpe, D., & Wiegand, T. (2007) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2007.905532 · Cited by 3,533
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 3,416
Two-Frame Motion Estimation Based on Polynomial Expansion
Farnebäck, G. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-45103-x_50 · Cited by 2,971
Arithmetic coding for data compression
Witten, I. H., Neal, R. M., & Cleary, J. G. (1987) · Communications of the ACM · doi:10.1145/214762.214771 · Cited by 2,877
MPEG: a video compression standard for multimedia applications
Le Gall, D. (1991) · Communications of the ACM · doi:10.1145/103085.103090 · Cited by 2,013
Pixel Recurrent Neural Networks
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.06759 · Cited by 1,437
End-to-end Optimized Image Compression
Ballé, J., Laparra, V., & Simoncelli, E. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01704 · Cited by 1,275
Cited by
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Wang, T.-C., Mallya, A., & Liu, M.-Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00991 · Cited by 444
Scale-Space Flow for End-to-End Optimized Video Compression
Agustsson, E., Minnen, D., Johnston, N., Ballé, J., Hwang, S. J., & Toderici, G. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00853 · Cited by 317
Deep Learning-Based Video Coding: A Review and a Case Study
Liu, D., Li, Y., Lin, J., Li, H., & Wu, F. (2021) · ACM Computing Surveys · doi:10.1145/3368405 · Cited by 192
Metadata
- Authors
- Wu, C.-Y., Singhal, N., & Krähenbühl, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 425-440
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963189365
- Cited by
- 316
- References
- 25
- FWCI
- 21.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- The paper
- Wu, C.-Y., Singhal, N., & Krähenbühl, P. (2018). Video Compression Through Image Interpolation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01237-3_26
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
