The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Scale-Space Flow for End-to-End Optimized Video Compression
Abstract
Despite considerable progress on end-to-end optimized deep networks for image compression, video coding remains a challenging task. Recently proposed methods for learned video compression use optical flow and bilinear warping for motion compensation and show competitive rate-distortion performance relative to hand-engineered codecs like H.264 and HEVC. However, these learning-based methods rely on complex architectures and training schemes including the use of pre-trained optical flow networks, sequential training of sub-networks, adaptive rate control, and buffering intermediate reconstructions to disk during training. In this paper, we show that a generalized warping operator that better handles common failure cases, e.g. disocclusions and fast motion, can provide competitive compression results with a greatly simplified model and training procedure. Specifically, we propose scale-space flow, an intuitive generalization of optical flow that adds a scale parameter to allow the network to better model uncertainty. Our experiments show that a low-latency video compression model (no B-frames) using scale-space flow for motion compensation can outperform analogous state-of-the art learned video compression models while being trained using a much simpler procedure and without any pre-trained optical flow networks.
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Citations
Cites
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BackgroundMethodCalcuation of average PSNR differences between RD-curves
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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
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
Bégaint, J., Racapé, F., Feltman, S., & Pushparaja, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03029 · Cited by 284
Notice of Removal: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., & Tan, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00984 · Cited by 144
Metadata
- Authors
- Agustsson, E., Minnen, D., Johnston, N., Ballé, J., Hwang, S. J., & Toderici, G.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8500-8509
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035195755
- Cited by
- 317
- References
- 30
- FWCI
- 25.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Agustsson, E., Minnen, D., Johnston, N., Ballé, J., Hwang, S. J., & Toderici, G. (2020). Scale-Space Flow for End-to-End Optimized Video Compression. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00853
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