Deep Learning-Based Video Coding: A Review and a Case Study
Open accessAbstract
The past decade has witnessed the great success of deep learning in many disciplines, especially in computer vision and image processing. However, deep learning-based video coding remains in its infancy. We review the representative works about using deep learning for image/video coding, an actively developing research area since 2015. We divide the related works into two categories: new coding schemes that are built primarily upon deep networks, and deep network-based coding tools that shall be used within traditional coding schemes. For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding and transform coding, respectively. For deep tools, there have been several techniques using deep learning to perform intra-picture prediction, inter-picture prediction, cross-channel prediction, probability distribution prediction, transform, post- or in-loop filtering, down- and up-sampling, as well as encoding optimizations. In the hope of advocating the research of deep learning-based video coding, we present a case study of our developed prototype video codec, Deep Learning Video Coding (DLVC). DLVC features two deep tools that are both based on convolutional neural network (CNN), namely CNN-based in-loop filter and CNN-based block adaptive resolution coding. The source code of DLVC has been released for future research.
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Multimedia coding has been advanced by deep learning [187].
“Another research topic that is close to AIoT is multimedia coding, which has also been advanced by deep learning [187].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [187]
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- Authors
- Liu, D., Li, Y., Lin, J., Li, H., & Wu, F.
- Affiliations
- University of Science and Technology of China (CN)
- Venue
- ACM Computing Surveys
- Published
- 2020-02-06
- Volume
- 53
- Issue
- 1
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2940954733
- Cited by
- 192
- References
- 162
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 36 · 2023: 30 · 2022: 27 · 2021: 37 · 2020: 23 · 2019: 2
- FWCI
- 15.84
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61772483)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- video compression, autoencoder, in-loop filtering, convolutional neural networks, encoding optimization
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