Overview of the Versatile Video Coding (VVC) Standard and its Applications

By Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J. Sullivan, Jens-Rainer Ohm. IEEE transactions on circuits and systems for video technology (Print), 2021

1,755 citations recorded by the source corpus.

Versatile Video Coding (VVC) was finalized in July 2020 as the most recent international video coding standard. It was developed by the Joint Video Experts Team (JVET) of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) to serve an ever-growing need for improved video compression as well as to support a wider variety of today’s media content and emerging applications. This paper provides an overview of the novel technical features for new applications and the core compression technologies for achieving significant bit rate reductions in the neighborhood of 50% over its predecessor for equal video quality, the High Efficiency Video Coding (HEVC) standard, and 75% over the currently most-used format, the Advanced Video Coding (AVC) standard. It is explained how these new features in VVC provide greater versatility for applications. Highlighted applications include video with resolutions beyond standard- and high-definition, video with high dynamic range and wide color gamut, adaptive streaming with resolution changes, computer-generated and screen-captured video, ultralow-delay streaming, 360° immersive video, and multilayer coding e.g., for scalability. Furthermore, early implementations are presented to show that the new VVC standard is implementable and ready for real-world deployment.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/721539c2d9b43e8d02b3d36eda05a26e19273592

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Yi Yang
5. Trevor Darrell
6. Xiaogang Wang
7. Kristen Grauman
8. Andrew Zisserman

In Signal Processing

1. Jiahao Li
2. Yan Lu
3. Bin Li
4. José Lezama
5. Ming-Hsuan Yang
6. Kihyuk Sohn
7. Irfan Essa
8. Jingkuan Song

Citations in the corpus

• Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression (2022, methodology): “, JPEG [33,39], BPG [6], HEIF [35], VVC [8], have been widely applied and adopted as fun-”
• Neural Video Compression with Diverse Contexts (2023, background): “For example, traditional codec adopts translational/affine motion models, geometric partition, bi-prediction, and so on modes to extract diverse temporal contexts [7].”
• Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation (2023, background): “) (Bross et al., 2021).”