The latest open-source video codec VP9 - An overview and preliminary results

By D. Mukherjee, Jim Bankoski, Adrian Grange, Jingning Han, John Koleszar, Paul Wilkins, Yaowu Xu, Ronald Bultje. 2013

252 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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. Trevor Darrell
3. Dacheng Tao
4. Jitendra Malik
5. Xiaogang Wang
6. Li Fei-Fei
7. Ming-Hsuan Yang
8. Andrew Zisserman

In Signal Processing

1. Jean-Marc Valin
2. Oren Rippel
3. Lubomir D. Bourdev
4. Steve Branson
5. Jan Skoglund
6. Frank K. Soong
7. Yossi Adi
8. Jade Copet

Citations in the corpus

• Learned Video Compression (2018, background): “Video codecs are designed for high compression efficiency, and achieve this by exploiting spatial and temporal redundancies within and across video frames ([51, 47, 36, 34] provide great overviews of commercial video coding techniques).”
• MuZero with Self-competition for Rate Control in VP9 Video Compression (2022, unknown): “[Mukherjee et al., 2013].”
• An Overview of Core Coding Tools in the AV1 Video Codec (2018, background): “Therefore, in an effort to create an open video format at par with the leading commercial choices, in mid 2013, Google launched and deployed the VP9 video codec[1].”