A Generative Compression Framework For Low Bandwidth Video Conference

Abstract

Video conferences introduce a new scenario for video transmission, which focuses on keeping the fidelity of faces even in the low bandwidth network environment. In this work, we propose VSBNet, one of the frameworks to utilize face landmarks in video compression. Our method utilizes the adversarial learning to reconstruct origin frames from the landmarks. To recover more details and keep the consistency of identity, we propose the concept of visual sensitivity to separate the contour of the face from the fast-moving parts, such as eyes and mouth. Experimental results demonstrate the superiority of our framework with a low bit rate of around 1KB/s.

Publication
2021 IEEE International Conference on Multimedia Expo Workshops (ICMEW)
Yan Huang
Yan Huang
PhD Student
Yiwei Zhang
Yiwei Zhang
Master Student
Jun Ling
Jun Ling
PhD Student

I’m now a PhD student at SJTU MediaLab, supervised by Prof. Li Song. Prior to join Song’s MediaLab, I had got my bachelor degree and master degree from University of Sience and Technology of China and Shanghai Jiao Tong University, in 2018 and 2021 respectively. My research interests focus on image and video generation, deep learning and computer vision.

Anni Tang
Anni Tang
Master Student
Li Song
Li Song
Professor, IEEE Senior Member

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