| Paper ID | MMSP-4.2 |
| Paper Title |
ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMATION |
| Authors |
Goluck Konuko, Telecom Paris- IP Paris, France; Giuseppe Valenzise, CNRS, CentraleSupelec, France; Stéphane Lathuilière, Telecom Paris- IP Paris, France |
| Session | MMSP-4: Image, Video and Point Cloud Coding |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Signal Processing for Multimedia Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 80% compared to HEVC. |
