  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.
