  Deep learning-based video compression is a challenging task, and many
previous state-of-the-art learning-based video codecs use optical flows to
exploit the temporal correlation between successive frames and then compress
the residual error. Although these two-stage models are end-to-end optimized,
the epistemic uncertainty in the motion estimation and the aleatoric
uncertainty from the quantization operation lead to errors in the intermediate
representations and introduce artifacts in the reconstructed frames. This
inherent flaw limits the potential for higher bit rate savings. To address this
issue, we propose an uncertainty-aware video compression model that can
effectively capture the predictive uncertainty with deep ensembles.
Additionally, we introduce an ensemble-aware loss to encourage the diversity
among ensemble members and investigate the benefits of incorporating
adversarial training in the video compression task. Experimental results on
1080p sequences show that our model can effectively save bits by more than 20%
compared to DVC Pro.
