  Recently, learning based video compression methods attract increasing
attention. However, the previous works suffer from error propagation due to the
accumulation of reconstructed error in inter predictive coding. Meanwhile, the
previous learning based video codecs are also not adaptive to different video
contents. To address these two problems, we propose a content adaptive and
error propagation aware video compression system. Specifically, our method
employs a joint training strategy by considering the compression performance of
multiple consecutive frames instead of a single frame. Based on the learned
long-term temporal information, our approach effectively alleviates error
propagation in reconstructed frames. More importantly, instead of using the
hand-crafted coding modes in the traditional compression systems, we design an
online encoder updating scheme in our system. The proposed approach updates the
parameters for encoder according to the rate-distortion criterion but keeps the
decoder unchanged in the inference stage. Therefore, the encoder is adaptive to
different video contents and achieves better compression performance by
reducing the domain gap between the training and testing datasets. Our method
is simple yet effective and outperforms the state-of-the-art learning based
video codecs on benchmark datasets without increasing the model size or
decreasing the decoding speed.
