Content Adaptive and Error Propagation Aware Deep Video Compression
Recently, learning based video compression methods attract increasing attention. However, previous works suffer from error propagation, which stems from the accumulation of reconstructed error in inter predictive coding. Meanwhile, 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 for the learned video compression. 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 efficiency 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 codec on benchmark datasets without increasing the model size or decreasing the decoding speed.