Neural Video Compression (NVC) techniques have achieved remarkable performance, even surpassing the best traditional lossy video codec. However, most existing NVC methods [1] heavily rely on transmitting Motion Vector (MV) to generate accurate contextual features, which has following drawbacks. (1) Compressing and transmitting MV requires specialized MV encoder and decoder, which makes modules redundant. (2) Due to the existence of MV Encoder-Decoder, the training strategy is complex. In this paper, we propose Single Stream Neural Video Compression, SS-NVC. It implicitly utilizes temporal information to eliminate temporal redundancy in video sequence. Without MV encoder-decoder [2] , it only needs to transmit single bit-stream in channel and use single-stage training strategy, which can greatly simplify training and compression process of NVC. Besides, we reimplement window-based attention intra-frame image compression with channel-wise and checkerboard auto-regression entropy model, enhance contextual encoder with mixing global and local context module, and redesign Dense-UNet frame generator with stronger generation capability to improve SSNVC’s compression performance. Experiment results show that SSNVC can achieve competitive performance on multiple benchmarks.

Using more reference frames can significantly improve the compression efficiency in neural video compression. However, in low-latency scenarios, most existing neural video compression frameworks usually use the previous one frame as reference. Or a few frameworks which use the previous multiple frames as reference only adopt a simple multi-reference frames propagation mechanism. In this paper, we present a more reasonable multi-reference frames propagation mechanism for neural video compression, called butterfly multi-reference frame propagation mechanism (Butterfly), which allows a more effective feature fusion of multireference frames. By this, we can generate more accurate temporal context conditional prior for Contextual Coding Module. Besides, when the number of decoded frames does not meet the required number of reference frames, we duplicate the nearest reference frame to achieve the requirement, which is better than duplicating the furthest one. Experiment results show that our method can significantly outperform the previous state-of-the-art (SOTA), and our neural codec can achieve -7.6% bitrate save on HEVC Class D dataset when compares with our base single-reference frame model with the same compression configuration.