  For neural video codec, it is critical, yet challenging, to design an
efficient entropy model which can accurately predict the probability
distribution of the quantized latent representation. However, most existing
video codecs directly use the ready-made entropy model from image codec to
encode the residual or motion, and do not fully leverage the spatial-temporal
characteristics in video. To this end, this paper proposes a powerful entropy
model which efficiently captures both spatial and temporal dependencies. In
particular, we introduce the latent prior which exploits the correlation among
the latent representation to squeeze the temporal redundancy. Meanwhile, the
dual spatial prior is proposed to reduce the spatial redundancy in a
parallel-friendly manner. In addition, our entropy model is also versatile.
Besides estimating the probability distribution, our entropy model also
generates the quantization step at spatial-channel-wise. This content-adaptive
quantization mechanism not only helps our codec achieve the smooth rate
adjustment in single model but also improves the final rate-distortion
performance by dynamic bit allocation. Experimental results show that, powered
by the proposed entropy model, our neural codec can achieve 18.2% bitrate
saving on UVG dataset when compared with H.266 (VTM) using the highest
compression ratio configuration. It makes a new milestone in the development of
neural video codec. The codes are at https://github.com/microsoft/DCVC.
