For any video codecs, the coding efficiency highly relies on whether the current signal to be
encoded can find the relevant contexts from the previous reconstructed signals. Traditional …

Most of the existing neural video compression methods adopt the predictive coding
framework, which first generates the predicted frame and then encodes its residue with the …

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 …

This paper presents an end-to-end learning-based video compression system, termed
CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video …

The emerging conditional coding-based neural video codec (NVC) shows superiority over
commonly-used residual coding-based codec and the latest NVC already claims to …

In most existing neural video codecs, the information flow therein is uni-directional, where
only motion coding provides motion vectors for frame coding. In this paper, we argue that …

Typical video compression systems consist of two main modules: motion coding and
residual coding. This general architecture is adopted by classical coding schemes (such as …

Over the past few years, learning-based video compression has become an active research
area. However, most works focus on P-frame coding. Learned B-frame coding is under …

Most existing neural video codecs (NVCs) only extract short-term temporal context by optical
flow-based motion compensation. However, such short-term temporal context suffers from …

In video compression, coding efficiency is improved by reusing pixels from previously
decoded frames via motion and residual compensation. We define two levels of hierarchical …