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 …

We propose a novel neural representation for videos (NeRV) which encodes videos in
neural networks. Unlike conventional representations that treat videos as frame sequences …

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 …

We show how transformers can be used to vastly simplify neural video compression.
Previous methods have been relying on an increasing number of architectural biases and …

Applying deep learning to video compression has attracted increasing attention in recent
few years. In this work, we address end-to-end learned video compression with a special …

Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

While learned video codecs have demonstrated great promise, they have yet to achieve
sufficient efficiency for practical deployment. In this work, we propose several ideas for …

Deep learning methods are increasingly being applied in the optimisation of video
compression algorithms and can achieve significantly enhanced coding gains, compared to …

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 …