In this paper we present aa deep generative model for lossy video compression. We employ
a model that consists of a 3D autoencoder with a discrete latent space and an …

We propose an end-to-end learned video compression scheme for low-latency scenarios.
Previous methods are limited in using the previous one frame as reference. Our method …

While there are many deep learning based approaches for single image compression, the
field of end-to-end learned video coding has remained much less explored. Therefore, in …

Recently, learning based video compression methods attract increasing attention. However,
the previous works suffer from error propagation due to the accumulation of reconstructed …

The usage of deep generative models for image compression has led to impressive
performance gains over classical codecs while neural video compression is still in its …

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 …

Traditional video compression approaches build upon the hybrid coding framework with
motion-compensated prediction and residual transform coding. In this paper, we propose the …

We propose a very simple and efficient video compression framework that only focuses on
modeling the conditional entropy between frames. Unlike prior learning-based approaches …

In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with
three hierarchical quality layers and a recurrent enhancement network. The frames in the …

Compression has been an important research topic for many decades, to produce a
significant impact on data transmission and storage. Recent advances have shown a great …