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 …

This paper offers an insightful examination of how currently top-trending AI technologies, ie,
generative artificial intelligence (Generative AI) and large language models (LLMs), are …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

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 …

Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …

Learning-based video compression is currently a popular research topic, offering the
potential to compete with conventional standard video codecs. In this context, Implicit Neural …

Relative entropy coding (REC) algorithms encode a sample from a target distribution $ Q $
using a proposal distribution $ P $ using as few bits as possible. Unlike entropy coding, REC …

Encoding videos as neural networks is a recently proposed approach that allows new forms
of video processing. However, traditional techniques still outperform such neural video …

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 …

In contrast to image compression, the key of video compression is to efficiently exploit the
temporal context for reducing the inter-frame redundancy. Existing learned video …