This paper provides a survey of the latest developments in visual signal coding and
processing with generative models. Specifically, our focus is on presenting the advancement …

Recent advances in implicit neural representation (INR)-based video coding have
demonstrated its potential to compete with both conventional and other learning-based …

Neural video compression has recently demonstrated significant potential to compete with
conventional video codecs in terms of rate-quality performance. These learned video codecs …

In this paper, we study hybrid neural representations for spherical data, a domain of
increasing relevance in scientific research. In particular, our work focuses on weather and …

Foundation models have recently been shown to be strong data compressors. However,
when accounting for their excessive parameter count, their compression ratios are actually …

The emerging large models have achieved notable progress in the fields of natural
language processing and computer vision. However, large models for neural video coding …

Deep video compression has made impressive process in recent years, with the majority of
advancements concentrated on P-frame coding. Although efforts to enhance B-frame coding …

Overfitted image codecs offer compelling compression performance and low decoder
complexity, through the overfitting of a lightweight decoder for each image. Such codecs …

Deep implicit functions have been found to be an effective tool for efficiently encoding all
manner of natural signals. Their attractiveness stems from their ability to compactly represent …

We propose a new continuous video modeling framework based on implicit neural
representations (INRs) called ActINR. At the core of our approach is the observation that …