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 …

Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the
acquisition of digital content and impelling the progress of visual compression towards …

Most video compression methods aim to improve the decoded video visual quality, instead
of particularly guaranteeing the semantic-completeness, which deteriorates downstream …

Video compression performance is closely related to the accuracy of inter prediction. It tends
to be difficult to obtain accurate inter prediction for the local video regions with inconsistent …

Neural video codecs have recently become competitive with standard codecs such as HEVC
in the low-delay setting. However, most neural codecs are large floating-point networks that …

Video coding that pursues the highest compression efficiency is the art of computing for rate-
distortion optimization. The optimization has been approached in different ways, exemplified …

In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
First, we reveal a fundamental relationship between bit allocation in NVC and Semi …

Video compression relies heavily on exploiting the temporal redundancy between video
frames, which is usually achieved by estimating and using the motion information. The …