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 …

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 …

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 …

Abstract 3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel
view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial …

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 …

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 …

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal
redundancies in videos. Index-based INRs ignore the content-specific spatial features and …

Implicit neural representations (INRs) have emerged as a promising approach for video
storage and processing showing remarkable versatility across various video tasks. However …

Most existing image compression approaches perform transform coding in the pixel space to
reduce its spatial redundancy. However they encounter difficulties in achieving both high …

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