Learned image compression (LIC) methods have exhibited promising progress and superior
rate-distortion performance compared with classical image compression standards. Most …

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 …

Recently, learned image compression techniques have achieved remarkable performance,
even surpassing the best manually designed lossy image coders. They are promising to be …

Learned image compression methods have exhibited superior rate-distortion performance
than classical image compression standards. Most existing learned image compression …

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 …

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 …

By optimizing the rate-distortion-realism trade-off, generative compression approaches
produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions …

One critical component in lossy deep image compression is the entropy model, which
predicts the probability distribution of the quantized latent representation in the encoding …

Many common types of data can be represented as functions that map coordinates to signal
values, such as pixel locations to RGB values in the case of an image. Based on this view …

Modeling latent variables with priors and hyperpriors is an essential problem in variational
image compression. Formally, trade-off between rate and distortion is handled well if priors …