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 has achieved remarkable performance. The entropy
model, which estimates the distribution of the latent representation, plays a crucial role in …

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 …

Entropy modeling is a key component for high-performance image compression algorithms.
Recent developments in autoregressive context modeling helped learning-based methods …

This paper addresses the problem of lossy image compression, a fundamental problem in
image processing and information theory that is involved in many real-world applications …

Recently, learned image compression methods have outperformed traditional hand-crafted
ones including BPG. One of the keys to this success is learned entropy models that estimate …

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 …

Recent work has shown a strong theoretical connection between variational autoencoders
(VAEs) and the rate distortion theory. Motivated by this, we consider the problem of lossy …

We propose an end-to-end image compression and analysis model with Transformers,
targeting to the cloud-based image classification application. Instead of placing an existing …

Recently, learned image compression has achieved impressive performance. The entropy
model, which estimates the distribution of the latent representation, plays a crucial role in …