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

In this paper, we design a new class of high-efficiency deep joint source-channel coding
methods to achieve end-to-end video transmission over wireless channels. The proposed …

Neural approximations of scalar-and vector fields, such as signed distance functions and
radiance fields, have emerged as accurate, high-quality representations. State-of-the-art …

In this paper, we propose a class of high-efficiency deep joint source-channel coding
methods that can closely adapt to the source distribution under the nonlinear transform, it …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We show how transformers can be used to vastly simplify neural video compression.
Previous methods have been relying on an increasing number of architectural biases and …

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 …

Before we start thinking about (deep) generative modeling, let us consider a simple
example. Imagine we have trained a deep neural network that classifies images (x∈ ℤ D) of …

Neural data compression based on nonlinear transform coding has made great progress
over the last few years, mainly due to improvements in prior models, quantization methods …

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 …