Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

Image compression is a fundamental research field and many well-known compression
standards have been developed for many decades. Recently, learned compression …

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large
scale image generation. To this end, we scale and enhance the autoregressive priors used …

It has long been established that predictive models can be transformed into lossless
compressors and vice versa. Incidentally, in recent years, the machine learning community …

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 review a class of methods that can be collected under the name nonlinear transform
coding (NTC), which over the past few years have become competitive with the best linear …

This article proposes an end-to-end learnt lossy image compression approach, which is built
on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure …

We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and
generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing …

Built on deep networks, end-to-end optimized image compression has made impressive
progress in the past few years. Previous studies usually adopt a compressive auto-encoder …