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 …

In the field of image processing and computer vision (CV), machine learning (ML)
architectures are widely used. Image compression problems can be solved using …

Recent models for learned image compression are based on autoencoders that learn
approximately invertible map**s from pixels to a quantized latent representation. The …

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

A large fraction of Internet traffic is now driven by requests from mobile devices with
relatively small screens and often stringent bandwidth requirements. Due to these factors, it …

We propose a method for lossy image compression based on recurrent, convolutional
neural networks that outper-forms BPG (4: 2: 0), WebP, JPEG2000, and JPEG as mea-sured …

We present a system to hide a full color image inside another of the same size with minimal
quality loss to either image. Deep neural networks are simultaneously trained to create the …

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 …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

Given that neural networks have been widely reported in the research community of medical
imaging, we provide a focused literature survey on recent neural network developments in …