Recent works have shown that the task of wireless transmission of images can be learned
with the use of machine learning techniques. Very promising results in end-to-end image …

Autoencoder-based communication systems use neural network channel models to
backwardly propagate message reconstruction error gradients across an approximation of …

We present PredProp, a method for optimization of weights and states in predictive coding
networks (PCNs) based on the precision of propagated errors and neural activity. PredProp …

Deepjoint source-channel coding (D-JSCC) provides several advantages over conventional
coding schemes, in which source and channel coding are designed separately. For …

We consider the problem of joint source and channel coding of structured data such as
natural language over a noisy channel. The typical approach to this problem in both theory …

We consider joint source and channel coding of natural language over a noisy channel
using deep learning. While the typical approach based on separate source and channel …

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 …

We present a novel adaptive deep joint source-channel coding (JSCC) scheme for wireless
image transmission. The proposed scheme supports multiple rates using a single deep …

A fundamental question in designing lossy data compression schemes is how well one can
do in comparison with the rate-distortion function, which describes the known theoretical …

Video compression is an indispensable technology in image/video communications, due to
its highly desirable ability to reduce the huge data volume. However, video compression …