Lossy compression algorithms are typically designed and analyzed through the lens of
Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion …

With the development of deep learning techniques, the combination of deep learning with
image compression has drawn lots of attention. Recently, learned image compression …

Assisted and automated driving functions in vehicles exploit sensor data to build situational
awareness, however, the data amount required by these functions might exceed the …

Training supervised image synthesis models requires a critic to compare two images: the
ground truth to the result. Yet, this basic functionality remains an open problem. A popular …

Could we compress images via standard codecs while avoiding visible artifacts? The
answer is obvious-this is doable as long as the bit budget is generous enough. What if the …

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for
a multivariate Gaussian source assuming jointly Gaussian reconstruction under mean …

Online Social Networks (OSNs) have attracted a huge number of users, who store and share
various images on a daily basis. As a well-known fact, most OSN platforms apply a series of …

This paper describes a method for pre-transforming images before image compression. This
method aims to preserve image recognition accuracy using deep neural network …

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for
discrete memoryless sources subject to a single-letter average distortion constraint and a …

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the
performance of practical DL implementations in terms of cloud bandwidth consumption …