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 …

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 …

Task-driven semantic video/image coding has drawn considerable attention with the
development of intelligent media applications, such as license plate detection, face …

Deep learning has revolutionized many computer vision fields in the last few years,
including learning-based image compression. In this paper, we propose a deep semantic …

Semantic communications are expected to accomplish various semantic tasks with relatively
less spectrum resource by exploiting the semantic feature of source data. To simultaneously …

Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing
category of Internet traffic is machine-to-machine communication. In particular, machine-to …

Accessibility to big training datasets together with current advances in computing power has
emerged interest in the leverage of deep learning to address image compression. This …

Collaborative intelligence is a new paradigm for efficient deployment of deep neural
networks across the mobile-cloud infrastructure. By dividing the network between the mobile …

Existing compression methods typically focus on the removal of signal-level redundancies,
while the potential and versatility of decomposing visual data into compact conceptual …