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

Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …

The vigorous developments of the Internet of Things make it possible to extend its computing
and storage capabilities to computing tasks in the aerial system with the collaboration of …

One critical component in lossy deep image compression is the entropy model, which
predicts the probability distribution of the quantized latent representation in the encoding …

Over the past several years, we have witnessed impressive progress in the field of learned
image compression. Recent learned image codecs are commonly based on autoencoders …

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

Image/video compression and communication need to serve both human vision and
machine vision. To address this need, we propose a scalable image compression solution …

It is a challenge to achieve high compression rates for remote sensing images because they
have rich information and complex backgrounds. Long-range context information can help …

With the increasing volume of high-resolution satellite images, image compression
technology has become a research hotspot in the field of remote sensing image processing; …

Deep image compression efficiency has been improved in the past years. However, to fully
exploit context information for compressing image objects of different scales and shapes …