As an increasing amount of image and video content will be analyzed by machines, there is
demand for a new codec paradigm that is capable of compressing visual input primarily for …

An increasing share of captured images and videos are transmitted for storage and remote
analysis by computer vision algorithms, rather than to be viewed by humans. Contrary to …

With the AI of Things (AIoT) development, a huge amount of visual data, eg, images and
videos, are produced in our daily work and life. These visual data are not only used for …

At present, and increasingly so in the future, much of the captured visual content will not be
seen by humans. Instead, it will be used for automated machine vision analytics and may …

With the development of 5G and edge computing, it is increasingly important to offload
intelligent media computing to edge device. Traditional media coding scheme codes the …

Recently, more and more images are compressed and sent to the back-end devices for
machine analysis tasks (eg, object detection) instead of being purely watched by humans …

Recent work has shown that learned image compression strategies can outperform standard
hand-crafted compression algorithms that have been developed over decades of intensive …

The visual signal compression is a long-standing problem. Fueled by the recent advances of
deep learning, exciting progress has been made. Despite better compression performance …

Image recognition techniques such as object detection are useful for assisting humans in
remote video surveillance tasks. However, compression algorithms used for efficient video …

Image compression at extremely low bit-rates has always been a challenging task in
bandwidth limited scenarios, such as aerospace and deep-sea explorations. Recent years …