Advances in both lossy image compression and semantic content understanding have been
greatly fueled by deep learning techniques, yet these two tasks have been developed …

Learned image compression (LIC) has illustrated good ability for reconstruction quality
driven tasks (eg PSNR, MS-SSIM) and machine vision tasks such as image understanding …

Entropy estimation is essential for the performance of learned image compression. It has
been demonstrated that a transformer-based entropy model is of critical importance for …

Video traffic comprises a large majority of the total traffic on the internet today.
Uncompressed visual data requires a very large data rate; lossy compression techniques …

Images are transmitted or stored in their compressed form and most of the AI tasks are
performed from the reconstructed domain. Convolutional neural network (CNN)-based …

Natural image compression has been greatly improved in the deep learning era. However,
the compression performance will be heavily degraded if the pretrained encoder is directly …

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 …

There has been much interest in deploying deep learning algorithms on low-powered
devices, including smartphones, drones, and medical sensors. However, full-scale deep …

Incorporating semantic analysis into image compression can significantly reduce the
repetitive computation of fundamental semantic analysis in downstream applications such …

There is a growing number of images that are analyzed by machines rather than just
humans. Recently, most machine vision tasks are based on decoded images which require …