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

Recently, learned image compression techniques have achieved remarkable performance,
even surpassing the best manually designed lossy image coders. They are promising to be …

Generative image modeling enables a wide range of applications but raises ethical
concerns about responsible deployment. This paper introduces an active strategy combining …

Learned image compression methods have exhibited superior rate-distortion performance
than classical image compression standards. Most existing learned image compression …

Most of the existing neural video compression methods adopt the predictive coding
framework, which first generates the predicted frame and then encodes its residue with the …

Learned image compression (LIC) methods have exhibited promising progress and superior
rate-distortion performance compared with classical image compression standards. Most …

This paper presents CompressAI, a platform that provides custom operations, layers, models
and tools to research, develop and evaluate end-to-end image and video compression …

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

For learned image compression, the autoregressive context model is proved effective in
improving the rate-distortion (RD) performance. Because it helps remove spatial …

Semantic communications has received growing interest since it can remarkably reduce the
amount of data to be transmitted without missing critical information. Most existing works …