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 …

Applying deep learning to video compression has attracted increasing attention in recent
few years. In this work, we address end-to-end learned video compression with a special …

Today's scientific simulations require a significant reduction of data volume because of
extremely large amounts of data they produce and the limited I/O bandwidth and storage …

Image compression is one of the most fundamental techniques and commonly used
applications in the image and video processing field. Earlier methods built a well-designed …

This paper presents an end-to-end learning-based video compression system, termed
CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video …

Image denoising aims to restore a clean image from an observed noisy one. Model-based
image denoising approaches can achieve good generalization ability over different noise …

This paper addresses the problem of lossy image compression, a fundamental problem in
image processing and information theory that is involved in many real-world applications …

We propose an end-to-end image compression and analysis model with Transformers,
targeting to the cloud-based image classification application. Instead of placing an existing …

Recently, learned image compression has achieved remarkable performance. The entropy
model, which estimates the distribution of the latent representation, plays a crucial role in …