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

The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

Recent models for learned image compression are based on autoencoders that learn
approximately invertible map**s from pixels to a quantized latent representation. The …

Image compression is a fundamental research field and many well-known compression
standards have been developed for many decades. Recently, learned compression …

Recently, deep learned enabled end-to-end communication systems have been developed
to merge all physical layer blocks in the traditional communication systems, which make joint …

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

Conventional video compression approaches use the predictive coding architecture and
encode the corresponding motion information and residual information. In this paper, taking …

We extensively study how to combine Generative Adversarial Networks and learned
compression to obtain a state-of-the-art generative lossy compression system. In particular …

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

We present a learned image compression system based on GANs, operating at extremely
low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi …