22 citations · 47 across the 12 of their papers we have counts for
4 papers · 1 filter
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression
Lucas Relic, Roberto Azevedo, Yang Zhang +2
Generative neural image compression supports data representation at extremely low bitrate, synthesizing details at the client and consistently producing highly realistic images. By…
Lossy Image Compression with Foundation Diffusion Models
Lucas Relic, Roberto Azevedo, Markus Gross +1
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates. Previous m…
Microdosing: Knowledge Distillation for GAN based Compression
Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah +2
Recently, significant progress has been made in learned image and video compression. In particular the usage of Generative Adversarial Networks has lead to impressive results in th…
Blind Image Restoration with Flow Based Priors
Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah +2
Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision wit…