3 citations · 3 across the 5 of their papers we have counts for
5 papers
DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning
Eren Çetin, Lucas Relic, Yuanyi Xue +3
We present a perceptually-driven video compression framework integrating implicit neural representations (INRs) and pre-trained video diffusion models to address the extremely low…
Region-Adaptive Generative Compression with Spatially Varying Diffusion Models
Lucas Relic, Roberto Azevedo, Yang Zhang +3
Generative image codecs aim to optimize perceptual quality, producing realistic and detailed reconstructions. However, they often overlook a key property of human vision: our tende…
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…