12 citations · 22 across the 10 of their papers we have counts for
12 papers
Knowledge Distillation for Visual Autoregressive Models
Elia Peruzzo, Aritra Bhowmik, Guillaume Sautiere +2
Autoregressive (AR) image generation models are highly expressive but computationally intensive, motivating effective model compression. Knowledge distillation (KD) is a natural ap…
Multi-Scale Local Speculative Decoding for Image Generation
Elia Peruzzo, Guillaume Sautière, Amirhossein Habibian
Autoregressive (AR) models have achieved remarkable success in image synthesis, yet their sequential nature imposes significant latency constraints. Speculative Decoding offers a p…
Low-Latency Neural Stereo Streaming
Qiqi Hou, Farzad Farhadzadeh, Amir Said +2
The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods, both in terms of rate-disto…
Clockwork Diffusion: Efficient Generation With Model-Step Distillation
Amirhossein Habibian, Amir Ghodrati, Noor Fathima +4
This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation…
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
Ties van Rozendaal, Tushar Singhal, Hoang Le +10
Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that…
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation
Noor Fathima Ghouse, Jens Petersen, Auke Wiggers +2
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models…
Boosting neural video codecs by exploiting hierarchical redundancy
Reza Pourreza, Hoang Le, Amir Said +2
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation. We define two levels of hierarchical redu…
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Hoang Le, Liang Zhang, Amir Said +6
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mob…
Region-of-Interest Based Neural Video Compression
Yura Perugachi-Diaz, Guillaume Sautière, Davide Abati +3
Humans do not perceive all parts of a scene with the same resolution, but rather focus on few regions of interest (ROIs). Traditional Object-Based codecs take advantage of this bio…
Lossy Compression with Distortion Constrained Optimization
Ties van Rozendaal, Guillaume Sautière, Taco S. Cohen
When training end-to-end learned models for lossy compression, one has to balance the rate and distortion losses. This is typically done by manually setting a tradeoff parameter $β…
Feedback Recurrent Autoencoder for Video Compression
Adam Golinski, Reza Pourreza, Yang Yang +2
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems…
Feedback Recurrent AutoEncoder
Yang Yang, Guillaume Sautière, J. Jon Ryu +1
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency.…