1.4k citations · 1.6k across the 7 of their papers we have counts for
12 papers
VCT: A Video Compression Transformer
Fabian Mentzer, George Toderici, David Minnen +4
We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, inc…
LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks
Berivan Isik, Philip A. Chou, Sung Jin Hwang +2
We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the…
Nonlinear Transform Coding
Johannes Ballé, Philip A. Chou, David Minnen +5
We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear trans…
Towards a Semantic Perceptual Image Metric
Troy Chinen, Johannes Ballé, Chunhui Gu +8
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 14…
Image-Dependent Local Entropy Models for Learned Image Compression
David Minnen, George Toderici, Saurabh Singh +2
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion…
Spatially adaptive image compression using a tiled deep network
David Minnen, George Toderici, Michele Covell +6
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural n…
Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh +2
We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies i…
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Michele Covell, Nick Johnston, David Minnen +5
We introduce a stop-code tolerant (SCT) approach to training recurrent convolutional neural networks for lossy image compression. Our methods introduce a multi-pass training method…
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston, Damien Vincent, David Minnen +6
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We…
Full Resolution Image Compression with Recurrent Neural Networks
George Toderici, Damien Vincent, Nick Johnston +4
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates…
Variable Rate Image Compression with Recurrent Neural Networks
George Toderici, Sean M. O'Malley, Sung Jin Hwang +5
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements. Due to these factors, i…
Shortest Path through Random Points
Sung Jin Hwang, Steven B. Damelin, Alfred O. Hero
Let be a complete -dimensional Riemannian manifold for . Let be a set of sample points in drawn randomly from a smooth Lebesgue density $…