116 citations · 162 across the 8 of their papers we have counts for
8 papers · 1 filter
Neural Image Decompression: Learning to Render Better Image Previews
Shumeet Baluja, Dave Marwood, Nick Johnston +1
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints. To satisfy these requests, it has bec…
Representing Images in 200 Bytes: Compression via Triangulation
David Marwood, Pascal Massimino, Michele Covell +1
A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has beco…
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…
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…