1 code implementation • 17 Nov 2021 • Berivan Isik, Philip A. Chou, Sung Jin Hwang, Nick Johnston, George Toderici
We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions.
no code implementations • 1 Aug 2018 • Troy Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean O'Malley, Charles Rosenberg, George Toderici
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
no code implementations • 31 May 2018 • David Minnen, George Toderici, Saurabh Singh, Sung Jin Hwang, Michele Covell
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 performance.
no code implementations • 7 Feb 2018 • David Minnen, George Toderici, Michele Covell, Troy Chinen, Nick Johnston, Joel Shor, Sung Jin Hwang, Damien Vincent, Saurabh Singh
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
15 code implementations • ICLR 2018 • Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston
We describe an end-to-end trainable model for image compression based on variational autoencoders.
no code implementations • 18 May 2017 • Michele Covell, Nick Johnston, David Minnen, Sung Jin Hwang, Joel Shor, Saurabh Singh, Damien Vincent, George Toderici
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
no code implementations • CVPR 2018 • Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici
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.
7 code implementations • CVPR 2017 • George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
1 code implementation • 19 Nov 2015 • George Toderici, Sean M. O'Malley, Sung Jin Hwang, Damien Vincent, David Minnen, Shumeet Baluja, Michele Covell, Rahul Sukthankar
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements.