no code implementations • 9 Feb 2021 • A. Murat Tekalp, Michele Covell, Radu Timofte, Chao Dong
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods.
no code implementations • 11 Jun 2019 • Michele Covell, David Marwood, Shumeet Baluja, Nick Johnston
We show results that are within 1. 6% of the reported, non-quantized performance on MobileNet using only 40 entries in our table.
no code implementations • 6 Dec 2018 • Shumeet Baluja, Dave Marwood, Nick Johnston, Michele Covell
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints.
no code implementations • 24 Sep 2018 • Shumeet Baluja, David Marwood, Michele Covell, Nick Johnston
For successful deployment of deep neural networks on highly--resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memory/power resources used during inference.
no code implementations • 7 Sep 2018 • David Marwood, Pascal Massimino, Michele Covell, Shumeet Baluja
A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints.
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.
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.
no code implementations • 3 Feb 2017 • Shumeet Baluja, Michele Covell, Rahul Sukthankar
Real-time optimization of traffic flow addresses important practical problems: reducing a driver's wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality.
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.