Search Results for author: Nick Johnston

Found 17 papers, 5 papers with code

The Need for Medically Aware Video Compression in Gastroenterology

no code implementations2 Nov 2022 Joel Shor, Nick Johnston

Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored.

Video Compression

LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks

1 code implementation17 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.

Neural Video Compression using GANs for Detail Synthesis and Propagation

no code implementations26 Jul 2021 Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici

Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods.

Video Compression

End-to-end Learning of Compressible Features

1 code implementation23 Jul 2020 Saurabh Singh, Sami Abu-El-Haija, Nick Johnston, Johannes Ballé, Abhinav Shrivastava, George Toderici

We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.


Computationally Efficient Neural Image Compression

no code implementations18 Dec 2019 Nick Johnston, Elad Eban, Ariel Gordon, Johannes Ballé

Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).

Image Compression

Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference

no code implementations11 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.


Neural Image Decompression: Learning to Render Better Image Previews

no code implementations6 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 Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference

no code implementations24 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.

Multi-class Classification

Towards a Semantic Perceptual Image Metric

no code implementations1 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.

Image Quality Assessment

Spatially adaptive image compression using a tiled deep network

no code implementations7 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.

Image Compression

Target-Quality Image Compression with Recurrent, Convolutional Neural Networks

no code implementations18 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.

Image Compression

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

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.

Image Compression MS-SSIM +1

Full Resolution Image Compression with Recurrent Neural Networks

5 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.

Image Compression

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations ICCV 2015 Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

1 code implementation5 Mar 2015 Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, Kevin Murphy

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.

Keyword Spotting

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