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Nick Johnston
Researcher at Google
Publications - 35
Citations - 3884
Nick Johnston is an academic researcher from Google. The author has contributed to research in topics: Image compression & Artificial neural network. The author has an hindex of 16, co-authored 35 publications receiving 2644 citations.
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Proceedings Article
Variational image compression with a scale hyperprior
TL;DR: In this paper, an end-to-end trainable model for image compression based on variational autoencoders is proposed, which incorporates a hyperprior to effectively capture spatial dependencies in the latent representation.
Proceedings ArticleDOI
Full Resolution Image Compression with Recurrent Neural Networks
George Toderici,Damien Vincent,Nick Johnston,Sung Jin Hwang,David Minnen,Joel Shor,Michele Covell +6 more
TL;DR: 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.
Posted Content
Full Resolution Image Compression with Recurrent Neural Networks
George Toderici,Damien Vincent,Nick Johnston,Sung Jin Hwang,David Minnen,Joel Shor,Michele Covell +6 more
TL;DR: In this paper, a set of full-resolution lossy image compression methods based on neural networks is presented, which can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once.
Proceedings ArticleDOI
Im2Calories: Towards an Automated Mobile Vision Food Diary
Austin Myers,Nick Johnston,Vivek Rathod,Anoop Korattikara,Alexander Gorban,Nathan Silberman,Sergio Guadarrama,George Papandreou,Jonathan Huang,Kevin Murphy +9 more
TL;DR: A system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories, is presented, significantly outperforming previous work.
Proceedings ArticleDOI
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Nick Johnston,Damien Vincent,David Minnen,Michele Covell,Saurabh Singh,Troy Chinen,Sung Jin Hwang,Joel Shor,George Toderici +8 more
TL;DR: In this paper, the authors 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.