S
Sung Jin Hwang
Researcher at Google
Publications - 23
Citations - 3835
Sung Jin Hwang 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 12, co-authored 21 publications receiving 2590 citations.
Papers
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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
Variable Rate Image Compression with Recurrent Neural Networks
George Toderici,Sean M. O'Malley,Sung Jin Hwang,Damien Vincent,David Minnen,Shumeet Baluja,Michele Covell,Rahul Sukthankar +7 more
TL;DR: A general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional LSTM recurrent networks are proposed, which provide better visual quality than (headerless) JPEG, JPEG2000 and WebP, with a storage size reduced by 10% or more.
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
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.