Journal ArticleDOI
Image compression with neural networks } A survey
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TLDR
This paper presents an extensive survey on the development of neural networks for image compression which covers three categories: direct image compression by neural networks; neural network implementation of existing techniques, and neural network based technology which provide improvement over traditional algorithms.Abstract:
Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding. Successful applications of neural networks to vector quantization have now become well established, and other aspects of neural network involvement in this area are stepping up to play significant roles in assisting with those traditional technologies. This paper presents an extensive survey on the development of neural networks for image compression which covers three categories: direct image compression by neural networks; neural network implementation of existing techniques, and neural network based technology which provide improvement over traditional algorithms.read more
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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.
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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.
Journal ArticleDOI
Medical image analysis with artificial neural networks.
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Posted Content
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: A method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG, WebP, JPEG2000, and JPEG as measured by MS-SSIM is proposed and it is shown that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics.
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An introduction to computing with neural nets
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