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Several deep learned lossy compression techniques have been proposed in the recent literature.
A mathematical theory of communication
C. E. Shannon · 1948
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Image compression using a neural network
S. Luttrell · 1988
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Image compression by back propagation: an example of extensional programming
P. Munro and D. Zipser · 1989
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The jpeg still picture compression standard
G. K. Wallace · 1992
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Png (portable network graphics) specification version 1.0
T. Boutell · 1997
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Kodak lossless true color image suite
R. Franzen · 1999
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Image compression with neural networks–a survey
J. Jiang · 1999
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The jpeg 2000 still image compression standard
A. Skodras, C. Christopoulos, and T. Ebrahimi · 2001
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Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, et al · 2004
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On single image scale-up using sparse-representations
R. Zeyde, M. Elad, and M. Protter · 2010
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Low-complexity single-image super-resolution based on nonnegative neighbor embedding
M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Bpg image format, 2014
F. Bellard · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Density modeling of images using a generalized normalization transformation
J. Ballé, V. Laparra, and E. P. Simoncelli · 2015
Cited alongside, same era.
Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks
N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. J. Hwang, J. Shor, and G. Toderici · 2017
Later among the works it cites.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Later among the works it cites.
Real-time adaptive image compression
O. Rippel and L. Bourdev · 2017
Later among the works it cites.
Enhancenet: Single image super-resolution through automated texture synthesis
M. S. Sajjadi, B. Schölkopf, and M. Hirsch · 2017
Later among the works it cites.
Lossy image compression with compressive autoencoders
L. Theis, W. Shi, A. Cunningham, and F. Huszár · 2017
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J. Ballé, V. Laparra, and E. P. Simoncelli · 2016
Cited alongside, same era.
Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
Cited alongside, same era.
Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deconvolution and checkerboard artifacts
A. Odena, V. Dumoulin, and C. Olah · 2016
Cited alongside, same era.
Pixel recurrent neural networks
A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
Cited alongside, same era.
Later among the works it cites.
Generative adversarial networks for extreme learned image compression
E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. Van Gool · 2018
Later among the works it cites.
Variational image compression with a scale hyperprior
J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston · 2018
Later among the works it cites.
Towards a semantic perceptual image metric
T. Chinen, J. Ballé, C. Gu, S. J. Hwang, S. Ioffe, N. Johnston, T. Leung, D. Minnen, S. O’Malley, C. Rosenberg, et al · 2018
Later among the works it cites.
Learning convolutional networks for content-weighted image compression
M. Li, W. Zuo, S. Gu, D. Zhao, and D. Zhang · 2018
Later among the works it cites.
Conditional probability models for deep image compression
F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool · 2018
Later among the works it cites.
Joint autoregressive and hierarchical priors for learned image compression
D. Minnen, J. Ballé, and G. D. Toderici · 2018
Later among the works it cites.
Generative compression
S. Santurkar, D. Budden, and N. Shavit · 2018
Later among the works it cites.
The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
Later among the works it cites.
The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, E. Efros A.A., Schectman, and W. O · 2018
Later among the works it cites.
Context-adaptive entropy model for end-to-end optimized image compression
J. Lee, S. Cho, and S.-K. Beack · 2019
Closest in time.
Human evaluations for image compression
Y. Patel, S. Appalaraju, and R. Manmatha · 2019
Closest in time.