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Recent neural compression methods have been based on the popular hyperprior framework.
Arithmetic coding for data compression
Ian H Witten, Radford M Neal, and John G Cleary · 1987
Earlier work this paper cites.
Image data compression using a neural network model
N Sonehara · 1989
Earlier work this paper cites.
Artificial neural network for image compression
GL Sicuranza, G Romponi, and Stefano Marsi · 1990
Earlier work this paper cites.
Elements of information theory
Thomas M Cover · 1991
Earlier work this paper cites.
Image coding using wavelet transform
Marc Antonini, Michel Barlaud, Pierre Mathieu, and Ingrid Daubechies · 1992
Earlier work this paper cites.
Kodak lossless true color image suite (photocd pcd0992), 1993
Eastman Kodak · 1993
Earlier work this paper cites.
High quality document image compression with “djvu”
Leon Bottou, Patrick Haffner, Paul G Howard, Patrice Simard, Yoshua Bengio, and Yann Le Cun · 1998
Earlier work this paper cites.
Bayesian networks for pattern classification, data compression, and channel coding
Brendan J Frey · 1998
Earlier work this paper cites.
Quantization
Robert M. Gray and David L. Neuhoff · 1998
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High performance scalable image compression with EBCOT
David Taubman · 2000
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Information Theory, Inference, and Learning Algorithms
David J. C. MacKay · 2003
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
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H. 264 and MPEG-4 video compression: video coding for next-generation multimedia
Iain E Richardson · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Oriented wavelet transform for image compression and denoising
Vivien Chappelier and Christine Guillemot · 2006
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Accurate image search using the contextual dissimilarity measure
H. Jégou, C. Schmid, H. Harzallah, and J. Verbeek · 2009
Earlier work this paper cites.
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Earlier work this paper cites.
Overview of the high efficiency video coding (HEVC) standard
Gary J Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand · 2012
Earlier work this paper cites.
Jpeg2000 image compression fundamentals, standards and practice: Image compression fundamentals, standards and practice
David Taubman and Michael Marcellin · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
TESTIMAGES: A large-scale archive for testing visual devices and basic image processing algorithms (SAMPLING 1200 RGB set)
N. Asuni and A. Giachetti · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
D. Kingma and M. Welling · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
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Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
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Super-resolution with deep convolutional sufficient statistics
Joan Bruna, Pablo Sprechmann, and Yann LeCun · 2016
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Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, and Daan Wierstra · 2016
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Voyager telecommunications
Roger Ludwig and Jim Taylor · 2016
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End-to-end optimized image compression
Johannes Ballé, Valero Laparra, and Eero P. Simoncelli · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues
Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K Sodickson, and Mehmet Akcakaya · 2020
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High-fidelity generative image compression
Fabian Mentzer, George D. Toderici, Michael Tschannen, and Eirikur Agustsson · 2020
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Channel-wise autoregressive entropy models for learned image compression
David Minnen and Saurabh Singh · 2020
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A U-Net based discriminator for generative adversarial networks
Edgar Schönfeld, Bernt Schiele, and Anna Khoreva · 2020
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Deep learning on image denoising: An overview
Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin · 2020
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Workshop and challenge on learned image compression (clic2020), 2020
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Photo-realistic single image super-resolution using a generative adversarial network
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Neural discrete representation learning
A. van den Oord, O. Vinyals, and K. Kavukcuoglu · 2017
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Parallel multiscale autoregressive density estimation
S. Reed, A. van den Oord, N. Kalchbrenner, S. Gómez Colmenarejo, Z. Wang, D. Belov, and N. de Freitas · 2017
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Real-time adaptive image compression
Oren Rippel and Lubomir Bourdev · 2017
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PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
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Lossy image compression with compressive autoencoders
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George Toderici, Wenzhe Shi, Radu Timofte, Johannes Balle Lucas Theis, Eirikur Agustsson, Nick Johnston, and Fabian Mentzer · 2020
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CogView: Mastering text-to-image generation via transformers
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Taming transformers for high-resolution image synthesis
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Masked autoencoders are scalable vision learners
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Zero-shot text-to-image generation
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Projected GANs converge faster
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Deep learning for image super-resolution: A survey
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Vector-quantized image modeling with improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, and Yonghui Wu Jason Baldridge · 2021
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Transformer-based transform coding
Yinhao Zhu, Yang Yang, and Taco Cohen · 2021
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BEiT: BERT pre-training of image transformers
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MaskGIT: Masked generative image transformer
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Improved masked image generation with token-critic
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High-resolution image synthesis with latent diffusion models
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Implicit neural representations for image compression
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