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By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models.
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Mehdi Mirza and Simon Osindero · 2014
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Real-time adaptive image compression
Oren Rippel and Lubomir Bourdev · 2017
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Shibani Santurkar, David Budden, and Nir Shavit · 2017
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Lossy image compression with compressive autoencoders
Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszar · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston · 2018
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Conditional probability models for deep image compression
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool · 2018
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Joint autoregressive and hierarchical priors for learned image compression
David Minnen, Johannes Ballé, and George D Toderici · 2018
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Deep generative models for distribution-preserving lossy compression
Michael Tschannen, Eirikur Agustsson, and Mario Lucic · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Generative adversarial networks for extreme learned image compression
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, and Luc Van Gool · 2019
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Rethinking lossy compression: The rate-distortion-perception tradeoff
Yochai Blau and Tomer Michaeli · 2019
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Nonlinear transform coding
Johannes Ballé, Philip A Chou, David Minnen, Saurabh Singh, Nick Johnston, Eirikur Agustsson, Sung Jin Hwang, and George Toderici · 2020
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Learned image compression with discretized gaussian mixture likelihoods and attention modules
Zhengxue Cheng, Heming Sun, Masaru Takeuchi, and Jiro Katto · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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
TensorFlow Compression: Learned data compression, 2022
Johannes Ballé, Sung Jin Hwang, and Eirikur Agustsson · 2022
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BPG Image format
Fabrice Bellard · 2022
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VTM 17.1
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David Minnen and Saurabh Singh · 2020
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CLIC 2020: Challenge on Learned Image Compression
George Toderici, Lucas Theis, Nick Johnston, Eirikur Agustsson, Fabian Mentzer, Johannes Ballé, Wenzhe Shi, and Radu Timofte · 2020
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Overview of the versatile video coding (vvc) standard and its applications
Benjamin Bross, Ye-Kui Wang, Yan Ye, Shan Liu, Jianle Chen, Gary J Sullivan, and Jens-Rainer Ohm · 2021
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Checkerboard context model for efficient learned image compression
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http://r0k.us/graphics/kodak/ , 2022
Kodak PhotoCD dataset · 2022
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Roi image codec optimized for visual quality
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Algorithms for the communication of samples
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Lossy compression with gaussian diffusion
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Scaling autoregressive models for content-rich text-to-image generation
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