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This paper outlines an end-to-end optimized lossy image compression framework using diffusion generative models.
The jpeg still picture compression standard
Wallace, G. K · 1991
Earlier work this paper cites.
Structural approaches to image quality assessment
Wang, Z., Bovik, A. C., and Simoncelli, E. P · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
True color kodak images, 2013
Franzen, R. W · 2013
Earlier work this paper cites.
Testimages: a large-scale archive for testing visual devices and basic image processing algorithms, stag - smart tools & apps for graphics conference, 2014., 2014
Asuni, N. and Giachetti, A · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A. and Brox, T · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
Earlier work this paper cites.
Visual network index cisco. forecast and methodology
Cisco · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
Lossy image compression with compressive autoencoders
Theis, L., Shi, W., Cunningham, A., and Huszár, F · 2017
Earlier work this paper cites.
Towards deeper understanding of variational autoencoding models
Zhao, S., Song, J., and Ermon, S · 2017
Earlier work this paper cites.
Variational image compression with a scale hyperprior
Ballé, J., Minnen, D., Singh, S., Hwang, S. J., and Johnston, N · 2018
Earlier work this paper cites.
Bpg image format, 2018
Bellard, F · 2018
Earlier work this paper cites.
Strong functional representation lemma and applications to coding theorems
Li, C. T. and El Gamal, A · 2018
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Joint autoregressive and hierarchical priors for learned image compression
Minnen, D., Ballé, J., and Toderici, G. D · 2018
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Deep generative models for distribution-preserving lossy compression
Tschannen, M., Agustsson, E., and Lucic, M · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Generative adversarial networks for extreme learned image compression
Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., and Gool, L. V · 2019
Transformer-based transform coding
Zhu, Y., Yang, Y., and Cohen, T · 2020
Later among the works it cites.
Autoregressive diffusion models
Hoogeboom, E., Gritsenko, A. A., Bastings, J., Poole, B., van den Berg, R., and Salimans, T · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Universal rate-distortion-perception representations for lossy compression
Zhang, G., Qian, J., Chen, J., and Khisti, A · 2021
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Multi-realism image compression with a conditional generator
Agustsson, E., Minnen, D., Toderici, G., and Mentzer, F · 2022
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