Generative Compression

Deep Generative Video Compression . The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video.

https://la.disneyresearch.com/publication/deep-generative-video-compression/

Video compression using deep generative models - Patent WO-2020191402-A1 - PubChem. National Center for Biotechnology Information. 8600 Rockville Pike, Bethesda, MD, 20894 USA. Contact. Policies. FOIA. National Library of Medicine. National Institutes of Health. Department of Health and Human Services.

https://pubchem.ncbi.nlm.nih.gov/patent/WO-2020191402-A1

The concept of generative compression , the compression of data using generative models, is described and it is suggested that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at deeper compression levels for both image and video data. Traditional image and video compression algorithms rely on hand ...

https://www.semanticscholar.org/paper/Generative-Compression-Santurkar-Budden/15b877e109dbbdd7f815b86cfdf60d83cb39ecda

Generative Compression . Abstract: Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. We describe the concept of generative compression , the compression of data using generative models, and suggest that it is a direction

https://ieeexplore.ieee.org/document/8456298

Deep Generative Models: Imitation Learning, Image Synthesis, and Compression Jonathan Ho Electrical Engineering and Computer Sciences University of California at Berkeley

https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-67.pdf

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression , the compression of data using generative models, and suggest that it is a direction worth pursuing to produce ...

https://arxiv.org/abs/1703.01467

Image compression is a very essential part of gaming experience with multiple applications related to storage and transmission of data. It is the key to making cloud game streaming possible by...

https://medium.com/deepgamingai/image-compression-with-gans-8d79f53f0470

Discrete Generative Models for Sentence Compression Yishu Miao1, Phil Blunsom 1;2 1University of Oxford, 2Google Deepmind {yishu.miao, phil.blunsom}@cs.ox.ac.uk Abstract In this work we explore deep generative mod-els of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a

https://aclanthology.org/D16-1031.pdf

Prior works on generative image compression include [14] and [15]. One advantage of generative compression is that it can encode images with extremely low bitrates (less than 0.1 bpp) and decode

https://www.researchgate.net/publication/327479514_Generative_Compression

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI IEEE Trans Med Imaging. 2019 Jan;38(1):167-179. doi: 10.1109/TMI.2018.2858752. Epub 2018 Jul 23. Authors Data Compression / methods* Databases, Factual Humans

https://pubmed.ncbi.nlm.nih.gov/30040634/

For compression , we input an image and process its 8 by 8 blocks in a sequence. For each block, we first try to predict its intensities based on previous blocks; then, ...

https://www.researchgate.net/publication/314255788_Generative_Compression

Deep Generative Models for Distribution-Preserving Lossy Compression Michael Tschannen ETH Z#252;rich [email protected] Eirikur Agustsson Google AI Perception [email protected] Mario Lucic Google Brain [email protected] Abstract We propose and study the problem of distribution-preserving lossy compression .

https://proceedings.neurips.cc/paper/2018/file/801fd8c2a4e79c1d24a40dc735c051ae-Paper.pdf

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression , the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually ...

https://ui.adsabs.harvard.edu/abs/2017arXiv170301467S/abstract

The promise of generative compression is to translate this perceptual redundancy into a reduction in code verbosity. Lossy ...

https://deepai.org/publication/generative-compression

New compression algorithms must address the dual constraints of increased flexibility while demonstrating improvement on traditional measures of compression quality. In this work, we propose a data-aware compression technique leveraging a class of machine learning models called generative models.

https://dspace.mit.edu/handle/1721.1/112048

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video.

https://papers.nips.cc/paper/9127-deep-generative-video-compression

This work extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system and bridges the gap between rate-distortion-perception theory and practice. We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art ...

https://www.semanticscholar.org/paper/High-Fidelity-Generative-Image-Compression-Mentzer-Toderici/9b6a7df58664000c9a9bc4e3141e2630e02ac177

We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually ...

https://arxiv.org/abs/2006.09965

compression rates of different deep generative models such as VAEs, GANs (and its variants) and adversarial autoencoders (AAE) on MNIST and CIFAR10, and arrive at a number of insights not obtainable from log-likelihoods alone. 1 Introduction Generative models of images represent one of the most exciting areas of rapid progress of AI (Brock

http://bayesiandeeplearning.org/2019/papers/99.pdf

1) A general paradigm for generative compression of sequential data. We propose a general framework for compressing sequential data by employing a sequential variational autoencoder (VAE) in conjuction with discretization and entropy coding to build an end-to-end trainable codec. 2) A new neural codec for video compression .

https://s3-us-west-1.amazonaws.com/disneyresearch/wp-content/uploads/20191209182703/Deep_Generative_Video_Compression.pdf

At the same time, recent work in neural image compression [mentzer2020high, agustsson2019extreme], formalized in “rate-distortion-perception theory” [blau2019rethinking, tschannen2018deep, theis2021coding, theis2021advantages], has shown how generative image compression methods can outperform the HEVC-based image codec BPG [bpgurl] in terms ...

https://deepai.org/publication/towards-generative-video-compression

We describe the concept of generative compression , the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at deeper compression levels ...

https://dspace.mit.edu/handle/1721.1/125883

Data Compression Conference - Home. The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications. Compression of specific types of data (text, images, video, etc.). Compression in networking, communications, and storage. Applications to bioinformatics.

https://www.cs.brandeis.edu/~dcc/

1) A general paradigm for generative compression of sequential data. We propose a general framework for compressing sequential data by employing a sequential variational autoencoder (VAE) in conjuction with discretization and entropy coding to build an end-to-end trainable codec. 2) A new neural codec for video compression .

https://proceedings.neurips.cc/paper/2019/file/f1ea154c843f7cf3677db7ce922a2d17-Paper.pdf

Abstract. Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. We describe the concept of generative compression , the compression of data using generative models, and suggest that it is a direction worth pursuing to produce

https://cris.tau.ac.il/en/publications/generative-compression

New compression algorithms must address the dual constraints of increased flexibility while demonstrating improvement on traditional measures of compression quality. In this work, we propose a data-aware compression technique leveraging a class of machine learning models called generative models.

https://dspace.mit.edu/handle/1721.1/112048