Cross-Domain Lossy Compression as Entropy Constrained Optimal Transport-Reference-Cited by-同舟云学术

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Cross-Domain Lossy Compression as Entropy Constrained Optimal Transport

Published:2022-09 Issue:3 Volume:3 Page:513-527
ISSN:2641-8770
Container-title:IEEE Journal on Selected Areas in Information Theory
language:
Short-container-title:IEEE J. Sel. Areas Inf. Theory

Author:

Liu Huan¹^ORCID,Zhang George²^ORCID,Chen Jun¹^ORCID,Khisti Ashish²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada

2. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

http://xplorestaging.ieee.org/ielx7/8700143/10077203/09993700.pdf?arnumber=9993700

Reference45 articles.

1. Universal rate-distortion-perception representations for lossy compression;zhang;arXiv 2106 10311,2021

2. Unpaired Learning of Deep Image Denoising

3. On perceptual lossy compression: The cost of perceptual reconstruction and an optimal training framework;yan;Proc Int Conf Mach Learn,2021

4. High-quality self-supervised deep image denoising;laine;Proc Adv Neural Inf Process Syst,2019

5. On the advantages of stochastic encoders;theis;arXiv 2102 09270,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Rate-limited Quantum-to-Classical Optimal Transport: A Lossy Source Coding Perspective;2023 IEEE International Symposium on Information Theory (ISIT);2023-06-25

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