Survey on Visual Signal Coding and Processing With Generative Models: Technologies, Standards, and Optimization-Reference-Cited by-同舟云学术

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Survey on Visual Signal Coding and Processing With Generative Models: Technologies, Standards, and Optimization

Published:2024-06 Issue:2 Volume:14 Page:149-171
ISSN:2156-3357
Container-title:IEEE Journal on Emerging and Selected Topics in Circuits and Systems
language:
Short-container-title:IEEE J. Emerg. Sel. Topics Circuits Syst.

Author:

Chen Zhibo¹^ORCID,Sun Heming²^ORCID,Zhang Li³^ORCID,Zhang Fan⁴^ORCID

Affiliation:

1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China

2. Faculty of Engineering, Yokohama National University, Kanagawa, Japan

3. Bytedance Inc., San Diego, CA, USA

4. School of Computer Science, University of Bristol, Bristol, U.K.

Funder

National Natural Science Foundation of China

JSPS KAKENHI

U.K. Research and Innovation (UKRI) MyWorld Strength in Places Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5503868/10578329/10535893.pdf?arnumber=10535893

Reference327 articles.

1. Conditional generative adversarial nets;Mirza;arXiv:1411.1784,2014

2. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets;Chen

3. Image-to-Image Translation: Methods and Applications

4. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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