Insights From Generative Modeling for Neural Video Compression-Reference-Cited by-同舟云学术

Insights From Generative Modeling for Neural Video Compression

Published:2023-08 Issue:8 Volume:45 Page:9908-9921
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Yang Ruihan¹^ORCID,Yang Yibo¹^ORCID,Marino Joseph²^ORCID,Mandt Stephan¹^ORCID

Affiliation:

1. Department of Computer Science, University of California Irvine, Irvine, CA, USA

2. DeepMind, California Institute of Technology, Pasadena, CA, USA

Funder

HPI Research Center in Machine Learning and Data Science at UC Irvine

National Science Foundation

NSF CAREER Award

NSF

U.S. Department of Energy

IN PART by gifts from Qualcomm and Disney

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

https://ieeexplore.ieee.org/ielam/34/10169863/10078276-aam.pdf

Reference73 articles.

1. M-LVC: Multiple Frames Prediction for Learned Video Compression

2. Stochastic variational video prediction;babaeizadeh;Proc Int Conf Learn Representations,2018

3. Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

4. FlowNet: Learning Optical Flow with Convolutional Networks

5. Deep generative video compression with temporal autoregressive transforms;yang;Proc ICML Workshop Invertible Neural Netw Normalizing Flows Explicit Likelihood Models,2020

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