Reduction of Video Compression Artifacts Based on Deep Temporal Networks-Reference-Cited by-同舟云学术

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Reduction of Video Compression Artifacts Based on Deep Temporal Networks

Published:2018 Issue: Volume:6 Page:63094-63106
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Soh Jae Woong,Park Jaewoo,Kim Yoonsik,Ahn Byeongyong,Lee Hyun-Seung,Moon Young-Su,Cho Nam Ik^ORCID

Funder

Samsung

Korean National Police Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8274985/08502045.pdf?arnumber=8502045

Cited by 22 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. Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution;2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW);2024-01-01

2. A stable variant of linex loss SVM for handling noise with reduced hyperparameters;Information Sciences;2023-10

3. Computer Network Redundancy Reduction Using Video Compression;Symmetry;2023-06-19

4. Survey On Parameters of Data Compression;REST Journal on Data Analytics and Artificial Intelligence;2023-03-01

5. Extended Openmax Approach for the Classification of Radar Images With a Rejection Option;IEEE Transactions on Aerospace and Electronic Systems;2023-02

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