Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming-Reference-Cited by-同舟云学术

Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming

Published:2024-09 Issue:9 Volume:46 Page:6023-6039
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Jeong Seunghwa¹^ORCID,Kim Bumki²^ORCID,Cha Seunghoon³^ORCID,Seo Kwanggyoon¹^ORCID,Chang Hayoung⁴^ORCID,Lee Jungjin⁵^ORCID,Kim Younghui²^ORCID,Noh Junyong¹^ORCID

Affiliation:

1. Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

2. KAI Inc., Daejeon, Republic of Korea

3. CJeS Studios, Seoul, Republic of Korea

4. LG Eletronics Inc., Seoul, Republic of Korea

5. Soongsil University, Seoul, Republic of Korea

Funder

Culture, Sports, and Tourism R&D Program

Ministry of Culture, Sports, and Tourism

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/34/10627928/10472651.pdf?arnumber=10472651

Reference63 articles.

1. Neural-Enhanced Live Streaming

2. Efficient Video Compression via Content-Adaptive Super-Resolution

3. Factors Affecting the Popularity of Video Content on Live-Streaming Services: Focusing on V Live, the South Korean Live-Streaming Service

4. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

5. Developing a predictive model of quality of experience for internet video

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