A Model-Agnostic Semantic-Quality Compatible Framework based on Self-Supervised Semantic Decoupling
Author:
Affiliation:
1. Communication University of Zhejiang, Hangzhou, China
2. Communication University of China, Beijing, China
3. Hangzhou Dianzi University, Hangzhou, China
Funder
The Public Welfare Technology Application Research Project of Zhengjiang Province,China
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3613775
Reference43 articles.
1. Anna C Belkina , Christopher O Ciccolella , Rina Anno , Richard Halpert , Josef Spidlen , and Jennifer E Snyder-Cappione . 2019. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature communications , Vol. 10 , 1 ( 2019 ), 5415. Anna C Belkina, Christopher O Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, and Jennifer E Snyder-Cappione. 2019. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature communications, Vol. 10, 1 (2019), 5415.
2. On the use of deep learning for blind image quality assessment
3. QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
4. Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
5. Real-world single image super-resolution: A brief review
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