A Model-Agnostic Semantic-Quality Compatible Framework based on Self-Supervised Semantic Decoupling

Author:

Ma Xiaoyu1ORCID,Feng Chenxi2ORCID,Wang Jiaojiao1ORCID,Lin Qiang1ORCID,Zhang Suiyu1ORCID,Zhu Jinchi1ORCID,Chen Xiaodiao3ORCID,Liu Chang1ORCID,Yu Dingguo1ORCID

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

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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