TY - GEN
T1 - Practical Video Quality Assessment of User Generated Content
AU - Korhonen, Jari
AU - Wen, Xuanzheng
AU - Cheng, Jun
AU - Wang, Xu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61772348 and 61871270, Guangdong ”Pearl River Talent Recruitment Program” under Grant 2019ZT08X603, and Shenzhen Fundamental Research Program under Grant JCYJ20200109110410133.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - During the past few years, video quality assessment (VQA) of user generated content (UGC) has attracted considerable attention in the research community. In this paper, we propose a practical architecture for a versatile video quality model, designed for assessing user generated videos in particular. The proposed architecture is based on our earlier design of two-level video quality model with a convolutional neural network (CNN-TLVQM), with various improvements and re-designed elements. We have built a fast implementation of the proposed model in C++, demonstrating that the model is practical for real-life applications. The implementation of the model has been submitted for evaluation in ICME UGCVQA Challenge in 2021.
AB - During the past few years, video quality assessment (VQA) of user generated content (UGC) has attracted considerable attention in the research community. In this paper, we propose a practical architecture for a versatile video quality model, designed for assessing user generated videos in particular. The proposed architecture is based on our earlier design of two-level video quality model with a convolutional neural network (CNN-TLVQM), with various improvements and re-designed elements. We have built a fast implementation of the proposed model in C++, demonstrating that the model is practical for real-life applications. The implementation of the model has been submitted for evaluation in ICME UGCVQA Challenge in 2021.
KW - Convolutional neural network
KW - Recurrent neural network
KW - User generated content
KW - Video quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85130732327&partnerID=8YFLogxK
U2 - 10.1109/ICMEW53276.2021.9455974
DO - 10.1109/ICMEW53276.2021.9455974
M3 - Published conference contribution
AN - SCOPUS:85130732327
T3 - IEEE International Conference on Multimedia and Expo Workshops
BT - 2021 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2021
Y2 - 5 July 2021 through 9 July 2021
ER -