A no-reference network video quality assessment method based on video content characteristics

doi:10.1145/3349341.3349493

IR > 计算机与通信学院

	A no-reference network video quality assessment method based on video content characteristics
	Zhao, Hong1 ; Chang, Zhaobin 1; Cao, Chang 1; Zeng, Xiangyan 2
	2019-07-12
会议名称	2019 International Conference on Artificial Intelligence and Computer Science, AICS 2019
会议录名称	ACM International Conference Proceeding Series
页码	698-704
会议日期	July 12, 2019 - July 13, 2019
会议地点	Wuhan, China
出版者	Association for Computing Machinery
摘要	A novel model of no-reference network video quality assessment is proposed in this study. First, a new definition of video content complexity is represented by the type of frame, the type of macro block, and the motion vector that consists of payload information. The proposed assessment model consists of a coding distortion module and a transmission distortion module. The coding distortion module adds the proposed complexity to a standard G.1070 model. The transmission distortion module describes the impact of frame loss rate, concentrative degree of frame loss and video content complexity on video quality. Several experiment results are presented to show that video content characteristics have a considerable influence on perceived quality. Thus, the proposed model provides a promising metric for video quality assessment. © 2019 Association for Computing Machinery.
关键词	Artificial intelligence Video recording Assessment models Coding distortion Frame loss Payload information Transmission distortion Video contents Video quality Video quality assessment
DOI	10.1145/3349341.3349493
收录类别	EI
语种	英语
EI入藏号	20194107525354
EI主题词	Complex networks
来源库	Compendex
分类代码	716.4 Television Systems and Equipment - 722 Computer Systems and Equipment - 723.4 Artificial Intelligence
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/118032
专题	计算机与通信学院
作者单位	1.School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China; 2.Department of Mathematics and Computer Science, Fort Valley State University, GA, United States
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhao, Hong,Chang, Zhaobin,Cao, Chang,et al. A no-reference network video quality assessment method based on video content characteristics[C]:Association for Computing Machinery,2019:698-704.