Paper Info
Title
Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change.
Abstract
Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users' Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users' perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users' QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.
Year
DOI
Venue
2017
10.1007/s11042-016-3558-0
Multimedia Tools Appl.
Keywords
Field
DocType
Video quality assessment,QoE,No-reference,Scene change,Fast motion,Compressed domain
Computer vision,Block-matching algorithm,Human visual system model,Computer science,Motion compensation,Subjective video quality,Video tracking,Artificial intelligence,Video quality,Video compression picture types,Rate–distortion optimization
Journal
Volume
Issue
ISSN
76
7
1380-7501
Citations 
PageRank 
References 
2
0.41
23
Authors
3
Name
Order
Citations
PageRank
Hong Zhang127626.98
Fan Li28516.83
Na Li3652106.02