Paper Info
Title
A Spatiotemporal No-Reference Video Quality Assessment Model
Abstract
Many researchers have been developing objective video quality assessment methods due to increasing demand for perceived video quality measurement results by end users to speed-up advancements of multimedia services. However, most of these methods are either Full-Reference (FR) metrics, which require the original video or Reduced-Reference (RR) metrics, which need some features extracted from the original video. No-Reference (NR) metrics, on the other hand, do not require any information about the original video; hence, are much more suitable for applications like video streaming. This paper presents a novel, objective, NR video quality assessment algorithm. The proposed algorithm is based on utilization of spatial extent of video, temporal extent of video using motion vectors, bit rate, and packet loss ratio. Test results obtained using LIVE video quality database demonstrate the accuracy and robustness of the proposed metric.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738012
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
Keywords
Field
DocType
Video quality assessment (VQA), no-reference metric, spatiotemporal information, packet loss, quality of experience (QoE)
Computer vision,Video processing,Computer science,Multiview Video Coding,Subjective video quality,PEVQ,Video tracking,Artificial intelligence,Video denoising,Video quality,Video compression picture types
Conference
ISSN
Citations 
PageRank 
1522-4880
6
0.53
References 
Authors
7
4
Name
Order
Citations
PageRank
Baris Konuk192.35
Emin Zerman2146.22
Gokce Nur3394.77
Gozde Bozdagi Akar412920.15