Video quality measurement for multimedia applications using reduced-reference signals

2011 ◽  
Vol 50 (6) ◽  
pp. 067403 ◽  
Author(s):  
Chulhee Lee
Keyword(s):  
Video Quality ◽  
Quality Measurement ◽  
Multimedia Applications ◽  
Reference Signals

Adaptive Retransmission Scheme for Video Streaming Applications

2010 ◽  
pp. 248-262
Author(s):  
Árpád Huszák
Keyword(s):  
Video Streaming ◽  
Mobile Networks ◽  
Video Quality ◽  
Multimedia Applications ◽  
Limited Bandwidth ◽  
Streaming Applications ◽  
Congestion Control Algorithm ◽  
Negative Effect ◽  
Audio Video

In this chapter we present a novel selective retransmission scheme, based on congestion control algorithm. Our method is efficient in narrowband networks for multimedia applications, which demand higher bandwidth. Multimedia applications are becoming increasingly popular in IP networks, while in mobile networks the limited bandwidth and the higher error rate arise in spite of its popularity. These are restraining factors for mobile clients using multimedia applications such as video streaming. In some conditions the retransmission of lost and corrupted packets should increase the quality of the multimedia service, but these retransmissions should be enabled only if the network is not in congested state. Otherwise the retransmitted packet will intensify the congestion and it will have negative effect on the audio/video quality. Our proposed mechanism selectively retransmits the corrupted packets based on the actual video bit rate and the TCP-Friendly Rate Control (TFRC), which is integrated to the preferred DCCP transport protocol.

Subjective video quality evaluation for multimedia applications

2006 ◽  
Author(s):  
Quan Huynh-Thu ◽  
Mohammed Ghanbari ◽  
David Hands ◽  
Matthew D. Brotherton
Keyword(s):  
Quality Evaluation ◽  
Video Quality ◽  
Multimedia Applications ◽  
Video Quality Evaluation

scholarly journals Hybrid video quality prediction: reviewing video quality measurement for widening application scope

2014 ◽  
Vol 74 (2) ◽  
pp. 323-343 ◽  
Author(s):  
Marcus Barkowsky ◽  
Iñigo Sedano ◽  
Kjell Brunnström ◽  
Mikołaj Leszczuk ◽  
Nicolas Staelens
Keyword(s):  
Video Quality ◽  
Quality Measurement ◽  
Quality Prediction ◽  
Application Scope

No-reference video quality measurement: added value of machine learning

2015 ◽  
Vol 24 (6) ◽  
pp. 061208 ◽  
Author(s):  
Decebal Constantin Mocanu ◽  
Jeevan Pokhrel ◽  
Juan Pablo Garella ◽  
Janne Seppänen ◽  
Eirini Liotou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Video Quality ◽  
Quality Measurement ◽  
Added Value

scholarly journals Hacking VMAF with Video Color and Contrast Distortion

2019 ◽  
Author(s):  
Анастасия Звездакова ◽  
Anastasia Zvezdakova ◽  
Сергей Звездаков ◽  
Sergey Zvezdakov ◽  
Дмитрий Куликов ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Video Quality ◽  
Quality Measurement ◽  
Quality Metrics ◽  
Video Codecs ◽  
Full Reference ◽  
Reference Quality ◽  
Different Color

Video quality measurement takes an important role in many applications. Full-reference quality metrics which are usually used in video codecs comparisons are expected to reflect any changes in videos. In this article, we consider different color corrections of compressed videos which increase the values of full-reference metric VMAF and almost don’t decrease other widely-used metric SSIM. The proposed video contrast enhancement approach shows the metric in-applicability in some cases for video codecs comparisons, as it may be used for cheating in the comparisons via tuning to improve this metric values.

Voice/Video Quality Measurement for LTE Services

2018 ◽  
Vol 25 (4) ◽  
pp. 96-103 ◽  
Author(s):  
Yingrong Coral Sung ◽  
Yung-Sheng Ho ◽  
Yi-Bing Lin ◽  
Jyh-Cheng Chen ◽  
Herman Chung-Hwa Rao
Keyword(s):  
Video Quality ◽  
Quality Measurement
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