A No-Reference Video Quality Estimation Model over Wireless Networks

2011 ◽  
Author(s):  
Yan Yang ◽  
Zhaoming Lu ◽  
Xiangming Wen ◽  
Wei Zheng ◽  
Ajing Zhang
Keyword(s):  
Wireless Networks ◽  
Video Quality ◽  
Estimation Model ◽  
Quality Estimation

scholarly journals DEMI: Deep Video Quality Estimation Model using Perceptual Video Quality Dimensions

2020 ◽  
Author(s):  
Saman Zadtootaghaj ◽  
Nabajeet Barman ◽  
Rakesh Rao Ramachandra Rao ◽  
Steve Goring ◽  
Maria G. Martini ◽  
...  
Keyword(s):  
Video Quality ◽  
Estimation Model ◽  
Quality Estimation ◽  
Perceptual Video Quality ◽  
Quality Dimensions

scholarly journals Quantitative Modeling of the Impact of Video Content in the ITU-T G.1070 Video Quality Estimation Function

2012 ◽  
Vol 14 (2) ◽  
Author(s):  
Jose Joskowicz ◽  
J. Carlos López Ardao ◽  
Rafael Sotelo
Keyword(s):  
Video Quality ◽  
Quantitative Modeling ◽  
Video Content ◽  
Estimation Model ◽  
Quality Estimation ◽  
Video Telephony ◽  
Video Clips ◽  
Wide Range ◽  
Estimation Function ◽  
The Impact

In this paper we present an enhancement to the video quality estimation model described in ITU-T Recommendation G.1070 “Opinion model for video-telephony applications”, in order to include the impact of video content, for different display sizes and codecs. This enhancement provides a much better approximation of the model results with respect to the perceptual MOS values for a wide range of video contents. SAD (Sum of Absolute Differences) is used as an estimation of the video spatial-temporal activity, and is included as a new parameter in the model. The results are based on more than 1500 processed video clips, coded in MPEG-2 and H.264/AVC, in bit rate ranges from 50 kb/s to 12 Mb/s, in SD, VGA, CIF and QCIF display formats.

scholarly journals Radio Link Quality Estimation in Low-Power Wireless Networks

2013 ◽  
Author(s):  
Nouha Baccour ◽  
Anis Koubâa ◽  
Claro Noda ◽  
Hossein Fotouhi ◽  
Mário Alves ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Low Power ◽  
Link Quality ◽  
Radio Link ◽  
Quality Estimation ◽  
Low Power Wireless Networks ◽  
Link Quality Estimation

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

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