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Volume 13, Issue 6
Mathematical Models for Quality Analysis of Mobile Video

S.-Q. Zhao, H. Jiang, C. Liang, S. Sherif & A. Tarraf

Int. J. Numer. Anal. Mod., 13 (2016), pp. 879-897.

Published online: 2016-11

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  • Abstract

With the explosive growth of mobile video applications, analysis of video quality becomes increasingly important because it is an important Key Performance Indicator (KPI) for Quality of Experience (QoE). In this paper, a framework for non-reference video quality analysis is proposed and applied to Video Telephony (VT) in LTE networks. Three metrics, blockiness, blur and freezing, are used to estimate the MOS. Blockiness is detected by taking the H.264 codec features into account, blur is estimated by utilizing the percentage of noticeable blurred edges in each frame, and freezing is evaluated by using a sigmoid function to mimic the effect of different freezing duration on the Human Visual System (HVS). Furthermore, the three metrics are combined into one objective MOS by considering different weighting factors and using the linear curve fitting. Above 90% correlation is achieved between the objective MOS score and subjective MOS score.

  • Keywords

Mathematical models, video quality analysis, quality of experience, mean opinion score, blockiness, blur, freezing, human visual system, modeling.

  • AMS Subject Headings

35R35, 49J40, 60G40

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-13-879, author = {S.-Q. Zhao, H. Jiang, C. Liang, S. Sherif and A. Tarraf}, title = {Mathematical Models for Quality Analysis of Mobile Video}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2016}, volume = {13}, number = {6}, pages = {879--897}, abstract = {

With the explosive growth of mobile video applications, analysis of video quality becomes increasingly important because it is an important Key Performance Indicator (KPI) for Quality of Experience (QoE). In this paper, a framework for non-reference video quality analysis is proposed and applied to Video Telephony (VT) in LTE networks. Three metrics, blockiness, blur and freezing, are used to estimate the MOS. Blockiness is detected by taking the H.264 codec features into account, blur is estimated by utilizing the percentage of noticeable blurred edges in each frame, and freezing is evaluated by using a sigmoid function to mimic the effect of different freezing duration on the Human Visual System (HVS). Furthermore, the three metrics are combined into one objective MOS by considering different weighting factors and using the linear curve fitting. Above 90% correlation is achieved between the objective MOS score and subjective MOS score.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/470.html} }
TY - JOUR T1 - Mathematical Models for Quality Analysis of Mobile Video AU - S.-Q. Zhao, H. Jiang, C. Liang, S. Sherif & A. Tarraf JO - International Journal of Numerical Analysis and Modeling VL - 6 SP - 879 EP - 897 PY - 2016 DA - 2016/11 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/470.html KW - Mathematical models, video quality analysis, quality of experience, mean opinion score, blockiness, blur, freezing, human visual system, modeling. AB -

With the explosive growth of mobile video applications, analysis of video quality becomes increasingly important because it is an important Key Performance Indicator (KPI) for Quality of Experience (QoE). In this paper, a framework for non-reference video quality analysis is proposed and applied to Video Telephony (VT) in LTE networks. Three metrics, blockiness, blur and freezing, are used to estimate the MOS. Blockiness is detected by taking the H.264 codec features into account, blur is estimated by utilizing the percentage of noticeable blurred edges in each frame, and freezing is evaluated by using a sigmoid function to mimic the effect of different freezing duration on the Human Visual System (HVS). Furthermore, the three metrics are combined into one objective MOS by considering different weighting factors and using the linear curve fitting. Above 90% correlation is achieved between the objective MOS score and subjective MOS score.

S.-Q. Zhao, H. Jiang, C. Liang, S. Sherif and A. Tarraf. (2016). Mathematical Models for Quality Analysis of Mobile Video. International Journal of Numerical Analysis and Modeling. 13 (6). 879-897. doi:
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