Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment
The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the c...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2016/1730814 |
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| author | Diego José Luis Botia Valderrama Natalia Gaviria Gómez |
| author_facet | Diego José Luis Botia Valderrama Natalia Gaviria Gómez |
| author_sort | Diego José Luis Botia Valderrama |
| collection | DOAJ |
| description | The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategy Diffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics. |
| format | Article |
| id | doaj-art-2cc01e7fc9f84850b89dc2d274870393 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-2cc01e7fc9f84850b89dc2d2748703932025-02-03T01:10:03ZengWileyAdvances in Multimedia1687-56801687-56992016-01-01201610.1155/2016/17308141730814Nonintrusive Method Based on Neural Networks for Video Quality of Experience AssessmentDiego José Luis Botia Valderrama0Natalia Gaviria Gómez1Engineering Department, Universidad de Antioquia, Medellín, ColombiaEngineering Department, Universidad de Antioquia, Medellín, ColombiaThe measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategy Diffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.http://dx.doi.org/10.1155/2016/1730814 |
| spellingShingle | Diego José Luis Botia Valderrama Natalia Gaviria Gómez Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment Advances in Multimedia |
| title | Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment |
| title_full | Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment |
| title_fullStr | Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment |
| title_full_unstemmed | Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment |
| title_short | Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment |
| title_sort | nonintrusive method based on neural networks for video quality of experience assessment |
| url | http://dx.doi.org/10.1155/2016/1730814 |
| work_keys_str_mv | AT diegojoseluisbotiavalderrama nonintrusivemethodbasedonneuralnetworksforvideoqualityofexperienceassessment AT nataliagaviriagomez nonintrusivemethodbasedonneuralnetworksforvideoqualityofexperienceassessment |