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Título : Nonintrusive method based on neural networks for video quality of experience assessment
Autor : Botia Valderrama, Diego José Luis
Gaviria Gómez, Natalia
Palabras clave : Quality of service
Complex networks
Mean square error
Neural networks
Quality control
Video signal processing
Calidad del servicio
Control de calidad
Procesamiento de señales
Redes neurales
Fecha de publicación : 2016
Editorial : Hindawi Publishing Corporation
Citación : Botia, D. J. & Gómez, N. (2016). Nonintrusive method based on neural networks for video quality of experience assessment. Advances in Multimedia, 2016, 1-17.
Abstract : 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 (RandomNeural Networks) is applied to evaluate the subjective qualitymetrics 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.
Grupo de INV. : Grupo de Investigación en Telecomunicaciones Aplicadas (GITA)
Documento del Gobiberno : 1730814
ISSN : 1687-5680
1687-5699 E
Aparece en las colecciones: CIA (Centro de Investigaciones Ambientales y de Ingeniería)

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