Nonintrusive method based on neural networks for video quality of experience assessment

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.

• Autores: Gaviria Gómez, Natalia
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2016
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/7912
• Acceso en línea: http://hdl.handle.net/10495/7912; http://dx.doi.org/10.1155/2016/1730814
• Palabra 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
• Rights: openAccess
• License: https://creativecommons.org/licenses/by/2.5/co/