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 strategyDiffserv. 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.
Desde el año 2020 el programa de Ingeniería de Sistemas de la Universidad de Antioquia ha trabajado en una propuesta de renovación curricular orientada a cumplir con los nuevos lineamientos del CNA (Consejo Nacional de Acreditación), lo cual implica una revisión y actualización a nivel meso y microcurricular, en donde se construyan nuevos resultados de aprendizaje, procesos para evaluación de los mismos, actualización de núcleos académicos, autoevaluación del currículo, entre otros procesos. De acuerdo con los referentes internacionales de currículo para programas relacionados con la informática, computación y sistemas como por ejemplo el ACM Computing Curricula 2020, se propuso un proceso de diagnóstico y mapeo curricular completo con el objetivo de generar una organización curricular del programa y que permite ajustarse a procesos importantes de flexibilidad y modernización curricular, doble titulación, internacionalización y acreditación.
For network operators (telcos) the quality assessment over multimedia services has become a hot topic in recent years. We propose a model to simplify the assessment and correlation metrics QoS/QoE over video services. We report the development of a methodology that use metrics Full Reference (FR) and Reduced Reference (RR), through network scenarios using QoS strategies (Diffserv). We used a multivariate correlation model and a model based on ANN as PSQA (Pseudo subjective Quality Assessment) for accurately predicting subjective quality MOS metric of the user, taking into account the QoS defined. Simulation experiments show values that are correlated nicely with each metric and allow validating QoE values adjusted for real user perception.
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