The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications\nto provide services with the expected quality for their users.However, factors like the network parameters and codification can\naffect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity\nto evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs\n(Back propagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics\nMOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video\nQuality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service)\nbased in the strategy Diffserv.The metrics were analyzed through Pearson�s and Spearman�s correlation coefficients, RMSE (Root\nMean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.
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