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Advances in Multimedia
Volume 2016 (2016), Article ID 1730814, 17 pages
http://dx.doi.org/10.1155/2016/1730814
Research Article

Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

Engineering Department, Universidad de Antioquia, Medellín, Colombia

Received 11 August 2015; Revised 4 December 2015; Accepted 17 December 2015

Academic Editor: Stefanos Kollias

Copyright © 2016 Diego José Luis Botia Valderrama and Natalia Gaviria Gómez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 (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.