## 6 Methodology for QoE/QoS Models  
The Video Quality Expert Group (VQEG) proposed a plan of tests to validate the performance of the different models proposed [45]. This plan allows a performance evaluation taking three factors into account: the (i) Prediction Accuracy; (ii) Prediction Monotonicity and (iii) Prediction Consistency [46]. The process followed for each aspect compares the results obtained from the proposed model to data obtained from subjective tests.  
1. Prediction Accuracy is expressed by the Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE). PLCC can assume values between \(-1\) and \(+1\); values closer to either \(-1\) or \(+1\) indicate higher accuracy. \(-1\) implies a completely negative correlation, zero implies an absence of correlation, and one implies a completely positive correlation [47]. The root mean square error (RMSE) is calculated based on quality estimation errors (i.e., the observed values and modelled values). It can assume values between zero and five; a value closer to zero indicates higher accuracy.
2. Prediction Monotonicity is expressed by the Spearman rank order correlation coefficient (SROCC) (also known as Rho of Spearman), which assesses the monotonic relation between two variables. In a monotonic relation, the variables tend to change simultaneously, albeit not necessarily in a constant, linear or logarithmic manner. The Spearman coefficient is calculated based on the data pair X, Y, and its value lies in \([-1,1]\); here, \(-1\) signifies that X can be represented as a declining/diminishing monotonic function of Y, and one signifies that X can be represented as an increasing monotonic function of Y [48].
3. Prediction Consistency is expressed by the Outlier Ratio (OR), which is defined as the percentage of the number of predictions that lie outside \(\pm\) 2 standard deviations of the results of the subjective tests. If N is the total number of data and N\({}^{\prime}\) is the number of atypical values, the percentage of atypical values is obtained by OR = N\({}^{\prime}\)/N. OR values lie between zero and one; zero implies the highest consistency [49].  
In our tests, each observer assessed a total of 70 videos (six test sequences, four sequences without distortion and sixty distorted sequences). We used the absolute classification with hidden reference (ACR-HR of recommendation ITU-T P.910) method to assess the video sequences, with a quality scale of nine levels. The video assessment was conducted in order to satisfy the requirements of Recommendation ITU-R BT.500-13. The lighting conditions and television screen were calibrated using an X-rite Color Munki Display A 32-inch Samsung (UN32D6000) television set with a native 1080 p resolution. The distance between the screen and observer was three times the height of the image [50].  
The model did not consider the sequences used to train the observers nor the post-processing of the subjective scoring system. The results for all the evaluated videos were recorded in a database, and the assessments obtained were processed to obtain the score associated with each video. Table 5 presents the results obtained by comparing the responses of the three proposed models with the subjective test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Models**} \\ \cline{2-4}
**Performance Factors** & **C00** & **I00** & **B00** \\ \hline PLCC & 0.566 & 0.772 & 0.825 \\ SROCC & 0.491 & 0.603 & 0.413 \\ RMSE & 0.4546 & 0.3147 & 0.3236 \\ OR & 0 & 0.25 & 0.066 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the performance tests for the proposed models.