## 4 Performance Evaluation and Discussion
### Performance Evaluation on Testing Videos  
After obtaining the necessary parameters for the proposed model, we quantitatively and qualitatively evaluated its prediction performance on eight distorted videos in the testing set. Alternatively, the discussion on the results was also performed.  
To quantitatively assess the prediction performance, the correlation between the subjective overall QoE obtained in the LFOVIA Video QoE Database [12] and our predicted cumulative QoE at the end of each video was computed. It is crucial to note that the subjective overall QoE can be considered as the cumulative perception of the user at the end of streaming session. Three evaluation metrics were utilized for evaluation: (1) Pearson Correlation Coefficient (PCC); (2) Spearman Rank Order Correlation Coefficient (SROCC); and (3) Root Mean Square Error (RMSE). Typically, PCC and SROCC quantify how well the predicted QoE tracks the actual QoE scores in the database, whereas RMSE indicates the closeness between them. We also compared our proposed model with a reference method in [32], using the same training set and testing set. The cumulative QoE model in [32] is characterized by the following equation:  
\[Q_{t}=\gamma Q_{t-1}+(1-\gamma)q_{t} \tag{10}\]  
where \(q_{t}\) is the instantaneous user experience at moment \(t\), \(Q_{t-1}\) is the cumulative QoE at the previous moment \(t-1\), and \(\gamma\) is the memory strength parameter. The correlation between the predicted cumulative QoE obtained from this model and the subjective overall QoE in LFOVIA database was also investigated through PCC, SROCC and RMSE metrics. We report the performance of our model and the reference method in Table 3. This result shows a superior prediction performance of our model. Figure 8 additionally emphasizes the competitive performance of our model. Thereby, the proposed model has effectively assessed cumulative perception over multiple scenarios in testing videos.  
In qualitative evaluation, our purpose was to validate the impact of memory effects and DoI on the cumulative QoE prediction over multiple scenarios on testing videos. Thereby, the prediction performance of the proposed model in a short period and longer period could be assessed. Thus, in Figure 9, we plot the predicted cumulative QoE in comparison with both subjective instantaneous QoE and subjective overall QoE that were obtained from the database. Hereafter, the terms of subjective instantaneous QoE and subjective overall QoE are referred to as instantaneous QoE and overall QoE, respectively.  
In general, the predicted cumulative QoE precisely reacts to any interruption at any moment while being close to the overall QoE at the end of the streaming session. For the initial interruption, it is always witnessed a significant deterioration in predicted cumulative QoE. Nevertheless, when such unpleasant events continuously occur, the predicted cumulative QoE tends to decrease at a lower rate. Additionally, a lower recovering rate is subsequently introduced after each event.  
\begin{table}
\begin{tabular}{c c c|c c} \hline \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) & \(\lambda_{1}\) & \(\lambda_{2}\) \\ \hline
0.0284 & 0.8492 & 0.1177 & 0.9809 & 0.0800 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of memory weight and the cumulative QoE model.  
\begin{table}
\begin{tabular}{l|l|c c c} \hline \hline  & & **PCC** & **SROCC** & **RMSE** \\ \hline \multirow{2}{*}{Training} & [32] & 0.7413 & 0.6420 & 10.6187 \\  & Proposed model & **0.9441** & **0.8604** & **4.1525** \\ \hline \multirow{2}{*}{Testing} & [32] & 0.2777 & 0.2381 & 7.5135 \\  & Proposed model & **0.7664** & **0.7857** & **4.6538** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction performance of the reference model and our proposed model over training and testing set.