## 3 Experimental Results and Analysis
### Performance Comparison with the State-of-the-Art Methods  
To verify the superiority of the proposed QoE model, we compared it with the existing four QoE assessment models. The four assessment models include:  
1. FTW model [5]: Consider re-buffering count and the re-buffering duration as the influencing factors and establish an exponential relationship between the influencing factors and MOS.
2. SQI model [11]: Use a linear combination of multiple exponential models to analyze the relationship between QoE and video compression, initial delay, re-buffering.
3. P.NATS model [17]: The random forest method is used to model the impact of re-buffering position, re-buffering duration, frame rate and video quality on the user's QoE.
4. [PERSON]'s model [28]: Take the effect of initial delay, re-buffering and quality fluctuation on QoE into account and establish the exponential and logarithmic models, separately. Then combine these two models as the QoE prediction model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Waterloo Streaming QoE Database-III**} & \multicolumn{2}{c}{**LIVE-NFLX-II**} \\ \cline{2-5}  & **Model 1** & **Model 6** & **Model 1** & **Model 6** \\ \hline Video content characteristics & \(\surd\) & SI \& TI & \(\surd\) & SI \& TI \\ Video quality & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Fluency & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Volatility & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Feature dimension & 68 & 20 & 62 & 14 \\
**PLCC** & **0.9570** & **0.8819** & **0.9756** & **0.9153** \\
**SROCC** & **0.9465** & **0.8699** & **0.9718** & **0.9092** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance comparison using the proposed deep spatial-temporal features and SI and TI.  
For a fair comparison, we test the above four models and the models proposed in this paper on the Waterloo Streaming QoE Database-III. The specific comparison results are listed in Table 6. The experimental results of the four models in the table are cited from the literature [23].  
Table 6 indicates that, compared with other models, the proposed QoE model can obtain much higher prediction accuracy with a large margin. This is because the proposed method not only considers video quality and fluency in parameter selection but also uses deep spatial-temporal features that can effectively characterize the content characteristics of the video, so it is obviously superior to other existing methods while predicting the user's QoE.  
We also compared the proposed method with the latest QoE assessment model NAVE [29] on LIVE-NFLX-II dataset. NAVE is a kind of No-reference Auto-encoder VidEo (NAVE) quality metric, which uses deep Auto Encoder (AE) network to extract the deep features of a video to estimate the overall visual quality. NAVE used DIIVINE to extract the NSS features of the video frame and then calculated the spatial-temporal indices. The total feature dimension is \(90\times\) N (N is the number of video frames), then the features are input into the automatic encoder for training.  
For a fair comparison, the same 10-fold cross-validation experiments as in the NAVE method is conducted in this paper and the results are listed in Table 7. The experimental results show that the proposed model can achieve more accurate prediction because the NAVE method only uses the characteristics of the video, while other influencing factors, such as re-buffering and quality switching are neglected.