## 4 Evaluation Results and Discussion
### Performance across Session Durations
#### 4.4.2 Second Model Group  
Figure 7 shows the results of the models in the second group. Along with the sum of stalling durations, the total number of stalling events are additionally fed in _[PERSON]'s_ and _ATLAS_ models. Hence, from Figures 6 and 7, it can be seen that their performance is significantly higher than or similar to that of _[PERSON]'s_ model for the _W-SQoE-I_, _TR04f_, _TR04p_, and _VLDB_ databases. Compared to the _[PERSON]'s_ model, the _ATLAS_ model produces better results but is still not very high for the _W-SQoE-II_ database that comprises sessions with only quality variations (i.e., PCC = 0.68, SROCC = 0.71, and RMSE = 0.53). This can be because this model relies on the frequency of quality switches to characterize the quality variations, which cannot account for the degrees of the quality switches (i.e., switching amplitude).  
In spite of using the same inputs to represent the impacts of stalling events, the _[PERSON]'s_ model brings out significantly higher performances than the _ATLAS_ model for the databases including sessions with stalling events (i.e., _W-SQoE-I_, _W-SQoE-III_, _VLDB_, and _TRDB_). This may be caused by the difference between the modeling approaches used in the two models. Interestingly, the _[PERSON]'s_ model that has higher performance utilizes the simple linear function, whereas the _ATLAS_ model uses the more sophisticated one of Support Vector Regression. However, in general, both the approaches are not very effective in quantifying the impact of stalling events (i.e., PCC \(\leq\) 0.81, SROCC \(\leq\) 0.85, and RMSE \(\geq\) 0.47). Hence, both _[PERSON]'s_ and _ATLAS_ models are not very effective to predict the QoE of streaming sessions. In addition, the use of more complicated modeling approaches does not always result in higher performance.  
For the _P.1203_, _CQM_, and _biQPS_ models, their performances are very high for the databases with medium and long sessions, namely _TR04p_, _VL04_, _VLDB_, _TRDB_, _TR06f_, _TR06p_, _VL13_, _VLCQ_, and _TRCQ_. In particular, their PCC and SROCC values are in the range from 0.82 to 0.95, and their RMSE values are between 0.29 and 0.43. The plausible explanation is that they are originally devoted to such session durations (i.e., 60 s-300 s for the _P.1203_ model and 60 s-360 s for the _CQM_ and _biQPS_ models). For short sessions of 13 and 28 s (i.e., in _W-SQoE-III_, _W-SQoE-IV_\(\_\)_ft264_, _W-SQoE-IV_\(\_\)_ph264_, and _W-SQoE-IV_\(\_\)_uH264_), the _P.1203_ model generally performs quite well (i.e., PCC \(\geq\) 0.76, SROCC \(\geq\) 0.79, and RMSE \(\leq\) 0.48). However, for shorter sessions of 8 and 10 s (i.e., in the _W-SQoE-I_ and _W-SQoE-II_ databases), its performance is drastically reduced (i.e., PCC \(\leq\) 0.40, SROCC \(\leq\) 0.53, and RMSE \(\geq\) 0.65). In a similar behavior, the performance of the _biQPS_ model is acceptable for 28-second long sessions in _W-SQoE-IV_ff264_, _W-SQoE-IV_pH264_, and _W-SQoE-IV_uhH264_ (i.e., PCC \(\geq\) 0.64, SROCC \(\geq\) 0.69, and RMSE \(\leq\) 0.63). However, its performance is unsatisfactory for shorter sessions (i.e., PCC \(\leq\) 0.45, SROCC \(\leq\) 0.45, and RMSE \(\geq\) 0.64). Meanwhile, the performance of the _CQM_ model is acceptable for all the databases including short sessions. However, these results are still not very high. Specifically, its PCC, SROCC, and RMSE values range respectively in [0.62, 0.80], [0.65, 0.84], and [0.73, 0.43]. This result implies that the _P.1203_, _CQM_, and _biQPS_ models should be employed for only medium and long sessions with the lengths from 60 to 360 s.