## 4 Evaluation Results and Discussion
### Performance across Session Durations
#### 4.4.3 Third Model Group  
Figure 8 shows the performance of the QoE models in the third group. Although _[PERSON]_'s and _[PERSON]_'s models are very simple, considering only the impact of quality variations, their performances in both the _ori_ and _mod_ cases are generally stable and acceptable for all the databases including those containing sessions with stalling events (i.e., PCC \(\geq\) 0.64, SROCC \(\geq\) 0.55, and RMSE \(\leq\) 0.72). They even provide quite high performances for many databases such as the _W-SQoE-I_, _W-SQoE-II_, _TR04f_, _TR06f_, and _TR06p_ databases (i.e., PCC \(\geq\) 0.81, SROCC \(\geq\) 0.76, and RMSE \(\leq\) 0.56).  
Figure 7: Performance of _[PERSON]’s_, _ATLAS_, _P.1203_, _CQM_, and _biQPS_ models in the second group over the test databases. (**a**) PCC; (**b**) SROCC; (**c**) RMSE.  
Although _[PERSON]'s_ and _ATLAS_ models additionally take into account the impacts of stalling events, their performances are not significantly higher and even, in some cases, lower than the simple models of _[PERSON]'s_ and _[PERSON]'s_. This can be explained by the fact that stalling events commonly appear after segment quality decreases. Therefore, to a certain extent, modeling the impact of quality variations can somewhat reflect the impact of stalling events. In addition, it is suggested that the impact of quality variations is a key component in QoE models. In addition, it is once again confirmed that complex models that contain more inputs and take into account more influence factors do not always have better performances.  
Within the same case of _ori_ or _mod_, it can be seen that difference in performance of these two models depends on session durations. In particular, the difference is small for short and medium sessions. In such cases, the maximum difference is 0.06 for PCC, 0.07 for SROCC, and 0.05 for RMSE. Meanwhile, in case of long sessions, the longer the duration is, the bigger the difference becomes. For the \(TRCQ\) database, the difference is up to 0.13 for PCC, 0.16 for SROCC, and 0.15 for RMSE. In addition, the _[PERSON]'s_ model tends to perform better than the _[PERSON]'s_ model. This result implies that, to represent the impact of quality variations, the statistics used in the _[PERSON]'s_ model (i.e., average and stdev of segment quality values, number of quality switches) are more effective than the ones employed in the _[PERSON]'s_ model (i.e., median and minimum segment quality values), especially for long sessions.  
From Figure 8, it can also be seen that the addition of the impact of initial delay to _[PERSON]'s_ and _[PERSON]'s_ models does not bring significant improvements as the difference between the two cases _ori_ and _mod_ is trivial for all the databases. In particular, the maximum gains of the _mod_ case in terms of PCC, SROCC, and RMSE are respectively 0.02, 0.02, and 0.02 for both _[PERSON]'s_ and _[PERSON]'s_ models. A possible reason is that the initial delay in these databases is  
Figure 8: Performance of _[PERSON]’s_ and _[PERSON]’s_ models in the third group over the test databases. (a) PCC; (b) SROCC; (c) RMSE.  
commonly short (e.g.,1 s, 2 s, or 5 s) and could be tolerant [3]. Therefore, for short initial delay, its impact is marginal.