## 4 Evaluation Results and Discussion
### Concluding Remarks  
In the above, our evaluation is divided into different cases, where models are evaluated in terms of codecs, viewing devices, and session durations. Based on different characteristics of models, a service provider may select an appropriate model. For example, for e-learning, a QoE model that supports PC screens and long sessions should be used, whereas a model that is good for smartphone screens and short sessions can be used for mobile streaming of short-form videos.  
Based on the above results and discussions, the following important remarks regarding the considered QoE models can be made.  
* In general, the performances of the models vary significantly across databases. Among them, the three models of _LSTM_, _SQI_, and _KSQI_ are found to be the most stable ones.
* Regarding encoding codecs, all the considered models result in better performance on H.264-encoded streaming sessions than HEVC-encoded ones. Surprisingly, even the models taking into account HEVC characteristics such as _P.1203_ and _KSQI_ have the similar behavior.
* With respect to viewing devices, the use of MOS and VMAF as segment quality metrics is quite effective to help the model perform more consistently. It is suggested that the _LSTM_, _SQI_, and _KSQI_ models can be employed for QoE prediction with different viewing devices.
* The models that are developed using short sessions such as the _[PERSON]_'s model may result in low and drastically variable performances for medium and long sessions.
* The ability to effectively quantify the impacts of (1) quality variations and (2) stalling events significantly affects the performance of QoE models. Hence, these two factors should be equally considered in order to build an effective model. In addition, it is found that the impact of stalling events can be partially covered by the impact of quality variations as seen in the case of the _[PERSON]_'s model. In addition, it is essential to consider temporal relations between impairment events in QoE models.
* Only one single statistic such as the QP average is insufficient to fully represent quality variations in a streaming session. Combination of several statistics such as the average of segment quality values, the switch frequency, and especially the degrees of quality switches (i.e., switch amplitudes) is found to be indispensable to effectively quantify the impact of quality variations.
* Similarly, the total stalling duration alone, which is employed in most models, is not able to characterize the factor of stalling. Other statistics such as the number of stalling events and the maximum stalling duration should be additionally considered.
* Developing complex models (i.e., more inputs and complicated modeling approaches) does not always result in better performance as found in the case of _[PERSON]'s_ and _ATLAS_ models.
* Among the considered models, the _LSTM_ model is the best one to predict the QoE of streaming sessions with different viewing devices and session durations. To evaluate the QoE of medium and long sessions (i.e., \(\geq\)1 min), the three models of _P.1203_, _CQM_, and _biQPS_ are also comparable.
* There is still room for improvements of the existing models, especially in the cases of various viewing devices (e.g., ultra high definition TV) and advanced video codecs (e.g., HEVC).