## 4 Performance Evaluation and Discussion
### Performance Evaluation on Testing Videos
#### 4.2.1 Impacts of Memory Effects  
In Patterns #2, #5, and #7, there is only one interruption with short duration occurring near the beginning of streaming sessions. As a result, the predicted cumulative QoE introduces a slight decrease, followed by a gradual recovery and convergence to a value as close to the overall QoE. These trends are consistent with those of instantaneous QoE. It means that the prediction accurately demonstrates the role of forgetting curve characteristic as well as the recency effect. More concretely, after the interruption finishes, the memory intensity about such event starts to exponentially decay, leading to the recovery in perceived video quality. At the end of streaming sessions, there is a possibility that the decay has completely finished; in other words, the memory of distorted events has vanished. Therefore, the recency effect becomes dominant, leading to the consistency among the predicted cumulative QoE, instantaneous QoE, and overall QoE.  
In Patterns #0 and #1, rebuffering event repeatedly occurs in the middle of streaming sessions. While the predicted cumulative QoE is consistent with the overall QoE at the end of sessions, the instantaneous QoE tends to continuously increase, creating a big gap to the overall QoE. At first sight, one might think that the overall QoE must be as high as the instantaneous QoE at the end of streaming sessions due to the recency effect. This inference is understandable because the moment at which the last interruption occurs is quite far from the end of sessions, thus the recency effect would have become dominant, resulting in the consistency among those QoE evaluations. However,  
Figure 8: Correlation between subjective overall QoE and predicted cumulative QoE at the end of streaming session.  
Figure 9: Predicted cumulative QoE in comparison with the subjective overall and instantaneous QoE over eight different playout patterns.  
when the interruption repeats many times, the impact of repetition characteristic become significantly obvious. Consequently, the user tends to provide an overall evaluation whose value is lower than the instantaneous QoE. On the other hand, by considering the recency effect and repetition characteristic, our proposed model can effectively provide the prediction consistency with the overall QoE.  
According to the hysteresis effect [31], the user is highly sensitive to a single unpleasant event and provides poor QoE scores immediately. However, when the interruption occurs many times as in Patterns #0, #1 and #3, the impact of the hysteresis effect will be shared with the repetition characteristic. This makes the user behave in the consideration of past annoying events to avoid the aggressive reaction. In addition, under the impact of repetition characteristic, such events are stuck in the user's memory and are recalled when the user provides the overall assessment. However, the instantaneous QoE always aggressively reacts to the distorted events, by dramatically decreasing and quickly recovering during a short period. This is because the instantaneous QoE is estimated locally without considering the global views of the streaming session. Oppositely, by weighting the instantaneous QoE by the memory effects (especially repetition characteristic), the predicted cumulative QoE can react calmly, and, eventually, correlates perfectly with the overall QoE.  
Interestingly, the predicted cumulative QoE also indicates a special behavior in human perception, which cannot be found in the instantaneous QoE and overall QoE. We call such the behavior as the _persistent evaluation_ where the user seems familiar with the distorted event and accepts it. The user does not even want to deteriorate their evaluation score or to quit from the streaming session. For instance, Patterns #0 and #3 visualize that the cumulative QoE dramatically falls after the occurrence of the first rebuffering event. However, it decreases with a significantly lower amplitude on the ones happening subsequently.