Configuring Quality of Experience models has its own dedicated tab in Qosium Scope. GQoSM and PSQA models can be selected and parameterized in this tab.
This tab consists of the following settings groups:
Setting this option from Off to Manual enables GQoSM samples in average results.
The model can use up to 4 QoS parameters in QoE calculation: Delay, jitter, packet loss, and connection break length. Each of these parameters can be enabled/disabled individually. Each parameter has 2 adjustments: Bad performance limit and form factor. For more information on how to configure this model, see Quality of Experience.
Pseudo-Subjective Quality Assessment (PSQA) uses a trained feed-forward neural network for determining quality. For more information on how to configure this model, see Quality of Experience.
The available options depend on the current
This listening model is applicable when the targeted traffic consists of a one-direction VoIP flow. The model has a few parameters:
This conversational model is applicable when the targeted traffic consists of a two-direction VoIP conversation flow. The model has a few parameters:
This streaming video model is applicable when the targeted traffic consists of a video stream. The model has a few parameters:
This streaming video model is applicable when the targeted traffic consists of a video stream. The model has a few parameters:
Sample averaging settings can be adjusted to pre-average QoE samples. This reduces sporadic fluctuations in the results when using small averaging interval, or when the quality model yields low scores for brief deterioration of network conditions not visible in the end-application.
When enabled, the average is calculated by using the weighted moving average algorithm. See Wikipedia article on weighted moving average.
When enabled, the average is calculated from a fixed number of most recent samples. The number can be adjusted manually.
Quality of Experience (QoE) indicates how satisfied the user is for using the application/service. QoE is always an application-specific measure, and the actual result can vary from person to person. This section introduces how QoE can be estimated automatically and in real-time for a connected application without consulting the user.