Automatic Quality of Experience Measuring for Video Conference in Real-Time | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 11, Volume 22, Issue 3, September 2022, Pages 118-127 PDF (1011.78 K) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.22.3.11 | ||
| Authors | ||
| Roaa E. Alhassany* ; Rana F. Ghani | ||
| Department of Computer Science, University of Technology, Iraq | ||
| Abstract | ||
| In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications is the user's perception of the quality of the experience. In this work, an Extreme Learning Machine (ELM) model was proposed for predicting video quality of experience. The proposed system extracts several features from videos that have a significant impact on the quality of the experience. The model performance was validated with unseen data. Spearman’s Rank Correlation Coefficient (SRCC), Root Mean Square Error (RMSE), Pearson’s Linear Correlation Coefficient (PLCC) metrics have been used to measure the accuracy of the model and correlation. The results demonstrate that the proposed model had better performance than models used by the previous researchers that were used for predicting video QoE in terms of precision, correlation, and running time. | ||
| Keywords | ||
| Extreme Learning Machine; Quality of Experience; quality of service; Video Streaming | ||
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