We have developed reduced reference parametric models for estimating perceived quality in audiovisual multimedia services. We have created 144 unique configurations for audiovisual content including various application and network parameters such as bitrates and distortions in terms of bandwidth, packet loss rate and jitter. To generate the data needed for model training and validation we have tasked 24 subjects, in a controlled environment, to rate the overall audiovisual quality on the absolute category rating (ACR) 5-level quality scale. We have developed models using Random Forest and Neural Network based machine learning methods in order to estimate Mean Opinion Scores (MOS) values. We have used information retrieved from the packet headers and side information provided as network parameters for model training. Random Forest based models have performed better in terms of Root Mean Square Error (RMSE) and Pearson correlation coefficient. The side information proved to be very effective in developing the model. We have found that, while the model performance might be improved by replacing the side information with more accurate bit stream level measurements, they are performing well in estimating perceived quality in audiovisual multimedia services. IEEE ICC 2016 -Communication QoS, Reliability and Modeling Symposium 978-1-4799-6664-6/16/$31.00 ©2016 IEEE
Finally, the authors in REF used ML for estimating audiovisual quality of multimedia service.
Towards reduced reference parametric models for estimating audiovisual quality in multimedia services
2016 IEEE International Conference on Communications (ICC)
2016 IEEE International Conference on Communications (ICC)
Computer Science