  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.
