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.
In 2010 @cite_0 conducted subjective experiments to explore methods to predict audiovisual quality objectively for video calls in wireless applications. They have presented subjective test results for 60 test conditions on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship, and investigated how network and application parameters affect overall audiovisual quality. In their analysis they have used a regression model to predict audiovisual quality from packet loss rate and frame rate.
@cite_0
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Mobile wireless multimedia applications (e.g., video calls and IPTV) have gained great momentum in recent years. An important issue is to monitor predict overall audiovisual quality, instead of audio-only or video-only quality, non-intrusively for technical or commercial reasons. Previous audiovisual modeling research mainly considered application parameters (e.g., codec and send bit rate). Little attention has been paid to how network parameters, e.g., Packet Error Rate (PER) affect audiovisual quality. The aim of this paper is to explore methods to predict audiovisual quality objectively for video calls in wireless applications. The contributions of the paper are twofold. Firstly, we present subjective test results on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship. Secondly, we investigated how network parameters (e.g., PER) and application parameters, e.g., video Frame Rate (FR) affect overall audiovisual quality. We developed a regression model to predict audiovisual quality from PER and FR which can be used to monitor predict audiovisual quality non-intrusively. We also explore the possibility to predict audiovisual quality from full-reference voice and video quality metrics (i.e., PESQ and PSNR) and from combined PESQ PSNR and network application parameters. The different predication accuracy obtained from these models (accuracy from 84 to 93 ) indicates the complex attributes in audiovisual quality prediction. An extended Evalvid NS-2 platform is developed to support simulation of video calls over wireless networks.