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.
The Video Quality Experts Group (VQEG) ran subjects tests through the same audiovisual material in six different international laboratories @cite_12 @cite_1 . Each of these six labs conducted the experiment in a controlled environment while four labs also repeated the same experiments in a public environment. They have reported that audiovisual subjective tests are highly repeatable from one laboratory and environment to the next and recommended 24 or more subjects for ACR tests in a lab and 35 and more subjects for the same sensitivity in the public environment.
@cite_1 @cite_12
2008903454 2004790454
In 2011, the Video Quality Experts Group (VQEG) ran subjects through the same audiovisual subjective test at six different international laboratories. That small dataset is now publically available for research and development purposes. Between discrete event simulation and evaluation within real networks, network emulation is a useful tool to study and evaluate the behaviour of applications. Using a real network as a basis to simulate another network's characteristics, it enables researchers to perform experiments in a wide range of conditions. After an overview of the various available network emulators, this paper focuses on three freely available and widely used network link emulators: Dummynet, NIST-Net, and the Linux Traffic Control subsystem. We start by comparing their features, then focus on the accuracy of their latency and bandwidth emulation, and discuss the way they are affected by the time source of the system. We expose several problems that cannot be ignored when using such tools. We also outline differences in their user interfaces, such as the interception point, and discuss possible solutions. This work aims at providing a complete overview of the different solutions for network emulation.