多媒体服务中视听质量估计的简化参考参数模型
Towards Reduced Reference Parametric Models for Estimating Audiovisual Quality in Multimedia Services
摘要：
我们已经开发了减少参考参数模型估计视听多媒体服务的感知质量。我们为视听内容创建了144种独特的配置，包括各种应用和网络参数，如带宽、丢包率和抖动方面的比特率和失真。为了生成模型训练和验证所需的数据，我们在受控环境中对24名受试者进行了任务，以绝对类别评级（ACR）5级质量量表对整体视听质量进行评级。我们使用基于随机森林和神经网络的机器学习方法开发了模型，以估计平均意见得分（MOS）值。我们使用了从数据包报头中检索的信息和作为网络参数提供的辅助信息来进行模型训练。基于随机森林的模型在均方根误差（RMSE）和皮尔逊相关系数方面表现更好。事实证明，边信息在开发模型方面非常有效。我们已经发现，虽然模型的性能可能会得到改善，用更准确的比特流水平测量取代边信息，他们在估计视听多媒体服务的感知质量表现良好。
英文摘要：
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.
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