论文标题
使用原始视频分析的网络状态估算：基于VQOS-GAN的非侵入性深度学习方法
Network state Estimation using Raw Video Analysis: vQoS-GAN based non-intrusive Deep Learning Approach
论文作者
论文摘要
基于内容的提供商将实时复杂信号（例如视频数据）从一个区域传输到另一个区域。在此传输过程中，这些信号通常最终会扭曲或退化，而视频中存在的实际信息丢失了。这通常发生在流视频服务应用程序中。因此，有必要知道接收器侧发生的降解水平。可以通过网络状态参数（例如数据速率和数据包损耗值）来估算此视频退化。我们提出的解决方案VQOS GAN（服务生成对抗网络的视频质量）可以使用半监督生成性对抗性网络算法的深度学习方法从降级接收到的视频数据中估算网络状态参数。深度学习网络模型的强大而独特的设计已通过视频数据以及数据速率和数据包丢失类标签进行培训，并达到了95％的培训准确性。提出的半监督生成对抗网络还可以将降级的视频数据重建为原始形式，以获得更好的最终用户体验。
Content based providers transmits real time complex signal such as video data from one region to another. During this transmission process, the signals usually end up distorted or degraded where the actual information present in the video is lost. This normally happens in the streaming video services applications. Hence there is a need to know the level of degradation that happened in the receiver side. This video degradation can be estimated by network state parameters like data rate and packet loss values. Our proposed solution vQoS GAN (video Quality of Service Generative Adversarial Network) can estimate the network state parameters from the degraded received video data using a deep learning approach of semi supervised generative adversarial network algorithm. A robust and unique design of deep learning network model has been trained with the video data along with data rate and packet loss class labels and achieves over 95 percent of training accuracy. The proposed semi supervised generative adversarial network can additionally reconstruct the degraded video data to its original form for a better end user experience.
