Abstract
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.
Abstract (translated)
内容提供商通过传输实时复杂信号（如视频数据）从一个地区传输到另一个地区。在传输过程中，信号通常会在实际视频信息丢失的地方出现扭曲或失真。这种情况通常发生在流媒体视频服务应用程序中。因此，需要在接收端了解发生了多少降解。这种视频降解可以通过网络状态参数（如数据率和丢失数据包数量）来估计。我们提出的解决方案vQoS GAN（视频服务质量生成对抗网络）使用半监督生成对抗网络算法的深度学习方法从失真的接收视频数据中估计网络状态参数。通过与视频数据和数据率及丢失数据包分类标签一起训练，该网络模型的鲁棒性和独特设计达到超过95%的训练准确率。与传统的基于规则的方法相比，我们的半监督生成对抗网络具有更高的准确性，并且可以重建失真的视频数据以提供更好的用户体验。
URL
https://arxiv.org/abs/2204.07062
