An Encrypted Video QoE Evaluation Method Based on Network Flow Feature Construction Learning
An evaluation method and network flow technology, applied in machine learning, data exchange network, digital transmission system, etc., can solve the problems of video frame HAS segmentation time length parameter acquisition, evaluation method is not comprehensive enough, etc., to achieve high accuracy effect
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Embodiment 1
[0028] This embodiment is an encrypted video QoE evaluation method based on TCP protocol and network flow characteristic construction learning. figure 1 A schematic block diagram of the process flow of the encrypted video QoE evaluation method based on TCP protocol-based network flow feature construction learning in Embodiment 1 is given. First extract the experimental data set D4 required for analysis from the data collection platform data set D3, then perform TCP flow feature extraction D5, and finally realize the QoE evaluation based on network quality D6.
[0029] The data acquisition platform is mainly composed of the terminal APP D1 and the data acquisition LAN D2. The data set D3 is the HAS video data set based on the TCP protocol collected by the terminal APPD1 through the data acquisition LAN D2 to access the content provider D7 while watching the video, including terminal information. , video information, user operation behavior, QoE parameters, MOS score and QoS par...
Embodiment 2
[0058] Embodiment 2 of the present invention is a UDP protocol-based encrypted video QoE evaluation method based on network flow feature construction learning. Figure 5 A schematic flow chart of the network quality-based QoE evaluation method for the HAS video stream service based on the UDP protocol in Embodiment 2 is given. The QoE evaluation method of the HAS video stream service based on the UDP protocol is the same as the process in Embodiment 1. The data collection platform is composed of the terminal APPF1, the data collection local area network F2 and the content provider F7, and the data based on the UDP protocol is obtained through the data collection local area network F2. Based on the set F3, first extract the experimental data set F4 required for analysis, and then perform UDP data flow feature extraction F5 from the downlink rate F9, and finally realize the QoE evaluation F6 based on network quality by modeling the MOS score F10 model. Experimental dataset F4 in...
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