Studay On Quality Assessment Methord For Networked Video

With the rapid development of the network, video applications are also increasingly popular. However, network video quality is influenced by many factors, such as the limited resources of transmission channel, time-varying characteristics of networks, compression coding distortion and so on, that will lead to the decline of video quality, reduce the experience quality of the user (Quality of Experience, QoE). Therefore, to guarantee the network video service quality has become the key of network video service, video quality assessment method research has received extensive attention.Subjective evaluation method of Video is time-consuming and not suitable for carrying out. In contrast, the objective assessment method is more suitable for network video quality assessment. Therefore, this paper combines the theoretical study and experimental simulation, based on network video quality objective assessment methods, mainly make original contribution in the following aspects:(1)Introduce and evaluate the classification of existing network video quality assessment method, Analysis a variety of factors that have an impact on the video quality.(2)Since the compression encoding impact video quality, do researches on evaluation method based on the encoding parameters. Analysis characteristic information in H.264stream which reflect video quality, using multiple linear regression method to establish the encoding parameters and the video quality assessment model, finally verify the validity of the model through the analysis of the objective value and subjective assessment of the correlation between the values. (3)Since the network performance impact on the video quality, do researches on the evaluation method based on the network parameters. Based on the analysis of the definition and relationship of QoE and QoS, with HTTP video stream as the research object, to establish three layers of mapping model between QoS network layer, application layer and QoE. First calculate video performance metrics by the network performance parameters, using application of neural network algorithm, set the video performance metrics as input, output video quality assessment results. Finally, make an analysis and comparison of the model simulation results and experimental test data, to prove the superiority of the model.