We propose amodular no-reference video quality predictionmodel for videos that are encoded with H.265/HEVC and VP9 codecs\nand viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending\nupon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS\nfactors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and\napplication QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding\nperceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an\noptimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired\nvideos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application\nQoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building\nthe final model.We also use an Artificial Neural Network approach for building the model and compare its performance with the\nregressive approach.
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