Inferring Video QoE in Real Time
Abbreviated Journal Title
Computer Science, Hardware & Architecture; Computer Science, Information; Systems; Engineering, Electrical & Electronic; Telecommunications
Inferring the subjective perception of a video stream in real time continues to be a stiff problem. This article presents MintMOS: a lightweight, no-reference, loadable kernel module to infer the QoE of a video stream in transit and offer suggestions to improve it. MintMOS revolves around one-time offline construction of a k-dimensional space, which we call the QoE space. A QoE space is a known characterization of subjective perception for any k parameters (network dependent/independent) that affect it. We create N partitions of the QoE space by generating N video samples for various values of the k parameters and conducting subjective surveys using them. Every partition then has an expected QoE associated with it. Instantaneous parameters of a real-time video stream are compared to the precomputed QoE space to both infer and offer suggestions to improve QoE. Inferring QoE is a lightweight algorithm that runs in linear time. We implemented MintMOS by creating an actual QoE space using three parameters and 27 partitions by conducting surveys with 77 human subjects. In a second set of surveys using 13 video clips, MintMOS's predictions were compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions.
"Inferring Video QoE in Real Time" (2011). Faculty Bibliography 2010s. 2045.