Assessing the QoE in Video Services Over Lossy Networks

Quality of experience is of critical importance in streaming video services, because the traditional quality of service cannot represent the quality perceived by viewers. This work evaluates several objective quality metrics under realistic bursty packet loss conditions in the network, with the support of a packet loss model. Alignment of reference and streamed video sequences (with different levels of spatial-temporal information) are also explored as a technique to prevent inaccurate computation of objective metrics due to frame loss. Finally, the correlation between subjective and objective metrics for each motion level and the computing time of metrics are analysed. The most suitable objective metrics to characterize the real degradation in the quality perceived by viewers, for both off-line and real-time assessment, are proposed. The integration of motion, busty packet loss, sequence alignment after frame loss and computing time of metrics are the main contributions of this research work.