The robust design and adaptation of multimedia networks relies on the study of the influence of potential network impairments on the perceived quality. Video quality may be affected by network impairments, such as delay, jitter, packet loss, and bandwidth, and the perceptual impact of these impairments may vary according to the video content. The effects of packet loss and encoding artifacts on the perceived quality have been widely addressed in the literature. However, the relationship between video content and network impairments on the perceived video quality has not been deeply investigated. A detailed analysis of ReTRiEVED test video dataset, designed by considering a set of potential network impairments, is presented, and the effects of transmission impairments on perceived quality are analyzed. Furthermore, the impact on the perceived quality of the video content in the presence of transmission impairments is studied by using video content descriptors. Finally, the performances of well-known quality metrics are tested on the proposed dataset.
In this article the effects of video content on Quality of Experience (QoE) have been presented. Delivery of the
video content with high level of QoE from bandwidth-limited and error-prone network is of crucial importance
for the service providers. Therefore, it is of fundamental importance to analyse the impact of the network
impairments and video content on perceived quality during the QoE metric design. The major contributions of
the article are in the study of i)the impact of network impairments together with video content, ii) impact of the
video content and ii) the impact of video content related parameters: spatial-temporal perceptual information,
video content, and frame size on QoE has been presented. The results show that when the impact of impairments
on perceived quality is low, the quality is significantly influenced by video content, and video content itself also
has a significant impact on QoE. Finally, the results strengthen the need for new parameter characterization, for
better QoE metric design.