ABSTRACT
Video-telephony applications have been widely used in people's daily life, such as online conferences, online education, and socialization. Especially during the COVID-19 pandemic, the business volume of video-telephony services has generally increased rapidly. This leads to a growing need for video-telephony service quality assessment and monitoring. This paper presents the subjective tests conducted in 'Computational model used as a QoE/QoS monitor to assess video-telephony services' (G.CMVTQS) project, which is under study in ITU-T SG12 Q.15. Two types of subjective tests are designed. Audiovisual material subjective test focuses on the quality of multimedia streams, while conversational subjective test focuses on the quality of interaction experience. These tests simulate real-life situations in video-telephony with different types of user terminals (PCs, TVs and mobile phones), environments (office, home, restaurant and outdoor), audio/video parameters (resolution, frame rate, bit rate and codec) and network impairments (packet loss, delay, jitter, bandwidth, synchronization). The final subjective database built collaboratively by five laboratories in different countries will be analyzed in the near future and provide more insights to develop new QoE/QoS monitor models for video-telephony. © 2022 IEEE.
ABSTRACT
This paper provides a subjective analysis of streaming video to understand human visual Quality of Experience (QoE). The emergence of the Corona pandemic has prompted a rise in demand for internet services, particularly for video streaming. A limited number of companies in Europe choose to reduce video transmission quality to escape Internet strain. The QoE markers are defined as either satisfactory if the video loads in less than 10 seconds and plays out efficiently or undesirable if the loading time is above 10 seconds or the playout suffers from stands. Online media will display various kinds of high motion sports and music videos. Such metrics would enable internet providers to assess the degree to which the QoE of video streaming customers can decline under bad network conditions. Video quality is influenced by consumer behavior and content and network and device efficiency. A more significant number of content types would help us to study human viewer behavior better. Network providers typically do not see this kind of traffic. QoE is now believed to be an essential aspect of the reactive traffic control mechanism within network operators. The researcher looked for the best to create a database that contains as many subject forms as possible. The mean opinion score (MOS) has an exponential relationship with the video playout's number of stalls. Today's Global Adaptive Streaming over HTTP (DASH) definition to distribute the content-based streaming services employ differential bitrate techniques. QoE tracking often considers a variety of video QoE influence factors (IFs) and contableuration schemes. Multimedia information QoE can be assessed using subjective or objective methods. © 2021 IEEE