Abstract
In addition to traditional Quality of Service (QoS), Quality of Experience (QoE) poses a real challenge for Internet service providers, audio-visual services, broadcasters and new Over-The-Top (OTT) services. Therefore, objective audio-visual metrics are frequently being dedicated in order to monitor, troubleshoot, investigate and set benchmarks of content applications working in real-time or off-line. The concept proposed here, Monitoring of Audio Visual Quality by Key Performance Indicators (MOAVI), is able to isolate and focus investigation, set-up algorithms, increase the monitoring period and guarantee better prediction of perceptual quality. MOAVI artefacts Key Performance Indicators (KPI) are classified into four categories, based on their origin: capturing, processing, transmission, and display. In the paper, we present experiments carried out over several steps with four experimental set-ups for concept verification. The methodology takes into the account annoyance visibility threshold. The experimental methodology is adapted from International Telecommunication Union – Telecommunication Standardization Sector (ITU-T) Recommendations: P.800, P.910 and P.930. We also present the results of KPI verification tests. Finally, we also describe the first implementation of MOAVI KPI in a commercial product: the NET-MOZAIC probe. Net Research, LLC, currently offers the probe as a part of NET-xTVMS Internet Protocol Television (IPTV) and Cable Television (CATV) monitoring system.
Highlights
In addition to traditional Quality of Service (QoS), Quality of Experience (QoE) poses a real challenge for Internet service providers, audiovisual services, broadcasters, and new OverThe-Top (OTT) services
Objective audiovisual metrics are frequently dedicated to monitoring, troubleshooting, investigating, and setting benchmarks of content applications working in real-time or off-line
The probe is offered by Net Research, LLC as part of NET-xTVMS Internet Protocol Television (IPTV) and Cable Television (CATV) monitoring systems
Summary
In addition to traditional Quality of Service (QoS), Quality of Experience (QoE) poses a real challenge for Internet service providers, audiovisual services, broadcasters, and new OverThe-Top (OTT) services. The classic quality metric approach cannot provide pertinent predictive scores with a quantitative description of specific (new) audiovisual artefacts, such as stripe error or exposure distortions. In realistic situations, when video quality decreases in audiovisual services, customers can call a helpline to describe the annoyance and visibility of the defects or degradations in order to describe the outage. They are not required to provide a Mean Opinion Score (MOS). We present our experiments carried out over several steps with four experimental set-ups for concept verification.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.