Key indicators for monitoring of audiovisual quality

Leszczuk, Mikołaj; Hanusiak, Mateusz; Blanco, Ignacio; Dziech, Andrzej; Derkacz, Jan; Wyckens, Emmanuel; Borer, Silvio

doi:10.1109/siu.2014.6830724

Cited by 15 publications

(28 citation statements)

References 9 publications

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“…Most of the models in ITU-T recommendations were validated on video databases that used one of the following hypotheses: frame freezes lasting up to 2 seconds; no degradation at the beginning or at the end of the video sequence; no skipped frames; clean video reference (no spatial or temporal distortions); minimum delay supported between video reference and video (sometimes with constant delay); and up or down-scaling operations not always taken into account [17].…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

“…Other KPIs mentioned in MOAVI are usually not taken into account (exposure time distortion, noise, block loss, freezing, slicing, etc.) in predicting MOS [17].…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

“…The history of the ITU-T recommendations for video quality metrics is shown in Table 1. Table 2 shows a synthesis of the set of metrics that are based on video signals [17]. As can be noticed from both tables, there is a lack of developments for the NR approach.…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

“…In a related research, Gustafsson et al [4] addressed the problem of measuring multimedia quality in mobile networks with an objective parametric model [17]. Closely related work are ongoing standardization activities at ITU-T SG12 on models for multimedia and Internet Protocol Television (IPTV) based on bit-stream information.…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

“…SG12 is currently working on models for IPTV. Q.14/12 is responsible for these projects, provisionally known as P.NAMS (non-intrusive parametric model for assessment of performance of multimedia streaming) and P.NBAMS (non-intrusive bit-stream model for assessment of performance of multimedia streaming) [17]. P.NAMS uses packet-header information (e.g., from IP through MPEG2-TS), while P.NBAMS also uses payload information (i.e., coded bit-stream) [23].…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

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Recent developments in visual quality monitoring by key performance indicators

Leszczuk

Hanusiak

Farias

et al. 2014

Multimed Tools Appl

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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)The research leading to these results has received funding from the European Regional Development Fund

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Section: State-of-the-art Backgroundmentioning

confidence: 99%

“…Other KPIs mentioned in MOAVI are usually not taken into account (exposure time distortion, noise, block loss, freezing, slicing, etc.) in predicting MOS [17].…”

Section: State-of-the-art Backgroundmentioning

confidence: 99%

Section: State-of-the-art Backgroundmentioning

confidence: 99%

Section: State-of-the-art Backgroundmentioning

confidence: 99%

Section: State-of-the-art Backgroundmentioning

confidence: 99%

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