Assessment of the rating performance of ITU-T recommended video quality metrics in the context of video freezes

Minhas, Tahir Nawaz; Shahid, Muhammad; Rossholm, Andreas; Lövström, Benny; Zepernick, Hans‐Jürgen; Fiedler, Markus

doi:10.1109/atnac.2013.6705382

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…The video freeze-and-jump has an adverse impact on the user QoE [15,16,29]. In case of a video jump, users may feel to be deprived for missing video content of an important moment, e.g., a goal or an attempt for goal in a soccer match, and this may degrade the user QoE.…”

Section: Live Video Streamingmentioning

confidence: 99%

“…where τ s > τ j (16) Data Loss (L): If τ o > τ s , then the data loss on the sender side equals the difference between the data sent during the network outage and data saved in the sender buffer:…”

Section: Sender Buffer Is Larger Than Jitter Buffermentioning

confidence: 99%

“…This phenomenon is more likely to appear in mobile and wireless networks as compared to wired networks. In the case of video streaming, video freezes degrade the QoE of the user remarkably [15,16]. Multiple freeze events have a severe impact on QoE, while the impact of data loss also depends on the video content [17].…”

Section: Introductionmentioning

confidence: 99%

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Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

Minhas

Fiedler

2021

Electronics

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With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of the video Quality of Experience (QoE). During live streaming, a network outage may result in video freezes and video jumps. To dampen the impact of a network outage on the video QoE, we propose the use of a well-sized sender buffer. We present the concept, derive key analytical relations, and perform a set of subjective tests. Based on those, we report a significant enhancement of user ratings due to the proposed sender buffer in the presence of network outages.

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Section: Live Video Streamingmentioning

confidence: 99%

Section: Sender Buffer Is Larger Than Jitter Buffermentioning

confidence: 99%

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Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

Minhas

Fiedler

2021

Electronics

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“…In [4], the author uses mathematical methods to combine features such as video motion attributes to form a video quality metric to measure the impact of video buffering issues on user QoE. In [5], the author compared MOS with the results of two measurement methods, Perceptual Evaluation of Video Quality (PEVQ) and Temporal Quality Metric (TQM), and summarized the pros and cons of different methods in measuring the visual quality changes caused by video buffering problems. However, most of them use objective parameters and do not evaluate video quality from the user's perspective.…”

Section: Introductionmentioning

confidence: 99%

Video Quality Measurement For Buffering Time Based On EEG Frequency Feature

Geng

Tao

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Currently, user-based quality of experience (QoE) measurement methods (e.g., mean opinion score, MOS) are often employed. However, their results might be affected by human subjective experience and thoughts. Physiological measurement methods can overcome these disadvantages. In the field of video quality models, the video buffering problem caused by poor network conditions is an important factor that affects QoE. In this paper, a reasonable psychophysiological measurement method, electroencephalography (EEG), is proposed to quantitatively analyze QoE changes when users face different levels of video buffering time. By extracting the band power of EEG signals as the feature and analyzing the correlation and variance, a more objective buffering time EEG (BT-EEG) score model of video quality under the single-factor video buffering time is established, which solves the problem of uneven subjective data quality.

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“…Both artifacts might trigger completely different user reactions, ranging from annoyance to abundance. Realizing that even well-reputed QoE evaluation tools are having issues with temporal disturbances [10], models that capture the dynamics of the disturbance processes, such as proposed in [11][12][13][14][15][16][17], keep emerging. Context factors, natural visual characteristics [14], visual attention [18] and short-and long-term pooling [19] have also been taken into account, and standards keep emanating [20].…”

Section: Introductionmentioning

confidence: 99%

Exponential On-Off Traffic Models for Quality of Experience and Quality of Service Assessment

Fiedler

Shaikh²,

Elepe

2014

PIK - Praxis Der Informationsverarbeitung Und Kommunikation

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Mobile connectivity typically exhibits on-off behavior, i.e. phases of undisturbed data transmission are interrupted by outages. Recent matching efforts have shown that the durations of the on-and off-phases can be matched by exponential distributions. The resulting exponential on-off models allow for elegant close-form solutions for performance metrics such as freeze probabilities in face of buffering, which amongst others allows for analysis-based interpretations of the impacts of various key parameters. Centering around exponential on-off behavior of mobile channels, this work provides a bridge between traffic measurements in mobile environments, closed-form traffic analysis based on Markov-modulated fluid flow models, and user perception of those kinds of disturbances that are typical for mobile environments. It also shows the need to focus Quality of Experience (QoE) studies on impact factors close to the source of the performance degradation, rather than on generic Quality of Service (QoS) parameters on packet level.

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Assessment of the rating performance of ITU-T recommended video quality metrics in the context of video freezes

Cited by 4 publications

References 17 publications

Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

Video Quality Measurement For Buffering Time Based On EEG Frequency Feature

Exponential On-Off Traffic Models for Quality of Experience and Quality of Service Assessment

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