Feature-based prediction of streaming video QoE: Distortions, stalling and memory

Bampis, Christos G.; Bovik, Alan C.

doi:10.1016/j.image.2018.05.017

Cited by 52 publications

(61 citation statements)

References 66 publications

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“…Concretely, Rassol et al [25] carried out subjective quality tests to validate the application of VMAF to traditional contents with 4K resolution, a resolution for which the metric is not trained, obtaining good results when trying to predict the VMAF score. Bampis et al [26] used the dataset created for VMAF to implement their quality predictor and compare the results obtained by VMAF with other typical metrics. Likewise, Bampis et al [27] proposed the SpatioTemporal-VMAF (ST-VMAF), an extension to the VMAF metric consisting in expanding the analysis of temporal features in video sequences to enhance the metric results.…”

Section: Introductionmentioning

confidence: 99%

Video Multimethod Assessment Fusion (VMAF) on 360VR Contents

Orduna

Díaz

Muñoz

et al. 2020

IEEE Trans. Consumer Electron.

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Section: Introductionmentioning

confidence: 99%

Video Multimethod Assessment Fusion (VMAF) on 360VR Contents

Orduna

Díaz

Muñoz

et al. 2020

IEEE Trans. Consumer Electron.

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“…According to their evaluation, network layer features is enough to get accurate results. Recently, [147] propose an ML model called Video Assessment of Temporal Artifacts and Stalls (ATLAS). It uses an objective video quality assessment (VQA) method by combine QoE-related features and memory features sources of information to predict QoE.…”

Section: Speechmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and addedvalue services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

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“…While machine learning algorithms have been used to model QoE for VoIP [12], video streaming [6] or Skype [23], its application to Web browsing is still lacking. One marked exception is the work by Gao et al [15], where authors formulate a ternary classification task (i.e., A is faster, B is faster, none is faster) and employ Random Forest and Gradient Boosting ML techniques with QoS metrics such as those described in Section 2.1 as input features.…”

Section: Web Qoe Modelsmentioning

confidence: 99%

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

Hora

Asrese

Christophides

et al. 2018

Lecture Notes in Computer Science

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Abstract. Page load time (PLT) is still the most common application Quality of Service (QoS) metric to estimate the Quality of Experience (QoE) of Web users. Yet, recent literature abounds with proposals for alternative metrics (e.g., Above The Fold, SpeedIndex and variants) that aim at better estimating user QoE. The main purpose of this work is thus to thoroughly investigate a mapping between established and recently proposed objective metrics and user QoE. We obtain ground truth QoE via user experiments where we collect and analyze 3,400 Web accesses annotated with QoS metrics and explicit user ratings in a scale of 1 to 5, which we make available to the community. In particular, we contrast domain expert models (such as ITU-T and IQX) fed with a single QoS metric, to models trained using our ground-truth dataset over multiple QoS metrics as features. Results of our experiments show that, albeit very simple, expert models have a comparable accuracy to machine learning approaches. Furthermore, the model accuracy improves considerably when building per-page QoE models, which may raise scalability concerns as we discuss.

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Feature-based prediction of streaming video QoE: Distortions, stalling and memory

Cited by 52 publications

References 66 publications

Video Multimethod Assessment Fusion (VMAF) on 360VR Contents

Video Multimethod Assessment Fusion (VMAF) on 360VR Contents

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

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