IP-Based Mobile and Fixed Network Audiovisual Media Services

Raake, Alexander; Gustafsson, Jörgen; Argyropoulos, Savvas; Garcia, Marie-Neige; Lindegren, David; Heikkila, G.; Pettersson, Martin; List, Peter; Feiten, Bernhard

doi:10.1109/msp.2011.942472

Cited by 42 publications

(35 citation statements)

References 37 publications

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“…While researchers may use them in the lab to conduct experiments "offline", they are typically implemented in network probing devices in order to allow for automated QoE calculations. We can classify QoE models based on the information they use, according to [35]: as shown in Fig. 2, we can define No-Reference (NR), Reduced-Reference (RR), and Full-Reference (FR) models, depending on whether they have no, partial, or full access to a source signal.…”

Section: Quality Modelsmentioning

confidence: 99%

“…Parametric models only operate on transmitted packet-level or bitstream-level information, or -in the case of so-called planning models -assume no actual transmission, but can be used for designing networks with a certain service in mind. Naturally, a model with access to more information (e.g., [35] decoded video frames instead of just packet headers) should provide a more accurate estimation of the quality, but in practice -and as we will see later -the amount of information accessible is often influenced by several extrinsic factors beyond control of the ISP.…”

Section: Quality Modelsmentioning

confidence: 99%

“…A comprehensive overview of these models was already given by Raake et al in 2011 [35], however, since then, several new models have been standardized. ITU-T Rec.…”

Section: Quality Modelsmentioning

confidence: 99%

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Challenges of future multimedia QoE monitoring for internet service providers

Robitza

Ahmad

Kara

et al. 2017

Multimed Tools Appl

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The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs Appl (2017) 76:22243-22266 focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers.

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Section: Quality Modelsmentioning

confidence: 99%

Section: Quality Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Challenges of future multimedia QoE monitoring for internet service providers

Robitza

Ahmad

Kara

et al. 2017

Multimed Tools Appl

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“…8 For the purpose of quality estimation, NR metrics make use of either the bitstream of the impaired video or the decoded pixels of it, or a combination of both to build NR hybrid metrics. 9,10 A detailed review of NR metrics using this classification can be found in Ref. 11.…”

Section: Introductionmentioning

confidence: 99%

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Shahid

Pandremmenou

Kondi

et al. 2016

J. Electron. Imaging

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This is the published version of a paper published in Journal of Electronic Imaging (JEI).Citation for the original published paper (version of record):Shahid, M. (2016) Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models. Journal of Electronic Imaging (JEI)Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Abstract. Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

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“…Thus, other approaches that provide better estimations of the visual experience of the viewers have been proposed. For example, worth noting are the works by Reibman [151] and and Raake [150] developing video quality metrics for monitoring systems. In fact, this problem has been considered in standardization activities by the ITU, resulting in the publication of two monitoring approaches: the recommendation P.1201 that is a packet-based metric [90], and the P.1202, which also allows decoding the signal [91].…”

Section: Introductionmentioning

confidence: 99%

Analysis of quality of experience in 3D video systems

Sánchez¹

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IP-Based Mobile and Fixed Network Audiovisual Media Services

Cited by 42 publications

References 37 publications

Challenges of future multimedia QoE monitoring for internet service providers

Challenges of future multimedia QoE monitoring for internet service providers

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Analysis of quality of experience in 3D video systems

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