QoE assessment model for multimedia streaming services using QoS parameters

Kim, Hyun Jong; Choi, Seong Gon

doi:10.1007/s11042-013-1507-8

Cited by 32 publications

(28 citation statements)

References 9 publications

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“…The assessment is based upon the quality of images contained in the video signal, packet loss, and the packet arrival time. Similar work has been done by authors in [27] for an IPTV service where the effects of delay, jitter, packet loss rate, error rate, bandwidth, and signal success rate are considered. Both these works primarily take into consideration network QoS factors and they are targeted towards watching videos on a big screen like television.…”

Section: Studies Of Correlation Between Qos and Qoementioning

confidence: 82%

“…Therefore, a regression based model is presented in (27) that integrates both the approaches for finding the final video quality due to the application factors. The regression model is build based on (25) and (26) along with the subjective score obtained from the 70 video sequences that have been impaired by all the concerned application QoS factors.…”

Section: Integrated Qoe Measurement For Application Factorsmentioning

confidence: 99%

“…Table 8. Based on the MOS scores obtained across these 78 sequences and (21) and (27); a regression approach is used to build the final video model. A stepwise method of variable entering scheme is used.…”

Section: Final Integrated Video Modelmentioning

confidence: 99%

See 2 more Smart Citations

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

Pal

Vanijja

2017

Advances in Multimedia

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We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.

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Section: Studies Of Correlation Between Qos and Qoementioning

confidence: 82%

Section: Integrated Qoe Measurement For Application Factorsmentioning

confidence: 99%

See 1 more Smart Citation

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

Pal

Vanijja

2017

Advances in Multimedia

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“…The point "b" on the graph represents the actual threshold value below which the user will probably stop using the service. There is no exact relationship that models this region of diminishing QoE [24,25]. However, a number of ITU recommendations like ITU-T G.1070, ITU-T G.1071, and ITU-T P.1201 attempt to model this scenario.…”

Section: The Relationship Between Qos and Qoementioning

confidence: 99%

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Pal

Triyason

2018

International Journal of Digital Multimedia Broadcasting

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Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research.

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“…Fiedler et al investigated the influence of single QoS parameter on QoE with neglecting the interactions among QoS parameters. Kim and Choi and Pokhrel et al combined multiple QoS parameters together to determine the QoE prediction model. To improve the accuracy of QoE prediction, multidimension influence factors were taken into consideration to establish QoE prediction model in previous studies .…”

Section: Introductionmentioning

confidence: 99%

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Wang

Huang

2018

Int J Communication

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Summary Satellite networks are promising alternatives to deal with the increasing traffic volume and provide universal access for end users. Quality of experience (QoE) as an aggregate of user perception and network quality will be a critical success factor for multimedia service delivery over satellite networks. Quality of experience prediction based on quality of service (QoS) has received much attention recently, but it has been little investigated in satellite networks. In this paper, on the basis of the modular neural network and deep belief networks (DBNs), we design a QoE/QoS mapping method for multimedia services over satellite networks to translate QoS parameters into user QoE. The task of QoE/QoS mapping for multimedia services is decomposed into several different subtasks by using traffic classification. Heterogeneous DBNs are used to learn the mapping relationships between QoE and QoS for different types of services (ie, subtask) simultaneously. An integrative approach based on relative distance is exploited to select subneural network(s) to predict resulting QoE collaboratively. To determine the weight parameters of the QoE/QoS correlation model, the dataset composed of QoS parameters and subjective opinion scores is built on the basis of satellite network simulator and subjective test. Finally, the effectiveness of our QoE/QoS mapping method is validated, and the relationship between adoption rating and opinion scores is analyzed.

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QoE assessment model for multimedia streaming services using QoS parameters

Cited by 32 publications

References 9 publications

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

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