Factors influencing QoE of video consultations

Oie, Even Brattbak; Koniuch, Kamil; Cieplińska, Natalia; Moor, Katrien De

doi:10.1109/qomex51781.2021.9465376

Cited by 5 publications

(2 citation statements)

References 25 publications

(25 reference statements)

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“…Moreover, mixed methods, such as open profiling of quality (OPQ) (Strohmeier et al, 2011), can provide quantitative results based on qualitative data. In other cases, mixed-method approaches have been used to deepen the understanding of quantitative data by means of a qualitative follow-up [see, e.g., (De Moor et al, 2019;Øie et al, 2021)]. All the abovementioned methods share a similar goal.…”

Section: Bottom-up Approachmentioning

confidence: 99%

Top-down and bottom-up approaches to video quality of experience studies; overview and proposal of a new model

Koniuch,

Baraković,

Husić

et al. 2024

Front. Comput. Sci.

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Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi) media researchers. They call this area of study “Quality of Experience” (QoE). In this paper, we present a theoretical model based on review of previous QoE’s models. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them; therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. This novel top-down model serves as a practical guide for structuring QoE experiments, ensuring the incorporation of influential factors in a confirmatory manner.

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Section: Bottom-up Approachmentioning

confidence: 99%

Top-down and bottom-up approaches to video quality of experience studies; overview and proposal of a new model

Koniuch,

Baraković,

Husić

et al. 2024

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Silva et al in [24] measured user annoyance for image blockiness and blurriness and found that the blockiness had the stronger impact on users between the two. In a recent study [25], Øie et al investigated a patient's and a doctor's QoE during video consultations. They found that the patient's age, medical condition, expectations, attitudes, prior experiences, and technical skills and competence of both patient and doctor affect their perception.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Mrvelj

Matulin

2021

Electronics

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Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service.

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Survey on user perceived system factors influencing the QoE of audiovisual calls on smartphones

Vucic

Baraković

Skorin‐Kapov

2022

Multimed Tools Appl

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With the widespread use of applications and services supporting audiovisual calls via smartphones, both in business and leisure contexts, a key challenge for service providers is meeting end user Quality of Experience (QoE) expectations and requirements. To successfully meet this challenge, there is a need to identify and analyze the key system-related factors impacting user perceived quality. In this paper, we contribute beyond state-of-the-art by conducting a large scale web-based questionnaire survey to investigate the system-related factors that subjects identify as most influential in contributing to their overall experience and quality perception. We focus in particular on leisure audiovisual calls, established via mobile devices. Our initial survey (Phase 1) was conducted in Feb. 2020, just prior to the outbreak of the COVID-19 pandemic (272 participants). To investigate if the importance of factors has changed due to increased usage of the service caused by the pandemic among the general population, we conducted a second survey (Phase 2) in October 2021 with 249 participants. Based on obtained results, we identify key system-related QoE influence factors belonging to three categories: media quality, functional support, and usability and service design. We observe no significant differences in user opinions and expectations prior to and during the period of increased service usage, despite different participant demographics and study time frames, thus contributing to generalizability of obtained results. Study results contribute to providing insights for designing future user studies investigating QoE, in terms of key factors that should be considered.

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Factors influencing QoE of video consultations

Cited by 5 publications

References 25 publications

Top-down and bottom-up approaches to video quality of experience studies; overview and proposal of a new model

Top-down and bottom-up approaches to video quality of experience studies; overview and proposal of a new model

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Survey on user perceived system factors influencing the QoE of audiovisual calls on smartphones

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