Quality Of Experience Model Research Articles

A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology

This study introduces a new adoption model for assessing the quality of experience (QoE) of radiologists using AI-based medical imaging technology. While AI has increasingly been used by radiologists for screening, diagnosis, and classification of medical images, previous investigations have primarily focused on metrics such as effectiveness, efficiency, and satisfaction. This research expands the evaluation criteria to include user experience and user interface (UX/UI) factors, integrating them within the broader QoE. QoE is conceptualized as a multifaceted construct influenced by both human and system factors, which affect cognitive perception, including hedonic and pragmatic aspects. Data were collected from 159 hospital radiologists with prior experience in using AI technology in medical imaging systems through a structured questionnaire. The data were then analyzed using structural equation modeling principles. The findings suggest that contextual content, human factors, and system characteristics significantly influence cognitive perception, which in turn affects the adoption and utilization of AI in medical imaging. This adoption model captures the radiologists' integration of AI throughout various stages of radiological procedures, including scheduling, scanning, acquisition, interpretation, reporting, and communication. The study also highlights the importance of data collection, storage, and sharing practices in compliance with privacy policies.

Intersecting realms: a cross-disciplinary examination of VR quality of experience research

The advent of virtual reality (VR) technology has necessitated a reevaluation of quality of experience (QoE) models. While numerous recent efforts have been dedicated to creating comprehensive QoE frameworks it seems that the majority of the factors studied as potential influencers of QoE are often limited to single disciplinary viewpoints or specific user-related aspects. Furthermore, the majority of literature reviews in this domain seem to have predominantly focused on academic sources, overlooking industry insights. To address these points, the current research took an interdisciplinary literature review approach to examine QoE literature covering both academic and industry sources from diverse fields (i.e., psychology, ergonomics, user experience, communication science, and engineering). Based on this rich dataset, we created a QoE model that illustrated 252 factors grouped into four branches - user, system, context, and content. The main finding of this review emphasized the substantial gap in the current research landscape, where complex interactions among user, system, context, and content factors in VR are overlooked. The current research not only identified this crucial disparity in existing QoE studies but also provided a substantial online repository of over 200 QoE-related factors. The repository serves as an indispensable tool for future researchers aiming to construct a more holistic understanding of QoE.

Assessing the quality of experience in wireless networks for multimedia applications: A comprehensive analysis utilizing deep learning-based techniques

In the context of the burgeoning progression of wireless network technology and the corresponding escalation in the demand for mobile Internet-based multimedia transmission services, the task of preserving and augmenting user satisfaction has emerged as an imperative concern. This necessitates a sophisticated and accurate evaluation of multimedia service quality within the sphere of wireless networks. To systematically address the nuanced issue of user experience quality, the present study introduces a novel method for evaluating multimedia Quality of Experience (QoE) in wireless networks, employing an advanced deep learning model as the underlying analytical framework. Initially, the research undertakes the task of modeling the video session process, giving due consideration to the status of each temporal interval within the session's architecture. Subsequently, the challenge of QoE prediction is dissected and investigated through the lens of recurrent neural networks (RNNs), culminating in the proposition of an all-encompassing QoE prediction model that harmoniously integrates video information, Quality of Service (QoS) data, user behavior analytics, and facial expression analysis. The empirical segment of this research serves to validate the efficacy of the suggested video QoE evaluation method, engaging both quantitative and qualitative comparison metrics with contemporaneous state-of-the-art QoE models, employing the RTVCQoE dataset as the empirical foundation. The experimental findings illuminate that the QoE model elucidated in this study transcends competing models in performance metrics such as PLCC, SRCC, and KRCC. Consequently, this investigation stands as a seminal contribution to academic literature, furnishing an exacting and dependable QoE evaluation methodology. Such a contribution augments the user experience landscape in multimedia services within wireless networks, and instigates further scholarly exploration and technological innovation in the mobile Internet domain.

Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.

A quality of experience model for live video in first-person-view drone control in cellular networks

Several upcoming 5G and 6G services will rely on unmanned aerial vehicles (UAV) sending live information to remote terminals. Thus, understanding the traffic flows that might influence end-user experience in these services is key for cellular network operators. One of these UAV-based services is first person view (FPV) drone control, consisting on the remote control of the UAV in Beyond Visual Line of Sight scenarios using only the live video visualized in a ground control station. This work focuses on the networking aspects of this service by presenting the assembly, integration and evaluation methodology of an UAV quadrotor teleoperated via FPV through a Long Term Evolution (LTE) network or WiFi radio access link. To assess system performance, three different connectivity schemes between UAV and ground control station are tested, namely server-based connection via LTE, direct LTE, and peer-to-peer WiFi connection. Then, several experiments are carried out in the testbed to characterize telemetry, control and video traffic for FPV service in the above schemes. Later, a methodology is defined to estimate Quality of Experience (QoE) for FPV service based on image quality and video latency measurements collected at network and application level. Results show that the QoE model for live video introduced in this work can be the basis of more sophisticated models for cellular FPV services.

ABRF: Adaptive BitRate-FEC Joint Control for Real-Time Video Streaming

Adaptive Forward Error Correction (AFEC) algorithms are proposed to achieve efficient Forward Error Correction (FEC) in Real-Time Communication (RTC). However, current AFEC approaches suffer two key limitations. (1) They do not consider the squeezing effect of redundancy on source bitrate while the squeezing usually happens in real RTC applications with limited bandwidth. (2) They estimate the future packet loss simply, ignoring some critical features of packet loss like randomness and multi-pattern. These drawbacks stop them from providing better Quality of Experience (QoE) in RTC services. We propose ABRF, a general QoE-oriented Adaptive BitRate-FEC joint control algorithm. ABRF makes predictions on the network loss pattern in the coming time and jointly calculates the optimal bitrate-FEC decision based on a QoE model for real-time video streaming. Moreover, ABRF is equipped with a fast adaptation method which helps it generalize across diverse network environments. In terms of Video Multi-method Assessment Fusion (VMAF), experimental results tell that ABRF decreases VMAF degradation caused by packet loss by 68%-95% compared with other AFEC algorithms in real-time video streaming in real-world Internet.

Do you agree? Contrasting Google’s Core Web Vitals and the impact of cookie consent banners with actual web QoE

Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, responsive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the importance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that additional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.

A Quality-of-Experience-Aware Framework for Versatile Video Coding-Based Video Transmission

Multimedia applications have expanded and resulted in huge demands for high resolution videos in order to enhance the end user Quality of Experience (QoE). Therefore, QoE becomes a critical factor that is often addressed in design considerations of Consumer Electronics (CE) devices. Furthermore, error prone transmission conditions can influence the QoE, potentially degrading the end-user multimedia experience. Although video coding standards such as Versatile Video Coding (VVC) take potential mitigation measures to improve the bandwidth reductions, it fails to address other significant network Quality of Service (QoS) parameters that impact transmission based QoE such as transmission delay and Packet Loss Rate (PLR) in the encoding processes. In response, this paper first derives a relationship between QoE that addresses the network QoS parameters and VVC dependent parameters. Subsequently, the derived QoE model is used with the VVC encoding process to adaptively optimize Lagrange parameters as well as the quantization parameter. Extensive experiments report significant improvements to VVC Test Model (VTM) 7.0, achieving superior QoE gains over the state-of-the-art research and up to 3.41% over the anchor implementations.

A QoE Model for Mulsemedia TV in a Smart Home Environment

The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user’s Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications.

Speeding Up Subjective Video Quality Assessment via Hybrid Active Learning

Subjective video quality assessment (VQA) is the most reliable way to get accurate quality scores, providing first-hand data on the research of quality of experience (QoE). However, traditional subjective VQA has the disadvantage of being cumbersome and time-consuming. In this paper, we propose an efficient subjective VQA framework based on hybrid information and active learning, named HA-SVQA. Built on the principle of active learning for data annotation, HA-SVQA allows iterative assessments on the most valuable or informative videos, which are selected based on hybrid information from the subject’s prior decisions and the objective quality predictions. By eliminating the redundant (or less valuable) videos to be assessed, HA-SVQA can speed up the process of subjective VQA. Concretely, our framework starts with a few quality-known videos to initialize dual-regression models. It then uses a scoring-difference stratified sampling strategy to iteratively select the next group of videos to be assessed, which contain high quality uncertainty. The newly scored videos are used to continually update every part of our framework. By this way, subjective VQA can be stopped early while meeting the acceptable goal of a full-time subjective study. We conducted simulation experiments on three different datasets: LIVE, LIVE VQC, and an underwater video quality dataset. The results show that HA-SVQA can effectively speed up the process of subjective VQA and reduce about 1/3 of the human workload or time cost in the presence of data redundancy. In order to investigate the effectiveness of HA-SVQA in more depth, we conducted a field experiment with deep-sea videos. We found that HA-SVQA is still effective in reducing about 1/3 of the overall human workload, which is consistent with the conclusion of simulation experiments. Finally, we discussed some of the factors that potentially affect the QoE modelling and the subjective VQA.

The effect of place attachment of geographical indication agricultural products on repurchase intention

[Objective] This study aims to investigate how place attachment and the quality of experience affect consumers' intention to repurchase Geographical Indication (GI) agricultural products. [Methodology] Based on the special functional value and symbolic meaning of GI agricultural products, we constructed a five-dimensional model of place attachment for GI agricultural products consumers. Expanding on the original two-dimensional place attachment model, we introduced three connection concepts: nature, society, and emotion. We also combined this model with the quality of experience model and consumer purchase intention model to form a research model of repurchase intention for GI agricultural products. A questionnaire survey was used to collect data (n = 340), and a partial least squares structural equation model was used to test the empirical evidence. [Research results] (1) Place attachment to Geographical Indication agricultural products significantly affects the quality of experience. (2) Quality of experience significantly affects consumer trust and repurchase intention. (3) Consumer trust significantly affects repurchase intention. (4) There is a mediating effect of experience quality between place attachment and repurchase intention; there is a mediating effect of trust in the relationship between quality of experience and repurchase intention. (5) There is a chain mediating effect of “place attachment→quality of experience→consumer trust→repurchase intention” in the model.

Industrial User Experience Index vs. Quality of Experience Models

Network operators have increased interest in understanding and quantifying the user perceived quality of experience (QoE). Appropriate QoE models allow mapping of measurable QoS parameters to QoE metrics like mean opinion scores (MOS) or poor-or-worse (PoW) ratios. Unfortunately, there are no QoE models for all applications available yet, and development and standardization is rather time-consuming. For that reason, industry often implements simple but effective approaches based on expert knowledge to get a user experience index able to deliver high value insights. This index is often defined based on thresholds of measurable QoS parameters to classify the resulting user experience. The question arises whether such a threshold-based user experience index can properly estimate the QoE in a system. We therefore answer in this work whether the user experience index is able to quantify the expected QoE in a system and if other QoE metrics like PoW ratios can be derived. Furthermore, we discuss the interpretation and the limits of the index. As a key contribution, we present a detailed systematic analysis of the threshold-based QoE concept and discuss its usability as QoE estimation methodology for industry.

QoE-Driven Adaptive Streaming for Point Clouds

With increasing popularity of virtual reality and augmented reality, application of point clouds is in critical demand as it enables users to freely navigate in an immersive scene with six degrees of freedom. However, point clouds usually comprise large amounts of data, and are thus difficult to stream in bandwidth-constrained networks. It is therefore important, yet challenging, to efficiently stream the resource-intensive point clouds, such that the user's quality of experience (QoE) is guaranteed on a high-level but with a low bandwidth consumption. To this end, we propose a QoE-driven adaptive streaming approach for the tile-based point cloud transmission, to maximize the user's QoE while reducing the transmission redundancy. By exploiting the perspective projection, we specifically model the QoE of a 3D tile as a function of the bitrate of its representation, user's view frustum and spatial position, occlusion between tiles, and the resolution of rendering device. Based on this QoE model, we then formulate the QoE-optimized rate adaptation problem as a multiple-choice knapsack problem, which allocates bitrates for different tiles under a given transmission capacity. It is equivalently converted to a submodular function maximization problem subject to knapsack constraints, and solved by a practical greedy-based algorithm with a theoretical worst-case performance guarantee. The proposed algorithm is able to achieve a near-optimal performance, but with a very low computational complexity. Experimental results further demonstrate superiority of the proposed rate adaptation algorithm over existing schemes, in terms of both user's visual quality and transmission efficiency.

Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoEi. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%.

A Bayesian Quality-of-Experience Model for Adaptive Streaming Videos

The fundamental conflict between the enormous space of adaptive streaming videos and the limited capacity for subjective experiment casts significant challenges to objective Quality-of-Experience (QoE) prediction. Existing objective QoE models either employ pre-defined parametrization or exhibit complex functional form, achieving limited generalization capability in diverse streaming environments. In this study, we propose an objective QoE model, namely, the Bayesian streaming quality index (BSQI), to integrate prior knowledge on the human visual system and human annotated data in a principled way. By analyzing the subjective characteristics towards streaming videos from a corpus of subjective studies, we show that a family of QoE functions lies in a convex set. Using a variant of projected gradient descent, we optimize the objective QoE model over a database of training videos. The proposed BSQI demonstrates strong prediction accuracy in a broad range of streaming conditions, evident by state-of-the-art performance on four publicly available benchmark datasets and a novel analysis-by-synthesis visual experiment.

ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques

Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to application-specific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming. The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO’s point of view.

On the development of the web service quality modelling space

Most web service (WS) based systems consider the quality of service (QoS) to select an appropriate WS to run an application. Some authors have proposed QoS attributes that characterize WS at the technological layer to determine QoS. However, this is not enough to meet user needs and ensure user satisfaction at the domain layer. Consequently, there is a need to develop a deeper understanding of the user Quality of Experience (QoE) for WS. This paper proposes a QoE modelling space, which views and allows predicting QoE through QoS attributes, WS architecture layers, and stakeholder viewpoints from a particular perspective. The proposed WS quality modelling space was validated using the objective (the WS-DREAM dataset), and subjective (expert’s judgments) attributes to estimate QoS performance by ARAS and to predict QoE performance by a Fuzzy Control System (FCS). The obtained results show the strong positive linear and strong positive monotonic relationships between WS QoS performance and WS QoE performance. Therefore, the proposed WS quality modelling space is suitable for QoE performance prediction based on measurable QoS attributes.

360-Degree Video Bandwidth Reduction: Technique and Approaches Comprehensive Review

Recently, the usage of 360-degree videos has prevailed in various sectors such as education, real estate, medical, entertainment and more. The development of the Virtual World “Metaverse” demanded a Virtual Reality (VR) environment with high immersion and a smooth user experience. However, various challenges are faced to provide real-time streaming due to the nature of high-resolution 360-degree videos such as high bandwidth requirement, high computing power and low delay tolerance. To overcome these challenges, streaming methods such as Dynamic Adaptive Streaming over HTTP (DASH), Tiling, Viewport-Adaptive and Machine Learning (ML) are discussed. Moreover, the superiorities of the development of 5G and 6G networks, Mobile Edge Computing (MEC) and Caching and the Information-Centric Network (ICN) approaches to optimize the 360-degree video streaming are elaborated. All of these methods strike to improve the Quality of Experience (QoE) and Quality of Service (QoS) of VR services. Next, the challenges faced in QoE modeling and the existing objective and subjective QoE assessment methods of 360-degree video are presented. Lastly, potential future research that utilizes and further improves the existing methods substantially is discussed. With the efforts of various research studies and industries and the gradual development of the network in recent years, a deep fake virtual world, “Metaverse” with high immersion and conducive for daily life working, learning and socializing are around the corner.

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.

An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience

With the coming of the 5G network era, Quality of Experience (QoE), which is used to describe experience quality of customers in the Direct Connect (DC) network, is critical to operators and hence has been widely studied. The existing evaluation method for QoE is unable to dynamically adjust QoE parameters so there will be deviations to reflect the real experience of customers when complaint events occur. In this paper, a novel progressive QoE evaluation model for network products is formulated. Subsequently, considering the customer complaints occurrences which represent that the real customer experience and previously calculated QoE may mismatch, the rules based on application types to judge whether QoE parameters needs to be adjusted are made. Then, a dynamic adjustment model of QoE parameters is further developed to reflect complaint customer experience. Finally, the obtained experimental result verified that the proposed dynamic perception method for QoE can accurately characterize the real customer experience. The evaluation result for QoE model based on customer satisfaction questionnaires proves the effectiveness of the QoE-aware model. The evaluation result for adjusted QoE model based on business complaints shows that the adjusted QoE calculation variance reduction rate exceeds more than 50%, which indicates that the proposed method has better ability to reflect the real customer experience.