移动视频体验质量的量化评价模型

移动视频体验质量（QoE）的研究正变得日益重要,现有的QoE评价模型缺少相应的量化方法。结合移动视频业务的特点,提出了两层权值的QoE量化评价模型,即QoE由六种“影响层面”按各自的一级权值叠加而成;进一步地,每个“影响层面”又由多个“影响因素”构成。除了丢包和抖动两个“影响因素”外,其他“影响因素”均具有独立的二级权值。提出通过层次分析法（AHP）计算出所有的一级权值,并且进一步计算出除了丢包、抖动外的其他二级权值。由于丢包和抖动这两个影响因素相互依赖,无法计算各自的二级权值,因此通过网络模拟实验,找出两者的双变量函数f（丢包,抖动）,由此得到完整的两层权值QoE量化评价模型。所提QoE量化评价模型通过测评人员的QoE评分进行验证,实验结果表明该模型获得了较高的准确度,证明了上述模型量化方法的可靠性。     

Developing a Quality of Experience (QoE) model for Web Applications

Web‐based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Although models exist that assess the QoE of Web Application, significant challenges remain in: (1) Defining QoE factors from a Web engineering perspective, (2) quantifying the relationship between so‐called “objective” and “subjective” factors of relevance, and (3) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators and Key Quality Indicators. The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning. The development of proposed model is framed and guided by Design Science Research approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Although the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed.     

用户体验质量评估模型及KQI权重计算方法

根据影响用户体验质量(QoE)的技术因素和非技术因素,建立QoE评估模型,以较好地呈现用户主观感受和网络因素。利用该模型提出一种关键质量指标(KQI)权重计算方法,应用模糊层次分析法计算KQI指标初始权重值,并通过影响QoE的非技术因素动态改变KQI指标的权重,使静态QoE评估过程动态化、实时化。仿真实验结果表明,该方法能保证最先假定的技术因素和非技术因素对QoE的影响程度各占50%,较为直观和真实地反映用户感受。     

Analysis of Skype VoIP traffic in UMTS: End-to-end QoS and QoE measurements

In the future Internet, multi-network services will follow a new paradigm in which the intelligence of the network control is gradually moved to the edge of the network. This impacts both the objective Quality of Service (QoS) of the end-to-end connection as well as the subjective Quality of Experience (QoE) as perceived by the end user. Skype already offers such a multi-network Voice-over-IP (VoIP) telephony service today. Due to its ease of use and a high sound quality, it becomes increasingly popular in the wired Internet.UMTS operators promise to offer large data rates which should suffice to support VoIP calls in a mobile environment. However, the success of those applications strongly depends on the corresponding QoE. In this work, we analyze the theoretically achievable as well as the actually achieved quality of IP-based voice calls using Skype. This is done performing measurements in both a real UMTS network and a testbed environment. The latter is used to emulate rate control mechanisms and changing system conditions of UMTS networks. The results show in how far Skype over UMTS is able to keep pace with existing mobile telephony systems and how it reacts to different network characteristics. The investigated performance measures comprise the QoE in terms of the MOS value and the QoS in terms of network-based factors like throughput, packet interarrival times, or packet loss.     

基于用户体验评价模型的最优路由选择算法

网络视音频业务的兴起使网络运营商和服务提供商更加关注视音频的用户体验（QoE）,而传统的路由算法只能保证所选路径的服务质量（QoS）参数,如延迟、抖动等满足QoS约束的需求,并不能直接反映QoE的信息,从而不能保证所选路径满足QoE需求。基于QoE评价模型,给出以QoE为目标的最优路由选择算法。通过分析QoE表征参数与传统QoS参数的关系,利用QoE表征参数可分解性和QoE表征值非递减性两个性质,给出多项式时间复杂度为O(V log V+E)的QoE_DSP算法。实验和分析表明,该算法能保证所得路径满足QoE需求,同时具有良好的计算扩展性。     

Spherical image QoE approximations for vision augmentation scenarios

Augmented Reality (AR) devices are commonly head-worn to overlay context-dependent information into the field of view of the device operators. One particular scenario is the overlay of still images, for which we evaluate the interplay of user ratings as Quality of Experience (QoE) with (i) the non-referential BRISQUE objective image quality metric as Quality of Service (QoS) and (ii) human subject dry electrode EEG signals gathered with a commercial off-the-shelf device. We employ basic machine learning approaches to perform QoE and QoS predictions based on this data. We find strong correlations for QoS inputs with aggregated user ratings as Mean Opinion Scores with spherical images. For subject-specific EEG portfolios, overall predictability of the QoE for both media types can be attained. Our overall results can be employed in practical scenarios by content and network service providers to optimize the user experience in augmented reality scenarios with a passive human in-the-loop in the future.

     

IP网络中视频流QoE评价模型

网络视频业务的兴起使网络运营商和服务提供商更加关注视频的用户体验（QoE）,然而视频用户体验（QoE）值由于其主观性且评价过程复杂,难以在视频流传输中实时获取。通过实验分析了视频传输过程中服务质量（QoS）参数变化对视频QoE的影响,建立了客观、可测量的QoS参数与视频QoE之间映射模型,用可量化的QoS参数来评定视频QoE受网络性能的影响程度,以评估网络视频质量,该模型形式简单,能够实时监测视频质量。实验结果表明,该模型的评价结果能较好反映视频QoE。     

面向智能终端的数据业务QoE测量体系

为了探究面向智能终端的数据业务QoE测量方法,通过分析QoE研究现状和数据业务用户体验特性,以时延、抖动、信息丢失为依据,建立了针对现有数据业务QoE的立体分类模型。在分析终端对QoE影响的基础上,综合网络、业务、终端三方面影响因素,创新性地提取QoE终端参数影响因子,结合面向业务的网络影响因子提取方法,建立了数据业务的QoE测量体系模型。     

服务功能链中基于机器学习的QoE评估与预测

在软件定义网络与网络功能虚拟化协同的网络架构下,只考虑单个服务质量（QoS）指标的服务功能链部署无法满足用户的多业务体验需求。提出一种基于机器学习的服务功能链部署模型。基于层次分析法构造MPNQ2算法以建立QoS与体验质量（QoE）的映射关系,得出影响QoE的网络参数并评估其影响权重。在此基础上,利用具备较强综合学习和泛化能力的随机森林模型对服务功能链的QoE进行预测。实验结果表明,与梯度提升决策树、线性判别分析等机器学习模型相比,随机森林模型为预测QoE的最佳模型,同时在影响QoE的网络参数中,丢包率对服务功能链的部署影响最大。     

Improving learner quality of experience by content adaptation based on network conditions

Apart from user characteristics, properties of the network over which the content is delivered and device on which the content is displayed affect end-user perceived quality. This paper presents a learner quality of experience (QoE) model that apart from the user-related content adaptation, considers delivery performance-based content personalisation in order to improve user experience when interacting with an online learning system.A comparison-based study on the benefit of using the proposed learner QoE model in adaptive and personalized education was conducted involving the original AHA! and QoEAHA – a version of AHA! enhanced with the learner QoE model. Testing results demonstrate significant benefits in terms of learning achievement, learning performance, learner navigation and user QoE in favour of the learner QoE model-enhanced solution.     

Impact of video content and transmission impairments on quality of experience

The analysis of the impact of video content and transmission impairments on Quality of Experience (QoE) is a relevant topic for the robust design and adaptation of multimedia infrastructures, services, and applications. The goal of this paper is to study the impact of video content on QoE for different levels of impairments. In more details, this contribution aims at i) the study of the impact of delay, jitter, packet loss, and bandwidth on QoE, ii) the analysis of the impact of video content on QoE, and iii) the evaluation of the relationship between content related parameters (spatial-temporal perceptual information, motion, and data rate) and the QoE for different levels of impairments.     

Impact of packet loss on the perceived quality of UDP-based multimedia streaming: a study of user quality of experience in real-life environments

Multimedia content delivery has become one of the pillar services of modern day mobile and fixed networks. The variety of devices, platforms, and content providers together with increasing network capacity has impacted the popularity of this type of service. Considering this context, it is crucial to ensure end-to-end service quality that can fulfill users’ expectations. The user quality of experience (QoE) for multimedia streaming is tempered by numerous objective and subjective parameters; therefore, it is important to understand the relationships among them. In this paper, we thoroughly examine the impact of packet loss on user QoE in cases when multimedia streaming service is based on underlying User Datagram Protocol. The dependencies between the chosen objective and subjective parameters and the user QoE were examined in a real-life environment by conducting a survey with 602 test subjects who rated the quality of a 1-h documentary film (72 different test sequences were prepared for the rating process). Based on the obtained results, we ranked the objective parameters by their order of importance in relation to their impact on user QoE as follows: (1) total duration of packet loss occurrences (PLOs), i.e., quality distortions in a video; (2) number of PLOs; (3) packet loss rate; and (4) duration of a single PLO. We also demonstrated how the overall user experience can be redeemed, despite the perceived quality distortions, if the content is entertaining to the viewer. The user experience was also found to be influenced by the existence/non-existence of video subtitles.     

支持IPTV高清码流连续平滑播放的多级缓冲区设计与实现

把流媒体系统作为一个复杂的控制系统进行研究,给出支持多级反馈控制机制的系统框图,重点分析媒体播放器从流接收到播放的全过程.同时,为了提高用户体验质量,实现音视频流在接收端的连续平滑播放,设计了多级缓冲机制以减少IP网络抖动和丢包产生的影响,实现了根据接收端的解码能力,自适应地选择解码层次和调整帧播放速率.最终采用多线程技术实现了MPEG-2 TS复用H.264高清视频流的解码播放,并给出试验结果.     

个人云存储体验质量测量工具的设计与实现

随着用户日益增长的网络存储需求,涌现出了大量个人云存储（PCS）服务平台。个人云存储终端用户使用过程中体验质量（QoE）的测量是终端用户和服务提供商所共同关注的问题。通过从控制流与数据流之间的不同特性方面分析了影响个人云存储体验质量的因素,从终端用户的角度提出了能合理评估个人云存储体验质量的指标,设计了精确测量体验质量评估指标的方法。利用被动测量技术,实现了一个面向终端用户的个人云存储服务体验质量测量工具,同时给出了工具实现中的进程抓包、网络流分类等问题的解决方案。实验结果表明,测量工具运行健壮,测量数据准确,可以用于从用户终端测量个人云存储服务QoE。     

A survey of data-driven approach on multimedia QoE evaluation

With the development of mobile communication technology and the growth of mobile device, the requirements for user quality of experience (QoE) become higher and higher. Network operators and content providers are interested in QoE evaluation for improving users’ QoE. However, multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE. In this paper, we provide a survey of the state of the art about applying data-driven approach on QoE evaluation. Firstly, we describe the way to choose factors influencing QoE. Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users’ QoE. Finally, we describe our research work on how to evaluate QoE in imbalanced dataset.     

The Impact of Personality on the Subjective Assessment of Usability

The system usability scale (SUS) has been widely employed in both the field and the laboratory as a valid and reliable measure of system usability. Although its psychometric properties are relatively well understood, the impact that differences in users’ personality traits have on their perceived usability of products, services, and systems has not been deeply explored—even though people’s scores on personality traits have been shown to be reliable and predict a staggering array of societally important outcomes in work, school, and life domains. In this study, 268 users assessed the usability of 20 different products retrospectively with the SUS. Five broad personality traits were then measured using the Mini-IPIP scale. Results indicated that measured personality traits do correlate with the rated usability of products, where measures of Openness to Experience and Agreeableness have the strongest positive correlations with subjective usability assessment. Implications for practitioners, designers, and researchers are discussed.     

基于双向强化学习与动态码率调节的无线mesh网络协议

针对现有无线mesh网络协议的用户体验质量(QoE)较差的问题,提出一种基于双向强化学习与动态码率调节的无线mesh网络协议。首先,设计了兼容不同服务类型的无线mesh网络QoE度量框架;然后,设计了基于双向强化学习的无线mesh网络路由协议;最终,结合QoE感知的差异化报文调度策略与数据流源节点码率动态调节算法进一步优化终端用户的QoE质量。基于NS-2仿真平台的对比实验结果显示,本协议可明显地提高无线mesh网络的QoE指标,同时具有较低的控制开销。     

基于用户QoE的流媒体系统性能测评模型设计

影响流媒体用户服务质量体验QoE(Quality of Experience)的因素有很多,如何对用户QoE进行量化判别是一个复杂的问题。为此,以媒体传输指标MDI为基础,研究了用于测量流媒体用户QoE的基本指标,并提出了相关测量数据的获取方法。以流媒体用户的实际体验质量为依据,提出了一种有效的流媒体系统性能评价模型,该模型为流媒体系统的性能分析和优化调整提供了理论基础,具有广泛的应用价值。     

移动视频业务QoE自适应测量工具

针对不同无线环境（3G、WiFi）下获取用户体验质量（Quality of Experience,QoE）数据困难和不精确的问题,提出一种基于安卓（Android）移动终端视频业务QoE的自适应测量方法.通过实时测量并评估用户在线视频业务体验质量,提高用户体验质量评价的准确性和实用性.为此开发了能自动测量视频QoE的工具,测量服务质量（Quality of Service,QoS）客观参数,通过效用函数映射到主观QoE（MOS值）.通过对理论QoE评价模型（取自文献）与用户实际反馈相关性研究改进理论模型.结果表明,无线环境下改进的模型测量结果更接近用户实际反馈,可以更好地评价QoE.     

Compressed-domain-based no-reference video quality assessment model considering fast motion and scene change

Due to the variability of wireless channel state, video quality monitoring became very important for guaranteeing users’ Quality of Experience (QoE). QoE presents the overall perceptual quality of service from the subjective users’ perspective. However, because of diverse characteristics of video content, Human Visual System (HVS) cannot give the same attention to whole scene simultaneously when facing video sequence. In this paper, we proposed a video quality assessment model by considering the influence of fast motion and scene change. The motion change contribution factor and scene change contribution factor are defined to quantify the characteristics of video content, which is closely related to the users’ QoE. Based on G.1070, our proposed model considers the influential factors of loss nature of video coding, variability of practical network and video features. Also, the proposed model owns low computational complexity due to the compressed domain approach for the estimation of the model parameters. Therefore, the video quality is assessed without fully decoding the video stream. The performance of our proposed model has been compared with five existing models and the results also shown that our model has high prediction accuracy closing to human perception.