Predicting User-Perceived Quality Ratings from Streaming Media Data

Abstract—Media stream quality is highly dependent on under-lying network conditions, but identifying scalable, unambiguous metrics to discern the user-perceived quality of a media stream in the face of network congestion is a challenging problem. User-perceived quality can be approximated through the use of carefully chosen application layer metrics, precluding the need to poll users directly. We discuss the use of data mining prediction techniques to analyze application layer metrics to determine user-perceived quality ratings on media streams. We show that several such prediction techniques are able to assign correct (within a small tolerance) quality ratings to streams with a high degree of accuracy. The time it takes to train and tune the predictors and perform the actual prediction are short enough to make such a strategy feasible to be executed in real time and on real computer networks. I

QoE Modelling, Measurement and Prediction: A Review

In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. These devices connect to the Internet via network or telecommunications operators. Users usually have some expectations about the services provided to them by different operators. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. Thus, QoE measurement and prediction techniques may benefit users in availing personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. This paper presents a review of the state-the-art research in the area of QoE modelling, measurement and prediction. In particular, we investigate and discuss the strengths and shortcomings of existing techniques. Finally, we present future research directions for developing novel QoE measurement and prediction technique

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

Perceptual quality assessment of real-world images and videos

The development of online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of a seemingly limitless supply of visual content. However, a vast majority of these digital images and videos are often afflicted with annoying artifacts during acquisition, subsequent storage, and transmission over the network. All these factors impact the quality of the visual media as perceived by a human observer, thereby compromising their quality of experience (QoE). This dissertation focuses on constructing datasets that are representative of real-world image and video distortions as well as on designing algorithms that accurately predict the perceptual quality of images and videos. The primary goal of this research is to design and demonstrate automatic image and continuous-time video quality predictors that can effectively tackle the widely diverse authentic spatial, temporal, and network-induced distortions -- contrary to all present-day algorithms that operate on single, synthetic visual distortions and predict a single overall quality score for a given video. I introduce an image quality database which contains a large number of images captured using a representative variety of modern mobile devices and afflicted with a widely diverse authentic image distortions. I will also describe the design of an online crowdsourcing system which aided a very large-scale image quality assessment subjective study. This data collection facilitated the design of a new image quality predictor that is founded on the principles of natural scene statistics of images in different color spaces and transform domains. This new quality method is capable of assessing the quality of images with complex mixtures of distortions and yields high correlation with human perception. Pertaining to videos, this dissertation describes a video quality database created to understand the impact of network-induced distortions on an end user's quality of experience. I present the details of a large-scale subjective study that I conducted to gather continuous-time ground truth QoE scores on a collection of 180 videos afflicted with diverse stalling events. I also present my analysis of the temporal variations in the perceived QoE due to the time-varying video quality and present insights on the impact of relevant human cognitive aspects such as long-term and short-term memory and recency on quality perception. Next, I present a continuous-time objective QoE predicting model that effectively captures the complex interactions between the aforementioned human cognitive elements, spatial and temporal distortions, properties of stalling events, and models the state of any given client-side network buffer. I also show how the proposed framework can be extended by further supplementing with any number of additional inputs (or by eliminating any ineffective ones), based on the information available at the content providers during the design of adaptive stream-switching algorithms. This QoE predictor supports future research in the design of quality-aware stream-switching algorithms which could control the position, location, and length of stalls, given a network bandwidth budget and the end user's device information, such that the end user's QoE is maximized.Computer Science

Music is Social: From Online Social Features to Online Social Connectedness

Despite the widespread adoption of social networks, the potential value of implementing similar opportunities for social interaction into online content consumption websites has received limited attention. What little research has been done suggests that there is significant potential in implementing social features onto these sites, since increased interaction can lead to higher engagement with the site, and to an increase in users\u27 willingness to pay for the site. Considering that music in an inherently social good, and that consumers desire the opportunity to involve social aspects into their consumption of music, this study develops a new approach to examine the specific types of features that can lead to a feeling of online social connectedness. Results suggest that this distinction is valuable, and the type of features that is most likely to lead to feelings of connectedness is highlighted. Implications for site developers and researchers are also discussed

Modelling human factors in perceptual multimedia quality: On the role of personality and culture

Perception of multimedia quality is shaped by a rich interplay between system, context, and human factors. While system and context factors are widely researched, few studies in this area consider human factors as sources of systematic variance. This paper presents an analysis on the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the perception of multimedia quality. A set of 144 video sequences (from 12 short movie excerpts) were rated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture as human factors; and an optimistic model in which each participant is modeled as a random effect. An analysis shows that personality and cultural traits represent 9.3% of the variance attributable to human factors while human factors overall predict an equal or higher proportion of variance compared to system factors. In addition, the quality-enjoyment correlation varied across the movie excerpts. This suggests that human factors play an important role in perceptual multimedia quality, but further research to explore moderation effects and a broader range of human factors is warranted