Table of contents
About this book
This SpringerBrief discusses the most recent research in the field of multimedia QoE evaluation, with a focus on how to evaluate subjective multimedia QoE problems from objective techniques. Specifically, this SpringerBrief starts from a comprehensive overview of multimedia QoE definition, its influencing factors, traditional modeling and prediction methods. Subsequently, the authors introduce the procedure of multimedia service data collection, preprocessing and feature extractions. Then, describe several proposed multimedia QoE modeling and prediction techniques in details. Finally, the authors illustrate how to implement and demonstrate multimedia QoE evaluation in the big data platform. This SpringerBrief provides readers with a clear picture on how to make full use of multimedia service data to realize multimedia QoE evaluation.
With the exponential growth of the Internet technologies, multimedia services become immensely popular. Users can enjoy multimedia services from operators or content providers by TV, computers and mobile devices. User experience is important for network operators and multimedia content providers. Traditional QoS (quality of service) can not entirely and accurately describe user experience. It is natural to research the quality of multimedia service from the users’ perspective, defined as multimedia quality of experience (QoE). However, multimedia QoE evaluation is difficult, because user experience is abstract and subjective, hard to quantify and measure. Moreover, the explosion of multimedia service and emergence of big data, all call for a new and better understanding of multimedia QoE.
This SpringerBrief targets advanced-level students, professors and researchers studying and working in the fields of multimedia communications and information processing. Professionals, industry managers, and government research employees working in these same fields will also benefit from this SpringerBrief.
quality of experience QoE multimedia big data evaluation influencing factors feature extraction prediction modeling machine learning deep neural network broad learning system ITPV video on demand Spark implementation
- DOI https://doi.org/10.1007/978-3-030-23350-1
- Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Print ISBN 978-3-030-23349-5
- Online ISBN 978-3-030-23350-1
- Series Print ISSN 2191-5768
- Series Online ISSN 2191-5776
- Buy this book on publisher's site