This Springer Brief discusses the most recent research in the field of multimedia Qo E evaluation, with a focus on how to evaluate subjective multimedia Qo E problems from objective techniques. Specifically, this Springer Brief starts from a comprehensive overview of multimedia Qo E 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 Qo E modeling and prediction techniques in details. Finally, the authors illustrate how to implement and demonstrate multimedia Qo E evaluation in the big data platform. This Springer Brief provides readers with a clear picture on how to make full use of multimedia service data to realize multimedia Qo E 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 Qo S (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 (Qo E). However, multimedia Qo E 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 Qo E.
This Springer Brief 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 Springer Brief.
表中的内容
1 Introduction.- 2 Technical Premise.- 3 Multimedia Service Data Preprocessing and Feature Extraction.- 4 Multimedia Qo E Modeling and Prediction.- 5 Implementation and Demonstration.- 6 Conclusion.
关于作者
Xin Wei received his Ph.D. degree major at Information and Communication Engineering from Southeast University, Nanjing, China in 2009. Now, he is an associate professor in Nanjing University of Posts and Telecommunications, China. His research interests are in the area of multimedia information processing, machine learning.
Liang Zhou received his Ph.D. degree major at Electronic Engineering both from Ecole Normale Superieure (E.N.S.), Cachan, France and Shanghai Jiao Tong University, Shanghai, China in 2009. Now, he is a professor in Nanjing University of Posts and Telecommunications, China. His research interests are in the area of multimedia communications and computing.
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