Buch, Englisch, 156 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 430 g
Buch, Englisch, 156 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 430 g
Reihe: T-Labs Series in Telecommunication Services
ISBN: 978-3-031-67550-8
Verlag: Springer Nature Switzerland
The book presents an in-depth exploration of the Individuality Assisted Estimation (IAE) model in the context of Quality of Experience (QoE) assessment for multimedia, specifically audiovisual communication. The book delves into how individual characteristics, including psychological traits and states, influence perceptions of multimedia quality. The book argues for the integration of individual differences into these assessments, hypothesizing that this approach can enhance the accuracy and relevance of QoE ratings. Through a series of experiments and analyses, the book rigorously evaluates the effectiveness of the IAE model, comparing it against traditional methods, and highlights its particular strengths in estimating task load. The research contributes to the field by emphasizing the importance of individuality in multimedia quality assessment and offering a practical, empirically validated approach for incorporating individual differences into QoE models.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
Introduction.- Fundamentals.- The Individuality Assisted Estimation Model of Experience.- Operationalization of the IAE Model Entities: Relevant Dimensions in the Context of this Work.- Relationship Validation.- Model Weights Analysis and Structural Model Refinement.- Comparative Performance Analysis: Advantage of IAE Over Signal-Only Models.- Discussion, Future Work, and Conclusions.