Quality of Experience - Concepts and Applications

2017-2018 at Technische Universität München (Lecturer)

The course Quality of Experience - Concepts and Applications (Master level) covers the concepts and applications of Quality of Experience (QoE) and the corresponding algorithms and assessment methodologies, but also how QoE complements in particular multimedia signal processing. The focus will be on audio-visual stimuli in the context of telecommunication- and multimedia-related applications, systems and services.

The course is part of the module Data Analysis for Quality of Experience Assessments, in which QoE prediction models will be created based on the concepts, methods and algortihms introduced in this course. Data Analysis covers the algorithmic concepts of analysing empirical data to determine an abstract data model for such data. This is closely connected to Big Data Applications. The course will focus on a subset of algorithms, relevant for the task of QoE assessments. All other practical aspects of data analysis shall also be addressed, such as cleaning data, outlier removal, handling missing data, noise removal, dimension reduction, visualization, cross-validation etc.

QoE described the quality or “goodness” of (audio-visual) stimuli as perceived by human observers in a specific context. It allows us to assess the influence of processing steps not only with respect to the physical properties of the signal itself e.g. SNR, but also how any degradations influence the observers’ experience by taking into account the psychophysical properties of human perception.

Integral part of the course is a project, in which the students implement QoE algorithms and conduct QoE assessment task

Evaluation report 2018

Christian Keimel
Christian Keimel
Artificial Intelligence Research Project Leader

My research interests include AI/Machine Learning in the context of Visual Information Processing.