
Dr.-Ing. Steve Göring
Head of Group (acting)
Team assistance and secretariat
Dipl.-Verw. BW (VWA) Monique Rodegast
Fax: +49 3677 69-1255
+49 3677 69-2757
monique.rodegast@tu-ilmenau.de
Visiting address
Helmholtzplatz 2,
Helmholtzbau, Room H 3533 (3rd floor)
98693 Ilmenau
Postal address
Technische Universität Ilmenau
Fakultät für Elektrotechnik und Informationstechnik
Fachgebiet Audiovisuelle Technik
Postfach 10 05 65
98684 Ilmenau
TU Ilmenau | Eckhardt SchönOur overarching research objective is to investigate the relationship between technical system properties and human perception and individual experience. This is done on the one hand through physical measurements of audiovisual media and on the other hand through the evaluation of the perception of these media.
The department is part of the Chemnitz-Ilmenau-Magdeburg Research and Innovation Network (CHIM).

Our research looks at the entire chain from production, processing, transmission and presentation through to perception and personal experience. Voice, audio and video systems and services are examined. Particular attention is paid to the convergence of media in IP-based networks with regard to higher-quality multimedia services.
Our research activities are divided into five subject areas:
The five subject areas are dealt with by several subgroups in different projects:
ViBe is a specialized research subgroup that focuses on improving the understanding of video quality and user behavior in multimedia applications. Our goal is to develop innovative methods and models that ensure superior multimedia experiences in new technologies while exploring the complex relationship between video quality and user behavior.
Head: Rakesh Rao Ramachandra Rao
The IVM subgroup deals with topics in the field of image and video processing in combination with machine learning. The aim is to evaluate the use of machine learning models and AI generators for visual content, e.g. high-resolution images/videos, 360-degree videos or point clouds as visual representations. Machine learning models can be used to predict quality aspects, e.g. taking into account the latest developments in compression technology. Or AI generators are used to generate content that is evaluated with singal-based features and deep learning models.
Head: Steve Göring