This year’s 21st Symposium on Vision, Modelling and Visualization (VMV) was hosted by the University of Bayreuth. International scientists presented their newest research in various sessions related to visual computing. Dr. Fabian Beck, member of the Visualization Research Center of the University of Stuttgart (VISUS) and associated to SFB-TRR 161, presented his interesting work on a matrix-based visual comparison of time series sports data .
As part of the VMV 2016, the SFB-TRR 161 co-organized a workshop session titled “Quantification – useful and needed?” Three leading german researchers of the visual computing community were invited to present their take on the role of quanitification in their respective fields of expertise.
Prof. Dr. Heidrun Schumann of the University of Rostock presented her teams current research on visual analytics. They combined incremental visualiziation with quality metrics in order to monitor the optimization process inside their visual analytics tool . Prof. Dr. Carsten Dachsbacher from the Karlsruhe Institute of Technology discussed the importance of quantification and quality metrics in the field of image synthesis. Although various techniques are already used inside the community, there is still a need for more suitable comparative metrics which incorporate human perception. Prof. Dr. Philipp Slussallek affiliated both to the University of Saarbruecken and the German Research Center for Artificial Intelligence outlined the existing challenges of displaying digital media. Newly emerging technologies like High Dynamic Range (HDR) in video production provide a quantifiable superior digital output from the technology perspective. However, there are no metrics available to predict the impact on the viewing experience.
During the symposium the attendants were offered a guided city tour through the historic city center of Bayreuth which was concluded with a reception in the Iwalewa House.
 Beck, F., Burch, M. Weiskopf, D. A Matrix-Based Visual Comparison of Time Series Sports Data. In: Vision, Modeling & Visualization (VMV 2016), 2016.
 Schulz, H.-J.; Angelini, M.; Santucci, G.; Schumann, H.: An Enhanced Visualization Process Model for Incremental Visualization. IEEE TVCG Vol.22, 2016.