Special sessions

Special Session Gaming

Focus and objectives of the special session:

Gaming has become a multi-billion dollar industry all over the world and significant amounts of money, time and effort are being invested in developing distinguished and high quality gaming experiences. The quality of game experience is dependent on many factors which may lie outside of the control of the game publisher (e.g., Internet connection, user equipment characteristics, user’s previous game experience etc.). While factors that contribute to the players’ QoE are known, still the influence of specific factors and the influence of their interaction is not well understood, quantified and modelled. It is the aim of this Special Session to bring together researchers and practitioners who work on Gaming QoE, and to provide a unique podium for discussion and scientific advances in the area of QoE for digital games.


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Special Session Crowdsourcing

Crowdsourcing has become a valuable tool for studying and evaluating the perceptual quality of systems. It provides an easy, fast, and cost effective way to access a large number of diverse users. However, due to the highly uncontrolled environment in which crowd-based experiments are conducted, well-established lab test setups and experimental methodologies cannot be deployed without adaptions. These adaptions might involve technical changes of the test setup to support the remote devices of the participants, or methodical changes like the introduction of reliability checks to verify that the experimental task has been understood and executed properly.

In this session, we aim to foster the discussion between researchers in the field of crowdsourcing and perceptual studies to further close the gap between subjective testing and crowdsourcing. We solicit contributions addressing novel techniques to ease the use of crowdsourcing for the study of the perceptual quality of systems, successful and unsuccessful examples of crowd-sourced subjective studies that can help deriving general best practices or pitfalls, and methodical approaches that, e.g.,  enable better reproducibility and comparability of crowdsourcing studies


Please find more information here.