Investigation of dynamic view expansion for head-mounted displays with head tracking in virtual environments

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Head mounted displays (HMD) are widely used for visual immersion in virtual reality (VR) systems. It is acknowledged that the narrow field of view (FOV) for most HMD models is the leading cause of insufficient quality of immersion, resulting in suboptimal user performance in various tasks in VR and early fatigue, too. Proposed solutions to this problem range from hardware-based approaches to software enhancements of the viewing process. There exist three major techniques of view expansion; minification or rendering graphics with a larger FOV than the display's FOV, motion amplification or amplifying user head rotation aiming to provide accelerated access to peripheral vision during wide sweeping head movements, and diverging left and right virtual cameras outwards in order to increase the combined binocular FOV. Static view expansion has been reported to increase user efficiency in search and navigation tasks, however the effectiveness of dynamic view expansion is not yet well understood. When applied, view expansion techniques modify the natural viewing process and alter familiar user reflex-response loops, which may result in motion sickness and poor user performance. Thus, it is vital to evaluate dynamic view expansion techniques in terms of task effectiveness and user workload. This paper details dynamic view expansion techniques, experimental settings and findings of the user study. In the user study, we investigate three view expansion techniques, applying them dynamically based on user behaviors. We evaluate the effectiveness of these methods quantitatively, by measuring and comparing user performance and user workload in a target search task. Also, we collect and compare qualitative feedback from the subjects in the experiment. Experimental results show that certain levels of minification and motion amplification increase performance by 8.2% and 6.0%, respectively, with comparable or even decreased subjective workload.