Multi-display environments (MDEs) of all kinds are used a lot nowadays. A
wide variety of devices helps to build a common display space. TVs, monitors,
projected surfaces, phones, tablets, everything that has the ability to display
visual information can be incorporated in multi-display environments.
While the main research emphasis so far has been on interaction techniques
and user experience within different MDEs, some research topics are dealing
with static and dynamic display reconfiguration. In fact, several studies
already work with MDEs that are capable of display reconfiguration on-the-fly.
Different frameworks can perform splitting, streaming and rendering of visual
data on large-scale displays with the ability of dynamic display
reconfiguration to calibrate multiple-projectors or to combine different
heterogeneous displays into one display wall dynamically. However, all of these
frameworks require different approaches
for display reconfiguration. Our goal is to create a model for display
reconfiguration which will be abstract, transparent, will work in real-time,
and will be easily deployable in any MDE.
In this work we present an extension to a software framework called Display as
a Service (DaaS). This extension is represented as a model for real-time
display reconfiguration using DaaS. The DaaS framework allows for generic and
transparent management of pixel-transport assuming only a network connection,
providing a simple high-level implementation for pixel-producing and
pixel-displaying applications. The main limitation of this approach is a
certain delay between pixel generation and display. However, the video encoding
and network transport are subject of improvements which will solve the problem
in the future.
As a proof of concept, we demonstrate three usage scenarios: manual dynamic
display reconfiguration, automatic display calibration, and real-time display
tracking. We also present a new algorithm for precise display calibration using
markers and a handheld camera. The calibration results are evaluated using
different tracking libraries. The additional precise calibration part for our
proposed algorithm makes the calibration accuracy several times better compared
to a naive approach.