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Abstract

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.