Video synchronization is an essential processing step in many multimedia applications, and various methods have been proposed in the literature each of which addresses the problem from a different point of vantage. In this article, we present an information theoretic approach to video synchronization, based on the state-of-the-art in hybrid video coding. Time series derived from the videos' instantaneous bitrate demand are correlated in a robust manner employing the recently published ConCor algorithm. We enhance ConCor with integrated normalization capabilities in order to improve its shape-oriented matching performance. Furthermore, we present a mathematical framework to derive the most suitable ConCor parameters given a specific class of input videos. In an extensive experimental analysis, we give an insight into the representation of synchronization-relevant scene changes with bitrate data, and examine the influence of encoding parameters on the synchronization performance. Experiments on diverse video input substantiate the reliable performance of our easy to implement, yet effective video synchronization algorithm which distinguishes itself in that it operates largely without manual intervention.

Cross-correlation is a classical similarity measure with broad applications in multimedia signal processing. While it is robust against uncorrelated noise in the input signals, it is severely affected by systematic disturbances which lead to biased results. To overcome this limitation, we propose in this paper consensus-based cross-correlation (ConCor) to deal with heavily corrupted signal parts that derail regular cross-correlation. ConCor builds upon the widely adopted RANSAC algorithm to reliably identify and eliminate corrupt signal parts at limited additional complexity. Our approach is universal in that it can be combined with existing cross-correlation variants. We apply ConCor in two example applications, namely video synchronization and template matching. Our experimental results demonstrate the improved robustness and accuracy when compared to classical cross-correlation.

We present a novel approach for the temporal synchronization of multiple videos which is based on cross-correlating bit rate profiles. The proposed scheme determines the temporal offset without major restrictions on viewing angles, camera properties and camera motion. We propose two extensions of the basic algorithm which reduce the influence of camera motion and distracting background objects. Additionally, we describe how to optimally combine different bit rate components in order to further improve the reliability of the synchronization scheme. The proposed approach, when combined with the three extensions, leads to a reliable, robust, and frame accurate temporal alignment of videos at remarkably low complexity.

We present a novel middleware approach for in-vehicle service discovery and device integration that is based on the concept of Service-oriented Architectures (SOA). In order to be able to identify a suitable SOA system, we define a series of criteria the SOA platform should fulfill. These criteria take into account the specific requirements of the automotive domain. Based on these criteria we compare nine different SOA standards and show why the Device Profile for Web Services (DPWS) standard is most suitable as the middleware of an in-car infotainment and communication system. Furthermore, we present a prototypical implementation of an in-vehicle network based on the DPWS middleware. Hence, we enable the integration of nomadic devices by providing their functionalities within the network and consequently including their graphical output into the in-vehicle Human Machine Interface (HMI). Due to the system design it is also possible to use nomadic devices as an additional display to present the HMI. Finally, an in-vehicle controller is included into our SOA and therefore available to all other devices within our system.