Automatic fine-grained area detection for thin client systems

The widespread availability of cloud infrastructures is fueling new interest in the thin client computing paradigm. However, current thin client protocols are not designed to handle new content types as often encountered in state-ofthe-art applications (e.g. multimedia editing, gaming, multimedia playback). Conveying this content using traditional thin client protocols typically results in a combination of excessive resource usage and low visual quality. In this paper, we propose an approach where the content type can vary for different portions of the screen (e.g. combination of static text and video). Once the different content types have been detected, each of them can be encoded using the most appropriate algorithm. We present two algorithms for this runtime detection. The first algorithm is operating at the pixel level, thereby being independent of the actual thin client protocol used. The second algorithm assumes the presence of a rectangle-based thin client protocol (such as the popular VNC protocol), trading independence for improved performance. The appropriate parameter settings for these algorithms are experimentally determined. Furthermore, their influence is studied in detail in terms of detection accuracy, and the time to perform the algorithms is analysed. Accurate hints are derived within less than 10 ms, indicating the high potential of this approach for use in next generation thin client systems.