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-of-the-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, the authors 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.