Measuring thin-client performance using slow-motion benchmarking
ACM Transactions on Computer Systems (TOCS)
IEEE Internet Computing
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
On the performance of wide-area thin-client computing
ACM Transactions on Computer Systems (TOCS)
Platform for distributed 3D gaming
International Journal of Computer Games Technology - Special issue on cyber games and interactive entertainment
Demystifying desktop virtualization
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
Virtualized Screen: A Third Element for Cloud-Mobile Convergence
IEEE MultiMedia
SHARC: A scalable 3D graphics virtual appliance delivery framework in cloud
Journal of Network and Computer Applications
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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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, 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 10ms, indicating the high potential of this approach for use in next generation thin client systems.