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Green Image
ISSN: 1077-2626
Jian Cui , Purdue University, West Lafayette
Zhiqiang Ma , Beijing University of Aeronautics and Astronautics, Beijing
Voicu Popescu , Purdue University, West Lafayette
Remote-visualization has become both a necessity, as dataset sizes have grown faster than computer network performance, and an opportunity, as mobile computing platforms have become ubiquitous. However, the conventional remote-visualization approach of sending new images from the server to the client for every view-parameter change suffers from reduced interactivity. One problem is high latency, as the network has to be traversed twice for each interaction of client with server. A second problem is reduced image quality due to aggressive compression or reduced resolution. We address these problems by constructing and transmitting enhanced images that are sufficient for quality output frame reconstruction at the client for a range of view-parameter values. The client reconstructs thousands of frames locally, without any additional data from the server, which avoids latency and aggressive compression. We introduce animated depth images, which not only store a color and depth sample at every pixel, but also store the trajectory of the samples for a given time interval. Sample trajectories are stored compactly by partitioning the image into semi-rigid sample clusters and by storing one sequence of rigid body transformations per cluster. Animated depth images leverage sample trajectory coherence to achieve a good compression of animation data, with a small and user-controllable approximation error.
Bounded error, Remote Visualization, Time-varying datasets, Animation data compression, Rigid-body decomposition

J. Cui, Z. Ma and V. Popescu, "Animated Depth Images for Interactive Remote Visualization of Time-Varying Datasets," in IEEE Transactions on Visualization & Computer Graphics.
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