Compression of 3-D Tele-Immersion Data

Jyh-Ming Lien, George Mason University and Ruzena Bajcsy, UC Berkeley


Data captured by a Tele-Immersion (TI) system can be very large. Compression is usually needed to ensure real-time data transmission. Our compression method takes advantage of prior knowledge of objects, e.g. human figures, in the TI environments and represents their motions using just a few parameters. The main steps of our approach include: motion estimation and residual computation as shown in the figure above. The proposed compression method provides tunable and high compression ratios (from 50:1 to 5000:1) with reasonable reconstruction quality. Moreover, the proposed method can estimate motions from the noisy data captured by our TI system in real time.

Multi-Camera Tele-immersion System with Real-Time Model Driven Data Compression, Jyh-Ming Lien, Gregorij Kurillo, and Ruzena Bajcsy, the Visual Computer, Springer, May 2009.
Full text: pdf

Skeleton-Based Compression of 3-D Tele-Immersion Data, Jyh-Ming Lien and Ruzena Bajcsy, Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Vienna, Austria, Sep. 2007.
Full text: pdf




download: avi (73.3 MB), divx avi (27.2 MB)

See also

List of MASC Research Pages
Computer Science @ George Mason University