Advances in Visual Computing

Volume 4841 of the series Lecture Notes in Computer Science pp 714-723

Skeleton-Based Data Compression for Multi-camera Tele-Immersion System

  • Jyh-Ming LienAffiliated withGeorge Mason University, Fairfax, VA
  • , Gregorij KurilloAffiliated withUniversity of California, Berkeley, CA
  • , Ruzena BajcsyAffiliated withUniversity of California, Berkeley, CA

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Image-based full body 3D reconstruction for tele-immersive applications generates large amount of data points, which have to be sent through the network in real-time. In this paper we introduce a skeleton-based compression method using motion estimation where kinematic parameters of the human body are extracted from the point cloud data in each frame. First we address the issues regarding the data capturing and transfer to a remote site for the tele-immersive collaboration. We compare the results of the existing compression methods and the proposed skeleton-based compression technique. We examine robustness and efficiency of the algorithm through experimental results with our multi-camera tele-immersion system. The proposed skeleton-based method provides high and flexible compression ratios (from 50:1 to 5000:1) with reasonable reconstruction quality (peak signal-to-noise ratio from 28 to 31 dB).