The alpha parallelogram predictor: A lossless compression method for motion capture data

  • Authors:

  • Pengjie Wang;Zhigeng Pan;Mingmin Zhang;Rynson W. H. Lau;Haiyu Song

  • Affiliations:

  • State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027, China and College of Computer Science & Engineering, Dalian Nationalities University, Dalian 116600, China and Department of ...;Digital Media and HCI Research Center, Hangzhou Normal University, Hangzhou 310023, China and State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027, China;State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027, China;Department of Computer Science, City University of Hong Kong, Hong Kong;College of Computer Science & Engineering, Dalian Nationalities University, Dalian 116600, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Motion capture data in an uncompressed form can be expensive to store, and slow to load and transmit. Current compression methods for motion capture data are primarily lossy and cause distortions in the motion data. In this paper, we present a lossless compression algorithm for motion capture data. First, we propose a novel Alpha Parallelogram Predictor (APP) to estimate the DOF (degree of freedom) of each child joint from those of its immediate neighbors and parents that have already been processed. The prediction parameter of the predictor, which is referred to as the alpha parameter, is adaptively chosen from a carefully designed lookup table. Second, we divide the predicted and actual values into three components: sign, exponent and mantissa. We then compress their corrections separately with context-based arithmetic coding. Compared with other lossless compression methods, our approach can achieve a higher compression ratio with a comparable compression time. It can be used in situations where lossy compression is not preferred.