Haptic data compression based on curve reconstruction

  • Authors:
  • Fenghua Guo;Yan He;Nizar Sakr;Jiying Zhao;Abdulmotaleb El Saddik

  • Affiliations:
  • School of Computer Science and Technology, Shandong University, P.R. China and School of Information Technology and Engineering, University of Ottawa, Canada;School of Computer Science and Technology, Shandong University, P.R. China;School of Information Technology and Engineering, University of Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Canada;School of Information Technology and Engineering, University of Ottawa, Canada

  • Venue:
  • AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
  • Year:
  • 2011

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Abstract

In this paper, the problem of haptic data compression is addressed. The algorithm partitions haptic data samples into subsets while relying on knowledge from human haptic perception. A geometric distance-based approach is used to reduce the number of haptic data subsets. In particular, to improve approximation precision, each haptic data subset is fitted by a quadratic curve. Accordingly, rather than directly using the original haptic data, only the coefficients of the quadratic curves are stored or transmitted. Experiments are performed to compare the suggested curve reconstruction method with the more common linear method. The results prove the effectiveness of the proposed approach in improving data reduction rate and approximation precision.