Haptic Data Compression Based on Curve Reconstruction
Identifieur interne : 000142 ( Istex/Checkpoint ); précédent : 000141; suivant : 000143Haptic Data Compression Based on Curve Reconstruction
Auteurs : Fenghua Guo [République populaire de Chine, Canada] ; Yan He [République populaire de Chine] ; Nizar Sakr [Canada] ; Jiying Zhao [Canada] ; Abdulmotaleb El Saddik [Canada]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
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.
Url:
DOI: 10.1007/978-3-642-21538-4_34
Affiliations:
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<front><div type="abstract" xml:lang="en">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.</div>
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