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Title: Low-latency compression of mocap data using learned spatial decorrelation transform
Authors: Hou, Junhui
Chau, Lap-Pui
Magnenat-Thalmann, Nadia
He, Ying
Keywords: DRNTU::Engineering::Computer science and engineering
Motion Capture
Data Compression
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2016
Source: Hou, J., Chau, L. P., Magnenat-Thalmann, N., & He, Y. (2016). Low-latency compression of mocap data using learned spatial decorrelation transform. Computer Aided Geometric Design, 43211-225. doi:10.1016/j.cagd.2016.02.002
Series/Report no.: Computer Aided Geometric Design
Abstract: Due to the growing needs of motion capture (mocap) in movie, video games, sports, etc., it is highly desired to compress mocap data for efficient storage and transmission. Unfortunately, the existing compression methods have either high latency or poor compression performance, making them less appealing for time- critical applications and/or network with limited bandwidth. This paper presents two efficient methods to compress mocap data with low latency. The first method processes the data in a frame-by-frame manner so that it is ideal for mocap data streaming. The second one is clip-oriented and provides a flexible trade-off between latency and compression performance. It can achieve higher compression performance while keeping the latency fairly low and controllable. Observing that mocap data exhibits some unique spatial characteristics, we learn an orthogonal transform to reduce the spatial redundancy. We formulate the learning problem as the least square of reconstruction error regularized by orthogonality and sparsity, and solve it via alternating iteration. We also adopt a predictive coding and temporal DCT for temporal decorrelation in the frame- and clip-oriented methods, respectively. Experimental results show that the proposed methods can produce higher compression performance at lower computational cost and latency than the state-of-the-art methods. Moreover, our methods are general and applicable to various types of mocap data.
ISSN: 0167-8396
DOI: 10.1016/j.cagd.2016.02.002
Rights: © 2016 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computer Aided Geometric Design, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

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