Reordering-based transform for compressing human motion capture data

This paper presents a simple yet effective algorithm for compressing human motion capture (mocap) data. With a reordering-based discrete wavelet transform and the standard discrete cosine transform, our method can effectively reduce the spatial and temporal correlation in mocap data. Our method is conceptually simple and easy to implement. Experimental results show that our method can achieve better compression performance with lower latency, compared to the state-of-the-art methods.

[1]  Michael Elad,et al.  Redundant Wavelets on Graphs and High Dimensional Data Clouds , 2011, IEEE Signal Processing Letters.

[2]  I-Chen Lin,et al.  Adaptive Motion Data Representation with Repeated Motion Analysis. , 2011, IEEE transactions on visualization and computer graphics.

[3]  Nadia Magnenat-Thalmann,et al.  Low-rank based compact representation of motion capture data , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  Libor Vása,et al.  Rate‐distortion optimized compression of motion capture data , 2014, Comput. Graph. Forum.

[5]  Neil D. Lawrence,et al.  MOCAP Toolbox for MATLAB , 2005 .

[6]  Zhihai He Peak Transform for Efficient Image Representation and Coding , 2007, IEEE Transactions on Image Processing.

[7]  Philippe Beaudoin,et al.  Adapting wavelet compression to human motion capture clips , 2007, GI '07.

[8]  Lap-Pui Chau,et al.  A Fuzzy Clustering Algorithm for Virtual Character Animation Representation , 2011, IEEE Transactions on Multimedia.

[9]  Okan Arikan Compression of motion capture databases , 2006, SIGGRAPH 2006.

[10]  Ivan V. Bajic,et al.  Hybrid low-delay compression of motion capture data , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[11]  Lap-Pui Chau,et al.  Scalable and Compact Representation for Motion Capture Data Using Tensor Decomposition , 2014, IEEE Signal Processing Letters.

[12]  Zhigang Deng,et al.  Quaternion space sparse decomposition for motion compression and retrieval , 2012, SCA '12.

[13]  Yi Li,et al.  Learning shift-invariant sparse representation of actions , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Irene Cheng,et al.  Perceptually Guided Fast Compression of 3-D Motion Capture Data , 2011, IEEE Transactions on Multimedia.

[15]  Craig Gotsman,et al.  Compression of soft-body animation sequences , 2004, Comput. Graph..

[16]  Ivan V. Bajic,et al.  Error concealment strategies for Motion Capture data streaming , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[17]  Guodong Liu,et al.  Segment-based human motion compression , 2006, SCA '06.