Low-latency compression of mocap data using learned spatial decorrelation transform
Junhui Hou, Lap‐Pui Chau, Nadia Magnenat‐Thalmann, Ying He · Computer Aided Geometric Design · 2016
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.