Model-based compression of nonstationary landmark shape sequences

We have proposed a novel model-based compression technique for nonstationary landmark shape data extracted from video sequences. The main goal is to develop a technique for the compact storage of landmark shape data. We use nonstationary shape activity (NSSA) to model the shape sequences. The shape data is encoded by applying differential pulse code modulation (DPCM) on the shape velocity coefficients under the NSSA model. We have studied the system performance in terms of compressibility-distortion trade off. NSSA based compression technique has been compared with two other methods based on existing shape modeling techniques namely, stationary shape activity (SSA) and active shape model (ASM). We tested our system with landmark shape data extracted from multiple video sequences of the CMU mocap database. It was found that NSSA outperforms both SSA and ASM in terms of compressibility for a given distortion tolerance. Thus NSSA based compression technique could be very useful in the applications like storage of large volumes of biomedical landmarks' data.