Lossless Pose Compression Based on Random Access Predictor

In current lossless compression methods, poses are usually encoded in a highly correlated space. Before current pose is decompressed, all poses related with it have to be processed. This will cost more decompression time and memory. In this paper, we propose a lossless pose compression method based on random access predictor. In our method, we compress well-organized poses. Firstly, the motion database is preprocessed by using a two step clustering process. After this process, similar poses are put together into one specific prediction space, ready for predicting and encoding. Secondly, a specially designed average predictor with quantized parameters is proposed to predict each pose independent of other poses. Finally, entropy encoding is introduced to compress the difference between the predicted value and the real value. Compared with previous lossless compression methods, we achieve higher compression ratio and better decompression time. The proposed lossless compression method can be widely used in character animation and virtual reality, where users normally demand high quality motion in real-time.