Perceptually Motivated Real-Time Compression of Motion Data Enhanced by Incremental Encoding and Parameter Tuning

Abstract

We address the problem of efficient real-time motion data compression considering human perception. Using incremental encoding plus a database of motion primitives for each key point, our method achieves a higher or competitive compression rate with less online overhead. Trade-off between visual quality and bandwidth usage can be tuned by varying a single threshold value. A user study was performed to measure the sensitivity of human subjects to reconstruction errors in key rotation angles. Based on these evaluations we are able to perform lossy compression on the motion data without noticeable degradation in rendered qualities. While achieving real-time performance, our technique outperforms other methods in our experiments by achieving a compression ratio exceeding 50 : 1 on regular sequences.