Prediction-based Haptic Data Reduction and Compression in Tele-Mentoring Systems

In this paper, a novel haptic data reduction and compression technique to reduce haptic data traffic in networked haptic tele-mentoring systems is presented. The suggested method follows a two-step procedure: (1) haptic data packets are not transmitted when they can be predicted within a predefined tolerable error; otherwise, (2) data packets are compressed prior to transmission. The prediction technique relies on the least-squares method. Knowledge from human haptic perception is incorporated into the architecture to assess the perceptual quality of the prediction results. Packet-payload compression is performed using uniform quantization and adaptive Golomb-Rice codes. The preliminary experimental results demonstrate the algorithm´s effectiveness as great haptic data reduction and compression is achieved, while preserving the overall quality of the tele-mentoring environment.