Efficient Vibrotactile Codec Based on Nbeats Network

Abstract

Within the domain of multimodal communication, the compression of audio, image, and video information is well-established, but compressing haptic signals, including vibrotactile signals, remains challenging. Particularly with the enhancement of haptic signal sampling rate and degrees of freedom, there is a substantial increase in data volume. While existing algorithms have made progress in vibrotactile codecs, there remains significant room for improvement in compression ratios. We propose an innovative Nbeats Network-based Vibrotactile Codec (NNVC) that leverages the statistical characteristics of vibrotactile data. This advanced codec integrates the Nbeats network for precise vibrotactile prediction, residual quantization, efficient Run-Length Encoding, and Huffman coding. The algorithm not only captures the intricate details of vibrotactile signals but also ensures high-efficiency data compression. It exhibits robust overall performance in terms of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR), significantly surpassing the state-of-the-art.