High Efficiency Vibrotactile Codec Based on Gate Recurrent Network

Abstract

The multimedia has achieved dominant positions in both local storage and internet bandwidth, which inevitably promotes the compression of audio, image and video information. Nowadays, the emerging haptic technology, which enhances the immersion in virtual reality and remote control, has also brought new challenges in its codec design. It is thus imperative to develop haptic codecs, including kinesthetic and vibrotactile codecs, with high efficiency and low delay. In this paper, we exploit statistical features of vibrotactile data to develop a Recurrent-Network-based Vibrotactile Codec (RNVC) with high compression efficiency and low coding delay. The proposed encoder consists of vibrotactile estimation by Gate Recurrent Unit (GRU), non-uniform quantization/compensation of residuals and an entropy encoder. In particular, the GRU-based recurrent network is utilized for its high efficiency to predict signals and low complexity to converge. The decoder consists of all counterparts of encoder. Experimental results show the proposed RNVC significantly reduces of original bitrates with negligible encoding delay, which achieves the state-of-the-art coding performance of vibrotactile signal.