Autoencoder for Vibrotactile Signal Compression
Zhuoran Li, Rania Hassen, Zhou Wang
Abstract
Vibrotactile signals contain rich haptic information about textured surfaces but their large data volume makes it a challenging task to transmit such signals to remote locations to create immersive and realistic user experiences. Inspired by the recent success of deep neural network (DNN) based autoencoder, we make the first attempt to apply autoencoder for lossy compression of haptic vibrotactile signals, where a convolutional neural network (CNN) and a rate-distortion (RD) function are used as the transform and cost functions, respectively. Performance comparisons with state-of-the-art methods using both peak signal-to-noise ratio (PSNR) and perceptually motivated spectral temporal similarity (ST-SIM) measures show that the proposed end-to-end vibrotactile autoencoder (EVA) is highly competitive at preserving signal quality while keeping the data rate low.