Deep Learning-Based Perceptual Vibrotactile Codec with Rate Scalability

We present a vibrotactile codec based on a convolutional neural network that is fully rate-scalable and perceptually optimized. Rate-scalability so far was a rarely addressed problem with deep learning-based codecs. This is achieved through a bit allocation algorithm that perceptually optimizes the signal quantization, utilizing bitrate estimation based on a gaussian entropy model. We compare different bit allocation approaches regarding signal quality and found that our algorithm is effective at reaching the desired bitrate while achieving higher performance than our previously designed classical codec.