Haptic feedback is becoming a crucial element for enhancing immersion in various media applications. To enrich this feedback, high-quality haptic content, an appropriate playback device, and efficient codecs for transmission are essential. This paper introduces a novel vibrotactile codec that employs an autoencoder architecture integrated with Convolutional Neural Networks (CNNs). It leverages a tailored perceptual model with a band structure derived from the audio domain, optimizing the perceived quality of the encoded signals during training. Additionally, we have developed and assessed multiple perceptual training losses to further enhance the performance of our codec.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Vibrotactile Signal Compression Using Perceptually Trained Autoencoders

  • Lars Nockenberg,
  • Eckehard Steinbach

摘要

Haptic feedback is becoming a crucial element for enhancing immersion in various media applications. To enrich this feedback, high-quality haptic content, an appropriate playback device, and efficient codecs for transmission are essential. This paper introduces a novel vibrotactile codec that employs an autoencoder architecture integrated with Convolutional Neural Networks (CNNs). It leverages a tailored perceptual model with a band structure derived from the audio domain, optimizing the perceived quality of the encoded signals during training. Additionally, we have developed and assessed multiple perceptual training losses to further enhance the performance of our codec.