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Efficient Vibrotactile Codec Based on Nbeats Network
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AI
DOI：10.1109/LSP.2024.3477251.png)
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摘要
En 中文
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.
Keyword:
Haptic interfaces
Encoding
Quantization (signal)
Codecs
Huffman coding
Databases
Decoding
Training
PSNR
Long short term memory
Multimodal communication
haptics
vibrotactile
signal compression