Article
Lossless Compression of Sensor Signals Using an Untrained
Multi-Channel Recurrent Neural Predictor
Qianhao Chen
1
, Wenqi Wu
1
and Wei Luo
2,
*
,†
Citation: Chen, Q.; Wu, W.; Luo, W.
Lossless Compression of Sensor
Signals Using an Untrained
Multi-Channel Recurrent Neural
Predictor. Appl. Sci. 2021, 11, 10240.
https://doi.org/10.3390/app112110240
Academic Editor: Nunzio Cennamo
Received: 8 September 2021
Accepted: 29 October 2021
Published: 1 November 2021
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4.0/).
1
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China;
chen_qh@zju.edu.cn (Q.C.); winkywow@zju.edu.cn (W.W.)
2
Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
* Correspondence: williamluo@cuhk.edu.hk
†
Wei Luo is now a research associate in the Chinese University of Hong Kong. He finished most of the work in
this paper when he was in Zhejiang University.
Abstract:
The use of sensor applications has been steadily increasing, leading to an urgent need
for efficient data compression techniques to facilitate the storage, transmission, and processing of
digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor
signals have more complex statistical characteristics. Specifically, in every signal point, each bit,
which corresponds to a specific precision scale, follows its own conditional distribution depending
on its history and even other bits. Therefore, applying existing general-purpose data compressors
usually leads to a relatively low compression ratio, since these compressors do not fully exploit
such internal features. What is worse, partitioning a bit stream into groups with a preset size
will sometimes break the integrity of each signal point. In this paper, we present a lossless data
compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural
architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU
models a specific precision range of each signal point without breaking data integrity. During
compressing and decompressing, the mirrored network will be trained on observed data; thus, no
pre-training is needed. The superiority of our approach over other compressors is demonstrated
experimentally on various types of sensor signals.
Keywords:
lossless compression; sensor signals; context-based compressor; entropy coding; recurrent
neural networks
1. Introduction
As digitalization advances continuously, sensor technology is undergoing tremen-
dous development and has been widely used in applications such as wearable medical
devices [
1
], climate change tracking [
2
], and infrastructure monitoring [
3
]. At the same time,
with the improvement of the resolution and sampling rate of analog-to-digital converters
(ADCs), the volume of sensor signals increases rapidly, leading to great pressure on data
storage. One way to alleviate such pressure is to reduce the redundancy existing in massive
data through data compression. Data compression techniques can be categorized into two
classes: lossless compression and lossy compression. Lossy compression of sensor signals
discards part of the secondary information, and the resulting distortion is limited within
an acceptable range. For example, An et al. proposed a data compression method based on
two-dimensional discrete cosine transform (DCT), which can effectively reduce the amount
of data since most natural signals are concentrated in the low frequency parts of DCT [
4
].
Zhang et al. proposed a compression method based on wavelet transform and obtained
a high signal-to-noise ratio [
5
]. These methods can produce a high compression ratio at
the cost of dropping part of the signal information. However, in many scenarios, such
as sensor debugging [
6
], sensor signals must be stored losslessly. In such cases, lossless
data compression is used. For example, Biagetti et al. explained the importance of lossless
Appl. Sci. 2021, 11, 10240. https://doi.org/10.3390/app112110240 https://www.mdpi.com/journal/applsci
