Abstract
This paper reports on an efficient lossless compression method for periodic signals based on adaptive dictionary predictive coding. Some previous methods for data compression, such as difference pulse coding (DPCM), discrete cosine transform (DCT), lifting wavelet transform (LWT) and KL transform (KLT), lack a suitable transformation method to make these data less redundant and better compressed. A new predictive coding approach, basing on the adaptive dictionary, is proposed to improve the compression ratio of the periodic signal. The main criterion of lossless compression is the compression ratio (CR). In order to verify the effectiveness of the adaptive dictionary predictive coding for periodic signal compression, different transform coding technologies, including DPCM, 2-D DCT, and 2-D LWT, are compared. The results obtained prove that the adaptive dictionary predictive coding can effectively improve data compression efficiency compared with traditional transform coding technology.
Highlights
With the popularization of digitalization, computers and data processing equipment have penetrated into all walks of life, and analog communication has almost been replaced by digital communication
To solve the problem of periodic signal compression, we propose an efficient lossless compression methodAppl
To solve the problem of periodic signal compression, we propose an efficient lossless directly code the signal, but codes the prediction error
Summary
With the popularization of digitalization, computers and data processing equipment have penetrated into all walks of life, and analog communication has almost been replaced by digital communication. S proposed a DPCM-based threshold data compression technology multi-channel linear prediction and adaptive Golomb-Rice coding [12]. For time-series signals, the ARIMA model and RNN model can accurately predict, but their famous is the partial matching prediction (PPM) algorithm, which was first proposed by Cleary and algorithm complexity is high, so they are mostly used for time series with low real-time requirements. In the process of data compression, massive predict, but their algorithm complexity is high, so they are mostly used for time series with low realdata is required, ARIMA model and the RNNUnfortunately, model take a lot timeprediction requirements such asand air the temperature and wind speed. Compression, massive data prediction is required, and the ARIMA model and the RNN model take a lot of time. Algorithm reduces Introduction the amount of data to obtain a high compression ratio
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