Prediction techniques for wavelet based 1-D signal compression

Abstract

This paper proposes a novel one-dimensional (1-D) signal compression technique. We first perform beat-alignment to transform a 1-D signal into 2-D, then use 2-D discrete wavelet transform (DWT) to further decompose the 2-D signal into multiple subbands. These coefficients in certain subbands are then coded using a simple differential pulse code modulation (DPCM). After which, we construct neural networks one for each subband (except the LL subband) to perform prediction. Based on the prediction results, we construct a type of pixel-wise context A to determine the activity of a given pixel. At last, the DWT coefficients and residues from DPCM are bit-plane coded using the Embedded Block Coding with Optimized Truncation (EBCOT) from JPEG2000. We analyzed our results using a well- known 1D signal, the ECG signals in the MIT-BIH database, and it demonstrated significant improvement over existing methods.