School of Information Science and Engineering, Central South University, Changsha, 410083, China; Hunan Vocational College of Commerce, Changsha 410025, China.
School of Information Science and Engineering, Central South University, Changsha, 410083, China; School of Computer Science and Educational Software, GuangZhou University, GuangZhou 510006, China.
Comput Methods Programs Biomed. 2017 Jul;145:157-166. doi: 10.1016/j.cmpb.2017.04.015. Epub 2017 Apr 25.
Embedded zerotree wavelet (EZW) is an efficient compression method that has advantages in coding, but its multilayer structure information coding reduces signal compression ratio. This paper studies the optimization of the EZW compression algorithm and aims to improve it. First, we used lifting wavelet transformation to process electrocardiograph (ECG) signals, focusing on the lifting algorithm. Second, we utilized the EZW compression coding algorithm, through the ECG information decomposition to determine the feature detection value. Then, according to the feature information, we weighted the wavelet coefficients of ECG (through the coefficient as a measure of weight) to achieve the goal of improved compression benefit.
嵌入式零树小波（EZW）是一种高效的压缩方法，在编码方面具有优势，但它的多层结构信息编码降低了信号的压缩比。本文研究了 EZW 压缩算法的优化，旨在改进它。首先，我们使用提升小波变换处理心电图（ECG）信号，重点研究提升算法。其次，我们利用 EZW 压缩编码算法，通过 ECG 信息分解来确定特征检测值。然后，根据特征信息，对 ECG 的小波系数进行加权（通过系数作为权重的度量），以达到提高压缩效益的目的。
