基于小波包变换的指纹图像分级压缩算法

摘要

Aiming at the fingerprint image characteristic of plentiful medium frequency, and oppositely less high frequency and low frequency, the fingerprint image level compression algorithm is presented based on wavelet packet transformation. The fingerprint image is divided into three levels according to energy distribution after wavelet packet transformation. The medium frequency image, which contained more energy, is encoded by lossless Differential Pulse Code Modulation (DPCM). The high frequency and low frequency images, which contained less energy, are encoded by Embedded Zero-tree Wavelet (EZW). The image reconstruction combines the compressed image code streams with the feature point information. The experimental results indicate that compared with the traditional Zero-tree Wavelet encoding, the presented algorithm improves the average compression ratio about 1.832 and the average signal-to-noise about 4.07, meanwhile, reduces the average computation time by about 26% on the basis of ensuring the reconstruction quality, which proves the validity of level compression algorithm.%针对指纹图像中频分量丰富,高频和低频分量相对较少的特点,利用小波包分析提出了一种指纹图像分级压缩算法.将小波包变换后的指纹图像按能量多少进行分级,对包含能量较多的中频子图像,采用无损差分脉冲编码调制(DPCM),对包含能量较少的低频和高频子图像,采用嵌入式零数编码(EZW)算法；并将压缩图像码流与特征点信息相结合进行图像重建.仿真实验表明,该算法在保证重建质量的前提下,比传统的小波零树编码算法压缩比平均提高了约1.832,信噪比平均提高了约4.07,平均运算时间减少了约26％.