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基于自适应稀疏变换的指纹图像压缩

马名浪 何小海 滕奇志 陈洪刚 卿粼波

马名浪, 何小海, 滕奇志, 陈洪刚, 卿粼波. 基于自适应稀疏变换的指纹图像压缩. 自动化学报, 2016, 42(8): 1274-1284. doi: 10.16383/j.aas.2016.c150815
引用本文: 马名浪, 何小海, 滕奇志, 陈洪刚, 卿粼波. 基于自适应稀疏变换的指纹图像压缩. 自动化学报, 2016, 42(8): 1274-1284. doi: 10.16383/j.aas.2016.c150815
MA Ming-Lang, HE Xiao-Hai, TENG Qi-Zhi, CHEN Hong-Gang, QING Lin-Bo. Fingerprint Image Compression Algorithm via Adaptive Sparse Transformation. ACTA AUTOMATICA SINICA, 2016, 42(8): 1274-1284. doi: 10.16383/j.aas.2016.c150815
Citation: MA Ming-Lang, HE Xiao-Hai, TENG Qi-Zhi, CHEN Hong-Gang, QING Lin-Bo. Fingerprint Image Compression Algorithm via Adaptive Sparse Transformation. ACTA AUTOMATICA SINICA, 2016, 42(8): 1274-1284. doi: 10.16383/j.aas.2016.c150815

基于自适应稀疏变换的指纹图像压缩

doi: 10.16383/j.aas.2016.c150815
基金项目: 

国家自然科学基金 61471248

四川省教育厅2014研究生教育改革创新项目 2014-Education-034

四川省科技计划项目 2015JY0189

详细信息
    作者简介:

    马名浪 四川大学电子信息学院硕士研究生.主要研究方向为图像超分辨率和图像压缩.E-mail:hellomaminglang@163.com;

    滕奇志 博士,四川大学电子信息学院教授.主要研究方向为图像处理,图像传输,模式识别和软件工程.E-mail:qzteng@scu.edu.cn;

    陈洪刚 四川大学电子信息学院博士研究生.主要研究方向为图像压缩,图像超分辨率,图像复原和压缩感知.E-mail:honggangchen.scu@gmail.com;

    卿粼波 博士,四川大学电子信息学院副教授.主要研究方向为图像压缩,视频编码与传输,信息理论.E-mail:_lb@scu.edu.cn

    通讯作者:

    何小海 博士,四川大学电子信息学院教授.主要研究方向为图像处理,模式识别和图像通信.本文通信作者.E-mail:hxh@scu.edu.cn

Fingerprint Image Compression Algorithm via Adaptive Sparse Transformation

Funds: 

National Natural Science Foundation of China 61471248

2014 Postgraduate Education Innovation Project of Sichuan Education Department 2014-Education-034

Technology Project of Sichuan Province 2015JY0189

More Information
    Author Bio:

    Master student at the College of Electronics and Information Engineering, Sichuan University. His research interest covers image super resolution and image compression.E-mail:

    Ph. D., professor at the College of Electronics and Information Engineering, Sichuan University. Her research interest covers image processing, image communication, pattern recognition, and software engineering.E-mail:

    Ph. D. candidate at the College of Electronics and Information Engineering, Sichuan University. His research interest covers image compression image super resolution, image restoration, and compressed sensing.E-mail:

    Ph. D., associate professor at the College of Electronics and Information Engineering, Sichuan University. His research interest covers image processing, video coding and transmission, and information theory.E-mail:

    Corresponding author: HE Xiao-Hai Ph. D., professor at the College of Electronics and Information Engineering, Sichuan University. His research interest covers image processing, pattern recognition, and image communication.
  • 摘要: 随着指纹识别技术的广泛应用,大量指纹图像需要被收集和存储.在指纹识别系统中,对于大容量的指纹数据库,指纹图像必须经过压缩后存储以减少存储空间,本文提出了基于自适应稀疏变换的指纹图像压缩算法.该算法在离线状态下提取指纹图像特征训练超完备字典;在编码过程中,首先利用差分预测编码和稀疏变换将待压缩指纹图像转换到稀疏域,然后对直流系数和稀疏表达系数进行量化和熵编码,从而实现图像信息的压缩.实验表明,在中低码率段,本文算法相比于JPEG、JPEG2000和WSQ等主流压缩算法表现出更优越的率失真性能;在相同码率时,本文算法生成的压缩图像的主观视觉效果更好,指纹识别率更高.
  • 图  1  本文提出的基于自适应稀疏变换的指纹图像压缩算法框架

    Fig.  1  The framework of the proposed fingerprint image compression algorithm via adaptive sparse transformation

    图  2  本文算法低频预测图像(a)与K-SVD-SR 算法低频图像 (b)块效应对比

    Fig.  2  The comparison of low-frequency predicted image between the proposed algorithm (a) and the K-SVD-SR algorithm(b)

    图  3  块间像素预测对最终编解码效果的影响

    Fig.  3  The effect of inter-block pixel prediction on the final codec

    图  4  灰度平均值的三种编码方向模式

    Fig.  4  The three coding direction modes for grayscale average

    图  5  分块尺寸分别为6× 6、7× 7、8× 8、9×9的率失真性能比较

    Fig.  5  The comparison of rate distortion performance between the blocks with the size of 6× 6,7× 7,8× 8,and 9× 9

    图  6  (a)~(d) 分别表示测试图像库中的数据库2~5在四种压缩算法下的平均率失真性能

    Fig.  6  The (a),(b),(c),(d) respectively denotes the average rate distortion performance of the test image library Database2,Database3,Database4,and Database5 at 4 compression algorithms

    图  7  从左至右分别表示原始图像和码率同为0.1 bpp的JPEG、JPEG2000、K-SVD-SR和本文算法的解码图像

    Fig.  7  From left to right respectively represents the original image and the decoded image of JPEG,JPEG2000,K-SVD-SR,and the proposed algorithm at the same rate as 0.1 bpp

    图  8  稀疏度自适应选择与固定稀疏度L=2、6、10、14对比

    Fig.  8  The contrast of the adaptive sparsity and the fixed sparsity of L=2,6,10,14

    图  9  MP与QOMP算法对比

    Fig.  9  The contrast of MP algorithm and QOMP algorithm

    图  10  两种量化模式对图像压缩性能的影响比较

    Fig.  10  The comparison of the impact on image compression performance between the two quantization modes

    图  11  “索引-权值”编码模式与“原子个数-索引-权值”编码模式对比

    Fig.  11  The contrast of the "index-weight" encoding mode and the "number of atoms-index-weight" encoding mode

    图  12  基于AGR的图像解码与K-SVD-SR的 直接解码对比

    Fig.  12  The contrast of the image decoding based on AGR and the direct decoding of K-SVD-SR

    图  13  测试图像库数据库2在四种压缩算法下的\\图像的平均特征匹配率

    Fig.  13  The average image feature matching rate of the test image library Database 2 at 4 compression algorithms

    表  1  四种压缩算法的时间复杂度比较 (s)

    Table  1  The comparison of time complexity about 4 compression algorithms (s)

    码率 (bpp) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
    JPEG0.160.160.160.160.170.170.170.170.17
    JPEG20000.080.080.080.080.070.080.080.080.08
    K-SVD-SR1.261.742.202.643.193.614.134.494.89
    本文算法2.132.803.262.884.534.955.835.766.84
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-12-07
  • 录用日期:  2016-03-10
  • 刊出日期:  2016-08-01

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