基于自适应稀疏变换的指纹图像压缩

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.

Fingerprint Image Compression Algorithm via Adaptive Sparse Transformation

With the wide application of fingerprint identification technology, a large number of fingerprint images need to be collected and stored. In fingerprint identification, as for the fingerprint database with large-capacity, the fingerprint images must be stored after compression to reduce the storage space. In this paper, a fingerprint image compression algorithm based on adaptive sparse transformation is proposed. The feature of the fingerprint image is extracted offline to train the over-complete dictionary. In the encoding process, the fingerprint image to be compressed is converted to sparse domain by utilizing the differential predictive coding and sparse transformation in the first place; after that the DC coefficients and the sparse coefficients are quantized and entropy coded to achieve the compression of the image information. Experimental results show that the proposed algorithm outperforms the mainstream compression methods, such as JPEG, JPEG2000 and WSQ, in terms of ratio-distortion performance of decoded fingerprint image, especially at low to medium bit rates. At the same bit rate, the compression image generated by the proposed algorithm exhibits better subjective visual effect and higher fingerprint recognition rate.