Kasaei Shohreh, Deriche Mohamed, Boashash Boualem
Sharif Univ. of Technol., Tehran, Iran.
IEEE Trans Image Process. 2002;11(12):1365-78. doi: 10.1109/TIP.2002.802534.
A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.
介绍了一种用于指纹图像的新型压缩算法。利用小波包和格矢量量化，提出了一种基于小波系数分布精确模型的新型矢量量化方案。该模型基于广义高斯分布。我们还讨论了一种确定均匀和分段均匀金字塔格所用格的最大半径及其缩放因子的新方法。所提出的算法旨在通过适应子图像的特征来实现最佳的率失真函数。在所提出的优化算法中，不对格参数做任何假设，也不需要训练和多量化。我们还表明，在所提出的算法中解决了与急剧分布的随机源相关的楔形区域问题。所提出的算法适应输入图像的变化和指定的比特率。与其他可用的图像压缩算法相比，所提出的算法在相同比特率下能得到更高质量的重建图像。
