FINGERPRINT COMPRESSION USING WAVELET PACKET TRANSFORM AND PYRAMID LATTICE VECTOR QUANTIZATION

A new compression algorithm for fingerprint images is introduced. A mo dified wavelet packet scheme which uses a fixed decomposition structur e. matched to the statistics of fingerprint images, is used. Based on statistical studies of the subbands, different compression techniques are chosen for different subbands. The decision is based on the effect of each subband on reconstructed image, taking into account the chara cteristics of the Human Visual System (HVS). A noise shaping bit alloc ation procedure which considers the HVS, is then used to assign the bi t rate among subbands. Using Lattice Vector Quantization (LVQ), a new technique for determining the largest radius of the Lattice and its sc aling factor is presented. The design is based on obtaining the smalle st possible Expected Total Distortion (ETD) measure, using the given b it budget. At low bit rates, for the coefficients with high-frequency content, we propose the Positive-Negative Mean (PNM) algorithm to impr ove the resolution of the reconstructed image. Furthermore, for the co efficients with low-frequency content, a lossless predictive compressi on scheme is developed. The proposed algorithm results in a high compr ession ratio and a high reconstructed image quality with a low computa tional load compared to other available algorithms.