This paper explains the Genetic Algorithm (GA)
evolution of optimized wavelet that surpass the cdf9/7
wavelet for fingerprint compression and reconstruction.
Optimized wavelets have already been evolved in previous
works in the literature, but they are highly computationally
complex and time consuming. Therefore, in this work, a simple
approach is made to reduce the computational complexity of
the evolution algorithm. A training image set comprised of
three 32x32 size cropped images performed much better than
the reported coefficients in literature. An average
improvement of 1.0059 dB in PSNR above the classical cdf9/7
wavelet over the 80 fingerprint images was achieved. In
addition, the computational speed was increased by 90.18 %.
The evolved coefficients for compression ratio (CR) 16:1
yielded better average PSNR for other CRs also. Improvement
in average PSNR was experienced for degraded and noisy
images as well
Description:
2012 International Conference on Advances in Computing and Communications