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Abstract:
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In this article, techniques have been presented for faster evolution of wavelet
lifting coefficients for fingerprint image compression (FIC). In addition to
increasing the computational speed by 81.35%, the coefficients performed much
better than the reported coefficients in literature. Generally, full-size images are
used for evolving wavelet coefficients, which is time consuming. To overcome
this, in this work, wavelets were evolved with resized, cropped, resized-average
and cropped-average images. On comparing the peak- signal-to-noise-ratios
(PSNR) offered by the evolved wavelets, it was found that the cropped
images excelled the resized images and is in par with the results reported till
date. Wavelet lifting coefficients evolved from an average of four 256 256
centre-cropped images took less than 1/5th the evolution time reported in
literature. It produced an improvement of 1.009 dB in average PSNR.
Improvement in average PSNR was observed for other compression ratios
(CR) and degraded images as well. The proposed technique gave better PSNR for
various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These
coefficients performed well with other fingerprint databases as well. |