In this paper, an improved technique for evolving
wavelet coefficients refined for compression and reconstruction
of fingerprint images is presented. The FBI fingerprint
compression standard [1, 2] uses the cdf 9/7 wavelet filter
coefficients. Lifting scheme is an efficient way to represent
classical wavelets with fewer filter coefficients [3, 4]. Here
Genetic algorithm (GA) is used to evolve better lifting filter
coefficients for cdf 9/7 wavelet to compress and reconstruct
fingerprint images with better quality. Since the lifting filter
coefficients are few in numbers compared to the corresponding
classical wavelet filter coefficients, they are evolved at a faster
rate using GA. A better reconstructed image quality in terms
of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best
lifting filter coefficients evolved for a compression ratio 16:1.
These evolved coefficients perform well for other compression
ratios also.
Description:
Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on
Devassia, V P; Dr. Tessamma, Thomas(Cochin University of Science & Technology, December , 2003)
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Abstract:
During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.
Description:
Department of Electronics, Cochin University of Science and Technology