Quantitative Multiscale Analysis using Different Wavelets in 1D Voice Signal and 2D Image
This is an incremental study applying existing wavelet methods to improve signal quality and compression in voice and image processing.
The paper tackled multiscale analysis of 1D voice signals and 2D images using various wavelets, achieving image compression performance up to 93.68% with specific wavelets like bior-4.4.
Mutiscale analysis represents multiresolution scrutiny of a signal to improve its signal quality. Multiresolution analysis of 1D voice signal and 2D image is conducted using DCT, FFT and different wavelets such as Haar, Deubachies, Morlet, Cauchy, Shannon, Biorthogonal, Symmlet and Coiflet deploying the cascaded filter banks based decomposition and reconstruction. The outstanding quantitative analysis of the specified wavelets is done to investigate the signal quality, mean square error, entropy and peak-to-peak SNR at multiscale stage-4 for both 1D voice signal and 2D image. In addition, the 2D image compression performance is significantly found 93.00% in DB-4, 93.68% in bior-4.4, 93.18% in Sym-4 and 92.20% in Coif-2 during the multiscale analysis.