This paper presents a novel architecture for discrete wavelet transform processing of gray level image. From several processor-time allocation scheme we found a quite optimal one that eflciently use the available processor-time slots. The algorithm is then mapped into an array architecture . The resulted architecture needs less processing elements than conventional architecture and has a simple...

This study presents two sets of translation invariant wavelet transforms for coding an arbitrarily shaped image. Each set can be viewed as a shape-adaptive discrete wavelet transform (SA-DWT) with the property of translation invariance. The proposed transform schemes have the following merits: (1) they are translation invariant, (2) no sharp transition appears at the edges of the image, (3) the...

Boundary functions for wavelets on a finite interval are often constructed as linear combinations of boundary-crossing scaling functions. An alternative approach is based on linear algebra techniques for truncating the infinite matrix of the Discrete Wavelet Transform to a finite one. In this article we show how an algorithm of Madych for scalar wavelets can be generalized to multiwavelets, giv...

Digital image compression with multiresolution singular value decomposition is compared with discrete cosine transform, discrete 9/7 biorthogonal wavelet transform, Karhunen–Loève transform, and combinations thereof. The coding methods used SPIHT and run-length with Huffmann coding. The performances of these methods differ little from each other. Generally, the 9/7 biorthogonal wavelet transfor...

Time-frequency analysis is a powerful tool for signal analysis and processing. The Fourier transform and wavelet transforms are used extensively as is the Short-Time Fourier Transform (or Gabor transform). In 1996 the Stockwell transform was introduced to maintain the phase of the Fourier transform, while also providing the progressive resolution of the wavelet transform. The discrete orthonorm...

Continuous and Discrete Wavelet Transform (WT) are used to create text-dependent robust to noise speaker recognition system. In this paper we investigate the accuracy of identification the speaker identity in nonstationary signals. Three methods are used to extract the essential speaker features based on Continuous, Discrete Wavelet Transform and Power Spectrum Density (PSD). To have better ide...

To my family, in Korea and in Atlanta. iii ACKNOWLEDGEMENTS I am deeply indebted to my advisor Dr. Brani Vidakovic for his guidance and support in countless ways. Without his enlightening instruction, great kindness and patience, unwavering enthusiasm and trust, I could not have completed my thesis. I also want to extend my gratitude to my thesis committee members, Dr. for all their careful rev...

We present new multiwavelet transforms for discrete signals. The transforms are implemented in two phases: 1. Pre (post)processing which transform the scalar signal into the vector one (and back). 2.Wavelet transforms of the vector signal. Both phases are performed in a lifting manner. We use the cubic interpolatory Hermite splines as a predicting aggregate in the vector wavelet transform. We p...

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of ...