A signal processed by the Discrete Wavelet Transform (DWT) must be periodic for perfect reconstruction to be possible. A signal that is non-periodic but which exists for a nite time can be \periodised" by repeating the signal again at its own end, making it suitable for processing by the DWT. For a continuous-time non-periodic signal, the signal has no end and as such it cannot be periodised an...

Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. The wavelet transform is one of the major processing components of image compression. The results of the compression change as per the basis and tap of the wavelet used. This paper compares compression performance of Daubechies, Biorthogonal, Coiflets and other wavelets along with res...

—ASIC hardware implementations of the discrete wavelet transform are required to cope with the intensive real-time computations of the transform. In this paper, we describe a parallel implementation of the wavelet transform using one type of reconfigurable ASICs; Filed Programmable Gate Arrays (FPGAs). The implementation is based on reformulating the transform using the distributed arithmetic a...

In this paper, we present a new predictive hybrid model using discrete wavelet transform (DWT), and the artificial neural network (ANN) to reduce the bullwhip effect of demand in supply chain to obtain a real amount of final customer demand. Also, we compare our result with more comprehensive sample of previous research to extend the scope of our study. In this new research our methodology is c...

Glossary WT1D The one-dimensional Wavelet Transform as defined in [1]. See also [2] in this volume. WT2D The two-dimensional Wavelet Transform. Discrete Ridgelet Trasnform (DRT) The discrete implementation of the continuous Ridgelet transform. Fast Slant Stack (FSS) An algebraically exact Radon transform of data on a Cartesian grid. First Generation Discrete Curvelet Transform (DCTG1) The discr...

In this paper we propose a new steganography technique which embeds secrete color image into color cover work in frequency domain. Unlike the spatial domain approach, secrete image or data are embedded in the low frequency coefficients resulted from Discrete Wavelet Transform, as it is much sensitive to Human Visual System several researchers are not preferring to embed data in low frequency co...

Classical discrete wavelet packet transforms are sensitive to changes in image orientation and translation. Therefore, it is hardly possible to extract rotation invariant features from images in the transform domain. This paper proposes several algorithms for invariant discrete wavelet decomposition to produce an invariant representation for an image. The procedure can be divided into several s...

In this paper image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is formed using two component orthogonal transforms. One is base transform which contributes to global features of an image and another transform contributes to local features. Here base transform is varied to observe its effect on image quality at different compression ratios. Different ...

A discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution as it captures both frequency and location information. DWT filter provides a high compression ratio and image reconstruction quality so as to serve the end user requirements. Some other...