Wavelet transform

About: Wavelet transform is a research topic. Over the lifetime, 52721 publications have been published within this topic receiving 954369 citations.

A new, fast, and efficient image codec based on set partitioning in hierarchical trees

Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.

Embedded image coding using zerotrees of wavelet coefficients

Abstract: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding >

Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

Abstract: We describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. affine (wavelet) frames. We propose a modification to the matching pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual (error) at every step and thereby leads to improved convergence. We refer to this modified algorithm as orthogonal matching pursuit (OMP). It is shown that all additional computation required for the OMP algorithm may be performed recursively. >

Singularity detection and processing with wavelets

Abstract: The mathematical characterization of singularities with Lipschitz exponents is reviewed. Theorems that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform are reviewed. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations has a particular behavior that is studied separately. The local frequency of such oscillations is measured from the wavelet transform modulus maxima. It has been shown numerically that one- and two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two dimensions, the wavelet transform maxima indicate the location of edges in images. >

An introduction to wavelets

Abstract: An Overview: From Fourier Analysis to Wavelet Analysis. The Integral Wavelet Transform and Time-Frequency Analysis. Inversion Formulas and Duals. Classification of Wavelets. Multiresolution Analysis, Splines, and Wavelets. Wavelet Decompositions and Reconstructions. Fourier Analysis: Fourier and Inverse Fourier Transforms. Continuous-Time Convolution and the Delta Function. Fourier Transform of Square-Integrable Functions. Fourier Series. Basic Convergence Theory and Poisson's Summation Formula. Wavelet Transforms and Time-Frequency Analysis: The Gabor Transform. Short-Time Fourier Transforms and the Uncertainty Principle. The Integral Wavelet Transform. Dyadic Wavelets and Inversions. Frames. Wavelet Series. Cardinal Spline Analysis: Cardinal Spline Spaces. B-Splines and Their Basic Properties. The Two-Scale Relation and an Interpolatory Graphical Display Algorithm. B-Net Representations and Computation of Cardinal Splines. Construction of Spline Approximation Formulas. Construction of Spline Interpolation Formulas. Scaling Functions and Wavelets: Multiresolution Analysis. Scaling Functions with Finite Two-Scale Relations. Direct-Sum Decompositions of L2(R). Wavelets and Their Duals. Linear-Phase Filtering. Compactly Supported Wavelets. Cardinal Spline-Wavelets: Interpolaratory Spline-Wavelets. Compactly Supported Spline-Wavelets. Computation of Cardinal Spline-Wavelets. Euler-Frobenius Polynomials. Error Analysis in Spline-Wavelet Decomposition. Total Positivity, Complete Oscillation, Zero-Crossings. Orthogonal Wavelets and Wavelet Packets: Examples of Orthogonal Wavelets. Identification of Orthogonal Two-Scale Symbols. Construction of Compactly Supported Orthogonal Wavelets. Orthogonal Wavelet Packets. Orthogonal Decomposition of Wavelet Series. Notes. References. Subject Index. Appendix.