3 research outputs found

    Discrete Wavelet Transforms

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    The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

    Low-Complexity Hyperspectral Image Coding Using Exogenous Orthogonal Optimal Spectral Transform (OrthOST) and Degree-2 Zerotrees

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    We introduce a low-complexity codec for lossy compression of hyperspectral images. These images have two kinds of redundancies: 1) spatial; and 2) spectral. Our coder is based on a compression scheme consisting in applying a 2-D discrete wavelet transform (DWT) to each component and a linear transform between components to reduce, respectively, spatial and spectral redundancies. The DWT used is the Daubechies 9/7. However, the spectral transform depends on the spectrometer sensor and the kind of images to be encoded. It is calculated once and for all on a set of images (the learning basis) from (only) one sensor, thanks to Akam Bita et al. 's OrthOST algorithm that returns an orthogonal spectral transform, whose optimality in high-rate coding has been recently proved under mild conditions. The spectral transform obtained in this way is applied to encode other images from the same sensor. Quantization and entropy coding are then achieved with a well-suited extension to hyperspectral images of the Said and Pearlman's SPIHT algorithm. Comparisons with a JPEG2000 codec using the Karhunen-Loève transform (KLT) to reduce spectral redundancy show good performance for our codec
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