Wavelet transform coding image compression is applied to two raw seismic data sets. The parameters of filter length, depth of decomposition, and quantization method are varied through 36 parameter settings and the rat...
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ISBN:
(纸本)0819425915
Wavelet transform coding image compression is applied to two raw seismic data sets. The parameters of filter length, depth of decomposition, and quantization method are varied through 36 parameter settings and the rate-distortion relation is plotted and fitted with a line. The lines are compared to judge which parameter setting produces the highest quality for a given compression ratio on the sample data. It is found that long filters, moderate decomposition depths, and frequency-weighted, variance-adjusted quantization yield the best results.
The framework for image coding system based on embedded zerotrees consists three stages: (i) wavelet transform (ii) an embedded zerotree encoding and (iii) adaptive arithmetic encoding. In this framework, the selectio...
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ISBN:
(纸本)0818681837
The framework for image coding system based on embedded zerotrees consists three stages: (i) wavelet transform (ii) an embedded zerotree encoding and (iii) adaptive arithmetic encoding. In this framework, the selection of wavelet filter becomes an important issue. In this paper, we present a modification to the scanning approach in the set partitioning algorithm proposed in [3] to exploit the correlation in a local neighborhood. Two new criteria are proposed for evaluating the performance of wavelets in lossless image compression applications: zero tree count and monotone spectral ordering of subbands produced after wavelet transform in a multiresolution scheme. We evaluate several wavelet filters to test the evaluation criteria and present experimental results to justify the proposed performance criteria.
We study the decomposition and compression of one-way wave propagation and imaging operators using wavelet transform. We show that the matrix representation of the Kirchhoff imaging operator (Kirchhoff migration opera...
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ISBN:
(纸本)0819425915
We study the decomposition and compression of one-way wave propagation and imaging operators using wavelet transform. We show that the matrix representation of the Kirchhoff imaging operator (Kirchhoff migration operator) in space domain is a dense matrix, while the compressed beamlet-operator matrix which is the wavelet decomposition of the Kirchhoff operator, is a highly sparse matrix. The beamlet imaging operator represents the backpropagation of multiscale orthonormal beams (beamlets) at different positions with different angles. The beamlet-operator behaves differently in different wavelet bases. For sharp and short bases, such as the Daubechies 4 (D4), both the interscale and intrascale coupling are strong. On the other hand, the interscale coupling is relatively weak for smooth bases, such as higher-order Daubechies wavelets, Coiflets, and spline wavelets. The images obtained by the compressed beamlet operators are almost identical to the images from a full-aperture Kirchhoff operator. Compared with the conventional limited-aperture Kirchhoff migration (imaging), beamlet migration (imaging) can retain the wide effective aperture of a full-aperture operator, and hence achieve higher resolution and image quality with reduced computational cost. The compression ratio of the imaging operator ranges from a few times to a few hundred times, depending on the frequency, step length and the wavelet basis.
Wavelet transforms have been one of the important signalprocessing developments in the last decade, especially for applications such as time-frequency analysis, data compression, segmentation and vision. Although sev...
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Wavelet transforms have been one of the important signalprocessing developments in the last decade, especially for applications such as time-frequency analysis, data compression, segmentation and vision. Although several efficient implementations of wavelet transforms have been derived, their computational burden is still considerable. The paper describes two generic parallel implementations of wavelet transforms, based on the pipeline processor farming methodology, which have the potential to achieve real-time performance. Results show that the parallel implementation of the oversampled wavelet transform achieves virtually linear speedup, while the parallel implementation of the discrete wavelet transform (DWT) also outperforms the sequential version, provided that the filter order is large. The DWT parallelisation performance improves with increasing data length and filter order, while the frequency-domain implementation performance is independent of wavelet filter order. Parallel pipeline implementations are currently suitable for processing multidimensional images with data length at least 512 pixels.
An approach is proposed for detecting macro defects in color LCDs (liquid crystal displays) by using a family of 2-D Gabor wavelets. The absolute values of the wavelet coefficients are used to detect macro defects suc...
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ISBN:
(纸本)0819425915
An approach is proposed for detecting macro defects in color LCDs (liquid crystal displays) by using a family of 2-D Gabor wavelets. The absolute values of the wavelet coefficients are used to detect macro defects such as periodic or streak patterns. To measure the filter performance, we introduced the improvement rate of the signal-to-noise ratio. The paper also proposes a new method for reconstructing images with enhanced defects by using linear combinations of the real parts of Gabor wavelet coefficients. We obtained the frame bounds of 2-D Gabor wavelets which have a discrete sampling step. The reconstructed images can help human evaluators to verify defects. The uniqueness of this method is that not all coefficients are used, but only those that contribute to defect detection. This method is practical in terms of its computational simplicity, and can therefore be used for on-line automatic inspection. Experiments using actual images with defects showed the effectiveness of the method.
The wavelet transform developed during the last years into a mature and very pragmatic formalism for the analysis of the scale behaviour of signals. However, it also remains a tool to serve its very initial goal: the ...
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ISBN:
(纸本)0819426490
The wavelet transform developed during the last years into a mature and very pragmatic formalism for the analysis of the scale behaviour of signals. However, it also remains a tool to serve its very initial goal: the time-frequency analysis. In this article we summarize the basics of time-frequency-scale formalism for signal representation and analysis, and we overview several applications with promising results for the Synthetic Aperture Radar (SAR) signalprocessing.
We present a technique to compress scalar functions defined on 2-manifolds of arbitrary topology. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous deve...
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ISBN:
(纸本)0819425869
We present a technique to compress scalar functions defined on 2-manifolds of arbitrary topology. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous developments: the lifting scheme, spherical wavelets, and embedded zerotree coding methods. applications lie in the efficient storage and rapid transmission of complex data sets. Typical data sets are earth topography, satellite images, and surface parametrizations. Our contribution in this paper is the novel combination and application of these techniques to general 2-manifolds. Keywords: compression, wavelets, manifolds, 30 objects, zerotree, multiresolution analysis, progressive coding
We define and construct a new family of compactly supported, nonseparable two-dimensional wavelets, `biorthogonal quincunx Coifman wavelets' (BQCWs), from their one-dimensional counterparts using the McClellan tra...
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We define and construct a new family of compactly supported, nonseparable two-dimensional wavelets, `biorthogonal quincunx Coifman wavelets' (BQCWs), from their one-dimensional counterparts using the McClellan transformation. The resulting filter banks possess many interesting properties such as perfect reconstruction, vanishing moments, symmetry, diamond-shaped passbands, and dyadic fractional filter coefficients. We derive explicit formulas for the frequency responses of these filter banks. Both the analysis and synthesis lowpass filters converge to an ideal diamond-shaped halfband lowpass filter as the order of the corresponding BQCW system tends to infinity. Hence, they are promising in image and multidimensional signalprocessingapplications. In addition, the synthesis scaling function in a BQCW system of any order is interpolating (or cardinal), which has been known as a desired merit in numerical analysis.
We presents the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inh...
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ISBN:
(纸本)0819424935
We presents the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inherent parallel processing. In theory, it has the nearly optimal minimax compression characteristics. The simulation shows that the interval interpolating wavelet transform are more qualified and remove the artificial blocking effects (which is serious by DCT). The interval wavelets is introduced to deal with the boundary points of the finite localized image. This method do not only improve the compress rate, but also delete the quantization aliasing of the boundary pixels.
Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike addi...
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ISBN:
(纸本)0819425915
Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike additive Gaussian noise, Poisson noise is signal-dependent, and consequently separating signal from noise is a very difficult task. In this paper, we develop a novel wavelet-domain filtering procedure for noise removal in photon imaging systems. The filter adapts to both the signal and the noise and balances the trade-off between noise removal and excessive smoothing of image details. Designed using the statistical method of cross-validation, the filter is simultaneously optimal in a small-sample predictive sum of squares sense and asymptotically optimal in the mean square error sense. The filtering procedure has a simple interpretation as a joint edge detection/estimation process. Moreover, we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself. The performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery.
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