Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-...
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Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The advantages of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that in 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. In this paper we propose an approach based on building a sparse representation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-terms non-linear approximations in general dictionaries is, in most cases, a NP-hard problem and sub-optimal approaches have to be followed. In this work we use a greedy strategy, also known as Matching Pursuit to compute the expansion. The residual, that we suppose to be the smooth and texture part, is then coded using wavelets. A rate distortion optimization procedure chooses the number of functions from the redundant dictionary and the wavelet basis. (C) 2005 Published by Elsevier B.V.
Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions have been desig...
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Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions have been designed to take into account the characteristics of the input signal/image. In our previous work(20,22,23) we have introduced an adaptive lifting framework, that does not require bookkeeping but has the property that it processes edges and homogeneous image regions in a different fashion. The current paper discusses the effects of quantization in such an adaptive wavelet decomposition. We provide conditions for recovering the original decisions at the synthesis and for relating the reconstruction error to the quantization error. Such an analysis is essential for the application of these adaptive decompositions in image compression.
The lifting scheme is well known to be an efficient tool for constructing second generation wavelets and is often used to design a class of biorthogonal wavelet filter banks. For its efficiency, the lifting implementa...
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The lifting scheme is well known to be an efficient tool for constructing second generation wavelets and is often used to design a class of biorthogonal wavelet filter banks. For its efficiency, the lifting implementation has been adopted in the international standard JPEG2000. It is known that the orthogonality of wavelets is an important property for many applications. This paper presents how to implement a class of infinite-impulse-response (IIR) orthogonal wavelet filter banks by using the lifting scheme with two lifting steps. It is shown that a class of IIR orthogonal wavelet filter banks can be realized by using allpass filters in the lifting steps. Then, the design of the proposed IIR orthogonal wavelet filter banks is discussed. The designed IIR orthogonal wavelet filter banks have approximately linear phase responses. Finally, the proposed IIR orthogonal wavelet filter banks are applied to the image compression, and then the coding performance of the proposed IIR filter banks is evaluated and compared with the conventional wavelet transforms.
The problem of separating linear features from a textured background is of importance in many applications. It has been shown that the Fourier transform can be used in conjunction with polar transformation to "li...
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The problem of separating linear features from a textured background is of importance in many applications. It has been shown that the Fourier transform can be used in conjunction with polar transformation to "lift" linear features from the background texture. However, while the Fourier transform works well with lines that are spread throughout the entire image, it is less effective when the linear features are of varied length and thickness. We propose approaches based on a windowed Fourier transform and wavelet packet decomposition to lift randomly located lines of varied lengths and thickness. The reasoning underlying the development of the approaches is presented along with comparative examples. (C) 2006 SPIE and IS&T.
In this paper, a robust watermarking algorithm using balanced multiwavelet transform is proposed. The latter transform achieves simultaneous orthogonality and symmetry without requiring any input prefiltering. Therefo...
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In this paper, a robust watermarking algorithm using balanced multiwavelet transform is proposed. The latter transform achieves simultaneous orthogonality and symmetry without requiring any input prefiltering. Therefore, considerable reduction in computational complexity is possible, making this transform a good candidate for real-time watermarking implementations such as audio broadcast monitoring and DVD video watermarking. The embedding scheme is image adaptive using a modified version of a well-established perceptual model. Therefore, the strength of the embedded watermark is controlled according to the local properties of the host image. This has been achieved by the proposed perceptual model, which is only dependent on the image activity and is not dependent on the multifilter sets used, unlike those developed for scalar wavelets. This adaptivity is a key factor for achieving the imperceptibility requirement often encountered in watermarking applications. In addition, the watermark embedding scheme is based on the principles of spread-spectrum communications to achieve higher watermark robustness. The optimal bounds for the embedding capacity are derived using a statistical model for balanced multiwavelet coefficients of the host image. The statistical model is based on a generalized Gaussian distribution. Limits of data hiding capacity clearly show that balanced multiwavelets provide higher watermarking rates. This increase could also be exploited as a side channel for embedding watermark synchronization recovery data. Finally, the analytical expressions are contrasted with experimental results where the robustness of the proposed watermarking system is evaluated against standard watermarking attacks.
A discrete wavelet transform is-one of the effective methodologies for compressing the image data and extracting the major characteristics from various data, but it always requires a number of target data composed of ...
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A discrete wavelet transform is-one of the effective methodologies for compressing the image data and extracting the major characteristics from various data, but it always requires a number of target data composed of a power of 2. To overcome this difficulty without losing any original data information, we propose here a novel approach based on the Fourier transform. The key idea is simple but effective because it-keeps all of the frequency components comprising the target data exactly. The raw data is firstly transformed to the Fourier coefficients by Fourier transform. Then, the inverse Fourier transform makes it possible to the number of data comprising a power of 2. We have applied this interpolation for the wind vector image data, and we have tried to compress the data by the multiresolution analysis by using the three-dimensional discrete wavelet transform. Several examples demonstrate the usefulness of our new method to work out the graphical communication tools.
The discrete wavelet transform has taken its place at the forefront of research for the development of signal and imageprocessingapplications. These wavelet-based approaches have outperformed existing strategies in ...
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The discrete wavelet transform has taken its place at the forefront of research for the development of signal and imageprocessingapplications. These wavelet-based approaches have outperformed existing strategies in many areas including telecommunication, numerical analysis and, most notably, image/video compression. The authors present an investigation into the design and implementation of 1-D and 2-D discrete biorthogonal wavelet transforms (DBWTs) using a field programmable gate array (FPGA)-based rapid prototyping environment. The proposed architectures for DBWTs are scalable, modular and have less area and time complexity when compared with existing structures. FPGA implementation results based on a xilinx Virtex-2000E device have shown that the proposed system provides an efficient solution for the processing of DBWTs in real-time.
We describe a new fusion method for time-frequency distribution (TFD) that increases the ability to detect and classify time-varying signals while suppressing signal-dependent artifacts and noise. This is achieved by ...
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We describe a new fusion method for time-frequency distribution (TFD) that increases the ability to detect and classify time-varying signals while suppressing signal-dependent artifacts and noise. This is achieved by applying a least-squares algorithm to estimate the second-order approximation Volterra series coefficients of the outputs of selected TFDs. These coefficients are used for the fusion of the selected TFDs and generate a new TFD. The proposed fusion method is compared with four other fusion methods in terms of resolution and signal-to-noise ratio (SNR) in the time-frequency (TF) plane. Five representative TFDs are fused to generate a new TFD and their performances are analyzed. The results show that the new fusion method considerably increases sharpness ( resolution) and strength ( SNR) of the signal in the TF plane and, furthermore, achieves better signal description over other fusion methods and the traditional TFDs. (C) 2006 SPIE and IS&T.
State-of-the-art signal compression and reconstruction techniques utilize wavelets. However, recently published research demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet inverse transforms...
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ISBN:
(纸本)0819462845
State-of-the-art signal compression and reconstruction techniques utilize wavelets. However, recently published research demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet inverse transforms that consistently outperform wavelets when used to reconstruct one- and two-dimensional signals under conditions subject to quantization error. This paper summarizes the results of a series of three follow-on experiments. First, a GA is developed to evolve matched forward and inverse transform pairs that simultaneously minimize the compressed file size (FS) and the squared error (SE) in the reconstructed file. Second, this GA is extended to evolve a single set of coefficients that may be used at every level of a multi-resolution analysis (MRA) transform. Third, this GA is expanded to achieve additional SE reduction by evolving a different set of coefficients for each level of an MRA transform. Test results indicate that coefficients evolved against a single representative training image generalize to effectively reduce SE for a broad class of reconstructed images.
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