In this paper, the relationship between wavelet transform and Differential Mapping Singularities Theory (DMST) is discussed in the context of image compression. DMST maps 3-D surfaces accurately, with exact results, a...
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ISBN:
(纸本)0819425915
In this paper, the relationship between wavelet transform and Differential Mapping Singularities Theory (DMST) is discussed in the context of image compression. DMST maps 3-D surfaces accurately, with exact results, and to construct an image compression algorithm based on an expanded set of operations. This set includes shift, scaling rotation, and homogenous nonlinear transformations. This approach permits the mathematical description of a full set of singularities that describe edges and other specific points of objects. The edges and specific points (degenerate critical points) are the product of mapping smooth 3-D surfaces, which can be described by a simple set of polynomials that are suitable for image compression and Automatic Target Recognition (ATR). In signal and imageprocessing, wavelets have been used for several years to provide multi-resolution data representation [1] Originally, wavelets were developed for one-dimensional signal decomposition. Subsequently, they were generalized to 2-D image coding. Now, wavelet transform is used to hierarchically decompose an input signal into a series of lower resolution reference signals and associated detail signals. At each level, a reference signal and its associated detail signal contain information required to reconstruct the reference signal at the next higher resolution level. Efficient image coding is enabled by allocating the bandwidth according to the relative importance of information in the reference and detail signals, and then applying the next level of the lossy and lossless compression algorithm.
image watermarking is in use for proving ownership for a fairly long time. For most of the study on this area, a pseudo random number sequence PRSN or a binary image logo is embedded as watermark. Nowadays the owner&#...
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ISBN:
(纸本)9781467373869
image watermarking is in use for proving ownership for a fairly long time. For most of the study on this area, a pseudo random number sequence PRSN or a binary image logo is embedded as watermark. Nowadays the owner's face or sound is also embedded as biometric watermark. image is transferred to discrete wavelet transform domain, watermark is embedded to DWT values, then DWT values are retransformed to spatial domain to obtain watermarked image. Embedding a vector image logo as watermark was not tried in previous works. In this work, non-blind robust watermarking is applied using a vector image as watermark. Various attacks are applied to watermarked images and for each of these attacks vector image watermark is obtained equal or almost equal to the original. Embedding vector image as watermark will bring a new discipline for image watermarking and a new development will arise in this perspective.
In this paper, we propose a method of efficient computation of wavelet coefficients from DCT-based coded image/video signals. Block transform domain filtering is well suited for transcoding of such data. First direct ...
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ISBN:
(纸本)0819422134
In this paper, we propose a method of efficient computation of wavelet coefficients from DCT-based coded image/video signals. Block transform domain filtering is well suited for transcoding of such data. First direct transform domain processing removes the necessary of inverse transform. Second, the number of nonzero elements in the blocks are significantly smaller than spatial domain. Therefore, the amount of computation can be reduced accordingly. Finally, the block processing algorithm provides a parallel processing method. Hence a fast implementation of the algorithm is well suited.
This paper presents a method that detects edge orientations in still images. Edge orientation is a crucial information when one wants to optimize the quality of edges after different processings. The detection is carr...
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ISBN:
(纸本)9780819479280
This paper presents a method that detects edge orientations in still images. Edge orientation is a crucial information when one wants to optimize the quality of edges after different processings. The detection is carried out in the wavelet domain to take advantage of the multi-resolution features of the wavelet spaces, and locally adapts the resolution to the characteristics of edges. Our orientation detection method consists of finding the local direction along which the wavelet coefficients are the most regular. To do so, the image is divided in square blocks of varying size, in which Bresenham lines are drawn to represent different directions. The direction of the Bresenham line that contains the most regular wavelet coefficients, according to a criterion defined in the paper, is considered to be the direction of the edge inside the block. The choice of the Bresenham line drawing algorithm is justified in this paper, and we show that it considerably increases the angle precision compared to other methods as for instance, the method used for the construction of bandlet bases. An optimal segmentation is then computed in order to adapt the size of the blocks to the edge localization and to isolate in each block at most one contour orientation. Examples and applications on image interpolation are shown on real images.
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
ISBN:
(纸本)0819425915
We give many examples of bivariate nonseparable compactly supported orthonormal wavelets which are supported over [0,3]x[0,3]. The Holder continuity properties of these wavelets are studied.
In this paper, we present a new method of image coding using two popular imaging tools, Zernike moments and wavelets. The main idea is that we can produce appropriate image descriptors by involving an appropriate numb...
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ISBN:
(纸本)0780375033
In this paper, we present a new method of image coding using two popular imaging tools, Zernike moments and wavelets. The main idea is that we can produce appropriate image descriptors by involving an appropriate number of moments, compressed in a form suitable to represent an image with low reconstruction error for pattern recognition applications. At this point the concept of wavelet compression is involved, which has already been discussed in many technical papers. We use an existent wavelet based compression algorithm, to compress not the 2-D image, but the resulted moment based 1-D signal. So, using this formulation we can achieve a compressed representation of the image, suitable for pattern recognition purposes and image retrieval tasks. It is very important to notice here the ability of Zernike moments to provide a very high level of image reconstruction, using the inverse wavelet transform, establishing a useful method.
In this work, we introduce a nonlinear geometric transform, called peak transform, for efficient image representation and coding. Coupled with wavelet transform and subband decomposition, the peak transform is able to...
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ISBN:
(纸本)9781424414369
In this work, we introduce a nonlinear geometric transform, called peak transform, for efficient image representation and coding. Coupled with wavelet transform and subband decomposition, the peak transform is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient data representation and compression. Based on peak transform (PT), we design an image encoder, called PT encoder, for efficient image compression. Our extensive experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a peak transform is applied. The PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in PSNR (peak signal-to-noise ratio), especially for images with a significant amount of high-frequency components.
The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, sta...
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ISBN:
(纸本)0780362985
The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, state-of-the-art denoising techniques use wavelet bases instead of Fourier bases since wavelet domain processing provides local adaptation in smooth and non-smooth parts due to the theoretical link between wavelets and smoothness spaces. In this paper the same smoothness spaces are used to propose a way of performing Sharpening and Smoothing of signals with wavelets (WSS) in Besov spaces. As an application the completely wavelet-based enhancement of a scanned document is discussed.
The wavelet transform is widely used in both speckle reduction and data compression of SAR images. Thus, it is very efficient to integrate these two procedures in a single process. In this research, an input image is ...
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ISBN:
(纸本)0819437646
The wavelet transform is widely used in both speckle reduction and data compression of SAR images. Thus, it is very efficient to integrate these two procedures in a single process. In this research, an input image is first subject to a logarithmic operation. The image is then transformed by using multiple level wavelet decomposition. The variance of noise is estimated from the data to determine the threshold, which is used for soft-thresholding the wavelet coefficients. For each subband, the obtained wavelet coefficients ate quantized and finally entropy encoded to produce the output bit stream of the image. The advantage of this method is that both speckle reduction and image compression are performed in wavelet domain. Experimental results on JERS-1/SAR images are also given.
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.
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