This paper presents compression of grey-scale fingerprint images, using a wavelet transform guided by a multifractal measure to obtain the best reconstructed image in terms of a higher peak signal to noise ratio, PSNR...
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This paper presents compression of grey-scale fingerprint images, using a wavelet transform guided by a multifractal measure to obtain the best reconstructed image in terms of a higher peak signal to noise ratio, PSNR, at the lowest bit rate. The fingerprint images and the corresponding wavelet coefficients are considered to be approximation of strange attractors and can be analyzed by their multifractality. The wavelet can provide not only the grouping of subbands information and the highest compression for optimum bit allocation (quantization), but also an optimum synthesis (combination of subbands) by the inverse wavelet transform to achieve the highest image quality. The motivation for this paper is to find the best combination of the subbands for both the quantization and image quality by applying the Mandelbrot (1983) singularity measure to the coefficients in various subbands.
The paper presents a perceptual image representation technique based on fractal surface interpolation (FSI), This technique is motivated from the observation that images taken from the real world contain many textures...
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The paper presents a perceptual image representation technique based on fractal surface interpolation (FSI), This technique is motivated from the observation that images taken from the real world contain many textures that are self similar, or fractal, in nature. The fractal surface interpolation representation is then compressed using a zero tree wavelet compression subsystem with lossless entropy encoding. The fractal surface interpolation technique described relies on the extraction and reconstruction of self affine fractal surfaces with measured Hurst exponents H*. This gives statistically self similar fractal surfaces used to represent textures in a real world image. Fractional Brownian motion (fBm) through a modified midpoint displacement (MPD) algorithm provides the basis for generating these self affine fractal surfaces between interpolation points.
A novel paradigm for fractal coding selectively corrects the fractal code for selected domain blocks with an image-adaptive VQ codebook. The codebook is generated from the initial uncorrected fractal code and is there...
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A novel paradigm for fractal coding selectively corrects the fractal code for selected domain blocks with an image-adaptive VQ codebook. The codebook is generated from the initial uncorrected fractal code and is therefore available at the decoder. An efficient trade-off results between incremental performance and bit rate.
This paper presents a new approach in processing nonstationary signals-such as speech signals and images-through singularity characterization. In this approach, we associate a singular measure /spl mu//sub f(t/) (r) w...
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This paper presents a new approach in processing nonstationary signals-such as speech signals and images-through singularity characterization. In this approach, we associate a singular measure /spl mu//sub f(t/) (r) with a transient at time t of a signal f(t) (where a real number r>0 is a time perturbation around t) and use the singularity behaviour of the measure for the characterization of the signal nonstationarity. The approach is capable of characterizing isolated transients through Holder exponents (or singularity strength), as well as mixture transients (e.g. singularity everywhere) through the concept of fractality and multifractality. The paper discusses the concept and the practicality of applying this approach to signals. The paper also shows that this approach can provide a unifying framework for previously published work on applying nonlinear, chaotic, fractal, and multifractal analysis to signals. We show that the main conceptual issue in applying fractality and multifractality to signals using this framework is the proper selection of signal measures.
This paper presents a perceptual image compression technique using fractal surface interpolation (FSI). This technique relies on the extraction and reconstruction of self-affine fractal surfaces with a specified Hurst...
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This paper presents a perceptual image compression technique using fractal surface interpolation (FSI). This technique relies on the extraction and reconstruction of self-affine fractal surfaces with a specified Hurst exponent H*. Fractional Brownian motion (fBm) through midpoint displacement (MPD) provides the basis for generating these fractal surfaces between interpolation points. The MPD algorithm is designed to eliminate creasing often associated with MPD in two dimensions. Experimental results show that when applied to 512/spl times/512 8-bit gray-level images, perceptually good results are achieved with a bit rate of 0.35 bpp.
This paper presents a new approach to lossy image compression through singularity preservation to obtain high compression ratios. The concept of this approach is based on a conjecture that image singularities carry mo...
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This paper presents a new approach to lossy image compression through singularity preservation to obtain high compression ratios. The concept of this approach is based on a conjecture that image singularities carry most of the perceptual information, hence the essential part of an image should be represented by its singularity as opposed to its energy alone. Wavelet maxima have been chosen to represent signal singularity because of their ability to characterize image singularity fully. There are algorithms to reconstruct the original image faithfully from wavelet maxima. A compression scheme can then be designed to reduce the bit rate while preserving singularities. The resulting low bit-rate image has sharp edges without distortions, such as blockiness or blurs. This approach has been used to compress aerial ortho images, in which the perceptual quality of a 27 peak signal-to-noise ratio (PSNR) singularity-preserving image outperforms that of a 30 dB PSNR energy-preserving joint-photographic expert group (JPEG) image at a 15:1 compression ratio.
This paper examines the process of image denoising to improve the efficiency of the reduced-search fractal block coding (FBC) of greyscale images by reducing the first-order entropy of the image. The reduced-search FB...
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This paper examines the process of image denoising to improve the efficiency of the reduced-search fractal block coding (FBC) of greyscale images by reducing the first-order entropy of the image. The reduced-search FBC is a lossy compression technique that exploits the block-wise self-affinity of an image where portions of the image are represented by scaled and isometrically transformed copies of other portions of the image. The efficiency of this process increases with increased redundancy which is the result of lowering the entropy. Image denoising is concerned with separating noise from an image and then suppressing the noise as much as possible without altering the image itself. In this paper spatial smoothing and wavelet denoising are compared. It is shown that denoising increases the efficiency of reduced-search FBC. Spatial smoothing, however, causes a loss of signal that wavelet denoising does not. In either case, the reconstruction qualities of the peak-signal-to-noise ratio at approximately 34 dB and compression ratios of 18.9:1 and higher have been achieved. This is an improvement over the 31 dB and 18.1:1 for non-denoised images.
Abstract only given. Discusses the compression of an important class of computer images, called aerial ortho images, that result from geodetic transformation computations [Kinsner, 1994]. The computations introduce nu...
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Abstract only given. Discusses the compression of an important class of computer images, called aerial ortho images, that result from geodetic transformation computations [Kinsner, 1994]. The computations introduce numerical noise, making the images nearly incompressible losslessly because of their high entropy. The use of classical lossy compression schemes is also not desirable because their effects on the original image are unknown. We then propose the use of image denoising coupled with lossless image compression, that preserves selected image characteristics. Two denoising schemes for a compression ratio of 2:1 are compared. The first scheme is based on a Donoho's (1992) wavelet shrinking scheme which preserves image smoothness. We study the effect of various shrinking parameter values on the compression ratio and image quality, where 35.5 dB peak signal-to-noise ratio (PSNR) is obtained for a compression ratio of 2.03:1. This approach preserves high-frequency information, so that sharp edges do not become blurred as in classical filtering methods. This is critically important, because the main feature of ortho images is in its flatness and its precision of edge position. The second scheme is based on preserving pixel predictability [Kostelich and Schreiber, 1993), leading to a variant of planar predictive coding. This approach adds, to the edge preserving capability, the limitation in pixel deviation between the original and denoised images to be within one grayscale level. As a result, two different predictive coding schemes achieve a compression ratio of 2:1 at 49.9 dB and 51.2 dB PSNR.
This paper presents a new method for the capture, analysis, and classification of radio transmitter transients. This method involves the use of a capturing subsystem consisting of an Icom IC-R7000 communications recei...
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This paper presents a new method for the capture, analysis, and classification of radio transmitter transients. This method involves the use of a capturing subsystem consisting of an Icom IC-R7000 communications receiver and a Sound Blaster 16 sound card running on a PC. The radio transients are sampled at 44,100 samples per second and have 16 bits accuracy. Once the transmitter transient has been captured, a genetic algorithm selects the critical features from the wavelet coefficients for classification. The selected wavelet coefficients are considered to be fingerprints, and are presented to a back propagation neural network for transmitter classification. The capturing and analysis system, ODO-1, is able to classify both transients of the same model type as well as individual transmitters with 100% accuracy on a small data base of transmitter fingerprints.
We present a low bit rate multimode speech coding algorithm which applies a suitable spectral model and a custom quantization scheme to each frame according to the selected mode. For unvoiced speech, the spectrum is c...
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