The emphasis of many algorithms that have been proposed for the compression of binary images has been the efficient coding of local redundancy in data. We propose that increased compression may be achieved by a decomp...
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The emphasis of many algorithms that have been proposed for the compression of binary images has been the efficient coding of local redundancy in data. We propose that increased compression may be achieved by a decomposition of the compression problem into two steps. The goal of the first step is to extract the global redundancy in an image. This is achieved by a color shrinking algorithm, The goal of the second step is to code the resulting localized data.
作者：
BURT, PJImage Processing Laboratory
Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy New York 12181
A common task in image analysis is that of measuring image properties within local windows. Often usefulness of these property estimates is determined by characteristics of the windows themselves. Critical factors inc...
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A common task in image analysis is that of measuring image properties within local windows. Often usefulness of these property estimates is determined by characteristics of the windows themselves. Critical factors include the window size and shape, and the contribution the window makes to the cost of computation, A highly efficient procedure for computing property estimates within Gaussian-like windows is described. Estimates are obtained within windows of many sizes simultaneously.
A computer or microprocessor-based adaptive digital filter can easily be constructed to adapt structure as well as weights. These totally adaptive filters will always find a structure and set of weights which offer eq...
A computer or microprocessor-based adaptive digital filter can easily be constructed to adapt structure as well as weights. These totally adaptive filters will always find a structure and set of weights which offer equal or better error than the standard FIR adaptive filter yet avoid most of the difficulties encountered with IIR adaptive filters. computer simulations of the totally adaptive filter in a host of filtering applications confirm its superior performance when compared with any fixed-structure adaptive filter with an equal number of weights.
The main contribution of this paper is the unified treatment of convergence analysis for both LMS and NLMS adaptive algorithms. The following new results are obtained: (i) necessary and sufficient conditions of conver...
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The main contribution of this paper is the unified treatment of convergence analysis for both LMS and NLMS adaptive algorithms. The following new results are obtained: (i) necessary and sufficient conditions of convergence, (ii) optimal adjustment gains and optimal convergence rates, (iii) interrelationship between LMS and NLMS gains, and (iv) non-stationary algorithm design.
In this paper the authors derive simple approximate formulas for the performance of entropy-encoded DPCM for a Gaussian random process and a frequency-weighted mean-square distortion measure. Using these results they ...
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In this paper the authors derive simple approximate formulas for the performance of entropy-encoded DPCM for a Gaussian random process and a frequency-weighted mean-square distortion measure. Using these results they compare the performance of DPCM to the information theoretic rate-distortion bound. They study the effect on the performance of DPCM of the spectrum of the input process, the frequency weight in the distortion measure, and the number of prediction coefficients. They also examine briefly the case of achromatic still images using line-by-line and two-dimensional DPCM encoding with intrafield and intraframe information.
For several signal processing applications, the usefulness of Fast Unitary Transforms (FUT) is now well recognized [1-7]. For signal representation, filtering and encoding, it is well known that the Karhunen-Loeve (KL...
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In this paper, we consider application of 2-D stochastic models discussed in [1] to develop noncausal FIR filters for restoration of images degraded by additive white noise. The semicausal model of [1] is used to desi...
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In this paper, we consider application of 2-D stochastic models discussed in [1] to develop noncausal FIR filters for restoration of images degraded by additive white noise. The semicausal model of [1] is used to design masks for edge extraction from the noisy images. The results presented here indicate that good restorations and robust edge detection are possible using relatively simple algorithms.
For several signal processing applications, the usefulness of Fast Unitary Transforms (FUT) is now well recognized [1-7]. For signal representation, filtering and encoding, it is well known that the Karhunen-Loeve (KL...
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For several signal processing applications, the usefulness of Fast Unitary Transforms (FUT) is now well recognized [1-7]. For signal representation, filtering and encoding, it is well known that the Karhunen-Loeve (KL) Transform, based on signal statistics, is optimum in various senses, but the KL Transform is slow. Suboptimum FUT's allow a trade-off between performance and speed. In this paper, we compare and rank the KL, Fourier, Walsh-Hadamard, Haar, Discrete Cosine, Slant Walsh Hadamard and Slant Haar Transforms by their performance in applications and by the number of elementary operations they require. In encoding and filtering, recursive techniques are widely used and are generally fast. By considering both performance and computations we are able to compare directly recursive and transform algorithms. The comparison brings to light a performance versus computation bound for the two classes of processing techniques.
With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the w...
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With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.
Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional ...
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Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several image processing problems, including image restoration, smoothing, enhancement, data compression, spectral estimation, and filter design, are discussed and examples given.
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