Basic Compression Techniques

Netravali, Arun N.; Haskell, Barry G.

doi:10.1007/978-1-4899-6950-7_5

Cited by 30 publications

(37 citation statements)

References 40 publications

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“…It is acknowledged that noise is useful in breaking up the quantization pattern in a video signal, 11 in the random dithering of analog-to-digital converters, 12 in the area of Brownian ratchets, 13 and in the physics of granular flow. 14 -16 Also it is known that when training a neural network, adding noise to the training data set can improve network generalization, i.e., the neural network's ability to extrapolate outside of the initial training data set.…”

Section: Introductionmentioning

confidence: 99%

Overview: Unsolved problems of noise and fluctuations

Abbott

2001

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Noise and fluctuations are at the seat of all physical phenomena. It is well known that, in linear systems, noise plays a destructive role. However, an emerging paradigm for nonlinear systems is that noise can play a constructive role-in some cases information transfer can be optimized at nonzero noise levels. Another use of noise is that its measured characteristics can tell us useful information about the system itself. Problems associated with fluctuations have been studied since 1826 and this Focus Issue brings together a collection of articles that highlight some of the emerging hot unsolved noise problems to point the way for future research. © 2001 American Institute of Physics. ͓DOI: 10.1063/1.1398543͔The study of fluctuations crosses many discipline boundaries as ''noise'' is a ubiquitous phenomenon. Noise is traditionally thought of as an unwanted effect that degrades the performance of a system. However, the emerging paradigm now recognizes that noise can play both a destructive or constructive role, depending on the circumstances. Thus there is now an intense interest in noise in many biological, physical, and other systems. The collection of papers in this Focus Issue examines open problems and debates surrounding the role of noise in both chaotic and nonchaotic systems. In this Overview we introduce the concepts of stochastic resonance, Brownian ratchets, 1Õf noise and vacuum fluctuations, and some problems surrounding them.

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Section: Introductionmentioning

confidence: 99%

Overview: Unsolved problems of noise and fluctuations

Abbott

2001

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…(1) [11,12], and then the PSNR can be derived, as in Eq. (2) [13][14][15]. Here, "O " and "C " are the original image and the covered image pixel values (binary), respectively, to be compared, and the image size is 'm × n'.…”

Section: Resultsmentioning

confidence: 99%

Data hiding in digital images using a partial optimization technique based on the classical LSB method

Akar¹,

Yalman²,

Varol³

2013

Turk J Elec Eng & Comp Sci

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This paper presents a new partial optimization approach for the least significant bit (LSB) data hiding technique that can be used for protecting any secret information or data. A deterioration effect of as little as possible in an image is intended using the LSB data hiding technique and this is well realized utilizing the proposed partial optimization approach achieving the same data embedding bit rates. In the proposed approach, all of the image pixels are classified into 8 regions and then the 8 distinct ordering codings are applied to each region by the developed partial optimization encoder. Thus, the most effective outcome that has been obtained from the 8 regions means that the number of the altered bits is kept minimized. Hence, the minimal values that have been attained from the 8 regions enable decoding that ensures relatively small distortions on the extracted cover image.

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“…Second, the MSE metric is sensitive to the brightness of the original image. Therefore, a more objective image quality measurement is known as the peak signal-to-noise ratio (PSNR) [18]. This metric is defined for N Â N images with a [0, 1] or [0, 255] grey-scale range, in dB as…”

Section: Resultsmentioning

confidence: 99%

Copasetic analysis: a framework for the blind analysis of microarray imagery

et al. 2004

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From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix w when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis.

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Basic Compression Techniques

Cited by 30 publications

References 40 publications

Overview: Unsolved problems of noise and fluctuations

Overview: Unsolved problems of noise and fluctuations

Data hiding in digital images using a partial optimization technique based on the classical LSB method

Copasetic analysis: a framework for the blind analysis of microarray imagery

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