Adaptive subband video coding using bivariate generalized Gaussian distribution model

Coban, Muhammed; Mersereau, R.M.

doi:10.1109/icassp.1996.544844

Cited by 18 publications

(12 citation statements)

References 4 publications

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“…, N]. The result outlined in (15) is identical to that in [1] for circular sources, i.e., the term E{s 2 i } = 0 when source si is circular.…”

Section: A1 Stability Conditions Of Cost Function (5)supporting

confidence: 49%

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On quantifying the effects of noncircularity on the complex fastica algorithm

Novey

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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The complex fast independent component analysis (c-FastICA) algorithm is one of the most popular methods for solving the ICA problem with complex-valued data. In this study, we extend the work of Bingham and Hyvärinen [1] by deriving conditions for local stability for the more general case of noncircular sources. We use the results of the analysis to quantify the effects of noncircularity on the performance of the algorithm using various nonlinearities and source distributions. Simulations are presented to demonstrate the results of our analysis.

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“…, N]. The result outlined in (15) is identical to that in [1] for circular sources, i.e., the term E{s 2 i } = 0 when source si is circular.…”

Section: A1 Stability Conditions Of Cost Function (5)supporting

confidence: 49%

“…We modify the bivariate ggd model from [15] where p is the shape parameter, γ = Γ(4/p) Γ(2/p) , m = E{s 2 } and E{ss * } = 1. By adjusting the shape parameter, we can generate Gaussian variates with p = 2, sub-Gaussian with p > 2, and super-Gaussian with 0 < p < 2.…”

Section: A2 Generalized Gaussian Distributionmentioning

confidence: 99%

On quantifying the effects of noncircularity on the complex fastica algorithm

Novey

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…In this section, we compare our model with the bivariate GG distribution, which seems to be a general reliable model. The bivariate GG distribution for the random vector [29]:…”

Section: Model Assessmentmentioning

confidence: 99%

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

Forouzanfar

Moghaddam

Gity

2009

SIViP

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This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale

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“…This pdf corresponds to the bivariate generalized Gaussian distribution [8] parameterized by the shape parameter c > 0 and the covariance matrix…”

Section: A Distirbution Of Complex Subband Coefficientsmentioning

confidence: 99%

UDCT complex coefficient statistics based rotation invariant texture characterization

Rouis

Jaballah

Abdallah

et al. 2014

2014 IEEE Visual Communications and Image Processing Conference

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We propose a discriminative texture feature based on a recent discrete implementation of the curvelet transform, namely the uniform discrete curvelet transform (UDCT). Several approaches including either statistical methods or spectral methods have been considered to describe the characteristics of textured surface. Anyhow, most of the proposed texture features are sensitive to rotation variations. In this paper, statistical properties of complex subband coefficients are captured more accurately by using the efficiency of the UDCT in extracting edge and linear information, and an accurate statistical modeling of complex coefficient distributions based on the bivariate generalized Gaussian distribution. Texture classification performances are carried out to investigate the robustness of the proposed descriptor. The results show that the classification rate of the proposed feature outperforms these of compared feature extraction methods considering marginal distributions, while achieving the invariance property to rotated image patterns.

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Adaptive subband video coding using bivariate generalized Gaussian distribution model

Cited by 18 publications

References 4 publications

On quantifying the effects of noncircularity on the complex fastica algorithm

On quantifying the effects of noncircularity on the complex fastica algorithm

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

UDCT complex coefficient statistics based rotation invariant texture characterization

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