A Fast Parameter Estimation of Generalized Gaussian Distribution

Wang, Taiyue; Li, Hongwei; Li, Zhiming; Wang, Zhihua

doi:10.1109/icosp.2006.345546

Cited by 25 publications

(13 citation statements)

References 9 publications

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“…Furthermore, in previous researches, the evaluation of accuracy of estimates for both large and small samples for GGD models among classic statistical methods shows that the ML estimator is significantly superior for heavy-tailed distribution, which is often the case for wavelet subband coefficients ( Wang, Li, Li, & Wang, 2007). We now describe an ML estimator for GGD.…”

Section: Ggd Model and ML Estimatormentioning

confidence: 98%

Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network

Liu

Zuo

Vroman

et al. 2010

Journal of the Textile Institute

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A joint method to identify nonwoven uniformity by combining wavelet transform, generalized Gaussian density (GGD) and generalized dynamic fuzzy (GDF) neural network is presented in this paper. Six hundred and twenty-five nonwoven images of five different grades, 125 images of each grade, are decomposed at three different levels with coif4 wavelet base. Wavelet coefficients in each subband are independently modeled by GGD model, while the scale and shape parameters of that are extracted as input features of GDF neural network. For comparison, two energybased features are also extracted from wavelet coefficients directly, the number of which is the same as the scale and shape parameters estimated from GGD model with maximum likelihood (ML) estimator. Experimental results on the 625 nonwoven images indicate the GGD model parameters are more expressive and powerful in characterizing textures than the energy-based ones. The proposed method has high identification accuracy, such as when the images are decomposed at Level 3 and described with GGD model parameters, the identification accuracies of five grades are all 100%. Additionally, to reduce the redundancy of the generated fuzzy rules, an effective complementary approach, fuzzy rule base reduction based on 'CityBlock' distance is proposed.

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Section: Ggd Model and ML Estimatormentioning

confidence: 98%

Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network

Liu

Zuo

Vroman

et al. 2010

Journal of the Textile Institute

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“…First, the D-Q data of actual coding using JM16.0 and model calculation are obtained, where two different methods, data fitting and GGD parameter estimation [8] are applied to provide α and β for our D-Q model of equation (12). The encoder is configured to use CABAC for entropy coding with R-D optimal mode selection turned on.…”

Section: Source Model Validation In H264/avcmentioning

confidence: 99%

Novel rate-distortion modeling for H.264/AVC and its application in two-pass VBR coding

Duan

Sun

Guo

2012

2012 IEEE International Symposium on Circuits and Systems

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In this paper, we first employ rate-distortion (R-D) modeling to derive the source model for H.264 video coding. This source model consists of the distortion-quantization (D-Q) model and distortion-rate (D-R) model, which describe the relationship between rate, distortion and quantization step for generalized Gaussian distribution (GGD) under the quantization scheme of H.264/AVC. The accuracy of the source model is confirmed by comparing the estimated results with the actual data of JM16.0. And the parameter selection criteria are developed to better employ the source model in our two-pass rate control algorithm for H.264 VBR coding. Based on the D-Q and D-R models, the proposed algorithm is of high stability, low complexity and is easy to implement. Extensive experiments covering different resolutions and target rates demonstrates: 1) about 96.7% reduction in PSNR variance compared to the method of JM16.0 with the average rate control error of 1.95%, and 2) significant improvement in smoothing the video quality compared with the latest two-pass rate control method.

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“…This methodology consists of the combination of Generalized Gaussian Density (GGD) [10] [11], wavelet transform [12], defect segmentation and Learning Vector Quantization (LVQ) Neural Network [11]. This methodology is four staged process as shown in figure 05.…”

Section: Approach Of Wavelet-texture Analysis and Lvq Neural Networkmentioning

confidence: 99%

Comparative Analysis of Different Fabric Defects Detection Techniques

Javed¹,

Ullah²,

Aziz-Ur-Rehman³

2013

IJIGSP

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Abstract-In last few years' different textile companies aim to produce the quality fabrics. Major loss of any textile oriented company occurs due to defective fabrics. So the detection of faulty fabrics plays an important role in the success of any company. Till now most of the inspection is done using human visual. This way is too much time consuming, cumbersome and prone to human errors. In past, many advances are made in developing automated and computerized systems to reduce cost and time whereas, increasing the efficiency of the process. This paper aims at comparing some of these techniques on the basis of classification methods and accuracy.

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A Fast Parameter Estimation of Generalized Gaussian Distribution

Cited by 25 publications

References 9 publications

Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network

Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network

Novel rate-distortion modeling for H.264/AVC and its application in two-pass VBR coding

Comparative Analysis of Different Fabric Defects Detection Techniques

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