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generalized gaussian model for Learned Image Compression

IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 1950-1965页

作者： Zhang, Haotian Li, Li Liu, Dong Univ Sci & Technol China MOE Key Lab Brain Inspired Intelligent Percept & C Hefei 230093 Peoples R China

In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the gaussian mixture models, can fit the distribution of latent variables more precisely, but the corresponding complexity is higher. To balance the compression performance and complexity, we extend the gaussian model to the generalized gaussian family for more flexible latent distribution modeling, introducing only one additional shape parameter beta than the gaussian model. To enhance the performance of the generalized gaussian model by alleviating the train-test mismatch, we propose improved training methods, including beta -dependent lower bounds for scale parameters and gradient rectification. Our proposed generalized gaussian model, coupled with the improved training methods, is demonstrated to outperform the gaussian and gaussian mixture models on a variety of learned image compression networks.

关键词： Image coding Training Probabilistic logic Transforms Entropy Complexity theory Quantization (signal) Shape Table lookup Noise generalized gaussian model learned image compression probabilistic model

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Packaged food detection method based on the generalized gaussian model for line-scan Raman scattering images

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JOURNAL OF FOOD ENGINEERING 2019年第Oct.期258卷 9-17页

作者： Liu, Zhenfang Huang, Min Zhu, Qibing Qin, Jianwei Kim, Moon S. Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China USDA ARS Environm Microbial & Food Safety Lab Beltsville Agr Res Ctr Bldg 303BARC East10300 Baltimore Ave Beltsville MD 20705 USA

Packaged food safety has gained increasing attention worldwide. Existing analytical methods pose difficulties in accurately measuring food quality without destroying the packaging. In this study, a nondestructive detection method for packaged food was proposed based on the generalized gaussian model for Raman scattering images. The Raman peaks of the scattering image were extracted, and the attenuation information of the peaks far from the laser point were imported into the established generalized gaussian model. Analysis of the histogram of residual distribution revealed that the difference in residual distribution was enhanced, and an appropriate threshold was selected to separate the Raman baseline correction spectrum of the internal materials. Food-grade polyethylene sheets with thicknesses of 1, 2, and 3 mm were used as packaging materials for comparison experiments. The proposed model can accurately separate the Raman peak of the subsurface material when 1 mm-thick polyethylene was used as the packaging. Food-grade plastic sheets of polyethylene, polypropylene and high-density polyethylene were covered with pure substances such as melamine, sodium nitrite, and maleic anhydride. This model was considered suitable for most food-grade plastic packaging, and the subsurface materials did not influence the separation effect. Finally, evaluation of premium white granulated sugar demonstrated that the model effectively separated the Raman peak produced by packaged food and detected the packaged food without conferring damage.

关键词： Raman scattering image generalized gaussian model Residual analysis Nondestructive testing

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A Novel SAR Image Change Detection Based on Graph-Cut and generalized gaussian model

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第1期10卷 14-18页

作者： Zhang, Xiaohua Chen, Jiawei Meng, Hongyun Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Xidian Univ Dept Math Xian 710071 Peoples R China

In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy c-means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm.

关键词： Change detection expectation maximization generalized gaussian model graph cut

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Blind source separation based on generalized gaussian model

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Journal of Harbin Institute of Technology(New Series) 2007年第3期14卷 362-367页

作者：杨斌孔薇周越 Information Engineering College of Shanghai Maritime University Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University

Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-gaussian and other sub-gaussian model,they may not be efficient to separate sources with different *** to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate *** great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than gaussian mixture *** using maximum likelihood(ML)approach,the convergence of the proposed algorithm is *** computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.

关键词： blind source separation Independent Component Analysis generalized gaussian model Maxi- mum Likelihood

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SAR image adaptive MAP filtering based on the generalized gaussian model - art. no. 678711

SAR image adaptive MAP filtering based on the generalized Ga...

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Chen, Shaobo Liu, Jianguo Wang, Guoyou Li, Qiaoliang Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469519

Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle noise into additive noise. We model the RCS using the recently introduced generalized gaussian density function([1]), Which was proved to be the best described for the SAR Amplitude. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the MAP filter based on the generalized gaussian prior for the RCS is among the best for speckle removal.

关键词： SAR speckle MAP estimation generalized gaussian model Mellin transform

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An ICA Algorithm Based on generalized gaussian model for Evoked Potentials Estimation

An ICA Algorithm Based on Generalized Gaussian Model for Evo...

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The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

作者： Hong Xie Jie Yu Institute of Information Engineering Shanghai Maritime University Shanghai People's Republic of China

Independent Component Analysis (ICA) is a recently developed Blind Source Separation (BSS) algorithm based on single observation sample. The success of the algorithm depends on its probability density model can better fit the signal inherent statistical distribution. For the problem that existing algorithms can not well fit the probability density model of source signals, this paper proposes an ICA algorithm based on the generalized gaussian model (GGM). This new algorithm, combining with the Maximum Likelihood of ICA, utilizes GGM to fit the signal probability density model, and uses it to estimate Auditory Evoked Potential (AEP). Experiments show that the algorithm can fit the signal inherent statistical distribution very well and estimate purer Evoked Potential (EP) signals more effectively.

关键词： Evoked Potential Independent Component Analysis generalized gaussian model Mazimum Likelihood Estimation

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Writer identification using global wavelet-based features

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NEUROCOMPUTING 2008年第10-12期71卷 1832-1841页

作者： He, Zhenyu You, Xinge Tang, Yuan Yan Huazhong Univ Sci & Technol Dept Elect & Informat Sci Wuhan 430074 Hubei Province Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China

In the human society, it is very important to find out the true writer of an unknown handwriting document. Therefore handwriting-based writer identification has been a hot research topic in pattern recognition field since several decades before. In our research, we find that the global styles of different people's handwritings are obviously distinctive and the histogram of the wavelet coefficients of preprocessed handwriting image can be well characterized by the generalized gaussian model (GGD) in wavelet domain. As a consequence, in this paper, we propose a new method by combining wavelet transform and GGD model for writer identification of Chinese handwriting document. Tested by our experiment, this method achieves a satisfied identification result and computational efficiency as well. (C) 2008 Elsevier B.V. All rights reserved.

关键词： writer identification Chinese document wavelet generalized gaussian model 2-D Gabor model

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Speech signal enhancement based on MAP algorithm in the ICA space

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2008年第5期56卷 1812-1820页

作者： Zou, Xin Jancovic, Peter Liu, Ju Kokuer, Munevver Univ Birmingham Elect Elect & Comp Engn Sch Engn Birmingham B15 2TT W Midlands England Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

This paper presents a novel maximum a posteriori (MAP) denoising algorithm based on the independent component analysis (ICA). We demonstrate that the employment of individual ICA transformations for signal and noise can provide the best estimate within the linear framework. The signal enhancement problem is categorized based on the distribution of signal and noise being gaussian or non-gaussian and the estimation rule is derived for each of the categories. Our theoretical analysis shows that under the assumption of a gaussian noise the proposed algorithm leads to some well-known enhancement techniques, i.e., Wiener filter and sparse code shrinkage. The analysis of the denoising capability shows that the proposed algorithm is most efficient for non-gaussian signals corrupted by a non-gaussian noise. We employed the generalized gaussian model (GGM) to model the distributions of speech and noise. Experimental evaluation is performed in terms of signal-to-noise ratio (SNR) and spectral distortion measure. Experimental results show that the proposed algorithms achieve significant improvement on the enhancement performance in both gaussian and non-gaussian noise.

关键词： generalized gaussian model independent component analysis (ICA) maximum a posteriori (MAP) estimation non-gaussian noise sparse code shrinkage speech enhancement Wiener filter

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Atrial activity extraction from single lead ECG recordings: Evaluation of two novel methods

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第3期43卷 176-183页

作者： Dai, Huhe Jiang, Shouda Li, Ye Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Hlth Informat Shenzhen Peoples R China Harbin Inst Technol Automat Test & Control Inst Harbin 150006 Peoples R China

Two different methods for extracting atrial activity (AA) signal from single lead electrocardiogram (ECG) of atrial fibrillation were proposed. The first one is a weighted average beat subtraction (WABS) method. Coefficients of QRS complexes used for constructing QRS template were obtained by minimizing mean square error. The second method is based on maximum likelihood estimation (MLE). Probability density functions of AA signal and ventricular activity (VA) signals were estimated using generalized gaussian model. Then AA signal was extracted by maximizing likelihood function. Simulated signal and clinical ECG were used to evaluate the performance of ABS, WABS and MLE-based algorithm. In comparison with ABS, WABS and MLE-based algorithm reduced normal mean square error by 23.5% and 20.2%, respectively. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Atrial fibrillation Single lead electrocardiogram Weighted average beat subtraction Maximum likelihood estimation generalized gaussian model

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Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps

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COMPUTER VISION AND IMAGE UNDERSTANDING 2004年第2期93卷 155-174页

作者： Provost, JN Collet, C Rostaing, P Pérez, P Bouthemy, P IRENAV GTS F-29240 Brest France INRIA Rennes IRISA F-35042 Rennes France

This paper presents an unsupervised method to segment multispectral images, involving a correlated non-gaussian noise. The efficiency of the Markovian quadtree-based method we propose will be illustrated on a satellite image segmentation task with multispectral observations, in order to update nautical charts. The proposed method relies on a hierarchical Markovian modeling and includes the estimation of all involved parameters. The parameters of the prior model are automatically calibrated while the estimation of the noise parameters is solved by identifying generalized distribution mixtures [P. Rostaing, J.-N. Provost, C. Collet, Proc. International Workshop EMMCVPR'99: Energy Minimisation Methods in Computer Vision and Pattern Recognition, Springer Verlag, New York,. 1;999, p. 141], by means of an iterative conditional estimation (ICE) procedure. generalized gaussian (GG) distributions are considered to model various intensity distributions of the multispectral images. They are indeed well suited to a large variety of correlated multispectral data. Our segmentation method is applied to Satellite Pour l'Observation de la Terre (SPOT) remote multispectral images. Within each segmented region, a bathymetric inversion model is then estimated to recover the water depth map. Experiments on different real images have demonstrated the efficiency of the whole process and the accuracy of the obtained results has been assessed using ground truth data. The designed segmentation method can be extended to images for which it is required to segment a region of interest using an unsupervised approach. (C) 2003 Elsevier Inc. All rights reserved.

关键词： unsupervised segmentation Markovian quadtree generalized gaussian model SPOT multispectral data bathymetry

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