The Experts below are selected from a list of 19797 Experts worldwide ranked by ideXlab platform
Jun Zhang - One of the best experts on this subject based on the ideXlab platform.
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General least product relative error estimation for multiplicative regression models with or without multiplicative Distortion Measurement errors
Communications in Statistics - Simulation and Computation, 2020Co-Authors: Huili Zhou, Jun ZhangAbstract:We consider the parameter estimation for multiplicative linear regression models with or without multiplicative Distortion Measurement errors. For the latter, both the response variable and the cov...
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Partial index additive models with additive Distortion Measurement errors
Communications in Statistics - Simulation and Computation, 2020Co-Authors: Jun Zhang, Sanying Feng, Yujie GaiAbstract:This paper considers the estimation for a partial index additive regression model, when the response variable and covariates in the index part are observed with additive Distortion Measurement erro...
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Measuring symmetry and asymmetry of multiplicative Distortion Measurement errors data
Brazilian Journal of Probability and Statistics, 2020Co-Authors: Jun Zhang, Yujie Gai, Xia CuiAbstract:This paper studies the measure of symmetry or asymmetry of a continuous variable under the multiplicative Distortion Measurement errors setting. The unobservable variable is distorted in a multiplicative fashion by an observed confounding variable. First, two direct plug-in estimation procedures are proposed, and the empirical likelihood based confidence intervals are constructed to measure the symmetry or asymmetry of the unobserved variable. Next, we propose four test statistics for testing whether the unobserved variable is symmetric or not. The asymptotic properties of the proposed estimators and test statistics are examined. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a real dataset for an illustration.
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Conditional absolute mean calibration for partial linear multiplicative Distortion Measurement errors models
Computational Statistics & Data Analysis, 2020Co-Authors: Jun Zhang, Bingqing Lin, Zhenghui FengAbstract:Abstract In this paper we consider partial linear regression models when all the variables are measured with multiplicative Distortion Measurement errors. To eliminate the effect caused by the Distortion, we propose the conditional absolute mean calibration, which avoids to use the nonzero expectation conditions imposed on the variables. With these calibrated variables, a profile least squares estimator is obtained, associated with its normal approximation based and empirical likelihood based confidence intervals. For the hypothesis testing on parameters, a restricted estimator under the null hypothesis and a test statistic are proposed. A smoothly clipped absolute deviation penalty is employed to select the relevant variables. The resulting penalized estimators are shown to be asymptotically normal and have the oracle property. Lastly, a score-type test statistic is then proposed for checking the validity of partial linear models. We derive asymptotic distribution of the proposed test statistic. The quadratic form of the scaled test statistic has an asymptotic chi-squared distribution under the null hypothesis and follows a noncentral chi-squared distribution under local alternatives, which converge to the null hypothesis at a parametric rate. Simulation studies demonstrate the performance of our proposed procedure and a real example is analyzed as illustrate its practical usage.
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Multiplicative Distortion Measurement errors linear models with general moment identifiability condition
Journal of Statistical Computation and Simulation, 2019Co-Authors: Jun Zhang, Yujie GaiAbstract:This paper considers linear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative Distortion Measurement errors. The...
Zhou Wang - One of the best experts on this subject based on the ideXlab platform.
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PERCEPTUAL NORMALIZED INFORMATION DISTANCE FOR IMAGE Distortion ANALYSIS BASED ON KOLMOGOROV COMPLEXITY
2011Co-Authors: Nima Nikvand, Zhou WangAbstract:Image Distortion analysis is a fundamental issue in many image processing problems, including compression, restoration, recognition, classification, and retrieval. In this work, we investigate the problem of image Distortion Measurement based on the theories of Kolmogorov complexity and normalized information distance (NID), which have rarely been studied in the context of image processing. Based on a wavelet domain Gaussian scale mixture model of images, we approximate NID using a Shannon entropy based method. This leads to a series of novel Distortion measures that are competitive with state‐of‐the‐art image quality assessment approaches.
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video quality assessment based on structural Distortion Measurement
Signal Processing-image Communication, 2004Co-Authors: Zhou Wang, Ligang Lu, A.c. BovikAbstract:Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based meth- ods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural dis- tortion as an estimate of perceived visual Distortion. A com- putationally ecient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set. Keywords—Image quality assessment, video quality assess- ment, human visual system, error sensitivity, structural dis- tortion, video quality experts group (VQEG)
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Video quality assessment using structural Distortion Measurement
Proceedings. International Conference on Image Processing, 2002Co-Authors: Zhou Wang, Ligang Lu, A.c. BovikAbstract:Objective image/video quality measures play important roles in various image/video processing applications, such as compression, communication, printing, analysis, registration, restoration and enhancement. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. We follow a new philosophy in designing image/video quality metrics, which uses structural Distortion as an estimation of perceived visual Distortion. We develop a new approach for video quality assessment. Experiments on the video quality experts group (VQEG) test data set shows that the new quality measure has higher correlation with subjective quality Measurement than the proposed methods in VQEG's Phase I tests for full-reference video quality assessment.
A.c. Bovik - One of the best experts on this subject based on the ideXlab platform.
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video quality assessment based on structural Distortion Measurement
Signal Processing-image Communication, 2004Co-Authors: Zhou Wang, Ligang Lu, A.c. BovikAbstract:Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based meth- ods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural dis- tortion as an estimate of perceived visual Distortion. A com- putationally ecient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set. Keywords—Image quality assessment, video quality assess- ment, human visual system, error sensitivity, structural dis- tortion, video quality experts group (VQEG)
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Video quality assessment using structural Distortion Measurement
Proceedings. International Conference on Image Processing, 2002Co-Authors: Zhou Wang, Ligang Lu, A.c. BovikAbstract:Objective image/video quality measures play important roles in various image/video processing applications, such as compression, communication, printing, analysis, registration, restoration and enhancement. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. We follow a new philosophy in designing image/video quality metrics, which uses structural Distortion as an estimation of perceived visual Distortion. We develop a new approach for video quality assessment. Experiments on the video quality experts group (VQEG) test data set shows that the new quality measure has higher correlation with subjective quality Measurement than the proposed methods in VQEG's Phase I tests for full-reference video quality assessment.
Zhenghui Feng - One of the best experts on this subject based on the ideXlab platform.
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Conditional absolute mean calibration for partial linear multiplicative Distortion Measurement errors models
Computational Statistics & Data Analysis, 2020Co-Authors: Jun Zhang, Bingqing Lin, Zhenghui FengAbstract:Abstract In this paper we consider partial linear regression models when all the variables are measured with multiplicative Distortion Measurement errors. To eliminate the effect caused by the Distortion, we propose the conditional absolute mean calibration, which avoids to use the nonzero expectation conditions imposed on the variables. With these calibrated variables, a profile least squares estimator is obtained, associated with its normal approximation based and empirical likelihood based confidence intervals. For the hypothesis testing on parameters, a restricted estimator under the null hypothesis and a test statistic are proposed. A smoothly clipped absolute deviation penalty is employed to select the relevant variables. The resulting penalized estimators are shown to be asymptotically normal and have the oracle property. Lastly, a score-type test statistic is then proposed for checking the validity of partial linear models. We derive asymptotic distribution of the proposed test statistic. The quadratic form of the scaled test statistic has an asymptotic chi-squared distribution under the null hypothesis and follows a noncentral chi-squared distribution under local alternatives, which converge to the null hypothesis at a parametric rate. Simulation studies demonstrate the performance of our proposed procedure and a real example is analyzed as illustrate its practical usage.
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Statistical inference for linear regression models with additive Distortion Measurement errors
Statistical Papers, 2018Co-Authors: Zhenghui Feng, Jun Zhang, Qian ChenAbstract:We consider estimations and hypothesis test for linear regression Measurement error models when the response variable and covariates are measured with additive Distortion Measurement errors, which are unknown functions of a commonly observable confounding variable. In the parameter estimation and testing part, we first propose a residual-based least squares estimator under unrestricted and restricted conditions. Then, to test a hypothesis on the parametric components, we propose a test statistic based on the normalized difference between residual sums of squares under the null and alternative hypotheses. We establish asymptotic properties for the estimators and test statistics. Further, we employ the smoothly clipped absolute deviation penalty to select relevant variables. The resulting penalized estimators are shown to be asymptotically normal and have the oracle property. In the model checking part, we suggest two test statistics for checking the validity of linear regression models. One is a score-type test statistic and the other is a model- adaptive test statistic. The quadratic form of the scaled test statistic is asymptotically chi-squared distributed under the null hypothesis and follows a noncentral chi-squared distribution under local alternatives that converge to the null hypothesis. We also conduct simulation studies to demonstrate the performance of the proposed procedure and analyze a real example for illustration.
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Partial Linear Single Index Models with Additive Distortion Measurement Errors
Communications in Statistics - Theory and Methods, 2017Co-Authors: Jun Zhang, Zhenghui FengAbstract:We study partial linear single-index models (PLSiMs) when the response and the covariates in the parametric part are measured with additive Distortion Measurement errors. These Distortions are mode...
Wenyu Liu - One of the best experts on this subject based on the ideXlab platform.
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B-spline-based shape coding with accurate Distortion Measurement using analytical model
Neurocomputing, 2015Co-Authors: Zhongyuan Lai, Zhen Zuo, Zhe Wang, Zhijun Yao, Wenyu LiuAbstract:Abstract In this paper, we present a new model to measure the contour point Distortion for the vertex-based shape coding with B-splines, called accurate Distortion Measurement using analytical model (ADMAM). Different from existing Distortion Measurements containing approximation, quantization or parameterization process, our Distortion is defined on the original B-spline. It is modeled as the shortest distance of associated contour point from the original B-spline, which is in line with the subjective-based objective quality metric. The geometric relationships are introduced to simplify the model computation, followed by a hybrid admissible Distortion checking algorithm to reduce the execution time. Our theoretical analysis and experimental results demonstrate that the ADMAM can lead to the smallest bit-rate among all the Distortion Measurements that guarantee the admissible Distortion, when the operational rate-Distortion optimal shape coding framework is applied. Moreover, if the original contour has NC points, it takes only O ( N C ) time for both peak and mean-squared segment Distortion measuring paradigms, which is the lowest computational complexity among all the existing Distortion Measurements.
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ICIP - Accurate Distortion Measurement for B-spline-based shape coding
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: Zhongyuan Lai, Zhen Zuo, Zhe Wang, Zhijun Yao, Wenyu LiuAbstract:In this paper, we present a new contour point Distortion Measurement, called accurate Distortion Measurement for B-spline-based shape coding (ADMBSC). Different from existing Distortion Measurements containing approximation, quantization or parameterization, our Distortion is defined as the shortest distance from the original B-spline to the associated contour point. This is in line with the subjective-based objective quality metric. Geometric relationships are introduced to simplify computation, followed by a hybrid admissible Distortion checking algorithm to reduce execution time. Theoretical analysis and experimental results demonstrate that when the operational rate-Distortion optimal shape coding framework under the minimum-maximum criterion is applied, the ADMBSC can lead to the smallest bit-rate among all the Distortion Measurements that can guarantee the admissible Distortion. Moreover, if the original contour has N C points, it takes only O(N C ) time for segment Distortion measuring paradigms, whose computational complexity is the same as the lowest one among the existing Distortion Measurements.
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DCC - Accurate Distortion Measurement Using Analytical Model for the B-Spline-Based Shape Coding
2011 Data Compression Conference, 2011Co-Authors: Zhongyuan Lai, Zhen Zuo, Zhe Wang, Wenyu LiuAbstract:In this paper, we present a new model to measure the contour point Distortion for the B-spline-based shape coding, called accurate Distortion Measurement using analytical model (ADMAM). It models the Distortion as the shortest distance of associate contour point from the approximating B-spline. Observing that this shortest path is perpendicular to the tangent vector of the approximating B-spline, we construct a parametric cubic equation by setting their dot product to be zero and address it by using Cartan's formula. The theoretical property and the experiments presented demonstrate that ADMAM can lead to the smallest bit-rate among all the Distortion Measurements that can guarantee the admissible Distortion, when the operational rate-Distortion optimal shape coding framework is applied. Moreover, if the original contour has NC points, it takes only O(NC) time for both a peak and mean-squared segment Distortion measuring paradigms, which is the lowest complexity among all the existing Distortion Measurements.