The Experts below are selected from a list of 116187 Experts worldwide ranked by ideXlab platform
Chuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan ZhangAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan Zhang, Zhiqiu XiaAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
Chunpeng Wang - One of the best experts on this subject based on the ideXlab platform.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan ZhangAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan Zhang, Zhiqiu XiaAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
Alberto Leongarcia - One of the best experts on this subject based on the ideXlab platform.
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Information loss recovery for block based image coding techniques a fuzzy logic approach
IEEE Transactions on Image Processing, 1995Co-Authors: Xiaobing Lee, Yaqin Zhang, Alberto LeongarciaAbstract:A new technique to recover the Information loss in a block-based image coding system is developed in this paper. The proposed scheme is based on fuzzy logic reasoning and can be divided into three main steps: (1) hierarchical compass interpolation/extrapolation (HCIE) in the spatial domain for initial recovery of lost blocks that mainly contain Low-Frequency Information such as smooth background (2) coarse spectra interpretation by fuzzy logic reasoning for recovery of lost blocks that contain high-Frequency Information such as complex textures and fine features (3) sliding window iteration (SWI), which is performed in both spatial and spectral domains to efficiently integrate the results obtained in steps (1) and (2) such that the optimal result can be achieved in terms of surface continuity on block boundaries and a set of fuzzy inference rules. The proposed method, which is suitable for recovering both isolated and contiguous block losses, provides a new approach for error concealment of block-based image coding systems such as the JPEG coding standard and vector quantization-based coding algorithms. The principle of the proposed scheme can also be applied to block-based video compression schemes such as the H.261, MPEG, and HDTV standards. Simulation results are presented to illustrate the effectiveness of the proposed method. >
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Information loss recovery for block based image coding techniques a fuzzy logic approach
Visual Communications and Image Processing, 1993Co-Authors: Xiaobing Lee, Yaqin Zhang, Alberto LeongarciaAbstract:A new technique to recover the Information loss in a block-based image coding system is developed in this paper. The proposed scheme is based on the fuzzy logic reasoning and can be divided into three main steps: (1) hierarchical compass interpolation/extrapolation in the spatial domain for initial recovery of lost blocks that mainly contain Low-Frequency Information such as smooth background; (2) coarse spectra interpretation by fuzzy logic reasoning for recovery of lost blocks that contain high-Frequency Information such as complex textures and fine features; (3) sliding window iteration in both spatial and spectral domains to efficiently integrate the results obtained in step (1) and (2) such that optimal results can be achieved in terms of surface continuities on block boundaries and the established inference rules. The proposed method, suitable for recovering both isolated and contiguous block losses, provides a new approach for error concealment of block-based image coding systems such as the JPEG coding standard and vector quantization based coding algorithms. The principle of the proposed scheme can also be applied to block-based video compression schemes such as the H.261, MPEG, and HDTV standards. Simulation results are presented to illustrate the effectiveness of the proposed method.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Zhiqiu Xia - One of the best experts on this subject based on the ideXlab platform.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan Zhang, Zhiqiu XiaAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
Xingyuan Wang - One of the best experts on this subject based on the ideXlab platform.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan ZhangAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
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geometric correction based color image watermarking using fuzzy least squares support vector machine and bessel k form distribution
Signal Processing, 2017Co-Authors: Chunpeng Wang, Xingyuan Wang, Chuan Zhang, Zhiqiu XiaAbstract:This paper presents a robust color image watermarking algorithm based on fuzzy least squares support vector machine (FLS-SVM) and Bessel K form (BKF) distribution, which is a recently developed geometric correction algorithm. We firstly compute the quaternion discrete Fourier transform (QDFT) of the maximum central region of the original color image. Then the watermark is embedded into the magnitudes of Low-Frequency Information of QDFT. In watermark decoding process, the synchronous correction based on FLS-SVM model is used. When training the FLS-SVM model, we firstly perform the quaternion wavelet transform (QWT) of the grayscale images that correspond to the color training images, and then use BKF distribution to fit the empirical histogram of coefficients of the QWT, and finally use the shape parameters and scale parameters of BKF distribution to construct image feature vector. Experimental results show that the proposed algorithm is not only invisible, but also has outstanding robustness against common image processing attacks and geometric attacks. A color image watermarking algorithm using FLS-SVM and BKF distribution is proposed.The magnitude Information of QDFT is less sensitive to the change of images.The magnitude Information of QDFT is used to embed the watermark image.BKF distribution is first employed to estimate the geometric distortion parameters.FLS-SVM is first introduced into image watermarking algorithm.
