The Experts below are selected from a list of 28026 Experts worldwide ranked by ideXlab platform
Mohammad Taghi Manzuri Shalmani - One of the best experts on this subject based on the ideXlab platform.
-
high capacity error free Wavelet Domain speech steganography
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S S Shahreza, Mohammad Taghi Manzuri ShalmaniAbstract:Steganography is the art of hiding information in a cover media without attracting attention. One of the cover media which can be used for steganography is speech. In this paper, we propose a new speech steganography in Wavelet Domain. In this method, lifting scheme is used to create perfect reconstruction Int2Int Wavelets. The data is hidden in some of the Least Significant Bits (LSB) of detail Wavelet coefficients. The LSB bits for hiding are selected with a new adaptive algorithm. This algorithm does not hide information in silent parts, so there is no need for silent detection algorithms. This method has zero error in hiding/unhiding process, while normal Wavelet Domain LSB has about 0.2 % error in equal hiding capacity. This method is a high capacity steganography method which can hide information up to 20% of the input speech. The Signal-to- Noise Ratio (SNR) and listening tests show that the stegano audio is imperceptible from original audio.
-
ICASSP - High capacity error free Wavelet Domain Speech Steganography
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S S Shahreza, Mohammad Taghi Manzuri ShalmaniAbstract:Steganography is the art of hiding information in a cover media without attracting attention. One of the cover media which can be used for steganography is speech. In this paper, we propose a new speech steganography in Wavelet Domain. In this method, lifting scheme is used to create perfect reconstruction Int2Int Wavelets. The data is hidden in some of the Least Significant Bits (LSB) of detail Wavelet coefficients. The LSB bits for hiding are selected with a new adaptive algorithm. This algorithm does not hide information in silent parts, so there is no need for silent detection algorithms. This method has zero error in hiding/unhiding process, while normal Wavelet Domain LSB has about 0.2 % error in equal hiding capacity. This method is a high capacity steganography method which can hide information up to 20% of the input speech. The Signal-to- Noise Ratio (SNR) and listening tests show that the stegano audio is imperceptible from original audio.
S S Shahreza - One of the best experts on this subject based on the ideXlab platform.
-
high capacity error free Wavelet Domain speech steganography
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S S Shahreza, Mohammad Taghi Manzuri ShalmaniAbstract:Steganography is the art of hiding information in a cover media without attracting attention. One of the cover media which can be used for steganography is speech. In this paper, we propose a new speech steganography in Wavelet Domain. In this method, lifting scheme is used to create perfect reconstruction Int2Int Wavelets. The data is hidden in some of the Least Significant Bits (LSB) of detail Wavelet coefficients. The LSB bits for hiding are selected with a new adaptive algorithm. This algorithm does not hide information in silent parts, so there is no need for silent detection algorithms. This method has zero error in hiding/unhiding process, while normal Wavelet Domain LSB has about 0.2 % error in equal hiding capacity. This method is a high capacity steganography method which can hide information up to 20% of the input speech. The Signal-to- Noise Ratio (SNR) and listening tests show that the stegano audio is imperceptible from original audio.
-
ICASSP - High capacity error free Wavelet Domain Speech Steganography
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: S S Shahreza, Mohammad Taghi Manzuri ShalmaniAbstract:Steganography is the art of hiding information in a cover media without attracting attention. One of the cover media which can be used for steganography is speech. In this paper, we propose a new speech steganography in Wavelet Domain. In this method, lifting scheme is used to create perfect reconstruction Int2Int Wavelets. The data is hidden in some of the Least Significant Bits (LSB) of detail Wavelet coefficients. The LSB bits for hiding are selected with a new adaptive algorithm. This algorithm does not hide information in silent parts, so there is no need for silent detection algorithms. This method has zero error in hiding/unhiding process, while normal Wavelet Domain LSB has about 0.2 % error in equal hiding capacity. This method is a high capacity steganography method which can hide information up to 20% of the input speech. The Signal-to- Noise Ratio (SNR) and listening tests show that the stegano audio is imperceptible from original audio.
M.n.s. Swamy - One of the best experts on this subject based on the ideXlab platform.
-
MMSP - Low-complexity video noise reduction in Wavelet Domain
IEEE 6th Workshop on Multimedia Signal Processing 2004., 1Co-Authors: N. Gupta, M.n.s. Swamy, E.i. PlotkinAbstract:This paper proposes a novel spatio-temporal filter for video denoising that operates entirely in the Wavelet Domain and is based on temporal decorrelation. For effective noise reduction, the spatial and the temporal redundancies, which exist in the Wavelet Domain representation of a video signal, are exploited. Using simple and closed form expressions, the temporal information in the Wavelet Domain is first decorrelated in order to minimize the redundancy. The decorrelated noise-free coefficients are then modeled using a generalized Gaussian prior. For spatial filtering of the noisy Wavelet coefficients, a new, low-complexity Wavelet shrinkage method, which utilizes the correlation that exists between subsequent resolution levels, is proposed. Experimental results show that the proposed scheme outperforms state-of-the-art spatio-temporal filters in time and Wavelet Domains, both in terms of PSNR and visual quality.
-
ISCAS (5) - Bayesian algorithm for video noise reduction in the Wavelet Domain
2005 IEEE International Symposium on Circuits and Systems, 1Co-Authors: N. Gupta, E.i. Plotkin, M.n.s. SwamyAbstract:The paper proposes a Bayesian algorithm for the reduction of additive video noise in the Wavelet Domain. Spatial and temporal redundancies that exist in a video sequence in the time Domain also persist in the Wavelet Domain. This allows video motion to be captured in the Wavelet Domain. Based on this fact, a new statistical model is proposed for video sequences. We not only model the subband coefficients in individual frames, but also the Wavelet coefficient difference occurring between two consecutive frames using the generalized Laplacian distribution. Following this model, a Bayesian processor is developed that estimates the noise-free Wavelet coefficients in the current frame, conditioned on the noisy coefficients in the current frame and the filtered coefficients in the past frame. Rigorous experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in time and Wavelet Domains in terms of quantitative performance as well as visual quality.
Silong Peng - One of the best experts on this subject based on the ideXlab platform.
-
Wavelet-Domain HMT-based image super-resolution
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003Co-Authors: Shubin Zhao, Silong PengAbstract:In this paper we propose an image super-resolution algorithm using Wavelet-Domain hidden Markov tree (HMT) model. Wavelet-Domain HMT models the dependencies of multiscale Wavelet coefficients through the state probabilities of Wavelet coefficients, whose distribution densities can be approximated by the Gaussian mixture. Because Wavelet-Domain HMT accurately characterizes the statistics of real-world images, we reasonably specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem. And the cycle-spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images. Quantitative error analyses are provided and several experimental images are shown for subjective assessment.
-
ICIP (2) - Wavelet-Domain HMT-based image super-resolution
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 1Co-Authors: Shubin Zhao, Hua Han, Silong PengAbstract:In this paper we propose an image super-resolution algorithm using Wavelet-Domain hidden Markov tree (HMT) model. Wavelet-Domain HMT models the dependencies of multiscale Wavelet coefficients through the state probabilities of Wavelet coefficients, whose distribution densities can be approximated by the Gaussian mixture. Because Wavelet-Domain HMT accurately characterizes the statistics of real-world images, we reasonably specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem. And the cycle-spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images. Quantitative error analyses are provided and several experimental images are shown for subjective assessment.
-
Image restoration with edge-preserving regularization in Wavelet Domain
Proceedings. 2005 IEEE Networking Sensing and Control 2005., 1Co-Authors: Xueguang Cao, Xuelin Wang, Silong PengAbstract:Image restoration is an ill posed problem and must be regularized. Usually, the difficulty of regularization is to suppress the noise but not smooth the edges. In order to preserve the edges of restored image effectively, a general Wavelet-Domain edge-preserving regularization scheme which is analogous to the space-Domain maximum a posterior probability (MAP) estimation in Markov random field (MRF) is proposed. The corresponding solving strategy of the Wavelet-Domain regularization is also put forward. Several potential functions which have the ability of edge-preserving are analyzed and tested. To get rid of the Gibbs effects brought during the Wavelet-Domain restoration, the horizontal (or vertical) continuity in horizontal (or vertical) subband of natural image is employed in the form of an additional penalty. And, experiments are presented to verify the theoretical results.
Li Hong - One of the best experts on this subject based on the ideXlab platform.
-
Image Denoising Based on Wavelet Domain Wiener Filtering
Computer Simulation, 2005Co-Authors: Li HongAbstract:The parameters of traditional Wavelet Domain local Wiener filter are estimated from neighborhood,consisting of coefficients at adjacent spatial location and coefficients at adjacent scales.Because of the limited size of the neighborhood,the problem of the estimation accuracy arises.Aimed to resolve the problem,in this paper,analysis of the errors occurring in the traditional Wavelet Domain local Wiener filtering is presented,then according to the results of analysis,an improved method is proposed,which thresholds the Wavelet coefficients by an appropriate threshold before the Wiener filtering.For the test images corrupted by noise with different levels,simulation results show that the improved method can effectively improve the performance of Wavelet Domain Wiener filtering,and the higher the noise levels,the more obvious the improvement.
