Mahdad Nouri Shirazi

BPCS steganography using EZW lossy compressed images

This paper presents a steganography method based on an embedded zerotree wavelet compression scheme and bit-plane complexity segmentation steganography. The proposed steganography enables us to use lossy compressed images as dummy files in bit-plane-based steganographic algorithms. Large embedding rates of around 25% of the compressed image size were achieved with little noticeable degradation in image quality.

Application of bit-plane decomposition steganography to JPEG2000 encoded images

This letter presents a steganography method based on a JPEG2000 lossy compression scheme and bit-plane complexity segmentation (BPCS) steganography. It overcomes the lack of robustness of bit-plane-based steganography methods with respect to lossy compression of a dummy image: a critical shortcoming that has hampered deployment in a practical scenario. The proposed method is based on a seamless integration of the two schemes without compromising their desirable features and makes feasible the deployment of the merits of a BPCS steganography technique in a practical scenario where images are compressed before being transmitted over the network. Embedding rates of around 15% of the compressed image size were achieved for preembedding 1.0-bpp compressed images with no noticeable degradation in image quality.

MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.

Texture classification based on Markov modeling in wavelet feature space

Abstract One difficulty of texture analysis and classification in the past was the lack of adequate tools to characterize textures over different scales. Recent developments in multiresolution analysis, such as the wavelet transform, promise ways to overcome this difficulty. In this paper, we present a texture classification methodology that is based on a stochastic modeling of textures in the wavelet domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients, which are typically disregarded by wavelet-based statistical signal processing techniques. It provides an accurate multiscale texture representation and underlies a highly discriminative texture classification algorithm.

Bit-Plane Decomposition Steganography Combined with JPEG2000 Compression

This paper presents a steganography method based on JPEG2000 lossy compression scheme and bit-plane complexity segmentation (BPCS) steganography. In JPEG2000 compression, wavelet coefficients of an image are quantized into a bit-plane structure and therefore BPCS steganography can be applied in the wavelet domain. The proposed JPEG2000-BPCS steganography was implemented using JJ2000 Java software of JPEG2000 compression, with which the program module for BPCS steganography was integrated. The proposed steganography enables us to use JPEG2000 lossy compressed images as dummy files for embedding secret data. Embedding rates of around 15% of the compressed image size were achieved for pre-embedding 1.0bpp compressed images with no visually noticeable degradation in image quality.

Textured image segmentation using MRF in wavelet domain

One difficulty of textured image segmentation in the past was the lack of computationally efficient models which can capture statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on computational efficiency of multiscale representations have received attention. Most Previous research has been based on multiscale stochastic models which use the Gaussian pyramid decomposition as image decomposition scheme. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm which is based on a multiscale stochastic modeling over the wavelet decomposition of image. For the sake of computational efficiency, versions of the EM algorithm and MAP estimate, which are based on the mean-field decomposition of a posteriori probability, are used for estimating model parameters and the segmented image, respectively.

Application of bit-plane decomposition steganography to wavelet encoded images

This paper presents a steganography method based on a lossy wavelet compression scheme and bit-plane complexity segmentation (BPCS) steganography. This method utilizes the embedded zerotree wavelet (EZW) compression scheme, where wavelet coefficients of an image are quantized into a bit-plane structure. The proposed steganography enables us to use lossy compressed images as dummy files in bit-plane-based steganographic algorithms. Large embedding rates of around 25% of the compressed image size were achieved with little noticeable degradation in image quality. The proposed method can be applied to other wavelet-based lossy compression schemes like SPIHT and JPEG2000, because in these compression schemes the wavelet coefficients are also quantized into a bit-plane structure.

Texture modeling and classification in wavelet feature space

One difficulty of texture analysis and classification in the past was the lack of adequate tools to characterize textures over different scales. Previous developments in multiresolution analysis, such as the wavelet transform, promise ways to overcome this difficulty. We present a texture classification algorithm based on Markov modeling of intrascale and interscale statistical regularities of textures in the wavelet domain. The model provides an accurate multiscale texture representation and underlies a classification algorithm with a high classification rate.

Determination of Principal Components in Data

Publisher Summary Principal component analysis (PCA) is a statistical technique that is employed to reduce the dimensionality of a data set in which there are a large number of interrelated variables. This reduction is obtained by transforming to a new set of variables, the principal components, which are uncorrelated and which are ordered so that the first few retain most of the variation present in all of the original variables. In other words, PCA searches for a set of statistically de-correlated features as an efficient representation of data, while retaining most of the intrinsic information in the data. PCA is an old and well-known technique in multivariate analysis. It was independently developed as a technique to analyze the correlation structure of data. Applications of PCA include data compression, noise reduction, feature extraction, and data analysis. This chapter describes PCA; how it is determined; PCA neural networks; biological background of PCA neural networks; minor component analysis; and nonlinear PCA.

Steganography using wavelet compressed images

Internet bandwidth is in high demand, and one way that web sites lower the amount of bandwidth they use is by compressing their sites images. This lowers the amount of bandwidth used, and makes the site load much faster. There are of course many other useful applications for compressed images. Bit Plane Complexity Segmentation (BPCS) digital picture steganography is a technique to hide data inside an image file. BPCS achieves high embedding rates with low distortion based on the theory that noise-like regions in a bit-plane can be replaced with noise-like secret data without discernible loss in image quality. This is possible because the human eye, while very good at distinguishing anomalies in areas of homogenous texture, is bad at distinguishing anomalies in visually complex areas. However, BPCS is not a robust embedding scheme, and any lossy compression usually destroys the data. Wavelet image compression using the Discreet Wavelet Transform (DWT) is the basis of many modern compression schemes. The coefficients generated by certain wavelet transforms have many image-like qualities. These qualities can be exploited to allow BPCS to be performed on the coefficients. The results can then be losslessly encoded, combining the good compression of the DWT with the high embedding rates of BPCS.