Discrete Wavelet Transform Sub-bands Research Articles

A Secure Skin Tone based Steganography Using Wavelet Transform

Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement Steganography is skin tone region of images (1). Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach - DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. For data hiding two cases are considered, first is with cropping and other is without cropping. In both the cases different steps of data hiding are applied either by cropping an image interactively or without cropping i.e. on whole image. Both cases are compared and analyzed from different aspects. This is concluded that both cases offer enough security. Main feature of with cropping case is that this results into an enhanced security because cropped region works as a key at decoding side. Where as without cropping case uses embedding algorithm that preserves histogram of DWT coefficient after data embedding also by preventing histogram based attacks and leading to a more security. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And simulation result shows that satisfactory PSNR (Peak-Signal-to-Noise Ratio) is also obtained.

Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition

The main objective of developing an image-watermarking technique is to satisfy both imperceptibility and robustness requirements. To achieve this objective, a hybrid image-watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed in this paper. In our approach, the watermark is not embedded directly on the wavelet coefficients but rather than on the elements of singular values of the cover image's DWT subbands. Experimental results are provided to illustrate that the proposed approach is able to withstand a variety of image-processing attacks.

Semi-Fragile Zernike Moment-Based Image Watermarking for Authentication

We propose a content-based semi-fragile watermarking algorithm for image authentication. In content-based watermarking scheme for authentication, one of the most challenging issues is to define a computable feature vector that can capture the major content characteristics. We identify Zernike moments of the image to generate feature vector and demonstrate its good robustness and discriminative capability for authentication. The watermark is generated by quantizing Zernikemoments magnitudes (ZMMs) of the image and embedded into DWT (Discrete Wavelet Transform) subband. It is usually hard to locate the tampered area by using global feature in the content-based watermarking scheme. We propose a structural embedding method to locate the tampered areas by using the separability of Zernike moments-based feature vector. The authentication process does not need the original feature vector. By using the semi-fragilities of the feature vector and the watermark, the proposed authentication scheme is robust to content-preserved processing, while being fragile to malicious attacks. As an application of our algorithm, we apply it on Chinese digital seals and the results show that it works well. Compared with some existing algorithms, the proposed scheme achieves better performance in discriminating high-quality JPEG compression from malicious attacks.

A Progressive Image Compression Method Based on EZW Algorithm

A simple method based on the EZW algorithm is presented for improving image compression performance. Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's EZW(Embedded Zerotree Wavelets)(1), Said and Pearlman's SPIHT(Set Partitioning In Hierarchical Trees)(2), and Bing-Bing Chai's SLCCA(Significance-Linked Connected Component Analysis for Wavelet Image Coding)(3). The EZW algorithm is based on five key concepts: (1) a DWT(Discrete Wavelet Transform) or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, (4) universal lossless data compression which is achieved via adaptive arithmetic coding. and (5) DWT coefficients' degeneration from high scale subbands to low scale subbands. In this paper, we have improved the self-similarity statistical characteristic in concept (5) and present a progressive image compression method.

Wavelet-based texture analysis and synthesis using hidden Markov models

Wavelet-domain hidden Markov models (HMMs), in particular, hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the two-dimensional discrete wavelet transform (DWT), i.e., HL, LH, and HH, are independent. In this paper, we study wavelet-based texture analysis and synthesis using HMMs. Particularly, we develop a new HMM, called HMT-3S, for statistical texture characterization in the wavelet domain. In addition to the joint statistics captured by HMT, the new HMT-3S can also exploit the cross correlation across DWT subbands. Meanwhile, HMT-3S can be characterized by using the graphical grouping technique, and has the same tree structure as HMT. The proposed HMT-3S is applied to texture analysis, including classification and segmentation, and texture synthesis with improved performance over HMT. Specifically, for texture classification, we study four wavelet-based methods, and experimental results show that HMT-3S provides the highest percentage of correct classification of over 95% upon a set of 55 Brodatz textures. For texture segmentation, we demonstrate that more accurate texture characterization from HMT-3S allows the significant improvements in terms of both classification accuracy and boundary localization. For texture synthesis, we develop an iterative maximum likelihood-based texture synthesis algorithm which adopts HMT or HMT-3S to impose the joint statistics of the texture DWT, and it is shown that the new RMT-3S enables more visually similar results than HMT does.