基于HVS和分形自相似的数字水印算法

在传统分形图像编码的基础上,借用人类视觉系统(HVS)的掩蔽特性,对分形域上的水印方法进行了研究.在空间域上对原始图像进行预处理,使原始图像的纹理区得到了突出,对纹理区图像进行分形压缩编码的同时,实现了水印的嵌入和提取操作.水印提取时运用概率统计知识,提高了水印检测的准确度和水印抵抗攻击的鲁棒性.实验结果表明,该算法对剪切、噪声、滤波、JPEG等图像处理方法具有较好的鲁棒性.     

A semi-blind HVS based image watermarking scheme using elliptic curve cryptography

In the present paper, an advanced encryption technique commonly known as Elliptic Curve Cryptography (ECC) is used to embed a binary image as a watermark in five grayscale host images in a semi-blind manner. The ECC algorithm is a fast encryption technique which successfully encrypts the subject with significantly less number of bits as compared to other popular encryption algorithms such as Rivest-Shamir-Adleman (RSA) and Direct Selling Association (DSA). In the proposed watermarking scheme, embedding in the grayscale host images is carried out in DWT-SVD domain. First, entropy based Human Visual System (HVS) parameters are computed block wise to identify the most appropriate blocks in spatial domain. First level DWT is computed for these selected blocks and watermark embedding is carried out by using the calculated Singular Value Decomposition (SVD) parameters. Preliminary results of this work show that proposed scheme outperforms the other similar schemes carried out in DCT-SVD domain without using any encryption method. It is concluded that the use of DWT-SVD hybrid architecture along with the fast encryption technique ECC is responsible for better performance in present case. In the second part of this simulation, an established HVS model working in DCT domain is implemented and compared with the entropy based HVS model implemented in transform domain to embed the ECC encrypted binary watermark in images. In this case also, proposed scheme performs better both in terms of visual imperceptibility and robustness as compared to other scheme. It is concluded that HVS parameters – Luminance, Contrast and Edge Sensitivity are better placed in comparison to entropy parameters to examine image features and characteristics for watermarking purpose.     

基于HVS的多通道DCT彩色图像水印算法

提出了一种基于离散余弦变换(DCT)的彩色盲水印算法,首先将彩色图像由RGB彩色空间转换到YCbCr彩色空间,利用其良好的空间独立性,分别对三个空间进行DCT,并根据人眼视觉特性自适应选择嵌入量,嵌入水印.实验结果表明,嵌入算法容易实现,且该水印算法具有很好的不可见性,并对剪切和JPEG压缩等图像处理都有很好的鲁棒性.     

Hyperspectral image coding and transmission scheme based on wavelet transform and distributed source coding

Multimedia Tools and Applications - This paper presents a novel scheme for satellite hyperspectral images broadcasting over wireless channels. First, a simple pre-processing is performed. Then, a...     

An image transform coding scheme based on spatial domain considerations

Image transform coding is first briefly reviewed using conventional viewpoints. Then a new spatial domain interpretation is given to image transform coding. An improvement based on this viewpoint for the Fourier transform coding, which possesses simple spatial domain relations, is presented.     

An image encryption scheme based on DNA coding and permutation of hyper-image

An image encryption algorithm based on substitution and permutation is proposed in this paper. The original image is encoded into DNA sequence and hyper-image respectively for better disposal. The encryption algorithm is composed of substitution in the DNA format and permutation in the hyper-image format, both of which have eliminated the relation between adjacent pixels in the image and adjacent bit planes in one pixel sufficiently. Besides, a random sequence generator based on the hyper-chaotic system is proposed, which has been utilized both in deciding the complementary ‘nucleoside’ in the substitution process and in constructing the hyper-image for the permutation process. Large quantities of experiments have demonstrated the validity and efficiency of the proposed scheme.     

基于HVS特性的数字图像水印研究

提出了一种基于小波变换低频系数的数字水印嵌入和检测算法.其中使用了有意义的二值图像水印来替代随机序列,先将水印通过Arnold置乱加密后再全部嵌入到低频子带系数中.该算法利用了人眼视觉系统(HVS)特性对水印嵌入强度做自适应调节以增强水印的鲁棒性和保证水印的不可感知性.实验结果表明,使用该算法嵌入的数字水印具有很好的隐蔽性,并且嵌入水印的图像对有损压缩、滤波、加入随机噪声和旋转等操作具有较强的抵御攻击能力.     

Haar wavelet based processor scheme for image coding with low circuit complexity

A hardware-oriented image coding processing scheme based on the Haar wavelet transform is presented. The procedure computes a variant of the Haar wavelet transform that uses only addition and subtraction operations, after that, an optimized methodology performs the selection and coding of the coefficients, tailored for it with the main aim of attaining the lowest circuit complexity hardware implementation. A selection strategy, which does not require the previous ordering of coefficients, has been used. A non-conventional coding methodology, which uses an optimized combination of techniques adapted to the various groups of coefficients, has been devised for the coding of the selected coefficients leading to a compressed representation of the image and reducing the coding problems inherent in threshold selection. The compression level reached for images of 512 × 512 pixels with 256 grey levels is just over 22:1, (0.4 bits/pixel) with a normalized mean square error, nrmse, of 2-3%, with subjective qualities which can be classified as good. The whole compression circuitry has been described and simulated at HDL level for up to 4 consecutive images, obtaining consistent results. The complete processor (excluding memory) for images of 256 × 256 pixels has been implemented using only one general-purpose low-cost FPGA chip, thus proving the design reliability and its relative simplicity.     

Quantum image coding with a reference-frame-independent scheme

For binary images, or bit planes of non-binary images, we investigate the possibility of a quantum coding decodable by a receiver in the absence of reference frames shared with the emitter. Direct image coding with one qubit per pixel and non-aligned frames leads to decoding errors equivalent to a quantum bit-flip noise increasing with the misalignment. We show the feasibility of frame-invariant coding by using for each pixel a qubit pair prepared in one of two controlled entangled states. With just one common axis shared between the emitter and receiver, exact decoding for each pixel can be obtained by means of two two-outcome projective measurements operating separately on each qubit of the pair. With strictly no alignment information between the emitter and receiver, exact decoding can be obtained by means of a two-outcome projective measurement operating jointly on the qubit pair. In addition, the frame-invariant coding is shown much more resistant to quantum bit-flip noise compared to the direct non-invariant coding. For a cost per pixel of two (entangled) qubits instead of one, complete frame-invariant image coding and enhanced noise resistance are thus obtained.     

Improved reversible visible image watermarking based on HVS and ROI-selection

Multimedia Tools and Applications - Reversible visible watermarking is used to combat the copyright infringement which can perfectly restore the original cover image by removing the embedded...     

Multiresolution image registration based on tree data structures

This paper presents a new approach for image registration based on the partitioning of the two source images in binary-space and quadtree structures, obtained with a maximum mutual information gain algorithm. Two different implementation approaches that differ in the level at which information is considered have been studied. The first works at pixel level using the simplified images directly, while the second works at node level dealing with the tree data structure. The obtained results show an outstanding accuracy and robustness of the proposed methods. In particular, the use of binary-space partitioned images drastically reduces the grid effects in comparison with regular downsampled images. An important advantage of our approach comes from the reduced size of the data structures corresponding to the simplified images, which makes this method appropriate to be applied in a multiresolution framework and telemedicine applications.     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

Chaos-based image encryption scheme combining DNA coding and entropy

Information security has became more and more important issue in modern society, one of which is the digital image protection. In this paper, a secure image encryption scheme based on logistic and spatiotemporal chaotic systems is proposed. The extreme sensitivity of chaotic system can greatly increase the complexity of the proposed scheme. Further more, the scheme also takes advantage of DNA coding and eight DNA coding rules are mixed to enhance the efficiency of image confusion and diffusion. To resist the chosen-plaintext attack, information entropy of DNA coded image is modulated as the parameter of spatiotemporal chaotic system, which can also guarantee the sensitivity of plain image in the encryption process. So even a slight change in plain image can cause the complete change in cipher image. The experimental analysis shows that it can resistant different attacks, such as the brute-force attack, statistical attack and differential attack. What’s more, The image encryption scheme can be easily implemented by software and is promising in practical application.     

Piecewise-linear subband coding scheme for fast image decomposition

Transform coding methods play a very important role in the development of diverse multimedia technologies. The discrete cosine and wavelet transforms are frequently used in the field of image processing and set up a basis for contemporary image compression standards (JPEG, MPEG). In this paper a novel two-channel piecewise-linear subband coding scheme (PL-SBC) is introduced. Its most important advantage are exceptionally fast and easy for implementation computational algorithms combined with good reconstruction quality. The proposed PL-SBC system is based on the dual filter bank consisting of a single filter at the decomposition stage and a single one at the reconstruction stage. For the presented PL-SBC scheme, the conditions of perfect reconstruction and the piecewise-linear approximation filter bank have been defined. The compression properties of a new subband coding scheme have been analysed for different categories of images and in comparison with well-known signal transforms, such as cosine, piecewise-linear, piecewise-constant and wavelet transforms. The research includes a comparison of the reconstruction error both in objective as well as subjective approach.     

基于小波变换和HVS的彩色图像质量评价方法

提出了一种基于小波变换和人类视觉系统的图像质量评价方法。首先分别测得4级小波分解后高低频分量的SSIM值并用CSF曲线加权,然后对低频分量进行分块DCT变换,测得每块的中高频分量的SSIM值作为乘性系数与前面所得结果相乘,最后对彩色图像各个通道分别进行加权相加。实验结果表明,算法与人眼主观感受值更加吻合。     

Multiresolution color image segmentation

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov random fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed     

Multiresolution image dynamic thresholding

A pyramid-based method of dynamic thresholding where the Gaussian pyramid is used to support a coarse-to-fine search strategy is presented. At the top level of the pyramid the image is divided into four subimages, and in each subimage, the gray-level variance is analyzed to find whether there is an edge. The hierarchical search is continued until the bottom level of the pyramid, or the original image, is reached. At the bottom level the threshold values of those subimages where an edge is present are estimated, and the value of zero is assigned to those subimages where no edge is present. Finally, by using interpolations of subimage and pixel threshold values, the dynamic threshold values are found.     

An HVS based adaptive quantization scheme for the compression of color images

In this paper a Human Visual System based adaptive quantization scheme is proposed. The proposed algorithm supports perceptually lossless as well as lossy compression. The algorithm uses a transform based compression approach using the wavelet transform, and has incorporated vision models for the compression of both luminance and chrominance components. The major strength of the coder is the incorporation of the vision model for the chrominance components and the optimum way in which the scales are distributed among the luminance and chrominance components to achieve higher compression ratios. The perceptual model developed for the color components gives flexibility for giving more compression for the color components without causing any color degradations. For each image the visual thresholds are evaluated and an optimum bit allocation is done in such a way that the quantization error is always less than the visual distortion for the given rate. To validate the strength of the proposed algorithm, the perceptual quality of the images reconstructed using the proposed coder is compared with the images reconstructed with JPEG2000 standard coder, for the same compression. To evaluate the perceptual quality of the compressed images latest perceptual quality matrices such as Structural Similarity Index, Visual Information Fidelity and Visual Signal-to-Noise Ratio are used. The results obtained reveal that the proposed structure gives excellent improvement in perceptual quality compared to the existing schemes, for both lossy as well as lossless compression. These advantages make the proposed algorithm a good candidate for replacing the quantizer stage of the current image compression standards.     

基于人眼视觉特性的X线图像质量评价方法

图像质量评价是图像处理技术的基础.由于图像处理结果的最终接受者是人,因此在评价图像质量时必须考虑到人类视觉系统的特性.为此利用小波的多分辨率分析方法与人眼视觉系统(HVS)信息处理特性相类似的特点,结合对比敏感度函数的带通特性,提出了一种无参考的客观图像质量评价方法.实验结果表明,该评价方法优于传统的质量评价方法,而且能较好地与人的主观视觉感受保持一致.