一种基于自适应分块八叉树颜色量化的图像压缩技术

颜色量化(CQ)是减少图像颜色数量的过程,已广泛用于图像压缩。基于八叉树的颜色量化(OCQ)因其编码效率高、内存使用低和调色板选择效果良好而被认为是最流行的CQ算法之一。然而,OCQ应用的一个严峻挑战是如何有效地管理关键的本地颜色。提出了一种基于分块的自适应八叉树颜色量化(AB-OCQ)算法,实验结果表明,与传统的OCQ算法相比,由于增加了对局部颜色的适当处理,AB-OCQ可以显著提高图像质量。在图像压缩比方面,AB-OCQ的综合性能也优于OCQ的。同时,和主流图像文件格式相比,AB-OCQ算法可以在保持压缩的前提下拥有随机访问图像像素数据的特性,该特性能让应用程序在同等内存下存储更多的图像数据,为提高应用程序的效率提供了一种方法。     

基于模糊聚类分析和HVS的自适应彩色图像水印算法

结合模糊C均值聚类算法和人眼视觉特性,提出了一种新的自适应彩色图像水印算法。首先,将彩色图像经模糊聚类分析,选取出适合于水印嵌入的位置;然后,分别在R、G、B 3个通道中利用小波域的视觉掩蔽特性自适应地修改水印嵌入强度,提取时,不需要用到原始图像。实验结果证明,含水印的彩色图像没有出现任何可感知的视觉失真,同时,该算法对一定的图像处理操作具有较强的鲁棒性。     

A semantic compression scheme for digital images based on vector quantization and data hiding

Multimedia Tools and Applications - A novel semantic compression based on vector quantization (VQ) and data hiding is proposed. A compact version of the original image is generated, and then, VQ...     

基于HVS和小波变换的彩色图像水印算法

提出了一种将彩色水印图像嵌入到原始彩色图像中的数字水印算法.该算法对水印的加密采取了Arnold结合矩阵变换的方法,并利用人类视觉系统特性,将水印信息嵌入到载体图像多级小波分解后的中低频奇数行奇数列系数中.实验结果表明,该算法在对嵌入水印后的图像进行剪切、不规则色块污染、添加各种噪声等处理后,提取的水印表现出良好的鲁棒性,且主观视觉失真较小.     

彩色图像颜色量化问题的求解方法

针对K-means聚类算法在彩色图像颜色量化问题中对初始条件依赖性较强而易陷入局部最优的缺点,以及传统智能优化算法在寻优时只考虑了种群层内个体的相互竞争而忽略种群层间相互协作的问题,提出了一种基于K-means的金字塔结构演化策略（PES）彩色图像量化算法。首先,将K-means聚类算法中的聚类损失函数作为新算法的适应度函数;其次,运用PES对色彩进行种群初始化、分层、探索、加速以及聚类等操作;最后,利用新算法对4幅标准彩色测试图像进行不同色彩量化级的量化。实验结果表明,所提算法能够改善K-means聚类算法以及传统智能算法的上述缺陷,在类内均方误差评判准则下,图像的平均失真率比基于PES的算法低12.25%,比差分进化算法低15.52%,比粒子群优化（PSO）算法低58.33%,比K-means算法低15.06%,且随着色彩量化级的减少,算法量化后的图像失真率比其他算法降低更多,此外,算法量化图像的视觉效果优于其他算法。     

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.     

基于MCM和HVS的彩色图像盲隐写分析算法

针对典型算法在描述DCT域相关性方面存在的不足,所引起的隐写检测综合性能不理想的问题,将MCM与HVS进行有机结合,提出一种JPEG彩色图像通用隐写分析算法。设计8-邻域MCM型,全面描述DCT系数之间的相关性,基于HVS分别在YCbCr模型空间Y分量抽取Markov状态转移矩、在其相应RGB三通道抽取Markov状态转移矩的主对角线邻域相似熵作为特征统计量,并进行合理“绑定”,采用PCA技术对其进行优化选择,构建高效分类特征向量。实验结果表明该算法对于Jsteg、F5、Outguess、MB1、MB2攻击具有较高的可靠性和检测正确率。     

An adaptive watermarking scheme for e-government document images

This paper proposes an adaptive watermarking scheme for e-government document images. The adaptive scheme combines the discrete cosine transform (DCT) and the singular value decomposition (SVD) using luminance masking. As a core of masking model in the human visual system (HVS), luminance masking is implemented to improve noise sensitivity. Genetic algorithm (GA), subsequently, is employed for the optimization of the scaling factor of the masking. Involving a number of steps, the scheme proposed through this study begins by calculating the mask of the host image using luminance masking. It is then continued by transforming the mask on each area into all frequencies domain. The watermark image, following this, is embedded by modifying the singular values of DCT-transformed host image with singular values of mask coefficient of host image and the control parameter of DCT-transformed watermark image using Genetic Algorithm (GA). The use of both the singular values and the control parameter respectively, in this case, is not only to improve the sensitivity of the watermark performance but also to avoid the false positive problem. The watermark image, afterwards, is extracted from the distorted images. The experiment results show the improved adaptive performance of the proposed scheme is in resistant to several types of attacks in comparison with the previous schemes; the adaptive performance refers to the adaptive parameter of the luminance masking functioned to improve the performance or robustness of an image from any attacks.     

A fuzzy algorithm for color quantization of images

In this paper, we review a number of techniques for fuzzy color quantization. We show that the fuzzy membership paradigm is particularly suited to color quantization, where color cluster boundaries are not well defined. We propose a new fuzzy color quantization technique which incorporates a term for partition index. This algorithm produces better results than fuzzy C-means at a reduced computational cost. We test the results of the fuzzy algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial quality improvements are achieved.     

An adaptive HVS based video watermarking scheme for multiple watermarks using BAM neural networks and fuzzy inference system

An efficient reversible adaptive video watermarking scheme for multiple watermarks based on Bi-directional Associative Memory (BAM) Neural Networks and Fuzzy Inference System namely, Multi-BAM-FUZ scheme is proposed in this paper. The main goal of this paper is to design a robust video watermarking system which facilitates secure video transmission over a communication channel by maintaining a trade-off among imperceptibility, robustness and watermark capacity or payload. The BAM neural network supports creation of weight matrix (formed out of multiple images) and this matrix is embedded into the DWT uncorrelated mid frequency coefficients of all the components (Y, Cb, Cr) of every frames of the video with varying embedding strength ‘α’. This adaptive embedding strength is generated using the Fuzzy Inference System which takes HVS characteristics such as luminance, texture and edge of each frame as an input in the DWT transform. The simulations performed on various test videos demonstrate that the proposed Multi-BAM-FUZ not only outperforms other existing methods with respect to various video degradation processes, but also maintains a satisfactory image quality, robustness and payload. It is noted that, the implementation of the novel adaptive process enhances the visual quality of about 60.97 dB in terms of PSNR and 0.9998 in terms of SSIM, robustness of about nearly 1.0000 and 0.9999 in terms of Normalized Cross Correlation (NCC) value and Bit Correction Rate (BCR) respectively against various attacks. Moreover, the proposed scheme facilitates high level of payload without affecting the imperceptibility and robustness level.     

基于ETRN具有任意压缩率的彩色加密图像有损压缩

当前大多数的先加密后压缩ETC（encryption-then-compression）方法只能够获得有限固定的压缩率,而无法获取到实际需求的任意压缩率。针对此问题提出一种具有任意压缩率的加密彩色图像有损压缩算法,该算法采用均匀下采样和随机下采样有机结合的方式对加密图像进行压缩,以获得加密图像的任意压缩率。接收方接收到加密图像的压缩序列后通过解压解密获得解密图像,随后把从解密图像有损重构原始图像的过程表征为一个结合下采样压缩方式约束的最优化问题,并设计一种基于卷积神经网络的有损ETC系统图像重构模型ETRN（ETC-oriented reconstruction network）来求解该优化问题。ETRN模型包含浅层特征提取层SFE（shallow feature extraction）、残差堆叠模块RIR（residual in residual）、残差信息补充模块RCS（residual content supplementation）、下采样约束模块DC（down-sampling constraint）。实验仿真结果表明,提出的加密彩色图像有损压缩算法能够获得优秀的加密压缩和重构性能,充分体现了该方法的可行性和有效性。     

基于HVS和量化的自适应盲水印算法

提出了一种基于HVS和量化的自适应盲水印算法,基于HVS的视觉模型使用亮度掩蔽系数,纹理掩蔽系数,频率掩蔽系数来自适应地调整水印的嵌入强度。算法选择在DCT域,通过用JPEG量化表量化DCT系数,比较量化后的DC系数|DC|与选定的量化后的AC系数|AC|之间的关系来嵌入和提取水印,并实现了水印的盲检测。由于DCT系数基于JPEG量化表来量化,对JPEG压缩具有很好的抵抗能力,对叠加噪声、模糊等常规的图像操作也具有很好的鲁棒性。实验结果证明,该算法是一种行之有效的方法。     

An adjustable algorithm for color quantization

Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization.     

A multisecret sharing scheme for color images based on cellular automata

In this work a new multisecret sharing scheme for secret color images among a set of users is proposed. The protocol allows that each participant in the scheme to share a secret color image with the rest of participants in such a way that all of them can recover all the secret color images only if the whole set of participants pools their shadows. The proposed scheme is based on the use of bidimensional reversible cellular automata with memory. The security of the scheme is studied and it is proved that the protocol is ideal and perfect and that it resists the most important statistical attacks.     

一种基于IWT与自适应预测的无损压缩方案

提出一种基于IWT与自适应预测相结合的无损图像压缩方案IWTAP。详细阐述了该方案的理论依据,在此基础上,结合矩阵理论和低频子带的自相关模型,给出了自适应预测参数的简化计算公式。编码时,每次IWT后,用低频子带改进高频子带一次,改进时所需的参数依据低、高频子带的特征自适应确定;解码时,先进行高频子带还原,再进行逆IWT。实验结果表明,该方法能有效降低高频子带的信息熵,降低图像的无损压缩比特率,计算复杂度上升不大。     

An improved reversible data hiding scheme using best neighboring coding based on color images

Multimedia Tools and Applications - In this paper, an improved reversible data hiding (RDH) scheme, best neighboring coding (BNC), is proposed for vector quantization (VQ) compressed color images....     

矢量量化用于颜色图像检索的改进方法

为了消除基于颜色的图像检索在颜色空间分布描述方向的不足,提出一种新的基于颜色的检索方法。引入颜色转移矩阵描述颜色的空间分布,再结合颜色直方图和颜色转移矩阵进行复合图像检索。同时通过矢量量化方法量化图像颜色得到颜色直方图和颜色转移矩阵,实现了在压缩领域进行图像检索,减少了额外计算负担。实验表明,该方法能有效提高检索效率和精度。     

An efficient color quantization based on generic roughness measure

Color quantization is a process to compress image color space while minimizing visual distortion. The quantization based on preclustering has low computational complexity but cannot guarantee quantization precision. The quantization based on postclustering can produce high quality quantization results. However, it has to traverse image pixels iteratively and suffers heavy computational burden. Its computational complexity was not reduced although the revised versions have improved the precision. In the work of color quantization, balancing quantization quality and quantization complexity is always a challenging point. In this paper, a two-stage quantization framework is proposed to achieve this balance. In the first stage, high-resolution color space is initially compressed to a condensed color space by thresholding roughness indices. Instead of linear compression, we propose generic roughness measure to generate the delicate segmentation of image color. In this way, it causes less distortion to the image. In the second stage, the initially compressed colors are further clustered to a palette using Weighted Rough K-means to obtain final quantization results. Our objective is to design a postclustering quantization strategy at the color space level rather than the pixel level. Applying the quantization in the precisely compressed color space, the computational cost is greatly reduced; meanwhile, the quantization quality is maintained. The substantial experimental results validate the high efficiency of the proposed quantization method, which produces high quality color quantization while possessing low computational complexity.