Reversibility of image with balanced fidelity and capacity upon pixels differencing expansion

Reversible data hiding has attracted considerable attention in recent years. Being reversible, the decoder can extract hidden data and recover the original image completely, and the difference expansion (DE) scheme can lead to a lossless pixel after secret data exacting. Furthermore, despite achieving pixel reversibility based on the concept of expanded differencing, the difference expansion scheme can cause enormous image distortion because of the size of the difference. The proposed scheme in this paper describes a novel prediction for achieving predictive error based reversible data hiding by considering the relation between a pixel and its neighboring pixel and using the predictor to identify the projected difference in pixel value. Experimental results show that the proposed scheme is capable of providing great embedding capacity without causing noticeable distortion by selecting the minimal predictor based on pixel expansion. In multilevel cases, this proposed method performs better than other existing methods. Moreover, the proposed scheme is able to pass the Chi-square test, a test used to find whether an image utilizes LSB for data hiding.     

基于RGB相关性的彩色图像可逆信息隐藏方法

为了提高彩色图像可逆信息隐藏（RDH）的性能,提出了基于RGB相关性的彩色图像RDH方法。首先为了更加准确地预测像素值,设计了基于RGB相关性的像素预测算法。该算法包括粗糙纹理预测和精细纹理预测两个阶段。第一个阶段利用一个参考通道将像素分为平滑区域和纹理区域。在第二阶段,利用两个参考通道对平滑区域像素进行精细划分,将像素分为水平、垂直和平坦区域,从而更加准确地计算像素的预测值。其次,利用预测误差扩展技术嵌入秘密信息。实验结果表明提出的预测算法能够较准确地预测每一个通道的像素值,信息隐藏性能好于现有的彩色图像RDH方法。     

基于像素值排序与块再分的可逆数据隐藏算法

基于像素值排序的可逆数据隐藏算法通过修改图像块中的最大像素和最小像素嵌入数据,但并未充分利用图像块内的每一个像素,从而影响嵌入性能。结合块再分原理,提出基于像素值排序的可逆数据隐藏算法。将原始图像划分为3×3的图像块,计算每一个图像块的局部复杂度。设计12种分块模式将局部复杂度小于阈值的图像块细分为子块A和B。根据子块A和B的不同局部特征分别采用2种不同的扫描顺序读取像素。子块A的像素序列使用次小值预测最小值和次大值预测最大值的方法,获得2个预测误差值,子块B的像素序列利用中值像素连续预测其余4个像素的方法,得到4个预测误差值。在此基础上,利用图像块中预测误差值为0和1的像素嵌入隐藏数据。实验结果表明,该算法在一个图像块中最高可嵌入6 bit的数据,在较低计算复杂度的情况下能够有效提高像素的嵌入性能。     

基于各向异性扩散方程的并行图像去噪研究

各向异性扩散方程是一种非线性PDE模型,在图像去噪中,通过非线性扩散因子来滤除噪声,同时能保留原有的边缘和纹理。但是当图像很大时,求解PDE的差分运算量将很大,满足不了实时系统的要求。针对该模型,在MPI并行编程环境下,利用图像像素的独立性和PDE求解的并发性,采用并行方式对图像去噪,在保证去噪性能的同时,极大地降低计算时间。     

基于灰预测模型的边缘检测新方法

提出了一种基于灰预测理论的边缘检测新方法。该方法基于GM(1,1)模型，根据图像不同区域间灰度级有较大变化的特性，由同行(列)待检测像素之前的若干像素灰度值建立灰预测模型，并利用模型预测此像素灰度值，比较预测灰度值与实际灰度值之间的差值，由差值来判断该像素是否为边缘像素。通过对不同类型测试图像利用本算法进行边缘检测效果比较表明，该算法能取得比较好的效果。     

Inter-view local texture analysis based stereo image reversible data hiding

To enhance security of three-dimensional images, an inter-view local texture analysis (ILTA) based stereo image reversible data hiding method is presented. Due to low accuracy of existing predictors, two novel predictors are proposed to improve the prediction precision. In the first predictor, a texture analysis model is built by using ILTA, in which the texture similarity between a pair of matched pixels in the stereo image is used to classify pixels into horizontal texture, vertical texture, smooth and complex types. Thus, the accurate prediction is adaptively computed by considering the pixel type. Moreover, an intra-view based predictor as the second predictor is also described to predict pixels by optimal weights finding (OWF). Since ILTA and OWF predictors are combined to predict pixels in the stereo image, sharp prediction error histograms of two views are both constructed, and then multi-level histogram shifting is used to embed secret data reversibly for obtaining low image distortion and high embedding capacity. Experimental results demonstrates that ILTA and OWF predictors can obtain precise predicted values, and the proposed data hiding method outperforms some state-of-the-art data hiding methods in terms of embedding capacity and quality of stego stereo image.     

A fuzzy logic-based predictor for predictive coding of images

In this paper, we present a fuzzy logic-based nonlinear predictor for predictive coding of images. We define five local structure patterns of images: uniform area, horizontal contour (0°), vertical contour (90°), 45°, and 135° diagonal contours. Their membership functions are derived with the gradient-based edge detection method and predicted values for different patterns are defined by linear extrapolation from available neighborhood pixel values. The predicted value of the current pixel can be obtained based on the membership functions and the defined predicted values for the different patterns. A set of parameters to characterize the proposed fuzzy predictor are determined from empirical data. Success in the use of the proposed predictor is demonstrated, by using simulation results through the reduction in the entropy as compared to those of existing linear and nonlinear ones. It is also shown that the proposed fuzzy predictor can be efficiently implemented     

一种基于像素预测的图像可逆信息隐藏策略

基于像素点预测的可逆信息隐藏(Reversible Data Hiding,RDH)是当今一种低失真、高容量的信息隐藏策略,特别是对于差值扩展和直方图平移算法而言,准确预测可以同时提高数据容量和减小图像失真。文中提出了一种基于像素预测差的直方图平移方案。首先,采用Warped Distance算法来对像素值进行预测,并在此基础上利用图像的局部梯度来实现对像素值的更精确预测。综合上述两种策略,改进了现有的直方图平移算法,同时也给出了防止像素值在平移后溢出的建议。实验结果表明,相对于近年来的其他方案,该方案能够在保证图像质量的情况下有效提升数据嵌入量,并且通过调整数据嵌入层级,可以在具体使用该算法时针对图像质量和数据嵌入量进行权衡。这也进一步说明了利用图像的局部特性,特别是梯度与图像局部几何相似性,可以有效提升像素预测的精度,从而改善可逆信息隐藏的容量-失真性能。     

Multiple predictors hiding scheme using asymmetric histograms

In recent years, many data hiding techniques have been proposed, and they can be generally classified into two types according to the reversibility of the image; these two types are reversible and irreversible data hiding. This study focused on reversible data hiding, which makes recovering the cover image possible after the secret data has been extracted. In 2013, Chen et al. proposed an asymmetric-histogram reversible data hiding method. In their scheme, two prediction error histograms (maximum and minimum error histograms) were used to embed the secret message. Two histograms were shifted in opposite directions. Hence, some stego-pixels were shifted to their original values. The complementary embedding strategy is effective. However, the predictor in the method is rough. Only neighboring pixels were used to generate the prediction errors, thereby resulting in poor prediction efficiency. To enhance the prediction efficiency, this paper combines several well-known predictors such as gradient adjusted gap (GAP), median edge detect, and interpolation by neighboring pixel (INP) to generate prediction errors. Different predictors along with the asymmetric-histogram method can achieve better results. The predictor GAP used more neighboring pixels to obtain the prediction value; therefore, it is suitable for complex images. However, the predictor INP only considers that closer pixels can achieve great results for smooth images. Hence, the proposed scheme combines GAP and asymmetric histogram for complex images. However, the predictor INP along with asymmetric histogram is used for smooth images. Experimental results showed that the PSNR value of the proposed method is greater than that of the asymmetric-histogram shifting method and other recent approaches.     

基于K-SVD的偏微分方程模型在毫米波图像恢复中的应用

在图像被大噪声污染或具有较低分辨率时,传统的偏微分方程(PDE)模型的稳态解会产生明显的阶梯效应,恢复图像质量较差。针对此缺点,提出了一种新的基于K-奇异值分解(K-SVD)的PDE图像恢复方法,并应用于毫米波(MMW)图像的恢复。K-SVD是一种图像稀疏表示方法,对图像进行稀疏估计的同时实现去噪,对噪声方差较大的图像具有较好的去噪鲁棒性。首先采用K-SVD对MMW图像进行去噪,对去噪图像再应用全变分(TV)模型的PDE方法进行恢复。对所提出的算法分别使用模拟的MMW图像和真实的MMW图像进行测试,并进一步和K-SVD、PDE方法比较,同时使用峰值信噪比(PSNR)对恢复图像进行评价。根据不同噪声方差下的PSNR数据和恢复图像的视觉效果,实验结果证明了所提方法能够有效地恢复MMW图像。     

根据最佳预测模式概率进行帧内预测的快速方法

为了减少视频编码中帧内预测的计算量，提出一种称为AOPDE的快速算法．该算法利用H．26L中帧内预测编码的最佳预测模式概率模型来自适应调整预测过程，结合PDE方法，达到加快帧内图像编码速度的目的．基于H．26L参考模型TML8的实验结果表明，文中提出的算法具有很好的效果．     

Reversible watermarking based on multiple prediction modes and adaptive watermark embedding

A new reversible watermark scheme based on multiple prediction modes and adaptive watermark embedding is presented. Six prediction modes fully exploiting strong correlation between any pixel and its surrounding pixels, are designed in this paper. Under any prediction mode, each to-be-predicted pixel must be surrounded by several pixels (they constitute a local neighborhood, and any modification to this neighborhood is not allowed in the embedding process). This neighborhood has three main applications. The first one is that when it is exploited to interpolate some to-be-predicted pixel, the noticeable improvement in prediction accuracy is obtained. The second one is that its variance is employed to determine which classification (i.e., smooth or complex set) its surrounded pixel belongs to. For any to-be-predicted pixel, the number of embedded bits is adaptively determined according to this pixel’s belonging. The last one is that we can accurately evaluate the classification of watermarked pixels by analyzing the local complexity of their corresponding neighborhoods on the decoding side. Therefore, the payload can be largely increased as each to-be-predicted pixel in the smooth set can possibly carry more than 1 bit. Meanwhile, the embedding distortion is greatly controlled by embedding more bits into pixels belonging to smooth set and fewer bits into the others in complex set. Experimental results reveal the proposed method is effective.     

Sparse recovery based reversible data hiding method using the human visual system

In this paper, a novel sparse recovery based reversible data hiding (RDH) method using the human visual system (HVS) is presented. To improve the low accuracy of existing predictors, a sparse recovery based predictor is proposed. In the processes of sparse recovery, the most relevant neighbors can be adaptively chosen by using sparse representation to predict the current pixel accurately, and thus the concentrated prediction error histogram (PEH) is built to obtain good embedding performance. Moreover, to overcome the conflict between the embedding order of the traditional RDH method and the evaluation of HVS, a new embedding strategy based on just noticeable difference (JND) is designed. In this strategy, pixels are classified into sensitive and in-sensitive clusters according to JND values, and two corresponding PEHs are built. Accordingly, different inner regions of two PEHs are adjusted to meet the required embedding capacity, and the prediction error expansion (PEE) technique is utilized to embed data. Experimental results prove that the proposed method outperforms the state-of-the-art RDH methods, including JND related methods.     

用于图像预测编码的预测器的设计及应用

设计并实现了一个用于图像预测编码的预测器。在图像压缩中,预测器是影响整个图像压缩比以及图像质量的关键环节。为了提高预测器的性能,本文结合了线性与非线性预测器的设计方法,首先对图像像素及其邻域像素之间的相关性估计值进行统计计算,确定该预测器的最佳阶数为四阶,接下来以预测差值的最小均方差标准和差值图像的期望值恒为零的约束为前提,同时对邻域模板的平坦程度加以分类,统计分析各类图像得出大量实验数据,最终设计出性能较好的四阶预测器。由于该预测器应用于星载图像的压缩,其预测系数在设计时兼备了便于硬件实现的特点。     

基于像素纹理分类的图像预测编码方法

JPEG-LS标准实现对静止图像的无损压缩以及近无损高保真压缩,其预测编码仅采用简单的中值边缘检测法。将充分利用邻域像素纹理的连续性与相关性,研究参考像素的选取、基于纹理信息的非线性分类预测器的构建与预测器参数的设计,增强梯度检测能力,提出新的四阶分类预测器。实验证明,该算法在低运算复杂度的前提下,有效提高了预测编码的性能。     

基于前馈上下文和形状先验的平面标注方法

针对真实场景中由于互相遮挡导致的场景语义不能完全被理解的问题,提出了一种基于前馈上下文和形状先验的方法来对前景区域和被遮挡的背景区域进行语义标注。首先,将原始图像分割成超像素并提取像素点特征,采用加速决策树方法标注前景,同时采用改进的基于多尺度可形变的部件模型方法进行目标检测。其次,将可见对象信息与前馈上下文预测相结合来推测背景区域的被遮挡部分。然后,根据与当前标签置信度相匹配的多边形为每个标签提供形状先验知识。最后,结合像素预测与可视平面预测和多边形知识,以形成完整的场景标注图像。与现有方法相比,该方法能够得到与街道场景更相符的结果,并在人行道和公路较接近时的标注效果更好。     

基于LASSO的可逆图像水印算法

对于采用差值扩展-直方图平移的可逆水印算法,提高预测的准确度有利于减小预测误差,从而在同等嵌入失真时获得更大的嵌入容量。为了进一步提高图像像素预测的准确度,构造了一种基于LASSO （Least Absolute Shrinkage And Selection Operator）的局部预测算法。具体而言,根据图像存在边缘、纹理方向的特点,将图像像素预测问题表征为基于LASSO的优化问题;然后通过优化求解得到预测系数,进而得到预测误差;随后利用预测误差,结合差值扩展-直方图平移嵌入技术设计可逆图像水印算法。实验仿真结果表明,与当前预测性能较好的、基于最小二乘局部预测的可逆图像水印算法相比,所提算法在嵌入相同的数据时拥有更高的峰值信噪比（PSNR）。     

Single frame correction of motion artifacts in PMD-based time of flight cameras

One of the leading time of flight imaging technologies for depth sensing is based on Photonic Mixer Devices (PMD). In PMD sensors each pixel samples the correlation between emitted and received light signals. Current PMD cameras compute eight correlation samples per pixel in four sequential stages to obtain depth with invariance to signal amplitude and offset variations. With motion, PMD pixels capture different depths at each stage. As a result, correlation samples are not coherent with a single depth, producing artifacts. We propose to detect and remove motion artifacts from a single frame taken by a PMD camera. The algorithm we propose is very fast, simple and can be easily included in camera hardware. We recover depth of each pixel by exploiting consistency of the correlation samples and local neighbors of the pixel. In addition, our method obtains the motion flow of occluding contours in the image from a single frame. The system has been validated in real scenes using a commercial low-cost PMD camera and high speed dynamics. In all cases our method produces accurate results and it highly reduces motion artifacts.     

基于降低映射预测残差的高光谱图像压缩算法

CCSDS 123.0-B-1算法是空间数据系统咨询委员会为多/高光谱图像提出的自适应三维预测无损压缩标准,针对CCSDS 123.0-B-1算法中存在的未充分利用像素位置信息及谱间相关性、压缩率有待提高的问题,对该算法的预测器进行了优化,提出了RMPR算法。RMPR算法根据当前像元具体位置对预测点进行自适应选择,采用双向线性预测去除高光谱图像的谱间相关性,并使用优化的残差映射器提高预测精度、缩短压缩码长。利用10幅高光谱图像进行测试,结果表明,在保证无损压缩且压缩效率无显著差异的前提下,RMPR算法的压缩性能显著优于原算法。     

A high capacity reversible data hiding scheme with edge prediction and difference expansion

To enhance the embedding capacity of a reversible data hiding system, in this paper, a novel multiple-base lossless scheme based on JPEG-LS pixel value prediction and reversible difference expansion will be presented. The proposed scheme employs a pixel value prediction mechanism to decrease the distortion caused by the hiding of the secret data. In general, the prediction error value tends to be much smaller in smooth areas than in edge areas, and more secret data embedded in smooth areas still meets better stego-image quality. The multiple-base notational system, on the other hand, is applied to increase the payload of the image. With the system, the payload of each pixel, determined by the complexity of its neighboring pixels, can be very different. In addition, the cover image processed by the proposed scheme can be fully recovered without any distortion. Experimental results, as shown in this paper, have demonstrated that the proposed method is capable of hiding more secret data while keeping the stego-image quality degradation imperceptible.