基于纹理复杂度和差分的抗盲检测图像隐写算法

通过分析纹理复杂度对图像隐写分析的影响,提出一种基于纹理复杂度和像素差分的隐写算法。首先,将图像划分为大小相同的分块,通过分块中起点位置的不同计算分块中不同小块的纹理复杂度,选择复杂度最高的图像小块;然后,计算小块的相邻像素差分值,并根据秘密消息的长度和不同方向上的差分值分布确定嵌入阈值,在大于该阈值的差分值中嵌入秘密消息。对多种嵌入算法在不同图像库上做通用盲检测实验,实验结果表明,该算法在抵抗小波高阶统计量分析等盲检测算法能力方面,优于LSB匹配、图像边缘自适应嵌入等隐写算法。     

基于视觉感知与菱形编码的图像隐写算法

为提供较大的隐写容量和保持良好的载密图像质量,提出一种基于视觉感知和菱形编码的图像隐写算法。根据人类视觉系统,针对人眼对图像的高中低灰度区、纹理区及平滑区的敏感度的不同,在不同区域选择嵌入不同数量的秘密信息;同时为了尽可能减少对图像像素值的改变,采用改进的菱形编码来嵌入秘密信息。实验结果表明,与HVS-OPAP算法相比,该算法图像的嵌入容量提高了4.09%,获取的隐写图像具有更好的不可感知性,同时峰值信噪比平均提高了1.5dB,而且该算法具有抗RS隐写分析能力,安全性更高。     

一种用于隐写测评的图像纹理复杂度估计方法

在评估图像隐写算法安全性及隐写分析性能时,隐写嵌入率等参数是重要的评价指标,而图像纹理差异对评价的影响很容易被忽略。为此,基于图像高维统计模型,提出一种新的针对隐写应用的图像纹理复杂度度量方法,用于衡量图像间的纹理差异。根据纹理复杂度对图像进行分类,单独在每一类不同纹理复杂度的图像集上评估隐写分析的检测性能。实验结果表明,该方法能有效地评价隐写的安 全性。     

抗统计分析的BPCS隐写算法

位平面复杂度分割（BPCS）隐写在复杂度较高的位面小块嵌入秘密信息,有较好的隐蔽性,但会引起图像位面小块复杂度的统计特性的变化。通过深入分析图像位平面上相邻像素之间的相关性,本文提出了一种改进的BPCS隐写法。它更好地利用人的视觉特性,结合图像的局部特征来嵌入秘密信息。实验结果表明,改进后的算法能够抵御针对原BPCS隐写的分析方法,有较大的嵌入量,并对原图像所作的改变具有更好的不可见性。     

基于视觉特性的JPEG图像自适应隐写算法

提出一种基于视觉特性的图像隐写算法。该算法利用亮度和纹理阈值等人类视觉系统特性,并结合图像各个局部分块的亮度与纹理特征,将图像划分为强嵌入区、次嵌入区、弱嵌入区,计算各个局部分块亮度和纹理,分别嵌入不同比特数的秘密信息。实验结果表明,该算法能够增加图像秘密信息的嵌入容量,提高抗检测性能,以及保持较高的峰值信噪比。     

像素差自适应隐写方案*

提出了一种新的基于相邻像素差的自适应隐写算法。该算法结合图像位平面性质以及人眼对像素值起伏剧烈的区域敏感性较差的视觉特性,根据相邻像素高五位比特值之差的不同选择不同的嵌入策略,通过改变每对像素的最低三位比特值实现信息嵌入。通过理论分析和实验证明,该算法具有较高的嵌入容量,具有良好的隐写安全性,同时算法复杂度低,实现简单。     

基于图像复杂度的数字水印算法

对图像复杂度进行了分析,结合人眼视觉系统,提出一种基于图像复杂度的数字水印算法.该算法根据载体图像灰度共生矩阵的图像熵对载体图像的纹理特征区域进行划分,按照不同的纹理特征将载体图像的DCT(discrete cosine transform)块分成3类分别进行嵌入,并利用Watson视觉模型控制水印嵌入强度.仿真实验结果表明,本文算法不仅具有较大的水印容量和良好的遮蔽性,而且对噪声、滤波、有损压缩、剪切等常见攻击具有较好的鲁棒性.     

基于边缘复杂度和插值法的隐写算法

为了在提高隐写容量的同时保持较好的不可感知性, 依据人眼对图像边缘区域变化不敏感的特性, 提出一种新的自适应隐藏算法。该方案完善了图像边缘复杂度的描述方法, 利用边缘检测理论计算出像素的嵌入强度, 然后通过改进的插值法, 根据嵌入强度, 对图像像素进行插值, 构造出插值点与基准点之间的冗余空间, 从而嵌入相应数据量的秘密信息。实验结果表明, 与传统算法相比, 该方法提高了隐写容量并具有较好的隐蔽性。     

使用四叉树分割进行自适应空域隐写

目的 针对自适应隐写术可有效避免对载体敏感区大量修改的关键问题,为间接提高安全性和增大隐写容量,在四叉树分割和自适应像素对匹配（APPM）的基础上提出一种自适应空域隐写术。方法 首先该方法以图像块的纹理复杂度作为一致性测度并且设置图像块大小为判别准则进行图像分割,根据四叉树分割结果中面积较小的图像块属于复杂区域,较大的属于平滑区域,按照图像块面积大小将图像分成由高复杂、中复杂、低复杂三大区域构成。其次嵌密方式采用APPM,根据密信容量和载体图像选择进制数B。最后,为了保证安全性和提高容量,优先选择高复杂区嵌入不低于B进制的密信,在中复杂区进行B进制的密信嵌入,在低复杂区选择不高于B进制的密信嵌入。结果 为了验证提出的方法,选8幅经典图作为实验,在嵌入率1.92 bit/pixel的情况下,与已有PVD系列算法和DE算法相比具有更高的PSNR值,PSNR值高达48 dB。此外与APPM算法比较,在嵌入率2.5 bit/pixel情况下,该算法的平均KL距离相比传统APPM算法减小了25.37%,平均一阶Markov安全指标值相比传统APPM算法减小了12.11%,对应的平均PSNR值相比传统APPM算法提高0.43%,在嵌入率1.5 bit/pixel情况下,该算法的平均KL距离相比传统APPM算法减小了37.84%,平均一阶Markov安全指标值相比传统APPM算法减小了26.61%,对应的平均PSNR值相比传统APPM算法提高1.56%。此外,从RSP图库中随机选1 000幅图作为数据集,在嵌入率0.5,0.6,0.7,0.8,0.9和1.0 bit/pixel条件下,结合SPAM特征和SVM分类器的最小平均错误率均高于LSB系列经典算法和APPM算法。结论 1）考虑了人类视觉系统对图像不同区域的敏感性不同,通过对图像进行四叉树分割预处理,优先选择非敏感区进行隐写,保证了一定的安全性要求,低嵌入率下抗SPAM检测和统计不可见性方面比较有优势。2）在四叉树分割中,对于隐写前后图像的四叉树分割结果不同的异常情况,采用一种图像块纹理复杂度调整方案,保证了密信正确完整提取。3）利用了APPM算法的大容量特性,可以隐写嵌入率大于1 bit/pixel的密信,比较适用于大容量的密信隐写,而且可以嵌入任意进制的密信,最大程度地减少嵌入失真,此外,进行了四叉树分割预处理,在安全性方面优于传统APPM算法。     

结合纹理复杂度和JND模型的图像水印算法

针对目前基于灰度共生矩阵相关水印算法存在容量较小的问题,提出一种将纹理复杂度和DCT域JND模型相结合的图像水印算法。首先将原图像分成子块,利用各子块灰度共生矩阵的四个纹理特性计算其纹理复杂度,并据此对子块进行排序以确定水印嵌入的子块位置后对原图像素矩阵进行DCT变换,结合新的分区方式计算各块JND值,根据JND值以及新的嵌入规则确定子块内水印嵌入方式。算法有效考虑了图像块的纹理特性和人眼视觉敏感性,加强了嵌入水印后的图像质量,提高了水印嵌入容量。实验结果表明,所提方法在嵌入相同容量的水印时,图像平均峰值信噪比（PSNR）较现有方法高出4.27%左右,在嵌入超过原方法容量上限一倍的水印时,图像平均PSNR仍有53.449 8 dB。     

Effects of increasing visual load on aurally and visually guided target acquisition in a virtual environment

The aim of the present study is to investigate interactions between vision and audition during a target acquisition task performed in a virtual environment. We measured the time taken to locate a visual target (acquisition time) signalled by auditory and/or visual cues in conditions of variable visual load. Visual load was increased by introducing a secondary visual task. The auditory cue was constructed using virtual three-dimensional (3D) sound techniques. The visual cue was constructed in the form of a 3D updating arrow. The results suggested that both auditory and visual cues reduced acquisition time as compared to an uncued condition. Whereas the visual cue elicited faster acquisition time than the auditory cue, the combination of the two cues produced the fastest acquisition time. The introduction of secondary visual task differentially affected acquisition time depending on cue modality. In conditions of high visual load, acquiring a target signalled by the auditory cue led to slower and more error-prone performance than acquiring a target signalled by either the visual cue alone or by both the visual and auditory cues.     

Constructing a discriminative visual vocabulary with macro and micro sense of visual words

Visual vocabulary representation approach has been successfully applied to many multimedia and vision applications, including visual recognition, image retrieval, and scene modeling/categorization. The idea behind the visual vocabulary representation is that an image can be represented by visual words, a collection of local features of images. In this work, we will develop a new scheme for the construction of visual vocabulary based on the analysis of visual word contents. By considering the content homogeneity of visual words, we design a visual vocabulary which contains macro-sense and micro-sense visual words. The two types of visual words are appropriately further combined to describe an image effectively. We also apply the visual vocabulary to construct image retrieving and categorization systems. The performance evaluation for the two systems indicates that the proposed visual vocabulary achieves promising results.     

Automated Visual Presentation: From Heterogeneous Information to Coherent Visual Discourse

Automated visual presentation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information. They should also be able to work in static or interactive environments and capable of employing a wide range of visual media and visual techniques. In this paper, we focus on three tasks in building visual production systems: establishing a thorough understanding of the presentation-related characteristics of domain-specific information; classifying several types of visual information and capturing their distinct syntactic, semantic, and pragmatic features; and formulating a set of design principles.We define a data-analysis taxonomy to characterize heterogeneous information. In addition, we have modeled presentation context information such as audience identity to produce user-centered visual design. To utilize and manipulate visual information, we have classified it into visual objects and visual tools based on its role in the visual production process. To guide the visual design process, we have formulated a set of design principles that ensure the expressiveness and effectiveness of a design. To test and evaluate our work, we have developed a prototype system called IMPROVISE based on the research results. We use examples generated by IMPROVISE to illustrate how it constructs visual presentations.     

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

The visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.     

Defining visual languages for interactive computing

A novel definition of visual languages allows a uniform approach to satisfying the needs of visual reasoning faced in visual human-computer interaction. The way the machine associates a computational meaning with an image, and conversely, the way it generates an image on the screen from a computation are formally described. A definition of a visual sentence and of a visual language as a set of visual sentences is discussed. A hierarchy of visual languages is derived in relation to the requirements for intelligible, manageable and trustable interaction between humans and computers     

视觉场景描述及其效果评价

作为计算机视觉、多媒体、人工智能和自然语言处理等领域的交叉性研究课题,视觉场景描述的研究内容是自动生成一个或多个语句用于描述图像或视频中呈现的视觉场景信息.视觉场景中内容的丰富性和自然语言表达的多样性使得视觉场景描述成为一项充满挑战的任务,综述了现有视觉场景描述方法及其效果评价.首先,论述了视觉场景描述的定义、研究任务及方法分类,简要分析了视觉场景描述与多模态检索、跨模态学习、场景分类、视觉关系检测等相关技术的关系;然后分类讨论视觉场景描述的主要方法、模型及研究进展,归纳日渐增多的基准数据集;接下来,梳理客观评价视觉场景描述效果的主要指标和视觉场景描述技术面临的问题与挑战,最后讨论未来的应用前景.     

Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?

IntroductionThe main purpose of this cross-sectional study was to investigate whether visual discomfort acts as a mediating factor between perceived visual ergonomic working conditions and self-rated visual performance among office workers who carry out administrative tasks and computer-based work at the Swedish Tax Agency.MethodsA questionnaire was sent to 94 office workers addressing: 1) perceived visual quality of the visual display units; 2) prevalence of eye symptoms; and 3) self-rated visual performance. Eighty-six persons (54 women (63%), 31 men (36%), and 1 of unspecified sex) answered the questionnaire. Multiple regression analysis investigated the association between visual ergonomic working conditions and visual performance, both with and without visual discomfort as a mediator.ResultsThe group mean of the Indexed survey questions indicated a reasonably good quality of visual ergonomic working conditions, a relative absence of eye symptoms, and acceptable self-rated visual performance. Results from multiple regression analysis showed a significant association between perceived visual ergonomic working conditions and self-rated visual performance (r² = 0.30, β = 0.327, p < 0.01). When visual discomfort was used as a mediator, the association between perceived visual ergonomic working conditions and self-rated visual performance remained the same (r² = 0.32, β = 0.315, p < 0.01).DiscussionIt was remarkable to discover that self-rated visual performance was independent of visual discomfort. Possible explanations include exposure factors not included in the current study, such as dry air and sensory irritation in the eyes, psychosocial stress, time spent performing near work activities, or time exposed to visually deficient working conditions.Relevance to industryThe strong connection between satisfaction with visual ergonomic working conditions and productivity in this study has implications for workplace profitability and staff satisfaction. If productivity is enhanced by better visual ergonomic working conditions, then managers of workplaces may be able to improve work outcomes by optimizing the physical work environment.     

Perceived Visual Complexity and Visual Search Performance of Automotive Instrument Cluster: A Quantitative Measurement Study

Advancements in technology have spurred the development of new in-vehicle applications. Drivers are faced with different driving contexts due to an increase in the number of devices that provide a wealth of diverse information. However, such a scenario can cause drivers to become distracted. Therefore, research on how the presentation of visual information can affect drivers’ performance is important. In this study, an analysis of quantifiable measurements that affect drivers’ perception of visual complexity and visual search performance was conducted. A questionnaire was administered to assess subjective perception of visual complexity, and a user experiment using eye tracking was designed to explore participants’ visual search performance. The results of subjective visual complexity perception and visual search performance suggested that some objective measurement variables were significantly related only to perceived visual complexity, whereas others affected both subjective and behavioral measurements. Thus it is possible to predict which quantifiable measurement variables affect subjective perception of visual complexity and which affect visual search performance. Therefore, this study allows understanding and explaining of perception of visual complexity by quantifiable measurements and the different ways by which these measurements affect visual search performance.