利用VC实现真彩色位图到灰度图的转换

给出了一种真彩色位图到灰度位图的转换方法 ,该方法采用创建好灰度图像调色板 ,让系统自动按照最接近的调色板颜色去转换 2 4位真彩色位图。结果表明 ,转换后的图像能保留原图像的除去颜色以外的所有信息 ,且磁盘存储容量缩小了 2 /3。     

量子索引图像的描述方法与隐写算法

为解决量子计算机上索引图像的描述问题,提出一种量子索引图像描述方法.该方法中量子索引图像包含量子数据矩阵和量子调色板矩阵2种数据结构,每种数据结构均基于量子比特序列的基态进行信息表示;在此基础上,提出一种基于EzStego的量子索引图像信息隐写算法,首先计算量子调色板矩阵中每个颜色的相对亮度值,然后根据相对亮度值对颜色排序并分配亮度序号,最后根据每个颜色的亮度序号及其位置索引,结合嵌入的秘密信息对量子数据矩阵中的像素索引值进行更新,得到嵌入秘密信息后的量子数据矩阵.利用Matlab在经典计算机上采用USC-SIPI图像数据库数据进行仿真实验,从视觉质量、嵌入容量和鲁棒性三方面验证了该方法的可行性和有效性.实验结果表明,文中方法可在将来的量子计算机上执行.     

代表性调色板提取及图像重着色EI北大核心CSCD

基于调色板的图像编辑技术是近年的热门研究方向,在海报制作、服装设计、电影、短视频编辑等方向有着广泛的应用.通过计算凸包提取图像调色板是一个较新技术.然而,其目前仍然存在2个问题:一是忽略了凸包内部的颜色分布,使得调色板整体上缺乏代表性;二是插值权重缺乏稀疏性,难以实现针对性的局部编辑.针对上述问题,提出一种新颖的调色板提取算法,并在此基础上实现高效的图像重着色编辑.首先,提取图像在RGB空间的凸包并简化;其次,通过聚类算法捕捉凸包内部的颜色分布,构造代表性调色板;最后,在RGB空间对调色板颜色进行四面体剖分,并对图像像素进行插值.为了验证该算法的有效性,从互联网上获取了40余幅图像组成数据集进行实验,对插值权重的稀疏性、图像重着色效果等进行了对比分析和用户调研.大量实验结果表明,该算法提取的调色板具有更好的代表性,插值权重具有更好的稀疏性,实现了更精确的局部编辑.     

基于标准调色板的彩色图像显示技术

无论是真彩色图像（２４ｂｉｔｓ），还是２５６色图像（８ｂｉｓｔ），在ＶＧＡ卡上显示都要处理调色板问题，如果每幅彩色图像使用不现的调色板，同屏显示多幅图像便不能实现，而且显示一幅图像后，屏幕上原有文字的颜色往往要被改变，本文介绍了一种使用标准调色板的彩色图像显示技术，采用了这种技术后，多幅彩色图像可同屏显示，并且不影响原屏幕上的文字的颜色，处理速度快，十分方便实用。     

基于直方图映射和分层的图像迁移算法研究

本文提出了一种基于直方图映射和分层迁移的灰度图像上色HisT算法.对于给定的颜色图像和需要迁移的灰度形状图像,先从RGB色彩空间转换到一个正交空间lαβ,消除空间各分量之间的强相关性.然后分别统计两幅图像的灰度直方图,对于灰度图像中的象素点,保留其灰度值,而利用颜色图像中与之对应或相邻灰度等级的颜色信息为灰度图像进行粗糙上色,得到一幅过渡图像.参照灰度直方图,将颜色图像和过渡图像进行分层处理,并利用统计学方法对图像各层和整体统计量进行分析,提取颜色图像的颜色信息和过渡图像的形状信息,最终实现颜色图像到灰度形状图像的上色过程.实验证明,该算法能快速准确地实现灰度图像的上色,具有广泛的应用前景.     

梯度与视觉显著度下的图像灰度重叠区域识别

采用目前方法识别图像中存在的灰度重叠区域时,没有构建图像的显著性图,存在识别精度低、查全率低和识别效率低的问题.提出梯度与视觉显著度下的图像灰度重叠区域识别方法,根据Gestalt前背景分离原则对图像中存在的梯度通道和颜色通道进行随机阈值化处理,获得对应的二进制布尔图,采用线性平均融合方法融合利用上述获取的二进制布尔图生成视觉注意图.通过分块区域分割技术识别显著性图中存在的空间位置信息,为图像灰度重叠区域的识别提供点云数据,在云计算模式中结合局部空间降噪方法消除噪声,定位去噪处理后显著性图空置区域中存在的特征点,提取灰度重叠区域的动态特征,建立对应的灰度直方图,最后利用云检测技术识别图像中存在的灰度重叠区域.仿真结果表明,所提方法的识别精度高、查全率高、识别效率高.     

用VC开发图像处理系统

本文从开发一个图像处理系统的角度出发，对数字图像的基本概念、Windows下调色板、BMP文件格式等进行了深入浅出的介绍；并重点剖析了图像平滑去噪、灰度直方图的绘制、真彩色图像的灰度化、图像的闽值选取及二值化等典型图像处理技术方面的基本原理。特别是，本文在介绍理论知识的同时，给出了基于VC6．0环境开发图像处理系统的关键实现代码。     

一种基于聚类的彩色图像分色算法

图像分色在纺织和印刷等行业中有着广泛而重要的应用，其目的是用尽量少的颜色采描述一幅彩色图像，使得到的分色图像与原图像尽可能的接近。提出一种基于单遍聚类和K-均值聚类相结合的自适应图像分色算法。该算法首先对原图像颜色进行统计学习，由单遍聚类产生初始调色板，然后根据该调色板对原图像的像素点进行K-均值聚类，产生分色图像。实验结果表明，与单纯K-均值聚类算法相比，该算法能在提高分色图像质量的同时进一步减少颜色数。     

一种新的基于颜色特征的图像检索方法

提出一种新的基于颜色特征的彩色图像检索算法.该方法首先利用颜色的距离矩阵,对样本图像进行非监督的颜色量化,得到调色板,然后将待检索图像按最小距离映射到该调色板中,这样对于所有图像,就可以得到基于样本图像调色板信息的统一的颜色直方图.这种颜色量化算法精度高于有监督的量化,而速度又明显高于传统的无监督的颜色聚类.此外,结合颜色在图像中的散布情况,综合颜色的统计特征与空间分布特征来描述图像内容,保证图像内容描述的精确性.实验证明,利用该算法的检索效率较高,检索结果也能够较好的满足人的视觉感受.     

一种基于RGB分量排序的索引替代信息隐藏算法*

提出了一种基于调色板图像的信息隐藏算法.该算法根据人眼视锥细胞对不同颜色分量的敏感度不同,通过对调色板颜色分量进行三次排序,得到最接近的像素颜色集及颜色孤立像素集,在像素颜色极为接近的集合中利用像素索引的最低有效位与嵌入比特位的一致性进行颜色替代来实现对信息的隐藏.分析了该算法的安全性及信息隐藏量.通过实验可看到在GIF图片中隐藏信息后人眼无法察觉,具有良好的隐蔽性.     

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.     

Progressive color transfer for images of arbitrary dynamic range

Image manipulation takes many forms. A powerful approach involves image adjustment by example. To make color edits more intuitive, the intelligent transfer of a user-specified target image's color palette can achieve a multitude of creative effects, provided the user is supplied with a small set of straightforward parameters. We present a novel histogram reshaping technique which allows significantly better control than previous methods and transfers the color palette between images of arbitrary dynamic range. We achieve this by manipulating histograms at different scales, which allows coarse and fine features to be considered separately. We compare our approach to a number of existing color transfer and tonemapping techniques and demonstrate its performance for a wide range of images.     

基于调色板图像的信息隐藏技术研究

文章对已有的调色板图像的信息隐藏技术进行了分析,指出其存在的脆弱性问题,提出了一种空域高效鲁棒性信息隐藏算法,该算法可以抵抗“全选”、“拷贝”、“粘贴”到原始图像的操作,可以抵抗对调色板颜色的轻微修改,可以抵抗调色板图像在真彩和索引图像之间的转换,隐藏容量高达25%。出于信息隐藏安全性考虑,文章提出了一种改进算法,牺牲了隐藏容量,提高了视觉效果。     

LoCoPalettes: Local Control for Palette-based Image Editing

Palette-based image editing takes advantage of the fact that color palettes are intuitive abstractions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image-space constraints and automatically splitting the image into semantic sub-regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult-to-achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method.     

平均误差向量加速的[K]-Means色彩量化方法

针对[K]-Means色彩量化方法在运行时间上过于冗长的问题,提出一种用平均误差向量加速的色彩量化方法。随机生成[K]种色彩作为初始的调色盘,用该调色盘对欲量化的图像进行一次量化。根据量化后的版本,计算其每个颜色分量的量化误差,获得平均误差向量。用该平均误差向量对调色盘进行更新,获得另一更优的调色盘。通过若干次迭代运算,获得最终收敛的调色盘,并用该调色盘进行最后的色彩量化。实验结果表明,该加速算法能对[K]-Means量化方法平均加速70~150倍,同时,原有[K]-Means方法的量化效果还得到了保持。     

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.     

An Improved Geometric Approach for Palette‐based Image Decomposition and Recoloring

Palette‐based image decomposition has attracted increasing attention in recent years. A specific class of approaches have been proposed basing on the RGB‐space geometry, which manage to construct convex hulls whose vertices act as palette colors. However, such palettes do not guarantee to have the representative colors which actually appear in the image, thus making it less intuitive and less predictable when editing palette colors to perform recoloring. Hence, we proposed an improved geometric approach to address this issue. We use a polyhedron, but not necessarily a convex hull, in the RGB space to represent the color palette. We then formulate the task of palette extraction as an optimization problem which could be solved in a few seconds. Our palette has a higher degree of representativeness and maintains a relatively similar level of accuracy compared with previous methods. For layer decomposition, we compute layer opacities via simple mean value coordinates, which could achieve instant feedbacks without precomputations. We have demonstrated our method for image recoloring on a variety of examples. In comparison with state‐of‐the‐art works, our approach is generally more intuitive and efficient with fewer artifacts.