CoQuWeiP--使用加权积的色彩量化算法

提出一种新颖的色彩量化算法——CoQuWeiP．该算法通过设立像素个数和色彩距离的权重，综合考虑了色彩层次感和关键细节的要求，并可以调节权重以满足不同色彩量化任务对色彩层次感和关键细节的不同要求．实验表明，文中算法在调节量化结果方面是有效的，量化性能较好．     

一种可调节的多级颜色量化算法

针对当量化图像的颜色数量较少时如何调节颜色层次和微弱颜色,本文提出了一种新颖的多级颜色量化算法.根据Kohonen自组织神经网络和线性像素置换计算第一级调色板;然后多次迭代找出最相似的颜色对,删除其中的一个,得到第二级调色板;最后根据调色板中颜色的像素数量和与基色距离的加权和,选出最终的代表颜色.实验结果表明,该算法能够通过设置适当的像素取样率、像素数量权重和与基色的距离权重而有效地选择满足不同需要的量化结果,并且保持较好的量化图像质量.     

真实图形的颜色量化

颜色量是真实图形显示的一个重要环节。它的主要任务是将一幅有Ｎ个象素点的真实图用不超过Ｋ（Ｋ《Ｎ）种颜色近似表示，以使这幅图能在颜色表长为Ｋ的显示设备上输出。本文提出了一种基于编码的颜色量化算法，引算法速度快，所生成的图形效果较好。     

ℒ0 Gradient‐Preserving Color Transfer

This paper presents a new two‐step color transfer method which includes color mapping and detail preservation. To map source colors to target colors, which are from an image or palette, the proposed similarity‐preserving color mapping algorithm uses the similarities between pixel color and dominant colors as existing algorithms and emphasizes the similarities between source image pixel colors. Detail preservation is performed by an ?0 gradient‐preserving algorithm. It relaxes the large gradients of the sparse pixels along color region boundaries and preserves the small gradients of pixels within color regions. The proposed method preserves source image color similarity and image details well. Extensive experiments demonstrate that the proposed approach has achieved a state‐of‐art visual performance.     

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.     

一个基于链表的快速色彩量化算法

为了提高色彩量化算法的质量和速度,作者考虑在设计调色板的过程中充分利用分裂算法的快速性和聚类算法的准确性,试图改进像素匹配过程,然后在其基础上提出一个新的实时色彩量化算法.该算法将图像中的所有颜色按照其出现次数的大小排列成一个数据结构链表.整个色彩量化过程可视为关于该链表的一系列操作.实验结果表明,该算法能够获得期望的...     

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。     

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.     

一种用于CBIR系统的主色提取及表示方法

颜色是彩色图像最重要的视觉特征之一,在基于内容的图像检索（ＣＢＩＲ）系统中,都将颜色信息作为重要内容参与匹配和检索。针对图像中起主要视觉作用的是图像的主色这一问题,提出一种基于聚类分析的提取和表示图像主色的方法,给出一种用于聚类算法的停止准则,和等量量化方法相比,用本方法提取的颜色信息,具有特征维数低、颜色表示准的优点。     

A Hybrid Color Image Quantization Algorithm Based on k-Means and Harmony Search Algorithms

Color quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.     

基于颜色对的色彩量化算法

提出了一种新颖的基于颜色对的色彩量化算法。综合考虑了色彩层次感和关键细节的要求，并且可以根据具体色彩量化任务的要求，对它们的偏重性做出调整，以得到最满意的量化结果。     

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.     

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.     

Quantization of color images for display/printing on limited color output devices

A large number of output devices in use today are either bilevel or can produce only a limited number of display levels (gray-scale or color). Most color graphics terminals conforming to Enhanced Graphics Adapter (EGA), Professional Graphics Adapter (PGA), or Video Graphics Array (VGA) standards can display from 16–256 colors, whereas real-world (externally acquired) images constitute typically 16M colors. In this paper, a new color quantization algorithm has been proposed which maps an original image into an output image with a limited number of colors, while still preserving the image quality. The algorithm itself is based on the concepts of vector quantization where a color vector is defined by red, green, and blue components and, based on a random sampling of the input image, a color mapping table is generated. The random sampling provides an estimate of the color distribution of the input image, which is then further combined by a clustering technique to derive the desired number of output colors. A mapping process results in a limited-color output image which is optionally preprocessed (in cases where the number of output colors is very small) by a pseudo-random dithering algorithm rendering a high-quality output. This postprocessing step is particularly useful in images with very few output colors, e.g., 16. Through examples, it is shown that input images with over 16M colors can be easily displayed in as few as 16 colors, with negligible degradation in quality.     

基于聚类分析的色彩量化新算法及其应用

针对针织提花,植绒、印染以及金属表面花纹处理等电脑设计中的要求,研究图像重新量化成仅有几种颜色的色彩量化问题,提出一种基于聚类分析的色彩量化新算法,量化图像较好地兼顾了原图像的总体风貌和设计者希望保留的一些特征,该算法计算量小,容易在微电脑中实现,已成功地应用于电脑提花圆机花型ＣＡＤ系统。该算法对一般的色彩量化具有重要意义。     

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.     

Medical image reconstruction using a multi-objective genetic local search algorithm

Image reconstruction from projections is a key problem in medical image analysis. In this paper, we cast image reconstruction from projections as a multi-objective problem. It is essential to choose some proper objective functions of the problem. We choose the square error, smoothness of the reconstructed image, and the maximum entropy as our objective functions of the problem. Then we introduce a hybrid algorithm comprising of multi-objective genetic and local search algorithms to reconstruct the image. Our algorithm has remarkable global performance. Our experiments show that we can get different results when we give different weights to different objective functions. We can also control the noise by giving different weights on different objective function. At the same time, we can adjust the parameter to let it have good local performance. Though the computation demands of the hybrid algorithm tends to be larger because of the random search of the GA, it is really a common feature of the global optimization method. Our results show that the hybrid algorithm is a more effective than the conventional method. We think our method is very promising for the medical imaging field.     

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Fisher判据的自适应彩色图像量化算法

提出了一种基于Fisher判据的自适应彩色图像量化算法。首先用八叉树算法把原始图像量化为256种颜色,然后根据人类的视觉特性,参照NBS距离与人类视觉对颜色差别的定量关系,自动确定初始聚类中心及聚类数目,在此基础上,用Fisher判据自动确定出初始类中心的一个同组,从而实现图像的量化。实验结果表明所提算法无需事先给定颜色量化数目,在量化数目相同的情况下,量化效果明显优于八叉树算法和k均值算法。     

一种改进的快速中位切割彩色图像量化算法

色彩量化的主要目标是选择一个使量化前后图像之间差异尽可能小的最佳调色板。通过对中位切割技术的研究,提出了一种改进的中位切割算法,采用提高预量化精度、利用方差计算切割位置以及反向查找颜色映射等方法,使彩色图像的色彩量化在速度和质量上都获得了较大提升,实验证明了该算法的有效性。