A multiscale framework for spatial gamut mapping.

Image reproduction devices, such as displays or printers, can reproduce only a limited set of colors, denoted the color gamut. The gamut depends on both theoretical and technical limitations. Reproduction device gamuts are significantly different from acquisition device gamuts. These facts raise the problem of reproducing similar color images across different devices. This is well known as the gamut mapping problem. Gamut mapping algorithms have been developed mainly using colorimetric pixel-wise principles, without considering the spatial properties of the image. The recently proposed multilevel gamut mapping approach takes spatial properties into account and has been demonstrated to outperform spatially invariant approaches. However, they have some important drawbacks. To analyze these drawbacks, we build a common framework that encompasses at least two important previous multilevel gamut mapping algorithms. Then, when the causes of the drawbacks are understood, we solve the typical problem of possible hue shifts. Next, we design appropriate operators and functions to strongly reduce both haloing and possible undesired over compression. We use challenging synthetic images, as well as real photographs, to practically show that the improvements give the expected results.     

Retaining local image information in gamut mapping algorithms.

Our topic is the potential of combining global gamut mapping with spatial methods to retain the percepted local image information in gamut mapping algorithms. The main goal is to recover the original local contrast between neighboring pixels in addition to the usual optimization of preserving lightness, saturation, and global contrast. Special emphasis is placed on avoiding artifacts introduced by the gamut mapping algorithm itself. We present an unsharp masking technique based on an edge-preserving smoothing algorithm allowing to avoid halo artifacts. The good performance of the presented approach is verified by a psycho-visual experiment using newspaper printing as a representative of a small destination gamut application. Furthermore, the improved mapping properties are documented with local mapping histograms.     

Natural scene-illuminant estimation using the sensor correlation

This paper describes practical algorithms and experimental results concerning illuminant classification. Specifically, we review the sensor correlation algorithm for illuminant classification and we discuss four changes that improve the algorithm's estimation accuracy and broaden its applicability. First, we space the classification illuminants evenly along the reciprocal scale of color temperature, called "mired," rather than the original color-temperature scale. This improves the perceptual uniformity of the illuminant classification set. Second, we calculate correlation values between the image color gamut and the reference illuminant gamut, rather than between the image pixels and the illuminant gamuts. This change makes the algorithm more reliable. Third, we introduce a new image scaling operation to adjust for overall intensity differences between images. Fourth, we develop the three-dimensional classification algorithms using all three-color channels and compare this with the original two algorithms from the viewpoint of accuracy and computational efficiency. The image processing algorithms incorporating these changes are evaluated using a real image database with calibrated scene illuminants     

Color image enhancement with high saturation using piecewise linear gamut mapping

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing.     

Improving gamut mapping color constancy

The color constancy problem, that is, estimating the color of the scene illuminant from a set of image data recorded under an unknown light, is an important problem in computer vision and digital photography. The gamut mapping approach to color constancy is, to date, one of the most successful solutions to this problem. In this algorithm the set of mappings taking the image colors recorded under an unknown illuminant to the gamut of all colors observed under a standard illuminant is characterized. Then, at a second stage, a single mapping is selected from this feasible set. In the first version of this algorithm Forsyth (1990) mapped sensor values recorded under one illuminant to those recorded under a second, using a three-dimensional (3-D) diagonal matrix. However because the intensity of the scene illuminant cannot be recovered Finlayson (see IEEE Trans. Pattern Anal. Machine Intell. vol.18, no.10, p.1034-38, 1996) modified Forsyth's algorithm to work in a two-dimensional (2-D) chromaticity space and set out to recover only 2-D chromaticity mappings. While the chromaticity mapping overcomes the intensity problem it is not clear that something has not been lost in the process. The first result of this paper is to show that only intensity information is lost. Formally, we prove that the feasible set calculated by Forsyth's original algorithm, projected into 2-D, is the same as the feasible set calculated by the 2-D algorithm. Thus, there is no advantage in using the 3-D algorithm and we can use the simpler, 2-D version of the algorithm to characterize the set of feasible illuminants. Another problem with the chromaticity mapping is that it is perspective in nature and so chromaticities and chromaticity maps are perspectively distorted. Previous work demonstrated that the effects of perspective distortion were serious for the 2-D algorithm. Indeed, in order to select a sensible single mapping from the feasible set this set must first be mapped back up to 3-D. We extend this work to the case where a constraint on the possible color of the illuminant is factored into the gamut mapping algorithm. We show here that the illumination constraint can be enforced during selection without explicitly intersecting the two constraint sets. In the final part of this paper we reappraise the selection task. Gamut mapping returns the set of feasible illuminant maps. Our new algorithm is tested using real and synthetic images. The results of these tests show that the algorithm presented delivers excellent color constancy.     

基于色调恒常空间的色彩管理

 针对CIELAB空间色调非恒常影响色域匹配及色差计算准确度问题,研究了色调恒常校正方法,建立了一种新的校正空间tLAB,并用其替代CIELAB空间实施色彩管理.首先,以孟塞尔空间为标准,分析了CIELAB空间的色调非恒常性.然后,利用孟塞尔新标数据对CIELAB空间实施色调校正,构建了色调恒常的颜色空间tLAB;借助三维查找表(3D_-LUT)描述CIELAB和tLAB之间的非线性关系,提出了不增加时空代价的色彩管理工程实现方案.最后,给出了利用tLAB空间进行色域匹配的结果,以及新的色差评价指标体系.实验表明,利用tLAB空间进行色域匹配的效果明显优于传统方法,且用其替代CIELAB空间实施色彩管理具有很好的实时性和普适性.     

Space-dependent color gamut mapping: a variational approach.

Gamut mapping deals with the need to adjust a color image to fit into the constrained color gamut of a given rendering medium. A typical use for this tool is the reproduction of a color image prior to its printing, such that it exploits best the given printer/medium color gamut, namely the colors the printer can produce on the given medium. Most of the classical gamut mapping methods involve a pixel-by-pixel mapping and ignore the spatial color configuration. Recently proposed spatial-dependent approaches for gamut mapping are either based on heuristic assumptions or involve a high computational cost. In this paper, we present a new variational approach for space-dependent gamut mapping. Our treatment starts with the presentation of a new measure for the problem, closely related to a recent measure proposed for Retinex. We also link our method to recent measures that attempt to couple spectral and spatial perceptual measures. It is shown that the gamut mapping problem leads to a quadratic programming formulation, guaranteed to have a unique solution if the gamut of the target device is convex. An efficient numerical solution is proposed with promising results.     

Perceptual image quality: Effects of tone characteristics

Tone mapping refers to the conversion of luminance values recorded by a digital camera or other acquisition device, to the luminance levels available from an output device, such as a monitor or a printer. Tone mapping can improve the appearance of rendered images. Although there are a variety of algorithms available, there is little information about the image tone characteristics that produce pleasing images. We devised an experiment where preferences for images with different tone characteristics were measured. The results indicate that there is a systematic relation between image tone characteristics and perceptual image quality for images containing faces. For these images, a mean face luminance level of 46-49 CIELAB L* units and a luminance standard deviation (taken over the whole image) of 18 CIELAB L* units produced the best renderings. This information is relevant for the design of tone-mapping algorithms, particularly as many images taken by digital camera users include faces.     

Hue-preserving color image enhancement without gamut problem

The first step in many techniques for processing intensity and saturation in color images keeping hue unaltered is the transformation of the image data from RGB space to other color spaces such as LHS, HSI, YIQ, HSV, etc. Transforming from one space to another and processing in these spaces usually generate a gamut problem, i.e., the values of the variables may not be in their respective intervals. We study enhancement techniques for color images theoretically in a generalized setup. A principle is suggested to make the transformations gamut-problem free. Using the same principle, a class of hue-preserving, contrast-enhancing transformations is proposed; they generalize existing grey scale contrast intensification techniques to color images. These transformations are also seen to bypass the above mentioned color coordinate transformations for image enhancement. The developed principle is used to generalize the histogram equalization scheme for grey scale images to color images.     

Gamut Compression and Extension Algorithms Based on Observer Experimental Data

Gamut compression algorithms have traditionally been defined functionally and then tested with deductive methods, e.g., psychophysical experiments. Our study offers an alternative, an inductive method, in which observers judge image colors to represent the original images more accurately. We developed a computer‐controlled interactive tool that modifies the color appearance of pictorial images displayed on a monitor. In experiments, observers used the tool to alter color pixels according to the region of color space to which they belonged. We created three different gamut compression algorithms based on the observer experimental data. Observer groups evaluated the performance of the newly‐developed algorithms, existing gamut compression algorithms, and an image based on the average observers’ results from experiments in this study. The study of gamut extension is unlike the study of gamut compression in that it mainly deals with the degree of image pleasantness as judged by observers. The results of the gamut extension experiments in this study not only make available worthwhile data but also suggest a methodology for using the observer experimental tool for future gamut extension research.     

Gamut mapping optimization algorithm based on gamut-mapped image measure (GMIM)

This paper presented a novel gamut mapping optimization algorithm based on a special designed gamut-mapped image measure (GMIM). We formulated GMIM as an objective function in an iterative manner, where the iterative step changes based on the information weight which can be computed between the original image and the initial gamut-mapped images. A new metric, GMIM, is developed to compute the achromatic differences and the chromatic differences of the input images based on structural similarity index. For the achromatic part, we introduce the gradient information to detect the achromatic distortions between two images; the chromatic part aims to analyze the hue and chroma information. Further, the circular statistical theory is employed to calculate the hue value. In the iterative process, we change the iterative steps according to the information weight which can be computed by the information theory. The information map between images indicated regions in an image which human paid attentions to. Experimental results demonstrated that our new gamut mapping method can preserve the brightness, color, as well as detail information of the reference images.     

基于颜色色差的彩色图像压缩技术研究

根据图像DCT变换域系数特征和结合人眼辨别颜色色差的阈值,提出了一种基于颜色色差的彩色图像压缩技术方案。方案首先将彩色图像转化到与设备无关的CIELAB颜色空间,并进行DCT变换和色差计算;再通过人眼辨别颜色色差的阈值进行有选择的量化,并通过Huffman算法进行编码、压缩;最后利用其逆过程进行解压。通过仿真实验,结果表明,重建的彩色解压缩图像与源图像几乎一样,各分量亮度图的解压缩图像的PSNR均超过30dB,人眼几乎不能分辨其差异;衡量编码质量优劣的5个参数值均达到较好效果,且在保证解压缩图像质量的情况下,彩色图像的压缩比能够达到107.167 8,完全可以满足彩色图像压缩的需求。结果表明,提出的基于颜色色差的图像压缩技术方案是一种可行的、较好的彩色图像压缩技术。     

Tone mapping with contrast preservation and lightness correction in high dynamic range imaging

In real-world environments, the human visual system perceives a wide range of luminance in a scene. However, the full range of tones in a high dynamic range (HDR) scene cannot be displayed on standard display devices due to their low dynamic range (LDR). Therefore, tone mapping is necessary to faithfully display a HDR scene on an LDR display device. Existing tone mapping methods have problems because details and contrast in a scene are not preserved faithfully, and they also distort the colors in a scene. Thus, we propose a tone mapping method for preserving the detail in an HDR scene using a weighted least squares filter, which preserves the global contrast in a scene by using a competitive learning neural network, before applying a tone reproduction operator to preserve the color without shifting the lightness. According to the Helmholtz–Kohlrausch effect, the perception of brightness depends on the lightness, chroma, and hue of a color. For example, the luminance of pixels with specific colors such as red and blue is low in an HDR scene. The proposed method corrects the lightness of pixels according to the color (i.e., lightness, chroma, and hue) of a tone-mapped image. Experimental results with several test sets of images demonstrated that the proposed tone mapping method with contrast preservation and lightness correction is more suitable for dynamic range compression than existing tone mapping methods, while it also preserves the color of a scene in an effective way.     

Application of a model of color appearance to practical problems inimaging

Some of the reasons for needing a color appearance model in imaging are given and the use of such a model in deriving a color reproduction index is described. There are three reasons why a model of color appearance is a necessary tool in color imaging systems. First, because the viewing conditions for images are often different at different stages of reproduction systems, it is necessary to allow for their visual effects; conventional colorimetry does not do this, but a model of color appearance can supply the necessary adjustments. Second, different display media typically have different color gamuts and a regime of gamut mapping is therefore often required; it is more appropriate to conduct this mapping in an appearance space. Third, it is desirable to be able to assess the quality of color reproductions in terms of a color reproduction index and a model of color appearance can provide a metric that is suitable as the basis of such an index     

Image-dependent gamut mapping as optimization problem.

We explore the potential of image-dependent gamut mapping as a constrained optimization problem. The performance of our new approach is compared to standard reference gamut mapping algorithms in psycho-visual tests.     

CRT显示器显示图像对比度的研究

基于CRT显示器的显色特性,根据对立色的特点,采用CIELAB色空间作为表色系统,以CIE1976LAB色差公式为基础,提出了一种对立色彩色图像的对比度定义,通过对实际色块对比度的计算和人眼觉察以及与国外研究结果对比分析表明,此定义不仅同时考虑了图像亮度和色度的对比度,并且理论计算值与人眼视觉感受结果保持较好的一致性,计算简单,是一种实用的显示器输出图像对比度定义。     

Perception-based visualization of manifold-valued medical images using distance-preserving dimensionality reduction

A method for visualizing manifold-valued medical image data is proposed. The method operates on images in which each pixel is assumed to be sampled from an underlying manifold. For example, each pixel may contain a high dimensional vector, such as the time activity curve (TAC) in a dynamic positron emission tomography (dPET) or a dynamic single photon emission computed tomography (dSPECT) image, or the positive semi-definite tensor in a diffusion tensor magnetic resonance image (DTMRI). A nonlinear mapping reduces the dimensionality of the pixel data to achieve two goals: distance preservation and embedding into a perceptual color space. We use multidimensional scaling distance-preserving mapping to render similar pixels (e.g., DT or TAC pixels) with perceptually similar colors. The 3D CIELAB perceptual color space is adopted as the range of the distance preserving mapping, with a final similarity transform mapping colors to a maximum gamut size. Similarity between pixels is either determined analytically as geodesics on the manifold of pixels or is approximated using manifold learning techniques. In particular, dissimilarity between DTMRI pixels is evaluated via a Log-Euclidean Riemannian metric respecting the manifold of the rank 3, second-order positive semi-definite DTs, whereas the dissimilarity between TACs is approximated via ISOMAP. We demonstrate our approach via artificial high-dimensional, manifold-valued data, as well as case studies of normal and pathological clinical brain and heart DTMRI, dPET, and dSPECT images. Our results demonstrate the effectiveness of our approach in capturing, in a perceptually meaningful way, important features in the data.     

基于MATLAB GUI的色域平台设计与开发

为了实现色域算法一体化集成和数据的可视化分析,基于Matlab GUI设计开发了色域平台,能够完成二维和三维色域数据的处理,且能够实现与用户的交互.实验结果表明,本平台能够较快地处理数据,计算结果较为准确,且操作简单易行,对色域研究工作者提供了很大的方便.     

General chromaticity compression function for gamut mapping

A general chromaticity compression function for gamut mapping is presented. It combines linear and nonlinear compression and clipping into a compact representation and the degree of transition between linear mapping and clipping is determined by a single parameter. The presented function is developed to facilitate the process of finding an effective gamut mapping function for a given set of images and reproduction systems     

基于结构相似的CIE L*a*b*颜色信号向孟塞尔色序系统分级转换

针对传统数学建模法无法实现CIELAB颜色信号向孟塞尔色序系统精确转换的问题,提出一种基于结构相似的分级转换算法。首先使用Pearson相关系数分析CIELAB颜色系统与孟塞尔色序系统间的结构关系,接着在此基础上采用分级转换的方式先后得到CIELAB颜色信号对应的孟塞尔明度V、色调H和彩度C三色标,并在第二级转换求色标H时使用了一种多次插值求最小色差的新方法,以保证算法的计算精度。对2735组检验样本的测试结果表明,本文算法具有良好的计算精度和可靠性,并可被广泛应用于各类图像颜色信号处理系统中。