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.     

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.     

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.     

Generalized sampling: a variational approach .II. Applications

For pt.I see ibid., vol.50, no.8, p.1965-76 (2000). The variational reconstruction theory from a companion paper finds a solution consistent with some linear constraints and minimizing a quadratic plausibility criterion. It is suitable for treating vector and multidimensional signals. Here, we apply the theory to a generalized sampling system consisting of a multichannel filterbank followed by a nonuniform sampling. We provide ready-made formulas, which should permit application of the technique directly to problems at hand. We comment on the practical aspects of the method, such as numerical stability and speed. We show the reconstruction formula and apply it to several practical examples, including new variational formulation of derivative sampling, landmark warping, and tomographic reconstruction     

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.     

Psychophysical evaluation of gamut mapping techniques using simplerendered images and artificial gamut boundaries

Using a paired comparison paradigm, various gamut mapping algorithms were evaluated using simple rendered images and artificial gamut boundaries. The test images consisted of simple rendered spheres floating in front of a gray background. Using CIELAB as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combinations of clipping and mapping the original gamut in linear piecewise segments. Complete color space compression in RGB and CIELAB was also tested. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C(*)/L(*)) allowed to vary or retain the same values as in the original image. Pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test image that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Clipping all out-of-gamut colors was the best method for mapping chroma. For lightness mapping at low lightness levels and high lightness levels particular gamut mapping algorithms consistently produced images chosen as most like the original. The choice of device-independent color space may also influence which gamut mapping algorithms are best.     

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     

宽色域液晶显示系统的研究

针对普通显示液晶系统在色彩表现方面的不足,在现有色域扩展技术的基础上,设计了一种以白和宝石蓝两种颜色发光二极管作为背光源的宽色域液晶显示系统。本文通过分析普通显示系统的色域,以色域覆盖率最大化为准则,选择主波长为501nm的宝石蓝发光二极管作为扩展背光源,扩展普通显示系统未能显示的青绿色区域。从理论上分析该系统的色域范围,并且详细介绍了系统背光源阵列、数据转发等功能模块的电路实现。实验结果表明:本文所设计的宽色域液晶显示系统的色域扩展了青绿色区域且色域覆盖率可达到45.65%。相比与其他多原色显示系统,本文设计的宽色域液晶显示系统不仅简化了硬件设计的复杂度,而且能够重现更多青绿色区域的颜色。     

真实表面色色域三维分析及其色立体计算

为弥补二维色度分析的局限性,首先在CIE-L*a*b*色空间及其变换所得L*C*hab坐标系中,对真实表面色色域进行三维色度学分析。为克服二维色度图计算色域覆盖率的片面性,还提出了一种在三维色空间计算色立体体积的近似算法,并与笔者的前期算法进行了比较,表明新算法精度有所提高。     

基于图像质量评价的显示器色域映射

针对显示器图像颜色复现问题,提出了一种基于图像质量评价和图像分割-融合策略的色域映射算法。详细分析了各类色域映射算法的特点并选取4种算法作为基本色域映射算法。提出了一种色域映射图像质量评价算法,并选用SLIC超像素算法对源图像分割。使用基本色域映射算法对源图像进行映射处理,并计算每个分块内各映射图像与源图像的相似度。根据每个图像分块的相似度,对基本色域映射图像进行选择综合处理并融合成最终图像。最后以LED显示屏和LCD显示器为例,对本文提出的算法和其他基本色域映射算法进行主观评价对比实验。实验结果证明本文算法在图像颜色保真效果上要明显优于其他算法,但是在计算速度上仍有待优化。     

Perceptual color correction through variational techniques.

In this paper, we present a discussion about perceptual-based color correction of digital images in the framework of variational techniques. We propose a novel image functional whose minimization produces a perceptually inspired color enhanced version of the original. The variational formulation permits a more flexible local control of contrast adjustment and attachment to data. We show that a numerical implementation of the gradient descent technique applied to this energy functional coincides with the equation of automatic color enhancement (ACE), a particular perceptual-based model of color enhancement. Moreover, we prove that a numerical approximation of the Euler-Lagrange equation reduces the computational complexity of ACE from theta(N2) to theta(N log N), where N is the total number of pixels in the image.     

超高色域图案化量子点彩膜的研究

平板显示中影响色域的两个关键因素为背光源发光光谱特性和彩色滤光片的透过光谱特性。在TFT-LCD的高色域领域,为了实现BT2020标准,我们开发了量子点光刻胶替代彩色滤光片的方法。首先合成新型的量子点光刻胶,将其作为色彩转换膜制备在彩色滤光片下方,通过背光激发量子点自发光,可得到色纯度更高的红绿光。研究表明,自主开发的量子点光刻胶可以有效地进行色转换,可耐受光刻工艺,在高温烘烤和碱性显影下,可保持一定转换效率,且能实现pattern化,避免目前用白光直接搭配彩膜的漏光问题,有效地提高色域。     

基于流水线技术的色域扩展实现

介绍了采用流水线技术完成色域扩展的硬件实现方法.色域扩展的整个处理过程是在LCH色彩空间中进行,对亮度L和饱和度C分步处理,设计了一条6级流水线的处理结构,提高了处理数据的吞吐量,及设计的可综合频率.采用了0.18μm的工艺技术,可综合频率达到150MHz,能满足实时处理高清视频数据的要求.同时,采用了灵活的参数配置方式,能方便地应用于不同色调和不同标准的处理,以适应不同色域标准的宽色域高清显示设备的需要.     

Cortex segmentation: a fast variational geometric approach

An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.     

Disocclusion: a variational approach using level lines

Object recognition, robot vision, image and film restoration may require the ability to perform disocclusion. We call disocclusion the recovery of occluded areas in a digital image by interpolation from their vicinity. It is shown in this paper how disocclusion can be performed by means of the level-lines structure, which offers a reliable, complete and contrast-invariant representation of images. Level-lines based disocclusion yields a solution that may have strong discontinuities. The proposed method is compatible with Kanizsa's (1979) amodal completion theory.     

基于金属-电介质-金属结构的滤波器提升LCD色域的研究

为了拓宽LCD的色域,提出了在背光系统中添加基于TiO2-Ag-TiO2-Ag-Glass膜层结构的滤波器来优化背光源光谱。所提出的滤波器在可见光范围内可以仅通过红、绿和蓝3种颜色的光,而反射其他颜色的光。利用TechWiz 1D软件模拟了滤波器对于LCD色域的影响。当所提出的滤波器分别与黄色荧光粉(pc-WLED)、新红粉(KSF-LED)以及量子点(QD)背光源相结合时,LCD的色域分别从72%提升至102.6%NTSC,从92.3%提升至113.8%NTSC,从104.3%提升至119.9%NTSC。当入射光的角度从0°增加到40°时,3种背光源的色域变化都很小。模拟结果表明基于金属-电介质-金属(MDM)膜层结构的滤波器拥有小的角度依赖性,因此对实现宽色域LCD,是一种非常有效的方法。但其光利用率很低,3种背光源的光效率分别为26.5%、35.4%以及40.7%。     

准积分法构建视觉可感知三维色域的研究

为了更全面地评价显示器的色重显性能,用准积分法构建了人类视觉可感知三维色域.该算法用CIE标准色度观察者观察、混配等能单色辐射测得的CIE 1931 XYZ色度系统光谱三刺激值,按某一波长间隔求累加和,来近似实现未知函数表达式的多重积分运算.构建的色域囊括了人眼能识别的全部颜色,是宽色域、高动态范围显示设备的目标色域.还用三棱锥体积求和算法计算出该色域体积,验证了算法的可行性.进而由实测的LCD电视显示器数据,用同样方法计算其三维色域,并与视觉可感知三维色域进行定量比较.这些工作表明,本算法可为定量评价显示器的三维色域提供理论依据和可行算法,用这种方法给出的显示设备三维色域覆盖率可克服通常在二维色度图计算色域覆盖率的局限性.     

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.