基于色貌的跨媒体颜色复制

介绍了一种基于视觉匹配的跨媒体颜色复制方法。通过视觉匹配将一个环境下的一些色貌因素"映射"到另一个环境,是一种基于色貌的CRT特性化方法。该方法复制的22个Munsell色卡的平均视觉评价为6分制的5.17分。其中,红色调的复制色块视觉评价较好,蓝色调或蓝色占有较大比例的复制色块误差较大。sRGB作为目前流行的用于颜色通讯的标准色空间,在实验中也进行了比较。实验证明这种基于视觉匹配的特性化方法,已经包含了一些色貌因素,可以满足一般的应用要求,有着广泛的应用前景。     

基于中国颜色体系的色差公式分析

CIE均匀颜色空间及利用色差公式对色差进行计算对彩色复制品的评价具有很关键的作用。利用中国颜色体系在视觉上为等色差系统以及色调、彩度和明度可以单独改变的特性,分别从色调、彩度、明度三方面对CIEL*a*b*、CMC(l:c)、CIE94、CIEDE2000色差公式进行了比较分析。结果表明:用各色差公式计算出的平均色差值是不同的,它们的均匀性也不同。需要针对不同的明度、彩度、色调选择使用不同的色差公式。     

基于模式识别技术的色貌模型评价研究

选用Munsell新标数据集,采用基于模式识别技术的圆度、色相角偏差、明度线性度与空间投影点聚集度四个指标来分别评价目前八个典型色貌模型在彩度均匀性、色相预测准确性、明度均匀性、颜色的空间再现的能力。结果表明,不同的色貌模型在四个方面的颜色再现性上表现出不同的优势。八个色貌模型对彩度预测最好的是RLAB,每个色貌模型对红色和绿色预测比较好,CAM02在色相预测整体上占优势;明度的均匀性预测都比较好,尤其是CIELAB;LLAB的颜色的空间再现能力最好。     

Lightness and chroma enhancement for food images considering Helmholtz–Kohlrausch effect

The color appearance of a food image is very important for making it look delicious. Many food images acquired in low illumination environments do not give delicious impressions because of the deterioration of their color appearance. We propose an image enhancement method to improve the color appearance of a food image acquired in a low illumination environment. In the proposed method, the lightness of each pixel is first enhanced within a color gamut on an equal hue plane in the CIELAB color space. The boundary of the color gamut is given by a lookup table calculated in advance. Then, when the hue of the pixel is attributed to foods, the chroma is enhanced within the color gamut. In this case, we consider Helmholtz–Kohlrausch (H-K) effect, which indicates the relation of the hue, chroma, and lightness in human visual perception. According to H-K effect, the perceived lightness increases as the chroma is enhanced. Therefore, the perceived lightness is preserved after the chroma enhancement, so that the food image is not too enhanced. To verify the effectiveness of the proposed method, experiments are conducted using several food images.     

CIELAB与CIECAM02色空间均匀性比较研究

采用均匀色空间Munsell系统检验和验证了CIELAB和色貌模型CIECAM02色空间的均匀性。利用Mun- sell色卡所对应的三刺激值X,Y,Z作为模型的输入值,预测色空间模型的明度、彩度和色调,并通过预测结果比较和检验了CIELAB色空间和CIECAM02模型色空间的均匀性。研究表明:在预测明度方面,两个色空间的结果相似;在预测彩度时,CIECAM02模型较好于CIELAB色空间;在预测色调时,当色调逐渐从BG变化到B时,CIELAB所预测的色调角与由Munsell色空间计算出来的色调角之差逐渐增大,最大时超过6%,而CIECAM02所预测的色调角与Munsell色空间的色调角之差在2.5%以内,CIECAM02优于CIELAB色空间。     

复杂显示中诱导域彩度对目标色貌的影响

用双目不对称匹配法测试了在复杂显示中目标色貌与其周围诱导域彩度的关系。结果表明：当保持目标与周围的色调相同且亮度不变,只改变诱导域的彩度时,色诱导引起的目标色觉位移的大小随诱导域彩度的增加而增加,色觉位移的方向大多数不随诱导域彩度改变,基本指向该色调彩度降低的方向。     

一种基于HSI颜色模型的目标提取方法

在工业、农业、军事以及医疗等领域中,目标识别是一个关键技术,近年来在图像处理方面已成为一个重要的研究课题。提出了一种用于除草机器人的基于HSI(色调、明度和饱和度)颜色模型的彩色图像目标提取方法。该方法首先将具有某种色彩信息的杂草与农作物从背景土壤中分离出来,然后再根据杂草与农作物形状的不同,采用腐蚀和膨胀的方法进一步将杂草与农作物分离。该方法除了用于除草机器人以外,还可用于水果采摘等。仿真结果验证了该方法的有效性。     

Failure of color constancy for high luminance of a test patch that appears unnatural as an object in a space

The theory of the recognized visual space of illumination (RVSI) is that the color appearance of objects in a space is determined in relation to its recognition axis RX whose direction is determined by the brain action to adapt to the illumination in the space. Thus the color constancy holds. RX is applicable to objects in the space but not to an object or a portion that does not belong in the space in terms of illumination, that is to say, the luminance of which is too high based on the illumination for that space. In that case the color appearance would be determined in relation to the fundamental axis FX and the color constancy would not hold. In the present paper the chromaticity points were measured for a test patch that appeared achromatic for various luminance of the patch. The points were close to the color of illumination to indicate the color constancy when the luminance was low enough to assure its appearance as the object color, but they departed from the color of illumination and approached the colorimetrically achromatic color. The color constancy gradually failed for the test patch with the high luminance when the color mode became an unnatural object color and then a light color.     

Impacts of appearance parameters on perceived image quality for mobile-phone displays

In order to investigate the perceived image quality (IQ) influenced by different image appearance parameters, a psychophysical experiment was carried out on two mobile-phone displays of different technologies, the in-plane switching (IPS) and the active matrix organic light emitting diode (AMOLED). The test images were generated within a wide range of variations by applying various manipulation methods, involving changes in several kinds of image appearance parameters, i.e. lightness, chroma, hue, and spatial frequency. The perceived attributes of naturalness, colourfulness, brightness, contrast, sharpness, clearness, preference, and overall IQ were visually assessed via categorical judgement method for several application types of test images. The impacts of the manipulation methods on individual attributes have been deeply discussed based on the interrelationships of these attributes, which reveal that the significant parameters towards better overall IQ for mobile-phone displays include high lightness, suitable chroma and lightness frequency distribution, and impartial hue, accordingly better image quality will be accomplished after all these parameters have been optimised at the same time. The achievements of this study would provide constructive instructions on achieving excellent display effects for researchers and manufactories of mobile-phone displays.     

Objective evaluation of target detectability in night vision color fusion images

An evaluation for objectively assessing the target detectability in night vision color fusion images is proposed.On the assumption that target detectability could be modeled as the perceptual color variation between the target and its optimal sensitive background region,we propose an objective target detectability metric in CIELAB color space defined by four color information features:target luminance,region perceptual luminance variation in human vision system,region hue difference,and region chroma difference.Experimental results show that this proposed metric is perceptually meaningful because it corresponds well with subjective evaluation.     

基于色差分析的不同光源下颜色稳定性研究

产品颜色是衡量产品质量的重要指标，由于产品流通环境中光源的变化无法避免，产品颜色必然受到光源变化的影响，因此研究不同光源下颜色的稳定性具有现实意义。以D65光源为标准光源，以A光源、F2光源为试验光源，选择大量色块作为样品，用不同光源下样品色差大小来衡量不同光源下样品颜色稳定性。通过对不同兴奋纯度的样品的分析，发现样品色差与其兴奋纯度是正相关的；通过对不同主波长/补色波长的样品进行分析，发现样品色差与其主波长/补色波长密切相关。样品主波长在479 nm时，色差较大，ΔE_ab*（D65/A）、ΔE_ab*（D65/F2）分别为14.50、13.81；样品主波长在561 nm时，色差较小，ΔE_ab*（D65/A）、ΔE_ab*（D65/F2）分别为5.17、2.78。将主波长/补色波长与色相对应后，发现紫、黄、黄绿等色相的样品在不同光源下色差较小，而红、青、浅蓝等色相的样品在不同光源下色差较大。结果表明，不同光源下颜色稳定性与其兴奋纯度以及主波长/补色波长有关。在实际生产与应用中，可以通过以下2种方式提高不同光源下产品颜色的稳定性:一是选择兴奋纯度较低的颜色；二是选择特定色相如紫、黄、黄绿等色相的颜色。     

基于多光谱和sRGB空间的自然植物色的重现

多光谱成像技术在颜色科学方面的应用主要在于准确的记录和复制拍摄场景的颜色信息。针对目前基于光谱的颜色重现设备昂贵、复杂和实现起来困难的问题,提出了物体颜色从反射光谱空间向sRGB颜色空间变换的方案。在遵循sRGB标准的设备上重现了D65光源下的218种自然植物颜色,所显示的颜色与植物反射光谱实物对应的名称一致。对8个自然植物在9种不同光源下和在sRGB空间下的颜色进行了预测显示。所提出的方案对在网络环境下实现相同实物在不同照明环境下的场景显示提供了可行的途径。     

NCS颜色空间标号的扩充方法研究

为了对现有NCS颜色空间的标号进行扩充,通过分析NCS图谱集上样本的CIE1976L a b 的值与NCS颜色空间坐标的关系,将NCS标号间隔单位由图谱集中的每10个黑度、艳度单位扩充至每1个黑度、艳度单位。应用样条插值、线性插值算法计算出这些扩充标号对应的CIELAB值,并将它们绘制在CIELAB空间图上。     

Approximating the CIECAM02 color appearance model by means of neural networks

An artificial neural network used to realize the approximating problem of the color appearance model (CAM) CIECAM02 in color management is demonstrated. GretagMacbeth ColorChecker Charts, which now are widely used in calibration of digital camera, are chosen as sanples to implement the forward and reverse color appearance models. When the predictive results are evaluated, for forward model, the output color appearance space is converted to the uniform color space based on CAM and is evaluated, while for reverse model, because the prediction precision is insufficient, we try to convert the color appearance space, which is the cylinder space, to the cube space similar to the red, green, and blue (RGB) space, and the results show that the precision is obviously improved.     

Background updating and shadow detection based on spatial, color, and texture information of detected objects

Background model updating is a vital process for any background subtraction technique. This paper presents an updating mechanism that can be applied efficiently to any background subtraction technique. This updating mechanism exploits the color and spatial features to characterize each detected object. Spatial and color features are used to classify each detected object as a moving background object, a ghost, or a real moving object. The starting position of each detected object is the cue for updating background images. In addition, this paper presents a hybrid scheme to detect and remove cast shadows based on texture and color features. The robustness of the proposed method and its effectiveness in overcoming challenging problems such as gradual and sudden illumination changes, ghost appearance, non-stationary background objects, the stability of moving objects most of the time, and cast shadows are verified quantitatively and qualitatively.     

One color contrast enhanced infrared and visible image fusion method

Color constancy (Toet and Franken, 2003 [2]; Toet, 2003 [7]) and color contrast (Scribner et al., 2000 [21]; Lee et al., 2005 [23]) are two important topics for color image fusion. The paper focuses on the low color contrast problem of linear fusion algorithms with color transfer method. Color transfer technology is popular in infrared (IR) and visible image fusion to give the fused image a natural day-time color appearance (Toet, 2003 [7]; Wang et al., 2007 [8]; Zheng and Essock, 2008 [9]). However, in the color transfer step, all three channels of the color space are processed with the same linear mapping without color enhancement, resulting in low color contrast between the target and the background (Wang et al., 2007 [8]). Based on the characteristics of the IR image, we introduce a ratio of local to global divergence of the IR image to improve the color contrast. The enhancement ratios for both hot and cold targets are larger than one, while it tends to one for the background. As a result, the proposed method pops out both hot and cold targets in color, where hot targets will appear intense red, and cold targets will appear cyan. Subjective results show visible color contrast enhancement effects. Target detection experiments through hue and saturation components of the fused image show an improvement in the hit rate for target detection, owing to larger color distance between the target and the background.     

Change of Color Appearance in Photopic,Mesopic and Scotopic Vision

Mesopic vision describes a range of light levels where vision is mediated by both cones and rods. The appearance of color in mesopic vision differs drastically from that in photopic vision, where only cones mediate visual information. We used a haploscopic color matching technique to investigate the color appearance under various illuminance levels, ranging from photopic to scotopic via mesopic levels. The observers did color matching between a test color chip under various illuminance levels and a matching color stimulus presented on the Cathode-Ray Tube (CRT) display under the photopic illuminance condition. The results showed that not only chroma and lightness but hue of most color chips changed with illuminance. The manner of the hue changed depended on the color of the test chip, while matching points approached a neutral gray with decrease in illuminance level for all test chips. Chroma reduced continuously with decrease of the illuminance level until 0.1 lx for reddish and yellowish color chips or until 1 lx for greenish and bluish ones. Beyond those illuminance levels, chroma was approximately constant. Lightness decreased with decreasing illuminance level for all test chips except bluish color chips, for which lightness did not decrease much in general and even increased in some cases as predicted by the Purkinje shift. The experimental results obtained in the present study provide critical features that should be considered in predicting the appearance of color at low light levels.     

Color Appearance of a Patch Explained by RVSI for the Conditions of Various Colors of Room Illumination and of Various Luminance Levels of the Patch

Whenever we enter a space illuminated differently from a previous space whether in color or in illuminance, we can quickly adapt to the new atmosphere and can again perceive white for the originally white object; this is known as color constancy. This phenomenon is explained by rotation of the recognition axis of the recognized visual space of illumination (RVSI) toward the illumination color. The explanation then predicts that the color appearance of a test patch changes radically toward the opposite direction from the color of illumination when the physical property of the test patch is kept unchanged at a neutral white. This prediction was confirmed by Experiment 1, where eight different colors of illumination were employed. The test patch appeared very vivid in color and shifted toward the opposite direction from the color of the illumination. In RVSI theory the light source color mode is explained by the release of the test patch from the restriction of RVSI. The release can be achieved by increasing the luminance of the test patch and the color appearance of the patch should then return to its own color as it is no longer controlled by RVSI. In Experiment 2 these predictions were investigated by increasing the luminance of the test patch to a much higher level than that of the objects in the lit room fixed at an illuminance of about 1001x. The color appearance of the test patch indeed became the light source color and returned to the original neutral white. Emphasis was given in the course of the experiments that the subjects were observing the test patch presented in a real 3D space where the subjects also stayed inside so that they could properly construct RVSI for the space.     

Prediction of Chinese color system appearance scales using various color appearance models

The chromaticities of the Chinese color system dataset are applied to eight color appearance models (CAMs). Models used are: CIELAB, Hunt, Nayatani, RLAB, LLAB, ZLAB, CIECAM97s, CIECAM02.     

基于感知颜色空间的灰度图像伪彩色编码

提出了一种基于感知颜色空间的灰度图像伪彩色编码的新方法。首先建立了灰度级和色调、饱和度和亮度等与人的感知颜色特征之间的函数关系。然后通过感知颜色空间到RGB空间的矩阵变换 ,得到相应的RGB值。最后将其分别送入彩色显示器的红、绿、蓝枪即可得到伪彩色图像。建立了相应的数学模型 ,给出了分析和实验结果 ,并对如何获得符合人们视觉习惯的伪彩色图像等问题进行了讨论。根据不同的实际需要 ,可以方便地建立灰度级与颜色的色调、饱和度与亮度的不同对应关系 ,使产生的伪彩色图像符合人们的感知习惯。用该方法得到的伪彩色是近似均匀的 ,即在不同的灰度级上 ,相邻两灰度级对应的色差近似相等