Device-independent Color Space Research Articles

Исследование цветовых отличий при воспроизведении памятных цветов на устройствах визуализации

This work is devoted to the study of changes in color coordinates on various visualization devices during color reproduction, in particular smartphones, as one of the most commonly used devices in the modern world, which is associated with the hardware dependence of the color reproduction system. The purpose of the work is to select visualization tools, determine their technical characteristics, determine test colors for visualization on various devices, determine the tolerances in reproduction of each color when using various viewing devices. To achieve the goal, such tasks were set as the selection of images containing memorable colors. These colors are fundamental in determining the tolerances in color reproduction, since information about them is inherent in each person on the basis of his life experience and knowledge, and a change in the reproduction of memorable colors, a violation in color rendering, is the most visually noticeable. Memorable colors are converted into samples – test objects, which are used to determine coordinates in a device-independent color space. Determination of tolerances is made when changing color reproduction through the use of selective (color) correction. When solving the problem, it was noted that there are colors in which, with small changes in color coordinates, the visual component changes to a sufficiently strong degree, while other colors, with a numerically identical change, do not visually change. For the selected colors on various visualization tools, the difference in color reproduction is calculated and the calculations of color differences for different models of viewing devices with a visual difference in perception are given.

A Correspondence-Free Color Chart Design for Color Calibration

Colorimetric calibration computes the necessary color space transformation to map a camera's device-specific color space to a device-independent perceptual color space. Color calibration is most commonly performed by imaging a color rendition chart with a fixed number of color patches with known colorimetric values (e. g., CIE XYZ values). The color space transformation is estimated based on the correspondences between the camera's image and the chart's colors. We present a new approach to colorimetric calibration that does not require explicit color correspondences. Our approach computes a color space transformation by aligning the color distributions of the captured image to the known distribution of a calibration chart containing thousands of colors. We show that a histogram-based colorimetric calibration approach provides results that are onpar with the traditional patch-based method without the need to establish correspondences.

Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.

Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

Currently, researchers are developing numerous plant diseases recognition model using image processing and soft computing. The models are mainly based on the extraction of discolored features and applying in various soft computing approaches to automate plant diseases recognition process. The extracted features are statistical, frequency, spatial-frequency or hybrid features of captured images accessed in device-dependent or device-independent color spaces. The performance of diseases recognition system is significantly dependent upon the selection of color spaces and extracted features. This paper presents a comprehensive review of the impact of color spaces and feature sets on machine learning and rule base automated plant diseases classifier. The review performed with six categories of rice plant images with two machine learning and two rule base classifiers. Initially, a thorough literature review performed on the previous investigation based on color spaces and used feature sets for designing diseases recognition model. Then common conditions created to extract feature sets in different color spaces, and applied machine learning and rule base classifier to analyze the impact of color spaces with feature sets. The review presents a detailed discussion on the correlation between color spaces, feature sets, and performance of diseases recognition system. The review results reveal the most relevant features on specific color space for machine learning and rule base classifier. It also deduces that the performance of plant diseases classifier highly dependent upon used color space and extracted features.

Beyond raw-RGB and sRGB: Advocating Access to a Colorimetric Image State

Most modern cameras allow captured images to be saved in two color spaces: (1) raw-RGB and (2) standard RGB (sRGB). The raw-RGB image represents a scene-referred sensor image whose RGB values are specific to the color sensitivities of the sensor's color filter array. The sRGB image represents a display-referred image that has been rendered through the camera's image signal processor (ISP). The rendering process involves several camera-specific photo-finishing manipulations intended to make the sRGB image visually pleasing. For applications that want to use a camera for purposes beyond photography, both the raw-RGB and sRGB color spaces are undesirable. For example, because the raw-RGB color space is dependent on the camera's sensor, it is challenging to develop applications that work across multiple cameras. Similarly, the camera-specific photo-finishing operations used to render sRGB images also hinder applications intended to run on different cameras. Interestingly, the ISP camera pipeline includes a colorimetric conversion stage where the raw-RGB images are converted to a device-independent color space. However, this image state is not accessible. In this paper, we advocate for the ability to access the colorimetric image state and recommend that cameras output a third image format that is based on this device-independent colorimetric space. To this end, we perform experiments to demonstrate that image pixel values in a colorimetric space are more similar across different makes and models than sRGB and raw-RGB.

A comprehensive study on color correction for medical facial images

The color images produced by digital cameras are usually not in conformity with their intrinsic colors. This will seriously affect computer-aided medical facial image analysis because it is on the basis of accurate rendering of color information. To solve that, we propose an optimized color correction scheme for medical facial images based on a comprehensive study in various aspects: color spaces, color patches and color correction algorithms. Firstly, we utilize undistorted facial images to demarcate complexion gamut. Secondly, we choose the whole color patches located inside the range of complexion gamut as our validation samples and select the most crucial color patches from the gamut as our optimized training samples, followed by the optimization criteria. Thirdly, we select an adaptive target device-independent color space for medical facial images color correction task. Finally, we evaluate the performance of three most popular color correction algorithms, and select the most suitable one to build our final regression model. Qualitative and quantitative experimental results show that the proposed scheme is superior to that based on the ColorChecker 24 and performs very closely to the benchmark training error results while only 24 colors are involved. More importantly, the acquisition environment is well-designed with the system color chromatic aberration of 5.3369, which is quite small and hard to improve, but the color difference still can be perceived by human vision. Through our optimized color correction scheme, we succeed in reducing the color chromatic aberration to 1.5898. Furthermore, the corrected facial images are more consistent with the observed colors by human beings. Compared with the previous works, our color correction scheme is characterized by mission dependence and statistical reliability. Besides, the optimized color correction model has low complexity and high accuracy. All of these features make this scheme distinctive from previous color correction methods and effective for medical facial images color correction task.

High dynamic range image reconstruction in device-independent color space based on camera colorimetric characterization

High dynamic range (HDR) image acquisition techniques can record both bright and dark areas of the real scene. In order to acquire accurate colorimetric information as well as the luminance of the HDR scene, a reconstruction method in CIEXYZ device-independent color space based on camera colorimetric method was proposed in this paper. The camera RGB values of individual pictures are firstly transformed into the corresponding CIEXYZ values by colorimetric characterization with unrestricted capture settings. Then the XYZ values are fused by the weighting function determined according to the characterization accuracy. The prediction accuracy of the proposed method was verified by two phases of experiments to outperform the traditional one, with the color difference being less than 2 CIELAB units under the constant relative spectral power distribution (SPD) of lighting and 3.5 CIELAB units for the illuminations of different correlated color temperatures (CCTs), respectively.

Accuracy Comparison of Application of Two Kinds of Cellular Neugebauer Models on Multicolor Separation

Neugebauer printing color model can be divided into tristimulus model and spectral model in form. After color subarea, if these two models are directly applied to multicolor separation, the accuracy of color separation is far from enough. A relatively effective way is to conduct cellular division for device independent color space and achieve higher accuracy through measuring more training samples. This article aims at CMYKRGB seven color separation applications and after performing color separation for two Neugebauer models, cellular divisions with different grades would be carried out successively. The predicted color accuracy of these two models after cellular divisions will be detected using color separation printing test color targets. The impact of cellular division correction on predicted color accuracy and calculation efficiency would be analyzed based on the test results finally.

Conversion of a digital camera into a non-contact colorimeter for use in stone cultural heritage: The application case to Spanish granites

In this study, a digital CMOS camera was calibrated for use as a non-contact colorimeter for measuring the color of granite artworks. The low chroma values of the granite, which yield similar stimulation of the three color channels of the camera, proved to be the most challenging aspect of the task. The appropriate parameters for converting the device-dependent RGB color space into a device-independent color space were established. For this purpose, the color of a large number of Munsell samples (corresponding to the previously defined color gamut of granite) was measured with a digital camera and with a spectrophotometer (reference instrument). The color data were then compared using the CIELAB color formulae. The best correlations between measurements were obtained when the camera works to 10-bits and the spectrophotometric measures in SCI mode. Finally, the calibrated instrument was used successfully to measure the color of six commercial varieties of Spanish granite.

Supervised feature selection for linear and non-linear regression of L*a*b* color from multispectral images of meat

In food quality monitoring, color is an important indicator factor of quality. The CIELab (L^@?a^@?b^@?) color space as a device independent color space is an appropriate means in this case. The co...

Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat

In food quality monitoring, color is an important indicator factor of quality. The CIELab (L⁎a⁎b⁎) color space as a device independent color space is an appropriate means in this case. The commonly used colorimeter instruments can neither measure the L⁎a⁎b color in a wide area over the target surface nor in a contact-less mode. However, developing algorithms for conversion of food items images into L⁎a⁎b color space can solve both of these issues. This paper addresses the problem of L⁎a⁎b color prediction from multispectral images of different types of raw meat. The efficiency of using multispectral images instead of the standard RGB is investigated. In addition, it is demonstrated that due to the fiber structure and transparency of raw meat, the prediction models built on the standard color patches do not work for raw meat test samples. As a result, multispectral images of different types of meat samples (430–970nm) were used for training and testing of the L⁎a⁎b prediction models. Finding a sparse solution or the use of a minimum number of bands is of particular interest to make an industrial vision set-up simpler and cost effective. In this paper, a wide range of linear, non-linear, kernel-based regression and sparse regression methods are compared. In order to improve the prediction results of these models, we propose a supervised feature selection strategy which is compared with the Principal component analysis (PCA) as a pre-processing step. The results showed that the proposed feature selection method outperforms the PCA for both linear and non-linear methods. The highest performance was obtained by linear ridge regression applied on the selected features from the proposed Elastic net (EN) -based feature selection strategy. All the best models use a reduced number of wavelengths for each of the L⁎a⁎b components.

Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence

The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.

Sampling Optimization for Printer Characterization by Direct Search

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the α = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.

Hue Preservation using Enhanced Integrated Multi-scale Retinex for Improved Color Correction

The images captured by most current digital cameras suffer from a narrow dynamic range that results in poor color fidelity and perceived image quality. Accordingly, this article proposes an enhanced integrated multi-scale Retinex (IMSR) method in a device-independent color space, namely, CIELAB, to preserve the hue and obtain a high contrast and naturalness. To achieve the desired objectives, a captured sRGB image is transformed into CIELAB color space using standard equations, then the IMSR is applied to only the L values, thereby preserving the balance of the color components, hue, and saturation. However, since this process causes unnatural undersaturation, a saturation adjustment is per- formed by applying a simple saturation compensation method, which maintains the chromatic ratio with respect to the sRGB gamut boundary according to the variation in the luminance. The adjusted CIELAB values are then transformed into sRGB using an inverse transform function. In experiments, the proposed method showed an improved visibility in both dark and bright regions, while reducing color distortion. Furthermore, in an observation preference test, the images resulting from the proposed method were perceived as hav- ing a higher visibility and naturalness. © 2011 Society for Imaging Science and Technology. DOI: 10.2352/J.ImagingSci.Technol.2011.55.1.010504

An Optimized Tongue Image Color Correction Scheme

The color images produced by digital cameras are usually device-dependent, i.e., the generated color information (usually presented in RGB color space) is dependent on the imaging characteristics of specific cameras. This is a serious problem in computer-aided tongue image analysis because it relies on the accurate rendering of color information. In this paper, we propose an optimized correction scheme that corrects the tongue images captured in different device-dependent color spaces to the target device-independent color space. The correction algorithm in this scheme is generated by comparing several popular correction algorithms, i.e., polynomial-based regression, ridge regression, support vector regression, and neural network mapping algorithms. We test the performance of the proposed scheme by computing the CIE L(*)a(*)b(*) color difference (∆E(ab)(*)) between estimated values and the target reference values. The experimental results on the colorchecker show that the color difference is less than 5 (∆E(ab)(*) < 5), while the experimental results on real tongue images show that the distorted tongue images (captured in various device-dependent color spaces) become more consistent with each other. In fact, the average color difference among them is greatly reduced by more than 95%.

Multispectral Colormapping Using Penalized Least Square Regression

The authors propose a novel method to map a multispectral image into the device independent color space CIE-XYZ. This method provides a way to visualize multispectral images by predicting colorvalues from spectral values while maintaining interpretability and is tested on a light emitting diode based multispectral system with a total of 11 channels in the visible area. To obtain interpretable models, the method estimates the projection coefficients with regard to their neighbors as well as the target. This results in relatively smooth coefficient curves which are correlated with the CIE-XYZ color matching functions. The target of the regression is a well known color chart, and the models are validated using leave one out cross validation in order to maintain best possible generalization ability. The authors compare the method with a direct linear regression and see that the interpretability improves significantly but comes at the cost of slightly worse predictability.

Predition of Textile Fabric Color by Digital Camera

The main objective of this study is to highlight the relationship between RGB values of digital camera as a device-dependent color space and a device-independent CIE color space. Calibration and testing databases are the colorimetric specifications of colored polyester fabrics. The colored fabrics were dyed with a variety of disperse dyestuffs. Camera characterization was done based on the polynomial regression technique. The methods adopted in the study were non-linear filtering applied to camera RGB values and a polynomial regression function directly applied to the CIELAB space. The performances of the methods improved by increasing the number of terms in the polynomial. The estimation errors of the linear polynomial regression to CIEXYZ, the nonlinear polynomial regression to CIEXYZ, the linear polynomial regression to CIELAB, and the nonlinear polynomial regression to CIELAB were 3.29, 4.43, 3.05, and 3.04 DE*ab respectively. The generalization capability decreased by increasing the number of terms in the polynomial regression. The best generalization capability was obtained by the linear polynomial regression to CIELAB. The best result was obtained by non-linear filtering while the second-best result was obtained by the polynomial regression to the CIELAB values.

Multiprimary Decomposition Method Based on a Three-Dimensional Look-Up Table in Linearized LAB Space for Reproduction of Smooth Tonal Change

With the recent development of multiprimary display devices, the three primary colors (red, green, and blue) of a conventional display need to be decomposed into control values for a multiprimary display (MPD) under the constraints of tristimulus matching. To achieve tristimulus matching between such different display systems, the MPD color signals need to be estimated based on a device-independent color space, such as CIEXYZ or CIELAB. Yet, since the focus of a MPD is to display motion picture data, the color space conversion and multiprimary control value decomposition must be simplified. Accordingly, this paper presents a color signal decomposition method for a MPD using a three-dimensional look-up-table (3D LUT) in linearized LAB space. Linearized LAB space satisfies the linearity and additivity required for the color space conversion, and can easily construct a 3D LUT that considers the lightness, chroma, and hue. In addition, to reproduce moving picture data in a MPD, the proposed decomposition method uses a 3D LUT structure to reduce the hardware complexity and processing time. First, a 3D LUT that contains the gamut boundary points of the MPD is created to decompose the multiprimary control values. The chroma and multiprimary color signals for the gamut boundary are then stored in the 3D LUT along with the quantized hue and lightness values. Next, a quadrangular pyramid composed of four gamut boundary points and one lightness point on an achromatic axis is generated according to the input linearized LAB values. Consequently, MPD color signals can be obtained for the input values by interpolating between the multiprimary color signals for the gamut boundary points and the lightness point on an achromatic axis. Furthermore, additional gamut boundary points within 10&#xB0; of the hue are used to interpolate the input values in regions that involve an abrupt change in the multiprimary control values to achieve a smooth change of hue. As a result, the proposed method guarantees computational efficiency and color signal continuity. Plus, less memory space is required when compared with conventional color decomposition methods.

Black Color Replacement using Gamut Extension Method

Besides having cyan, magenta and yellow colorants, most of color printers include at least one extra colorant, black (K) in order to increase the density for shadow colors and to reduce colorants for printing shadow colors. There are numerous advantages of using GCR (Gray Component Replacement) technique: the chief advantages are (1) extension of color gamuts, especially of denser black, (2) improvement of image definition, and (3) substitution of relative inexpensive black ink to more costly colored inks.Unlike gamut compression studies, gamut extension is mapping from narrower original gamuts to wider reproduction gamuts. In this study gamut extension method was used in increasing color gamut, the major benefit of using black ink. In new method, black replacement is done in device-independent color space, and gamut is extended with increasing lightness reproducible range (CIE L* or J) and increasing chroma range, sequentially. In addition, observer experimental data of gamut extension studies was used for searching optimal black replacement.Two different evaluation experiments, quantitative and qualitative, were carried out. In quantitative experiment, color gamuts were compared between methods of existing GCR methods in commercial color printers and of this study. Furthermore lightness contrast values, i.e., reproducible lightness range, were compared. In qualitative evaluation (psychophysical), color imaging experts compared reproductions through two different methods. The results show new method outperforms existing black color replacement method.

Multispectral color image capture using a liquid crystal tunable filter

We describe the experimental setup of a multispectral color image acquisition system consisting of a professional monochrome CCD camera and a tunable filter in which the spectral transmittance can be controlled electronically. We perform a spectral characterization of the acquisition system taking into account the acquisition noise. To convert the camera output signals to device-independent color data, two main approaches are proposed and evaluated. One consists in applying regression methods to convert from the K camera outputs to a device-independent color space such as CIEXYZ or CIELAB. Another method is based on a spectral model of the acquisition system. By inverting the model using a principal eigenvector approach, we estimate the spectral reflectance of each pixel of the imaged surface.