Product color emotional design considering color layout

Emotional experience and demand for product colors are important factors in users' decisions to buy and use a product. Therefore, accurately characterizing users' emotional responses of the product's color has become a significant consideration for product color design. However, a product color design problem exists in which it is challenging to accurately and efficiently position users' color image space because consumers have completely different image perceptions when encountering a large number of color schemes generated by different color spatial distributions. For this reason, this article proposes a product color emotional design method that considers the color layout. A table is built of an elemental composition for product color design, which contains color layout forms. This article also establishes a mapping model based on the semantic difference and back propagation neural network between the users' color image perceptions and the elements of the product's color design. The system recommends a color layout form that matches the users' emotional image goal through the k‐nearest neighbor algorithm, and then the form is initialized using a genetic algorithm. The system can realize the solution to the optimal product color scheme by optimizing and evaluating the population. Designers can make intuitive choices and decisions through the product color recommendation system. Through an example of color design for industrial vacuum cleaners, this article shows that the method has satisfactory feasibility and applicability for solving the problem of the optimization of product color design with color layout forms.     

Applying image‐based color palette for achieving high image quality of displays

In the highly competitive display market, manufacturers continuously develop new technologies to improve the image quality of displays. However, color measurement and visual assessment are time‐consuming to production lines. A new method to measure and improve color quality of the displays automatically therefore, is urgently needed to the manufacturers. This article proposes a familiar color correction strategy to optimize the colors of different displays by means of creating an image‐based color palette which enables color correction for familiar objects (e.g., facial skin, blue sky, or green grass) in the multidisplay systems. To produce the image‐based color palette, the 8‐bit RGB value of each pixel in an image is transformed to L*d*n* (lightness/dominant color/nondominant color) color channels, and the dominant‐color regions in an image are subsequently extracted from the dominant color (d*) channel. The memory color data of familiar objects can be set in reference monitor in advance to determine the dominant color (d*) channel. Then a series of palette colors are generated around a displayed image. The color palette will be displayed as a target for two‐dimensional colorimeter shooting to obtain the measured color data. The familiar color correction model was established based on a first‐order polynomial regression to achieve a polynomial fit between the measured color data and the reference color data on the color palette. The proposed method provides a solution to correct familiar colors on a displayed image, and maintains the original color gamut and tone characteristic in the multidisplay systems simultaneously. It is possible to achieve the preferred intent of the displayed images by using the proposed familiar color correction method. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 154–168, 2014     

Assessing color performance of whole‐slide imaging scanners for digital pathology

The color performance of two commercial whole‐slide imaging (WSI) scanners was compared against the ground truth and a hypothetical monochrome scanner. Three biological tissue slides were used to test the WSI scanners. A multispectral imaging system was developed to obtain the color truth of the biological tissue slides at the pixel level. The hypothetical monochrome scanner was derived from the color truth as a lower bound for comparison. The CIEDE2000 formula was used to measure color errors. Results show that color errors generated by the modern commercial WSI scanner, the legacy commercial WSI scanner, and the monochrome WSI scanner are in the range of [8.4, 13.0], [18.0, 26.33], and [17.4, 17.6] ΔE₀₀, respectively. The legacy commercial WSI scanner was outperformed by not only the modern commercial WSI scanner but also by the hypothetical monochrome scanner.     

Skin‐color correction method based on hue template mapping for wide color gamut liquid crystal display devices

The development of wide color gamut (WCG) liquid crystal display (LCD) plays an important role in the high‐quality television (TV) field. Nowadays, people want their TV or display devices to have the capability of showing vivid colors while keeping skin colors as natural as they remember. Therefore, it is necessary to develop color‐correction technologies for WCG LCD system. A new color‐correction method named “natural skin‐color mapping algorithm” (NSCMA) for WCG LCD is proposed in this study. It can solve the skin‐color contour problem in color‐corrected images with simple skin‐color detection. Its development is based on the concepts of performing color mapping between source hue colors and target hue colors on each hue page. The polynomial regression is also applied to calculate the color mapping conversion matrices. Two color mapping factors called template‐size factor and tone‐compression factor are designed in NSCMA. The template‐size factor is used to adjust target template sizes adequately. The tone‐compression factor is designed to control the degrees of image enhancement. For facial skin‐color pictures, the appropriate settings of template‐size factor and tone‐compression factor will get suitable color image rendering on the WCG LCD. It is demonstrated that the WCG LCD can be corrected to show vivid color pictures and keep facial skin colors as natural as possible when the proposed NSCMA is performed. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

Derivation of a color space for image color difference measurement

In digital image reproduction, it is often desirable to compute image difference of reproductions and the original images. The traditional CIE color difference formula, designed for simple color patches in controlled viewing conditions, is not adequate for computing image difference for spatially complex image stimuli. Zhang and Wandell [Proceedings of the SID Symposium, 1996; p 731–734] introduced the S‐CIELAB model to account for complex color stimuli using spatial filtering as a preprocessing stage. Building on S‐CIELAB, iCAM was designed to serve as both a color appearance model and also an image difference metric for complex color stimuli [IS&T/SID 10th Color Imaging Conference, 2002; p 33–38]. These image difference models follow a similar image processing path to approximate the behavior of human observers. Generally, image pairs are first converted into device‐independent coordinates such as CIE XYZ tristimulus values or approximate human cone responses (LMS), and then further transformed into opponent‐color channels approximating white‐black, red‐green, and yellow‐blue color perceptions. Once in the opponent space, the images are filtered with approximations of human contrast sensitivity functions (CSFs) to remove information that is invisible to the human visual system. The images are then transformed back to a color difference space such as CIELAB, and pixel‐by‐pixel color differences are calculated. The shape and effectiveness of the CSF spatial filters used in this type of modeling is highly dependent on the choice of opponent color space. For image difference calculations, the ideal opponent color space would be both linear and orthogonal such that the linear filtering is correct and any spatial processing on one channel does not affect the others. This article presents a review of historical opponent color spaces and an experimental derivation of a new color space and corresponding spatial filters specifically designed for image color difference calculations. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Use of gray system theory in product‐color planning

A quantitative evaluation method for the CIE color‐planning activity within the product design cycle is proposed in this article. The questionnaire‐based process that is traditionally employed to obtain objective color psychology tends to be time‐consuming. Accordingly, this study proposes the use of gray system theory to overcome this problem. In the CIE color system, colors are defined by three primary colors, R (red), G (green), and B (blue). Using these three principal hues with fixed equigap sequences to simulate specific basic color samples is an efficient means of investigating unicolor images on a personal computer. However, a gray relational generating operation can be used to simulate colors beyond these basic samples and to predict the corresponding membership values for semantic words. In addition, the gray clustering operation is introduced to predict the overall color image evaluation of multicolored products. The predicted evaluation results of the gray system theory and a back‐propagation neural network are both compared with experimentally verified results. The results indicate that the gray forecasting model is the more effective means of predicting the image evaluation, and therefore, the method is adopted within the color‐planning activity. Although this study takes the example of the Internet‐aided color planning of a baby walker as a case study, the proposed method can also be used on other products. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 222–231, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20009     

Pattern‐driven color pattern recognition for printed fabric motif design

Pattern‐driven design method is an important data‐driven design method for printed fabric motif design in textiles and clothing industry. We introduce a novel framework for automatic design of color patterns in real‐world fabric motif images. The novelty of our work is to formulate the recognition of an underlying color pattern element as a spatial, multi‐target tracking, classification, segmentation and similarity association process using a new and efficient color feature encoding method. The proposed design method is based on pattern‐driven color pattern recognition and indexing from the element image database. A series of color pattern recognition algorithms are used for color and pattern feature extraction. The local statistical corner features and Markov random field model are used for motif unit tiling detection and conversion. The color feature encoding problem is modeled in a gray‐scale color difference optimization problem, which can be solved quickly by existing algorithms. Color pattern feature matching, segmentation and indexing techniques are then used to locate and replace the elements in the motif unit image with similar elements in the database. Experiments show that the approach proposed in this study is effective for color pattern recognition and printed fabric motif design.     

Emotional response to color across media

In this study, the characteristics of emotional responses to color are explored in two empirical studies. In particular, the relationship between color attributes and emotional dimensions—valence, arousal, and dominance—is analyzed. To account for the cognitive quantity of color, 36 color stimuli were selected following hue and tone categorizations and based on the CIELAB LCh system. In one experiment, the colors were presented on A5‐size glossy paper whereas the identical colors were displayed on CRT monitors in the other experiment. In both experiments, the subjects assessed the emotional responses to each color stimulus using a Self‐Assessment‐Manikin (SAM), which consists of three rows of five pictograms illustrating the three dimensions of emotion, respectively. The empirical results provide evidence that the patterns of affective judgment of colors can be profiled in terms of the three dimensions of emotion (Reliability coefficient, Cronbach's alpha > 0.7). All three attributes of colors, i.e. hue, Chroma, and Lightness, influenced the emotional responses (repeated measurement One‐way ANOVA, P < 0.05), and especially, Chroma was always positively correlated with each of the three emotional dimensions (r > 0.60 P < 0.01). Moreover, the results indicate that emotional responses to color vary more strongly with regard to tone than to hue categories. Comparing the SAM ratings between the two experiments, a systemic explanation has yet to be found to conclude that there is a media effect on the emotional responses to colors. Furthermore, the process of affective judgment of colors and the limit of color as an emotion elicitor are discussed. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

Estimation of the structure of air entrained concrete using a flatbed scanner

This paper describes the possibilities of estimating the structure of concrete with the help of automatic image analysis using a high-resolution flatbed scanner. Sample preparation techniques and components of the image analysis system are described. Concerning the structure of the entrained air voids system, it was found that the results obtained using a high-resolution flatbed scanner were comparable to those obtained by conventional methods, i.e. using a system equipped with stereomicroscope. The possibility of automatically measuring the paste content in hardened concrete by analysis of plane sections, taking into account information available in RGB histograms, is also presented. A new procedure is proposed for the identification of the distance from the cement paste to periphery of the nearest air void, which enables rapid evaluation of the quality of the air entrainment treatment The procedure also allows some characterization of the aggregate component.     

Michromatic scope for enhancement of color difference

A michromatic (microscope plus chromatic) scope is a device that enhances the color discrimination between two spectral color datasets. Three spectral filters are required, instead of the conventional red, green, and blue filters, for the implementation of a michromatic camera. In this study, we describe two approaches to the design of these filters: in the first case, the design is based on the direct optimization of the filter characteristics (transmittance), whereas in the second case, the design is based on the nonnegative tensor factorization (NTF) of the spectral datasets. A michromatic camera can be implemented using these filters along with compatible postprocessing in‐camera firmware. Here, we performed experiments with two color datasets: one comprising skin and vein colors, and one comprising skin and cosmetics colors. These were further divided into a training set and a test set. The filters were defined using the training set, and the operation of the filters was tested and magnified using the test set. Our experiments demonstrated that the proposed approaches are suitable for color discrimination. For the first color dataset, the enhancement produced using the optimized filters was up to 252% of the original value, and the average color difference ΔE was increased from 2.82 to 9.93. NTF and preprocessing further enhanced the ΔE up to 21.84. For the second color dataset, NTF and postprocessing enhanced the ΔE from 4.33 to 29.19. The proposed discrimination enhancement could be physically implemented in a designated digital charge‐coupled device camera with proper filter installation and compatible postprocessing. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

The feasibility of establishing new color image scales using the magnitude estimation method

Color image is one of the most important factors in art and design. In general, artists and designers apply their own personal image meanings into their work. However, the image meaning for a specific work is frequently in conflict with those of the general observer. Thus it is necessary and important to derive one set of merit color image scales which can be utilized to predict the color image meanings of works in parallel with the average person's perception and which can also serve as a guide for artists and designers. In this study, the psychophysical method (magnitude estimation method), usually used in visual measurement of color appearance, was employed to attempt to establish new color image scales to evaluate the color image meanings of works matching those of the average person. The results show that new color image scales WIP are developed, and the relativity between the latest color image scales WIP and the color attributes (say Lightness L*, Hue h, and Chroma C*) of the CIELAB color space is also discussed. The total mean value of coefficient of variation for the results of visual assessment in the experiment of evaluating the color image meanings of the 207 color specimens used, in general, is about 36, similar to that for those experiments conducted using the psychological method. Also, a good relationship between the new color image scales and the color attributes of the CIELAB color space can be found. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 463–468, 2007     

Color‐emotion associations,designing color schemes for urban environment‐architectural settings

Color in urban design has become an important issue, each city may present different colors which help to define and describe its architectural features. In the study, color in urban design with architectural setting is studied, façade colors are analyzed with a specific emphasis on the following research questions; “Can color schemes be designed in respect to color‐emotion associations? ” and “Are color‐emotion associations affective while designing architectural setting‐urban environment?.” Non‐color experts, 170 people, from different European and non‐European countries were asked to match the most appropriate adjectives with the given street views in accordance to their color schemes. In the first step, the effect of color is identified in relation to architectural environment‐urban setting, second the relative effect of color is studied as a component of the material. A categorical specification on color cognition and linguistic level of representation is attempted. The results can be a starting point to highlight the importance of preparing color schemes in regard to color‐emotion associations. Abstract color schemes may also provide us an idea about image setting, especially at design process stage. In the study, keywords are linked as environment‐response pairs; such as quiet, calming, lively, exclusive, reserved, and natural. Human psychophysical structure such as “warm‐cool,” “heavy‐light” in regard to visualizing certain colors are evaluated and described in terms of building materials.     

Optimization of color reproduction on CRT‐color monitors

In the present experimental study, we quantify the influence of the brightness and contrast levels of a CRT‐color monitor in the color reproduction of 60 Munsell chips distributed throughout the chromatic diagram. The images were captured by two CCD cameras, and the color differences were evaluated after reproducing the chips on a color monitor (the experiment was performed with 3 different monitors) for 9 combinations of brightness‐contrast levels. We evaluated the color differences with 3 different formulas: CIELAB, CIELUV, and CIE94. The results indicate that the optimal settings of a monitor, to minimize the color differences, is a medium or minimum brightness level in combination with a maximum contrast level. This combination ensures a more faithful color reproduction with respect to the original image. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 207–213, 1999     

Fast image enhancement based on color space fusion

The current Retinex algorithm processes the RGB channels separately for color image enhancement. However, it changes the ratios of RGB components and also causes some serious problems, such as color distortion, color noise, and the halo artifacts. To solve these issues, we propose a novel algorithm based on color space fusion. The single scale Retinex with fast mean filtering is applied to the luminance component in hue‐saturation‐value (HSV) color space. An enhancement adjustment factor is introduced to avoid color distortion and noise amplification. Then, the surrounding function is replaced by a small scale Gaussian filter in RGB color space to eliminate the halo artifact. A parameter is involved to keep the color natural when the reflection is estimated. Finally, the enhanced color image is constructed from the weighted averaging results of these two steps. The subjective and objective evaluations of many different backlight images captured by different cameras are implemented to verify the validity of the proposed algorithm in our experiments. The experimental results show that the proposed algorithm can not only significantly suppress the halo effect and noise amplification, but can also remove color distortion. Our proposed algorithm is superior to the multi‐scale Retinex with color restoration approach and other state‐of‐the‐art methods. © 2014 Wiley Periodicals, Inc. Col Res Appl, 41, 22–31, 2016     

Dictionary‐based estimation of spectra for wide‐gamut color imaging

With the widespread use of commercialized wide‐gamut displays, the demand for wide‐gamut image content is increasing. To acquire wide‐gamut image content using camera systems, color information should be accurately reconstructed from recorded image signals for a wide range of colors. However, it is difficult to obtain color information accurately, especially for saturated colors, if conventional color cameras are used. Spectrum‐based color image reproduction can solve this problem; however, bulky spectral imaging systems are required for this purpose. To acquire spectral images more conveniently, a new spectral imaging scheme has been proposed that uses two types of data: high spatial‐resolution red, green, and blue (RGB) images and low spatial‐resolution spectral data measured from the same scene. Although this method estimates spectral images with high overall accuracy, the error becomes relatively large when multiple different colors, especially those with high saturation, are arranged in a small region. The main reason for this error is that the spectral data are utilized as low‐order spectral statistics of local spectra in this method. To solve this problem, in this study, a nonlinear estimation method based on sparse and redundant dictionaries was used for spectral image estimation—where the dictionary contains a number of spectra—without loss of information from the low spatial‐resolution spectral data. The estimated spectra are represented by a mixture of a few spectra included in the dictionary. Therefore, the respective feature of every spectrum is expected to be preserved in the estimation, and the color saturation is also preserved for any region. Experiments performed using the simulated data showed that the dictionary‐based estimation can be used to obtain saturated colors accurately, even when multiple colors are arranged in a small region. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013     

Set theoretic estimation for problems in subtractive color

The use of linear algebra and set theoretic estimation for problems in color science and imaging is reviewed. Through a product‐space formalism, the powerful projections onto convex sets (POCS) algorithm is extended to subtractive color systems satisfying convex constraints in the density domain. Several convex sets are defined, which are useful in color science and imaging, and projections onto these sets are presented. The usefulness of the new methods is demonstrated by applying them to three practical problems: (1) model‐based scanner calibration, (2) design of color scanning filters that are color mixture curves, and (3) colorant formulation. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 333–348, 2000     

Sensor interpixel correlation analysis and reduction for color filter array high dynamic range image reconstruction

High dynamic range imaging (HDRI) by bracketing of low dynamic range (LDR) images is demanding, as the sensor is deliberately operated at saturation. This exacerbates any crosstalk, interpixel capacitance, blooming and smear, all causing interpixel correlations (IC) and a deteriorated modulation transfer function (MTF). Established HDRI algorithms exclude saturated pixels, but generally overlook IC. This work presents a calibration method to estimate the affected region from saturated pixels for a color filter array (CFA) sensor, using the native CFA as a matched filter. The method minimizes color crosstalk given a set of candidates for proximity regions, and requires no special setup. Results are shown for a 21‐bit HDR output image with improved color fidelity and reduced noise. The calibration reduces IC in the LDR images and is performed only once for a given sensor. The improvement is applicable to any HDRI algorithm based on CFA image bracketing, irrespective of sensor technology. Generalizations to subsaturated and supersaturated pixels are described, facilitating a suggested irradiance‐exposure dependent point spread function charge repatriation strategy.