A cross‐cultural colour‐naming study. Part I: Using an unconstrained method

Colour naming by panels of British and Taiwanese subjects (speaking English and Mandarin, respectively) was used to study colour categorization, and the results applied to investigate differences of usage between the two languages. Fifty British and 40 Chinese subjects took part in experiments using an unconstrained method with 200 ISCC‐NBS colour samples. Data analysis was performed to calculate the frequency and codability of each colour name in each group and subgroup. These names were then grouped using 7‐category and 4‐category methods to find the culture and gender differences. It was confirmed that the 11 basic names found by Berlin and Kay were the most widely used for both languages. The results showed a close agreement between the two languages in terms of colour categories, but a large discrepancy in the use of secondary names due to cultural differences. The cross‐cultural comparison revealed a clear pattern of the linkage between language and concepts of colour. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 40–60, 2001     

A cross‐cultural colour‐naming study: Part II—Using a constrained method

This study aimed to investigate the differences in colour naming between the English (British) and Mandarin (Taiwanese) languages. A constrained method was employed, with 20 British and 20 Chinese adults. All the experiments were conducted under an artificial daylight, using 1526 colours from the Natural Color System (NCS). Each subject was asked to find the colour(s) corresponding to basic names, modifiers, and secondary names in terms of one colour (focal colour) or a colour region (colour volume). Little difference in chromaticness and hue was found between the two languages, but a systematic discrepancy was found in blackness. Because this could have been caused by different surrounds, i.e., gray and white walls used for the British and Chinese experiments, respectively, a verification experiment was carried out using a panel of ten Taiwanese subjects against a gray surround. The results proved that the lightness difference found earlier was indeed caused by the surround. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 193–208, 2001     

Evaluation of colour‐difference formulae for different colour‐difference magnitudes

Most of the colour‐difference formulae were developed to fit data sets having a limited range of colour‐difference magnitudes. Hence, their performances are uncertain when applying them to a range of colour differences from very small to very large colour differences. This article describes an experiment including three parts according to the colour‐difference magnitudes: large colour difference (LCD), small colour difference (SCD), and threshold colour difference (TCD) corresponding to mean ΔE values of 50.3, 3.5, and 0.6, respectively. Three visual assessment techniques were used: ratio judgement, pair comparison, and threshold for LCD, SCD, and TCD experiments, respectively. Three data sets were used to test six colour‐difference formulae and uniform colour spaces (CIELAB, CIE94, CIEDE2000, CAM02‐SCD, CAM02‐UCS, and CAM02‐LCD). The results showed that all formulae predicted visual results with great accuracy except CIELAB. CIEDE2000 worked effectively for the full range of colour differences, i.e., it performed the best for the TCD and SCD data and reasonably well for the LCD data. The three CIECAM02 based colour spaces gave quite satisfactory performance. © Wiley Periodicals, Inc. Col Res Appl, 2012     

The usability of the CIE colour‐matching functions in the case of CRT monitors

During the past years, several papers have been published that question the use of the CIE colour‐matching functions in the case of metameric samples. Visually matching samples produced on CRT (Cathode Ray Tube) monitors are metameric to most colour stimuli created by illuminating reflecting materials. As CRT monitors are often used in colour design applications, it seemed important to check how well CIE colorimetry will predict such colour matches. To investigate this problem, we set up an experiment in which painted samples were matched with samples produced on a CRT monitor. The colour of incandescent lamp irradiated Munsell samples were visually matched to the mixture of the RGB primaries of a CRT monitor. Both the reflected colour stimuli of the Munsell samples and the emitted stimuli of the monitor were measured spectroradiometrically. Our results imply that there is an observer‐dependent variability among the matches, but we could not find a major difference between the tristimulus data of the hard copy and soft copy presentations that would indicate errors in the CIE colour‐matching functions. The measurement accuracy, quantization errors of the monitor, and the achieved accuracy of the colour matches are treated in this study. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 436–441, 2001     

Comparing large colour‐difference data sets

This study investigated the differences between different large colour‐difference (LCD) data sets (with a mean ΔE value about 10). Six data sets were studied. For each data set, various CIELAB based colour difference models were derived to fit the data. These models were compared to shed light on the difference between the different data sets. It was found that all data sets have very similar characteristics except for the Munsell data. Detailed investigation showed that the discrepancy is mainly due to the balance between the lightness and chromatic differences used previously for the Munsell data set. It was found that one unit of Munsell Value appears to be three times as large colour difference as one unit of Munsell Chroma at least under the experimental conditions for the data sets studied here. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

Uniform colour spaces based on the DIN99 colour‐difference formula

Several colour‐difference formulas such as CMC, CIE94, and CIEDE2000 have been developed by modifying CIELAB. These formulas give much better fits for experimental data based on small colour differences than does CIELAB. None of these has an associated uniform colour space (UCS). The need for a UCS is demonstrated by the widespread use of the a*b* diagram despite the lack of uniformity. This article describes the development of formulas, with the same basic structure as the DIN99 formula, that predict the experimental data sets better than do the CMC and CIE94 colour‐difference formulas and only slightly worse than CIEDE2000 (which was optimized on the experimental data). However, these formulas all have an associated UCS. The spaces are similar in form to L*a*b*. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 282–290, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10066     

Testing uniform colour spaces and colour‐difference formulae using printed samples

A grey‐scale psychophysical experiment was carried out for evaluating colour differences using printed colour patches. In total, 446 pairs of printed samples were prepared surrounding 17 colour centers recommended by the CIE with an average δE of 3 units. Each pair was assessed 27 times by nine observers. The visual results were used to test some selected more advanced colour‐difference formulae and uniform colour spaces. The results showed that CIELAB and OSA performed the worst, and the advanced formulae and spaces gave quite satisfactory performance such as CIEDE2000, CIE94, DIN99d, CAM02‐UCS, and OSA‐GP‐Eu. The colour discrimination ellipses were used to compare with those of the earlier studies. The results showed that they agreed well with each other. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2012     

A colour harmony model for two‐colour combinations

This study investigates harmony in two‐colour combinations in order to develop a quantitative model. A total of 1431 colour pairs were used as stimuli in a psychophysical experiment for the visual assessment of harmony. These colour pairs were generated using 54 colours selected systematically from CIELAB colour space. During the experiment, observers were presented with colour pairs displayed individually against a medium gray background on a cathode ray tube monitor in a darkened room. Colour harmony was assessed for each colour pair using a 10‐category scale ranging from “extremely harmonious” to “extremely disharmonious.” The experimental results showed a general pattern of two‐colour harmony, from which a quantitative model was developed and principles for creating harmony were derived. This model was tested using an independent psychophysical data set and the results showed satisfactory performance for model prediction. The study also discusses critical issues including the definition of colour harmony, the relationship between harmony and pleasantness, and the relationship between harmony and order in colour. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 191–204, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20208     

A study of colour emotion and colour preference. Part II: Colour emotions for two‐colour combinations

Eleven colour‐emotion scales, warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, harmonious–disharmonious, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike, were investigated on 190 colour pairs with British and Chinese observers. Experimental results show that gender difference existed in masculine–feminine, whereas no significant cultural difference was found between British and Chinese observers. Three colour‐emotion factors were identified by the method of factor analysis and were labeled “colour activity,” “colour weight,” and “colour heat.” These factors were found similar to those extracted from the single colour emotions developed in Part I. This indicates a coherent framework of colour emotion factors for single colours and two‐colour combinations. An additivity relationship was found between single‐colour and colour‐combination emotions. This relationship predicts colour emotions for a colour pair by averaging the colour emotions of individual colours that generate the pair. However, it cannot be applied to colour preference prediction. By combining the additivity relationship with a single‐colour emotion model, such as those developed in Part I, a colour‐appearance‐based model was established for colour‐combination emotions. With this model one can predict colour emotions for a colour pair if colour‐appearance attributes of the component colours in that pair are known. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 292–298, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20024     

A data set for fuzzy colour naming

In computer vision, colour naming has been posed as a fuzzy‐set problem where each colour category is modeled by a function that assigns a membership value to any given sample. However, the success in the automation of this process relies on having an appropriate psychophysical data set for this purpose. In this article we present a data set obtained from a colour‐naming experiment. In this experiment, we used a scoring method to collect a set of judgments adequate for the fuzzy modeling of the colour‐naming task. The data set is composed of 387 colour reflectances, their CIELab and Munsell values, and the corresponding judgments provided by the subjects in the experiment. These judgments are the membership values to the 11 basic colour categories proposed by Berlin and Kay (Berlin B, Kay P. Berkeley: University of California; 1969). All these data have been made available online ( http://www.cvc.uab.es/color_naming ) and, in this article we provide a wide analysis of them. To prove the suitability of the proposed scoring methodology, we have computed a set of common statistics in colour‐naming experiments, such as consensus and consistency, on our data set. The results make it possible for us to conclude the coherence of our data with previous experiments and, thus, its usefulness for the fuzzy modeling of colour naming. © 2005 Wiley Periodicals, Inc. Col Res Appl, 31, 48–56, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20172     

Colour‐naming behavior among bilingual Malayalam‐English speakers: Evidence from English narrative discourse

Through an unconstrained colour‐naming task, researchers have found that bilinguals possess greater colour vocabulary knowledge than monolinguals. In the current study, we explore the colour‐naming behavior of bilinguals in spontaneous speech scenarios. The native language of these bilinguals holds complex colour labels for most of the colours, for which the second language holds only basic colour terms. For this purpose, a story narration task was developed where eight stories were designed with elementary and mixed colour conditions. Two groups of participants took part in the study: (a) 44 monolingual speakers and (b) 32 Malayalam‐English bilinguals. Qualitative and quantitative analyses revealed that there were clear colour label differences between the basic colour terms in English and Malayalam and those labeled by bilingual speakers. Bilingual speakers use translation equivalents of Malayalam colour terms for English colours.     

Color‐naming of M‐cone incremental flashes

To study which hues are associated with brief excitations of foveal middle‐wavelength (M) cones, two highly practiced color‐normal observers (the authors) gave basic color names to 500 and 530 nm increments. The test spots were presented to the fovea and foveola in conditions that included M‐cone isolation. 1° and 3.6 min arc tests were flashed for 200 ms on steady 8.6′, 1°, or 10° monochromatic adapting fields (481, 530, 610, and 630 nm), or on mixtures of these fields, or on a dark field. Tests were flashed at 1, 2, 4, and 8 × thresholds. Field hue had little overall effect. Yellow, green, blue‐green, and blue color‐names were elicited, both for the foveal 1° tests and for 3.6 min tests confined to the S‐cone free foveola. These data add to previous research by showing a contribution of M‐cones to blueness on monochromatic fields, as well as on achromatic fields. Because the 500 and 530 nm tests appear green when presented steadily on these same fields, the M‐cone contribution to blue may well be transitory. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 132–140, 2001     

A computer‐assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory

An aesthetic measure based approach for constructing a colour design/selection system is proposed in this article. In this model, an image data base for the relationships between the psychological preference of customers and clothing colour tones is built using the membership functions of a fuzzy set, and an aesthetic measure calculation method based on colour harmony is also proposed. In addition, a skin colour detection theory is proposed to construct a skin colour detection program to detect the skin colour of a customer, which is then taken as the major colour in matching the skin, polo shirt, and(or) pant colours to select the best colour combination. Integrating the skin colour detection theory, colour harmony theory, aesthetic measure method, and fuzzy set theory, a program is constructed to build an aesthetic measure based colour design/selection system. With the aid of this system, one can get proper cloth colours to match his/her skin colour and image requirement by starting with inputting one's colour photo, catching image with a camera, or inputting R, G, B values of his/her skin. The theoretical results for the ranks of clothing colours proposed by the system are examined with the experimental results and the result shows they are very close, suggesting that the proposed colour selection system is acceptable. Although the selection of clothing colours is taken as an example to specify the methodology, it can also be used to develop a system for other products. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 411–423, 2008     

A study of colour emotion and colour preference. Part III: Colour preference modeling

In this study three colour preference models for single colours were developed. The first model was developed on the basis of the colour emotions, clean–dirty, tense–relaxed, and heavy–light. In this model colour preference was found affected most by the emotional feeling “clean.” The second model was developed on the basis of the three colour‐emotion factors identified in Part I, colour activity, colour weight, and colour heat. By combining this model with the colour‐science‐based formulae of these three factors, which have been developed in Part I, one can predict colour preference of a test colour from its colour‐appearance attributes. The third colour preference model was directly developed from colour‐appearance attributes. In this model colour preference is determined by the colour difference between a test colour and the reference colour (L*, a*, b*) = (50, ?8, 30). The above approaches to modeling single‐colour preference were also adopted in modeling colour preference for colour combinations. The results show that it was difficult to predict colour‐combination preference by colour emotions only. This study also clarifies the relationship between colour preference and colour harmony. The results show that although colour preference is strongly correlated with colour harmony, there are still colours of which the two scales disagree with each other. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 381–389, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20047     

Gamut evaluation of an n‐colour printing process with the minimum number of measurements

The aim of this study was to use a minimum number of measured colour patches to evaluate the colour gamut of an n‐colour printing process. Traditionally, the colour gamut of a printing system has been derived by printing and then measuring a gamut target for example, a profiling chart. For an n‐colour printing (printing with more than four process inks), it is desirable to know the colour gamut of the given set of inks without having to print a large number of test patches. Different spectral printer models were used to predict the gamut of a 7‐colour printing process. The colorant space was divided into sectors each containing four inks. For each printer model, the colour gamut of the each four‐ink sector was predicted. All sector‐gamuts were then combined to predict the overall colour gamut of the n‐colour process. This predicted gamut was then compared with the gamut obtained by measurement using a gamut comparison index (GCI). The Yule–Nielsen modified spectral Neugebauer (YNSN) model gave the best accuracy, at the cost of a larger number of input measurements, than other models. A combination of the Kubelka–Munk (KM) and YNSN models performed well with the fewest input measurements. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 408–415, 2015     

Influence of learning effects on the results of the cap‐sorting test Roth 28‐hue (E) desaturated: Influence of learning effects on colour‐arrangement tests

The results obtained in colour vision tests can be influenced by many factors. It is possible that a learning effect disguises the fact that an acquired colour vision disturbance has progressively either deteriorated or been successfully treated. Therefore, the primary object of this study was to examine whether a possible learning effect occurred if screening by the colour vision test Roth 28‐hue (E) desaturated was repeated several times, and if this learning effect was age dependent. Sixty‐five ocularly and generally healthy subjects participated in the study and were divided into two age groups: group A: 20–39 years, n = 35; group B: 40–59 years, n = 30. Besides their ophthalmological status (visual acuity, refraction, intraocular pressure, cup/disk ratio, central fundus), the cap‐sorting test Roth 28‐hue (E) desaturated was performed under standardized test conditions. The measurements were repeated after 5 ± 1.72 days (T1), 15 ± 3.53 days (T2), 32 ± 6.97 days (T3), and 189 ± 16.85 days (T4). The ophthalmological parameters of all subjects were inconspicuous. The individual evaluation of the error scores in the cap‐sorting test Roth 28‐hue (E) desaturated showed large‐scale variations. For both age groups there was no statistically significant difference between the right and left eye at any time. The mean values of the younger group remained relatively constant after the first measurement. This age group showed a quick, clearly visible learning effect that persisted over the whole test period. With regard to the older age group the average values deteriorated, remained solid for one month increasing again after 6 months. The results showed an age‐related learning effect. Therefore, it is important to repeat the colour vision test within 5 days for the age group 20–39 years. This second test result will then serve as a stable basis for further comparative examinations within a period of 6 months. The subjects of the age group 40–59 years ought to repeat a first colour vision test after 5 and again after 15 days. The result of the second repetition will then offer stable basic values for subsequent tests. © 2006 Wiley Periodicals, Inc. Col Res Appl, 32, 16–21, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20282     

Preparation and properties of Fe(II)‐ion‐sensitive colour‐changing fabric

Textile fabrics were dyed with complexometric indicators (ionochromic dyes) to develop Fe(II) ionochromic fabric. Three kinds of ionochromic dye were used to dye silk fabric, and they were evaluated for colour changes triggered by Fe(II) solution. The K/S values and photos of the fabrics were then recorded. It was found that 1,10‐phenanthroline was the most suitable ionochromic dye in these dyes. Colour change from white to red could be clearly seen when 1,10‐phenanthroline‐dyed silk fabric was triggered by Fe(II) solution, but it showed no colour change when triggered by Cu(II), Mg(II), or Ca(II) solution. Moreover, 1,10‐phenanthroline‐dyed nylon, polyester, and cotton fabrics showed no obvious colour changes after triggering by Fe(II) solution. Ion concentration, pH value, and reaction time could affect the colour changes. When triggered by 8 mg l^?1 of Fe(II) solution at neutral pH for about 15 min, the ionochromic fabric showed a clear colour change. In addition, three coloured fabrics in green, blue, and yellow were also dyed with 1,10‐phenanthroline. It was found that they could also show clear colour changes when triggered by Fe(II) solution. These ionochromic fabrics may find broad application in many fields, such as Fe(II) detection, magic toys, anticounterfeiting materials, and bionic silk flowers.     

A cross‐cultural comparison of colour emotion for two‐colour combinations

Psychophysical experiments were conducted in the UK, Taiwan, France, Germany, Spain, Sweden, Argentina, and Iran to assess colour emotion for two‐colour combinations using semantic scales warm/cool, heavy/light, active/passive, and like/dislike. A total of 223 observers participated, each presented with 190 colour pairs as the stimuli, shown individually on a cathode ray tube display. The results show consistent responses across cultures only for warm/cool, heavy/light, and active/passive. The like/dislike scale, however, showed some differences between the observer groups, in particular between the Argentinian responses and those obtained from the other observers. Factor analysis reveals that the Argentinian observers preferred passive colour pairs to active ones more than the other observers. In addition to the cultural difference in like/dislike, the experimental results show some effects of gender, professional background (design vs. nondesign), and age. Female observers were found to prefer colour pairs with high‐lightness or low‐chroma values more than their male counterparts. Observers with a design background liked low‐chroma colour pairs or those containing colours of similar hue more than nondesign observers. Older observers liked colour pairs with high‐lightness or high‐chroma values more than young observers did. Based on the findings, a two‐level theory of colour emotion is proposed, in which warm/cool, heavy/light, and active/passive are identified as the reactive‐level responses and like/dislike the reflective‐level response. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2012     

How colour‐deficient observers see things,or not.

Colour deficiency or, colloquially, colour blindness, is common and has been observed and described in the scientific literature for ca. 200 years. In more recent times, algorithms have been developed that simulate the effect of colour deficiency to a colour‐normal observer. Sometimes these algorithms are used to indicate potential problems in the colour design, but often the implicit assumption is that a colour‐deficient observer actually sees things that way. But do they? This paper questions some of the underlying assumptions of the algorithms.     

An intelligent system for fashion colour prediction based on fuzzy C‐means and gray theory

For design and manufacturing industries, to be able to capture the fashion trend is an essential factor that leads to winning a sale. However, colour predicting process in many organizations is not visible to the public. In order to provide colour trend to industries in advance, a predicting method is proposed in this study. In the method, the fuzzy c‐means was used to separate the collected colour data, then the minimum mean‐square error was used to place the similar colour clusters within different time point together and the gray model was adopted for prediction. In order to verify the prognostication of the system, four data announced by Pantone from spring 2014 to fall, 2015 were taken as the predicted samples and the colour for spring 2016 was predicted to compare with that in Pantone spring, 2016. The results show that the system has a high accuracy for predicting colour. The residual modified model constructed with the colour samples rearranged with MMSE has the best‐predicted result that ranged from 83.3% to 99.4%. It indicates that the result obtained with the rearranged samples is higher than that without rearrangement. Besides, the accuracy of the gray predicted results with residual modification would be more precise than the one without residual modification. Moreover, the value of mean squared error is quite low, which was ranged from 0.000025 to 0.0277. Therefore, the current intelligent predicting system satisfies the criteria of capturing colour in trend for enterprises. Moreover, it enables industries to make decisions for selecting the colour trend. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 273–285, 2017