Geometric relations between scales of small colour differences

Varying magnitude of colour differences from threshold up to moderate size in painted sample pairs at five CIE colour centers was estimated by grey scale assessment. Painted samples were produced for constant step width along the main axes of previously determined threshold (x,y,Y)‐ellipsoids with lightness variation at constant (x,y)‐chromaticity starting with threshold length and enlarging it five times for moderate magnitude of colour difference. Pairs were formed for linear extensions along axes and for diagonal combinations at equal step width between axes. The model under test assumes additive linear scale extension in constant proportions of the threshold (x,y,Y)‐ellipsoid for increasing magnitude of perceived colour difference and correlates perceptual main colour characters with main ellipsoid axes. Both assumptions were falsified to some degree: in general, magnitude of colour difference varies differently, though close to linear, and slightly subadditive for the three axes and for the different colour centers; the short (x,y)‐ellipse axis in some cases is not correlated with a perceptual hue vector component, and the main lightness direction sometimes is tilted in relation to the (x,y)‐plane. Three colour‐difference formulae do not provide better global predictions than the local (x,y,Y)‐ellipsoid formulae. The results may be used for more detailed modeling of colour‐difference formulae and for tolerance settings at different ranges of colour difference. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 78–92, 1999     

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     

Semantic interpretation of color differences and color‐rendering indices

A series of visual experiments were carried out to rate the similarity of color appearance of two color stimuli on categorical and continuous semantic rating scales. Pairs of color stimuli included two copies of the same colored real or artificial object illuminated by a test light source and a reference light source. A formula was developed to predict a category of color similarity (e.g., “moderate” or “good”) from an instrumentally measured color difference. Given a numeric value of a color difference between the two members of a pair of colors, for example, 2.07, the formula is able to predict a category of color similarity, for example, “good.” Because color‐rendering indices are based on color differences, the formula could be applied to interpret the values of the new color‐rendering index (n‐CRI or CRI2012) in terms of such semantic categories. This semantic interpretation enables nonexpert users of light sources to understand the color‐rendering properties of light sources and the differences on the numeric scale of the color‐rendering index in terms of regular language. For example, a numeric value of 87 can be interpreted as “good.” © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 252–262, 2014; Published online 14 March 2013 in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/col.21798     

Colour appearance rating of familiar real objects

The determination of the long‐term memory colours of objects has been the subject of investigation for many years. Colour acceptance boundaries have been determined from the visual assessments of objects under variable illumination or by presenting manipulated images of objects on a calibrated computer display. However, a systematic and quantitative rating of the colour of real objects with respect to memory colour is not available at this moment. In this article, nine familiar real objects with colours distributed around the hue circle were positioned in a specially designed LED illumination box. For each object, approximately hundred real illumination spectra were synthesized in a random order keeping the luminance of the object approximately constant. Observers were asked to rate, on a five‐point scale, the similarity of the perceived object colour to their idea of what the object looked like in reality. By avoiding specular reflections, the observer was unable to identify any clues as to the colour of the illumination. For each object, similarity ratings showed a good intraobserver and interobserver agreement. The ratings of all the observers were pooled and successfully modeled in IPT colour space by a bivariate Gaussian distribution. It was found that the chromaticity corresponding to the highest rating tended to be shifted toward higher chroma in comparison with the chromaticity calculated under D65 illumination. The bivariate distributions could be very useful in applications where the quantitative evaluation of the colour appearance of an object stimulus is required, such as in the evaluation of the colour rendering capabilities of a light source. © 2010 Wiley Periodicals, Inc. Col Res Appl, 36, 192–200, 2011;     

Some aspects of the visual scaling of large colour differences

The formulation of a metric to provide numbers that correlate with visually perceived colour differences has proved a very difficult task. Most early experimental work was concerned with just-perceptible colour differences. Later the concept of perceptibility was expanded to acceptability, it being argued that many industrial tolerances were larger than just-perceptible. This led naturally to the concept of large colour differences and the question as to whether the current CIE colour-difference formulae, specified as appropriate for just-perceptible differences, can be applied to larger differences than those concerned with, for instance, colour matches experienced in the fabric dyeing industry. This article investigates the application of four colour-difference formulae to visual scaling of large colour differences between photographically prepared reflection colour samples at approximately constant lightness. It is shown that the scaling of colour differences depends on the directions of hue and chroma differences of a test sample when compared with a reference. It is also shown that, of the four candidate colour-difference metrics, the modified CIE 1976 L*a*b* colour difference, referred to as CIE1994 or , correlates best with visual scaling. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 298–307, 1997     

Investigation of texture effect on visual colour difference evaluation

The texture effect on visual colour difference evaluation was investigated in this study. Five colour centers were selected and textured colour pairs were generated using scanned textile woven fabrics and colour‐mapping technique. The textured and solid colour pairs were then displayed on a characterized cathode ray tube (CRT) monitor for colour difference evaluation. The colour difference values for the pairs with texture patterns are equal to 5.0 CIELAB units in lightness direction. The texture level was represented by the half‐width of histogram, which is called texture strength in this study. High correlation was found between texture strength and visual colour difference for textured colour pairs, which indicates that an increasing of 10 units of texture strength in luminance would cause a decreasing of 0.25 units visual difference for the five colour centers. The ratio of visual difference between textured and solid colour pairs also indicates a high parametric effect of texture. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 341–347, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Investigation of parametric effects using small colour differences

This experiment was carried out to investigate some viewing parameters affecting perceived colour differences. It was divided into eight phases. Each phase was conducted under a different set of experimental conditions including separations, neutral backgrounds, and psychophysical methods. Seventy‐five wool sample pairs were prepared corresponding to five CIE colour centers. The mean colour difference was three CIELAB units. Each pair was assessed by a panel of 21 observers using both the gray scale and pair comparison psychophysical methods. The assessments were carried out using the three different backgrounds (white, mid‐gray, and black) and a hairline gap between the samples. Assessments on the gray background were repeated using a large (3‐inch) gap between the samples. It was found that the visual results obtained from both psychophysical methods gave very similar results. The parametric effect was small, i.e., the largest effect was only 14% between the white and gray background conditions. These visual data were also used to test four colour‐difference formulae: CIELAB, CMC, BFD, and CIE94. The results showed that three advanced colour‐difference formulae performed much better than CIELAB. There was a good agreement between the current results and those from earlier studies. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 331–343, 1999     

Testing LED lighting for colour discrimination and colour rendering

Light‐emitting diode (LED) technology offers the possibility of obtaining white light, despite narrow‐band spectra. In order to characterize the colour discrimination efficiency of various LED clusters, we designed a classification test, composed of 32 caps equally distributed along the hue circle at about 3 ΔE* _ab‐unit intervals. Forty normal colour observers were screened under four different LED test light sources adjusted for best colour rendering, and under one control incandescent light of the same colour temperature. We used commercially available red, green, blue, and/or amber LED clusters. These yielded a poor colour rendering index (CRI). They also induced a significantly higher number of erroneous arrangements than did the control light. Errors are located around greenish‐blue and purplish‐red shades, parallel to the yellow‐axis direction, whereas when the distribution of light covers the full spectrum, the LED clusters achieve satisfactory colour discrimination efficiency. With respect to the lights we tested, the colour discrimination is correlated with the CIE CRIs as well as with a CRI based on our sample colours. We stress the fact that increasing the chroma of samples by lighting does not necessarily imply an improvement of colour discrimination. © 2008 Wiley Periodicals, Inc. Col Res Appl, 34, 8–17, 2009.     

Evaluation of the crispening effect using CRT‐displayed colour samples

In this study, the crispening effect was clearly observed when 38 neutral‐coloured sample pairs with only lightness differences were assessed under 5 neutral backgrounds of different lightness values. The sample pairs are CRT‐based colours, and they are selected along the CIELAB L* axis from 0 to 100. The magnitude of colour difference of each pair is 5.0 CIELAB units. The visual assessment results showed that there is a very large crispening effect. The colour differences of the same pair assessed under different backgrounds could differ by a factor of up to 8 for a sample pair with low lightness. The perceived colour difference was enlarged when the lightness of a sample pair was similar to that of the background. The extent of crispening effect and its quantification are discussed in this investigation. The performances of five colour‐difference equations were also tested, including the newly developed CIEDE2000. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 374–380, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20045     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.     

Some aspects of the visual scaling of large colour differences—II

In an earlier article the authors related visually‐ scaled large colour differences to ΔE* values calculated using four colour‐difference formulae. All four metrics yielded linear regressions from plots of visual colour difference against ΔE*, and ΔE gave the best linear fit, but the correlations were rather low. In an effort to clarify matters, the previous investigation is expanded to include data not hitherto examined. The link between visual colour difference and ΔE* colour metrics is further explored in terms of a power law relationship over a wide range of lightness, hue, and chroma variations within CIELAB colour space. It is shown that power‐law fits are superior to linear regressions in all cases, although correlations over large regions of the colour space are not very high. Partitioning of the experimental results to give reduced data sets in smaller regions is shown to improve correlations markedly, using power‐law fits. Conclusions are drawn concerning the uniformity of CIELAB space in the context of both linear and power‐law behavior. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 116–122, 2000     

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     

The use of sensory difference tests to investigate perceptible colour‐difference in a cosmetic product

Two sensory difference tests have been used to assess the ability of an untrained population to perceive colour difference in a cosmetic product. The two tests used were the triangle test and the “two‐out‐of‐five” test. Participants were presented with groups of samples with varying colour differences and asked to identify the odd sample in the triangle test and the pair in the two‐out‐of‐five test. From these data, the number of correct responses was correlated with the calculated colour difference using three colour‐difference equations (CMC, CIE94, and CIEDE2000). These correlations were optimized by varying the parameters in the colour‐difference equations. With the parameters optimized, each of the three colour‐difference equations gave a correlation coefficient of ～0.97 with the two‐out‐of‐five test and a correlation coefficient of ～0.79 with the triangle test. These correlation coefficients suggest that sensory difference testing can be used to investigate perception of colour difference. However, for the triangle test the correlation between the sensory data and the calculated colour difference is weak and the two‐out‐of‐five test should be preferred. The minimum perceptible colour difference was estimated from the regression plots between the optimised colour‐difference equations and the sensory data. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 299–304, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20025     

Experimental modeling of colour harmony

This study investigates colour harmony in visual experiments in order to develop a new quantitative colour harmony model. On the basis of new experimental results, colour harmony formulae were developed to predict colour harmony from the CIECAM02 hue, chroma, and lightness correlates of the members of two‐ or three‐colour combinations. In the experiments, observers were presented two‐ and three‐colour combinations displayed on a well‐characterized CRT monitor in a dark room. Colour harmony was estimated visually on an 11 category scale from ?5 (meaning completely disharmonious) to +5 (meaning completely harmonious), including 0 as the neutral colour harmony impression. From these results, mathematical models of colour harmony were developed. The visual results were also compared with classical colour harmony theories. Two supplementary experiments were also carried out: one of them tested the main principles of colour harmony with real Munsell colour chips, and another one compared the visual rating of the new models with existing colour harmony theories. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2010.     

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     

Toward colour rendering method of window glass

The aim of this study was to expand the understanding of modern glazing materials' effects on the colour distortion in interiors and to develop a rather simple colour rendering method useful for any type of glazing and based on colorimetry measurements and mathematical calculations. The qualitative and quantitative aspects of different light sources on colour rendering are frequently discussed topics among the researchers, but one of the issues touched only sporadically is the impact of tinted glazing on colour rendering of daylight. The study started from the following question: Is the colour rendering method proposed by Lynes reliable also for present‐day high‐tech glazing types? The experiment was carried out in the Norwegian University of Science and Technology's (NTNU) with the artificial sky, which enables mimicking of skylight of the following correlated colour temperatures: 2700 K, 6500 K and 8000 K. Three high‐tech glazing types were used in five different transmittance scenarios. Colorimetric measurements were taken with the SpectraScan PR655 spectroradiometer. The findings indicated that the Lynes method is reliable to predict which glazing have the biggest impact on all aspects of colour but only in 6500 K. New set of measures have been proposed: average colour shift distance for hue gamut area for Chroma and median transmittance for Value. However, for higher precision regarding the direction of shift and the overall perception of the respective colours in building context, experiments with subjects are needed.     

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