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     

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     

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     

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.     

Ordinal scale based description of colour rendering

Perceived colour differences of 17 test colour samples (uniform standalone patches) were evaluated visually between a test and a reference light source on three visual scales. Two graphical rating scales (a greyscale‐anchored colour difference scale and a similarity judgement scale) and a five‐step ordinal rating scale (excellent, good, acceptable, not acceptable or very bad colour rendering) were used. The experimental setup included tungsten halogen, gas discharge, fluorescent, and white LED light sources at two correlated colour temperatures, 2700 and 4500 K. There was an inverse relationship between similarity judgement and visual colour difference results. Each category of the five‐step ordinal rating scale had a characteristic mean visual colour difference value. Visual colour differences correlated best with the recently developed CIECAM02‐UCS colour difference metric. Latter metric was used to predict the observers' ratings of visual colour differences on the above five‐step ordinal rating scale. From the predicted ratings of 17 test‐colour samples under the test light source, a new ordinal rating scale based colour rendering index (RCRI) was defined and compared with previous colour rendering indices. RCRI correlated well with both the mean visual colour differences and the mean similarity judgements. Despite the significant interobserver differences of the visual assessment of colour differences, the RCRI method showed an overall performance of 73% in terms of good predictions of the rating categories. Validation experiments with complex still life (tabletop) stimuli are currently underway. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

The effect of texture on the pass/fail colour tolerances of injection‐molded plastics

The effect of surface texture on the pass/fail colour tolerance region was estimated using acrylonitrile–butadiene–styrene injection‐molded plaques in three different colours and with different surface textures. Variations in the measured colour coordinates due to differences in texture for three selected colours were observed using the specular component excluded mode (SCE) of the spectrophotometer. Such variations could not, however, be clearly distinguished in the specular component included mode. The colour tolerance regions were assessed through a combination of colour measurement and a psychometric study involving 35 observers. The limits of acceptance of colour differences varied substantially with the surface texture. In some cases, the observers accepted a larger colour difference for the textured than for the smooth surface, whereas in other cases, the reverse situation was noted. Thus, no general conclusion regarding the influence of texture on the tolerance region can be drawn, since the acceptable differences were colour‐ and/or surface texture‐dependent. In the case of textured surfaces, measurements made in the SCE mode of the spectrophotometer appear to be more meaningful for assessing perceived colour differences and tolerance regions. © 2006 Wiley Periodicals, Inc. Col Res Appl, 32, 47–54, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20284     

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     

Investigating the effect of texture on the performance of color difference formulae

To predict the perceived color differences, the effect of the surface texture on the performance of the color difference formulae was investigated. To this end, knitted polyester fabrics with eight different textures were prepared. The fabrics were dyed by seven dyestuffs in five different depths. The selected pairs from the five samples with different depths in each hue covered small to large color differences. The assessed pair of samples had the same texture and hue, but different depths. A panel of 23 observers assessed the color differences of the pairs by gray scale method. The results showed that for the textile samples with different texture structures, the CIEDE2000 (2:1:1) performed the best followed by CMC (2:1:1), CIE94 (2:1:1), and CIELAB with approximately same performance. In addition, the magnitude of color difference influenced the ability of the formulae to predict the visual assessments and the best performance obtained for medium color differences. The comparison between eight different texture groups indicated that the texture structures of the pairs significantly affected the performance of the color difference formulae. For instance, the PF/3 measures obtained for the eight texture groups by CIEDE2000 (2:1:1) color formula could be varied between 21.98 and 33.37 PF/3 units. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

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     

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     

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     

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     

The ULAB colour space

A new colour space, named ULAB, is developed. It is derived from the CIELAB colour space and can be converted to and from CIELAB. Unlike modified CIELAB colour‐difference formulae, ULAB incorporates corrections for lightness, chroma, and hue differences into its colour coordinates. For the small magnitude colour difference data, it shows the performance as good as more complicated formulae such as CIEDE2000. ULAB shows another chance of developing a colour space approximately more uniform than CIELAB. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 17–29, 2015     

Effect of observer metamerism on colour matching of display and surface colours

We studied the individual variability of asymmetric metameric colour matching between computer displays and object colour stimuli in conditions typical for the surface colour industries. Using two different computational techniques, we assessed the contribution of observer metamerism to this variability. In the studied conditions of spatially separated computer display and surface colour stimuli, this contribution was found to be insignificant for all colours but neutrals. In the chromaticness plane, the range of matches made by different observers practically coincides with the range of matches made by an individual observer. Consequently, we conclude that in the task of matching spatially separated display and surface colours, the range of matches made by a group of observers cannot be determined from variations in their colour‐matching functions, and thus the paradigm of the Standard Deviate Observer is shown to be inapplicable to the studied conditions. We suggest that individual variability in these conditions is governed by mechanisms of chromatic discrimination, and can be modeled by advanced colour difference formulae with suitably adjusted parametric coefficients. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 346–359, 2008     

The LLAB (l:c) colour model

A new colour model, named LLAB(l:c) is derived. It includes two parts: the BFD chromatic adaptation transform derived by Lam and Rigg, and a modified CIELAB uniform colour space. The model's performance was compared with the other spaces and models using the LUTCHI Colour Appearance Data Set. The results show that LLAB(l:c) model is capable of precisely quantifying the change of colour appearance under a wide range of viewing parameters such as light sources, surrounds/media, achromatic backgrounds, sizes of stimuli, and luminance levels. It had a similar performance as that of the Hunt colour appearance model. The LLAB(l:c) model was also tested using various colour difference datasets. The model gave a similar performance as the state-of-the-art colour difference formulae such as CMC, CIE94, and BFD. This performance is considered to be very satisfactory, and the model, therefore, should be considered for field trials in applications such as colour specification, colour difference evaluation, cross-image reproduction, gamut mapping, prediction of metamerism and colour constancy, and quantification of colour-rendering properties. The model does not give predictions for chroma (as distinct from colourfulness), or for brightness, and it does not include any rod response. © 1996 John Wiley & Sons, Inc.     

Age effects on colour emotion,preference, and harmony

Two psychophysical experiments were carried out to investigate whether or not colour emotion responses would change with the advance of the viewer's age. Two forms of stimuli were used: 30 single colours (for Experiment 1) and 190 colour pairs (for Experiment 2). Four word pairs, warm/cool, heavy/light, active/passive, and like/dislike, were used to assess colour emotion and preference in Experiment 1. In Experiment 2, harmonious/disharmonious was also used in addition to the four scales for Experiment 1. A total of 72 Taiwanese observers participated, including 40 (20 young and 20 older) for Experiment 1 and 32 (16 young and 16 older) for Experiment 2. The experimental results show that for single colours, all colour samples were rated as less active, less liked, and cooler for older observers than for young observers. For colour combinations, light colour pairs were rated as less active and cooler for older observers than for young observers; achromatic colour pairs and those consisting of colours in similar chroma were rated as cooler, less liked and less harmonious for older observers than for young observers. The findings may challenge a number of existing theories, including the adaptation mechanism for retaining consistent perception of colour appearance across the lifespan, the modeling of colour emotion based on relative colour appearance values, and the additive approach to prediction of colour‐combination emotion. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

Investigation of colour size effect for colour appearance assessment

Three psychophysical experiments were conducted to investigate the colour appearance changes between different sizes under various media or viewing conditions. The results are highly consistent that when increase stimulus size, the colour will appear to be lighter and more colourful with little change in hue. © 2010 Wiley Periodicals, Inc. Col Res Appl, 36, 201–209, 2011;     

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     

User evaluation of a virtual colour laboratory as a tool for demonstrating colour appearance

The aim with our research is to contribute to a better understanding of how colour research findings can be conveyed to a broader target audience, using digital media. This article presents a user study focusing on the popular science project the Virtual Colour Laboratory (VCL). The VCL is an interactive webpage for presenting and demonstrating existing research results on spatial colour phenomena. It was initiated and carried out with the intention of spreading knowledge of colour appearance and colour perception to a wider target audience, from the viewpoint of practice based architectural colour research. The VCL enables the user to investigate actively how colours appear in different situations, and provides information on relevant literature and links for further studies. In a questionnaire study, carried out in 2014, two groups of users including architecture students and professionals within architecture, art and design evaluated the usability of the VCL with a combination of qualitative and quantitative methods. The predominant result showed that the VCL generally was highly appreciated. However, the results also show differences in how the two groups experienced the visual and written content of the stations in the VCL, as well as differences in how they experienced the VCL as a whole. This highlights the importance of adjusting levels of information depending on the target group, as well as presents advantages and difficulties of showing research on spatial colour appearance on the web using digital visualization as a medium for presentation. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 611–625, 2016