On the colours dichromats see

Dichromatic colour vision is commonly believed to be a reduced form of trichromatic colour vision (referred to as the reductionist principle). In particular, the colour palette of the dichromats is believed to be a part of the colour palette of the trichromats. As the light‐colour palette differs from the object‐colour palette, the dichromatic colour palettes have been derived separately for light‐colours and object‐colours in this report. As to light‐colours, the results are in line with the widely accepted view that the dichromatic colour palettes contain only two hues. However, the dichromatic object‐colour palettes have proved to contain the same six component colours which constitute the trichromatic object‐colour palette (yellow, blue, red, green, black and white). Moreover, all the binary and tertiary combinations of the six component colours present in the trichromatic object‐colour palette also occur in the dichromatic object‐colour palettes. Yet, only five of the six component colours are experienced by dichromats as unitary (unique) object‐colours. The green unitary colour is absent in the dichromatic object‐colour palettes. The difference between the dichromatic and trichromatic object‐colour palettes arises from the fact that not every combination of the component‐colour magnitudes occurs in the dichromatic object‐colour palettes. For instance, in the dichromatic object‐colour palettes there is no colour with the strong green component colour. Furthermore, each achromatic (black or white) component colour of a particular magnitude is combined with the only combination of the chromatic components. In other words, the achromatic component colours are bound with the chromatic component combinations in dichromats. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 112–124, 2014     

A comparison of colour appearance for surface colours between outdoor and indoor environments

Psychophysical experiments of colour appearance, in terms of lightness, colourfulness, and hue, were conducted outdoors and indoors to investigate whether there was any difference in colour appearance between outdoor and indoor environments. A panel of 10 observers participated in the outdoor experiment, while 13 observers took part in the indoor experiment. The reference white had an average luminance of 12784 cd/m² in the outdoor experiment and 129 cd/m² in the indoor experiment. Test colours included 42 colour patches selected from the Practical Coordinate Color System to achieve a reasonable uniform distribution of samples in CIECAM02. Experimental results show that for both outdoor and indoor environments, there was good agreement between visual data and predicted values by CIECAM02 for the three colour appearance scales, with the coefficient of variation values all lower than 25 and the R² values all higher than 0.73, indicating little difference in the three dimensions of colour appearance between indoor and outdoor viewing conditions. Experimental data also suggest that the observers were more sensitive to variation in lightness for grayish colours than for highly saturated colours, a phenomenon that seems to relate with the Helmholtz-Kohlrausch effect. This phenomenon was modeled for predicting perceived lightness (J′) using the present experimental data. The new J′ model was tested using three extra sets of visual data obtained both outdoors and indoors, showing good predictive performance of the new model, with an average coefficient of variation of 14, an average R² of 0.88, and an average STRESS index of 14.18.     

Colour-difference ellipsoids for five CIE colour centres

Following the CIE guidelines for coordinated research on colour-difference evaluation, sets of dyed wool samples were produced near each of the five CIE reference colours, the colour differences within each set ranging from 1 to 9 CIE-LAB units. Ratio assessments were used to determine discrimination ellipsoids for illuminants D₆₅ and A. Ratio and grey-scale assessments involving pairs from all five colours were used to bring the visual data onto a common scale consistent with previous results. The ellipsoids for the red and green centres were compared with those obtained previously for the same centres. The agreement was rather better for the green colour, even though the substrates and sizes of colour-differences were different in this case. The chromaticity-discrimination ellipses generally agreed well with those obtained earlier for similar colours. The CMC (1 : 1) formula agreed better with the experimental results than did the CIELAB formula.     

Publications Sponsored by the Colour Measurement Committee—VIII

Methods of describing the extent of agreement between the ΔE values calculated from a colour-difference equation and the corresponding visual estimates of colour difference are discussed. Lack of agreement will be due to a combination of errors in the visual assessments, in the measurements, and in the colour-difference equation itself. If there were no error in the instrumental measurements, the equation error for a particular colour difference would be the difference between the calculated ΔE value and the mean visual assessment of a very large number of observers (ΔVtrue). Experimental data in the form of acceptances (%) can be converted to ΔV values directly proportional to the observed colour differences. The overall equation error for n colour differences can be calculated from Eqn 1. The Davidson and Friede data are considered to be the most satisfactory of those presently available for testing the suitability of equations for industrial colour-tolerance work and have been used to assess the accuracy of several well-known equations. After allowing for errors in the visual assessment and in the instrumental measurements, σ(log ΔE) for the 1964 CIE equation was 0.22. Similar values (0.16-0.23) were found for other equations. A lower a-value was found for a very simple empirical equation essentially based on the x, y chromaticity diagram rather than on any transformation of it. The usual transformations tend to be based on data covering the whole chromaticity gamut, whereas real surface colours cover only a fraction of the possible area. This fact, together with the knowledge that most equations are based on data corresponding to small fields of view or large colour differences, could account for the relative failure of the standard equations.     

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     

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     

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     

Psychophysical models of consumer expectations and colour harmony in the context of juice packaging

The aim of this study was to develop psychophysical models that predict the influence of pack colours on consumers' psychological responses of fruit juices, such as visually perceived expectations of freshness, quality, liking, and colour harmony. Two existing colour harmony models derived from experiments involving only uniform colour plaques were tested using the juice packaging experimental data. Both models failed to predict the visual results obtained. Nevertheless, two parameters relevant to chromatic difference and hue difference were somewhat associated with the visual results. This suggested that, among all colour harmony principles for uniform colours, only the equal‐hue and the equal‐chroma principles can be adopted to describe colour harmony of packaging used for juice. This has the implication that the principles of colour harmony may vary according to the context in which the colours are used. A new colour harmony model was developed for juice packaging, and a predictive model of freshness was derived. Both models adopted CIELAB colour attributes of the package colour and the fruit image colour to predict viewers' responses. Expected liking and juice quality can be predicted using the colour harmony model while expected freshness can be predicted using the predictive model of freshness. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 157–168, 2015     

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.     

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     

Revisiting the weighting function for lightness in the CIEDE2000 colour-difference formula

We have used 13 experimental datasets (7420 colour pairs) to study the performance of the weighting function for lightness proposed by the CIEDE2000 colour-difference formula, because it has been suggested that this function can be improved by using the weighting function for lightness S_L = 1 adopted by the CIE94 colour-difference formula. Using the standardised residual sum of squares (STRESS) index, it was found that: (i) replacing the S_L in CIEDE2000 with S_L = 1 improved the results for 7/13 datasets considered, but the improvement was statistically significant only for 1/13 datasets; (ii) a Whittle-type lightness-difference formula can be used to replace the term ∆L*/S_L in CIEDE2000, which led to a new colour-difference formula with no statistically significant difference with respect to CIEDE2000 for any of the 13 experimental datasets. A modification of the CIEDE2000 formula using a Whittle-type lightness formula is proposed.     

Regression analysis to determine the optimum colour tolerance level for instrumental shade sorting

An investigation of the correlation between visual colour assessment and instrumental colour acceptance determination using regression analysis has been carried out. Three colour-difference equations, CIELAB, CMC(2:1) and CIE94(2:1:1), were studied in order to determine which is the best for generating a uniform colour space/microspace for allocating the colour population in shade sorting. Determination of optimum colour tolerance for further shade sorting was also undertaken. Some 1320 pairs of dyed samples distributing around 20 shade standards were measured instrumentally and also evaluated visually by a panel of 32 observers. Percentage rejection was plotted against colour difference and different mathematical regression relationships were then imposed. As a result, both CMC and CIE94 showed better correlation between the two colour assessment methods than the CIELAB colour-difference equation. Consequently, optimum colour tolerance limits were determined for subsequent development of shade sorting, with the findings being equally applicable to colour acceptance (shade passing).     

BFD (l:c) colour-difference formula Part 2-Performance of the formula

The available experimental data relating to small colour differences between pairs of surface colours have been combined together into two sets including perceptibility results (CP) and acceptability results (CA). A new colour-difference formula, BFD(\:c) has been developed using the combined experimental results. For small colour differences, particularly when perceptibility judgements are involved, the new formula represents a substantial improvement over other fomulae. For large colour-differences the new formula is close to the best available formula. Different 1 values are required for perceptibility and acceptability judgements, and for large colour differences, but c=1 in all cases. The BFD formula gives the best possible results obtainable with currently available data. Analysis shows that results from different studies, including those based on perceptibility and acceptability judgements, are compatible in many respects. Discrepancies that were detected seemed to be due to experimental error, or problems in scaling the visual results. However, there are major differences between the results for small differences between surface colours and those implicit in the Munsell system and MacAdam ellipses.     

Publications Sponsored by the Colour Measurement Committee—IX

A new series of green paint samples has been prepared and used to study different methods of obtaining visual assessments of colour differences and of scaling the experimental results to give Δ V values directly proportional to the observed differences. The results from two standard methods (the ratio method and the paired-comparison method) were in good agreement with each other and with results from the ranking method used earlier, thus adding confidence to conclusions based on the use of the latter method. The extent of agreement between ΔE values calculated from some best-fit empirical colour-difference equations and the ΔV values has been calculated. Results obtained by Robinson on a series of blue-grey paint samples by a % acceptance method have been re-analysed. The scaling method used previously with visual results in the form of % acceptance values gives ΔV values directly proportional to calculated ΔE values. The visual observers can be subdivided into two groups based on whether they had experience of colour matching or not. No evidence was found for any difference between the ‘perceptibility’ and ‘acceptability’ of colour differences in the sense that different colour-difference equations might have been required to represent the two groups of observers.     

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     

Colour-difference Measurements in Relation to Visual Assessments in the Textile Field

Two sets of dyeings, each containing six samples, showing slight colour variations about a standard were prepared on bright viscose rayon satin and milled wool cloth, respectively. The two standards were centred in the green with luminance factors of about 10%, and were intended to be approximate and non-metameric matches. In each case the six colourvariations were chosen to be essentially in pairs: brighter-duller; stronger-weaker; andtwo showing a hue difference. In all cases the differences were isomeric about the standard and ranged from one to eight traces. According to industrial procedures and under illumination conforming to BS 950:1967, 32 observers in four organisations assessed visually the colour differences from standard in each set, and the colour differences were measured on 23 different instruments throughout seven organisations, largely on Colormaster and Color-Eye tristimulus colorimeters, but also on other types of colorimeters and on three spectrophotometers. The instrumental results, obtained in the CIE system and with reference to Illuminant C., were converted to single-number colour-difference values by the use of six typical formulae, including the 1964 CIE recommended formula. Reasonable, but not completely satisfactory, agreement was found between observers and between instruments, but the correlation of visual and instrumental results provided by the formulae was poor. Some improvement in instrumental performances should be possible, and a modified method of correlation is needed, which can be achieved satisfactorily for the limited number of colours considered here. However, further work is obviously required.     

Mathematical approach for predicting non‐negative tristimulus values using the CAT02 chromatic adaptation transform

It has been reported that for certain colour samples, the chromatic adaptation transform CAT02 imbedded in the CIECAM02 colour appearance model predicts corresponding colours with negative tristimulus values (TSVs), which can cause problems in certain applications. To overcome this problem, a mathematical approach is proposed for modifying CAT02. This approach combines a non‐negativity constraint for the TSVs of corresponding colours with the minimization of the colour differences between those values for the corresponding colours obtained by visual observations and the TSVs of the corresponding colours predicted by the model, which is a constrained non‐linear optimization problem. By solving the non‐linear optimization problem, a new matrix is found. The performance of the CAT02 transform with various matrices including the original CAT02 matrix, and the new matrix are tested using visual datasets and the optimum colours. Test results show that the CAT02 with the new matrix predicted corresponding colours without negative TSVs for all optimum colours and the colour matching functions of the two CIE standard observers under the test illuminants considered. However, the accuracy with the new matrix for predicting the visual data is approximately 1 CIELAB colour difference unit worse compared with the original CAT02. This indicates that accuracy has to be sacrificed to achieve the non‐negativity constraint for the TSVs of the corresponding colours. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

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     

Colour words and their uses

Popular usage of colour words as parts of speech obey certain rules according to whether they are population dependent and whether use demands a degree of colour vision. The word green refers to that colour most of us see, recognize and categorize as being of the colour called green. But, colours and colour words are to do with emotion as well as perception. What can we learn from the greatest writers, artists and musical composers; how do they, for example, regard green? From them we learn that we perceive colours with our ears as well as our eyes and, in an emotional sense, a colour word means or is associated with just what the writer intends. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 111–113, 2015