Colour correctability of a colour-matching recipe

The colour of a test dyeing/batch is frequently too far from the target colour to be accepted and some correction to the component concentrations in the recipe is needed. Therefore, it would be useful for a colourist, already at the time of match prediction, to have some a priori information about the possibility of correcting the colour of a particular recipe. In this article, the definitions of directional and overall colour correctability are introduced and the way to calculate their numerical estimates is developed. These values enable the comparison of the colour correctability of various different recipes for the same target colour in advance. In addition, the link between the colour sensitivity and the colour correctability of a recipe is discussed. A numerical example is provided for illustration. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 88–95, 1997     

Different measures of sensitivity of the recipe colour to random and proportional dye concentration error. Part 1: Definitions, mutual relations and estimates of maximal colour errors

The general concept of predicting the colour sensitivity to random colorant concentration errors and the colour correctability of a colour matching recipe are reviewed and generalised in this paper. The treatment of both quantities is unified either in the concentration space or, equivalently, in colour space. The concept of the recipe's colour balance is revised. Oulton's concept of recipe colour sensitivity to proportional concentration errors is also briefly reviewed and extended to obtain another measure of recipe sensitivity to random concentration errors. The differences and connections among the two measures of recipe sensitivity to random errors and Oulton's measure of recipe sensitivity to proportional errors are discussed and illustrated by numerical examples. Estimates of maximal colour error, caused by given maximal weighing and strength errors, are developed.     

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     

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     

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     

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     

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.     

A study of colour emotion and colour preference. Part I: Colour emotions for single colours

This article classifies colour emotions for single colours and develops colour‐science‐based colour emotion models. In a psychophysical experiment, 31 observers, including 14 British and 17 Chinese subjects assessed 20 colours on 10 colour‐emotion scales: warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike. Experimental results show no significant difference between male and female data, whereas different results were found between British and Chinese observers for the tense–relaxed and like–dislike scales. The factor analysis identified three colour‐emotion factors: colour activity, colour weight, and colour heat. The three factors agreed well with those found by Kobayashi and Sato et al. Four colour‐emotion models were developed, including warm–cool, heavy–light, active–passive, and hard–soft. These models were compared with those developed by Sato et al. and Xin and Cheng. The results show that for each colour emotion the models of the three studies agreed with each other, suggesting that the four colour emotions are culture‐independent across countries. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 232–240, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20010     

Why higher resolution graphics cards are needed in colour vision research

The colour resolution of a 14‐bit and an 8‐bit per channel graphics card were evaluated and compared with the just noticeable difference between colours (varying only in luminance) for: (1) a standard observer (based on the CIE 1976 L*u*v* colour space) and (2) real observers in a colour discrimination task. The results of this study show that an 8‐bit per channel graphics card seems adequate for colour discrimination experiments where stimuli only vary in luminance. However, considering that the resolution of the graphics card should be equal to the Nyquist rate, an 8‐bit per channel card turns out to be inadequate. For colour discrimination experiments where stimuli only vary in chromaticity, there is an undersampling of the colour space with respect to MacAdam ellipses when using 8‐bit per channel graphics cards. The extremely fine colour resolution of a 14‐bit per channel graphics card overcomes these problems. Its use allows more accurate measurements of achromatic and chromatic discrimination thresholds and avoids experimental (spatial or luminance) artefacts, such as bandings that can occur on achromatic or chromatic gradients. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

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.     

Vector-based modelling of colour difference: a pilot study of the DE2000 colour difference model

A novel approach to colour difference modelling is presented whereby for any given CMC (1:1) or CIE DE2000 ∆E, ∆C, ∆H, and ∆L colour difference, the equivalent CIE XYZ, L*a*b*, and L*C*h coordinate changes are derived by optimising the input RGB stimuli from which they are all calculated. Single-dimension L or C or H difference loci expressed in DE2000 difference units are thus generated, and the additive equivalence of tristimulus values is likewise projected forward onto each locus and also onto a set of CIE DE2000 three-unit ellipse boundaries. Using the datasets thus generated, it is then shown firstly that the derived ellipses have well-defined semi-axes, which explain the detailed orientation of the MacAdam ellipses in x,y,Y space. Unit CIE DE2000 difference is confirmed as a successful quantifying constant of visual difference over a wide range of chroma, hue, and lightness differences. As a constant, CIE DE2000 unit difference is shown to have a significantly variable value at high and low chroma: evidence is established for systematic changes in both chroma and hue difference sensitivity. A hitherto unresolved non-linearity is revealed in the C* dimension of L*C*h space that is not replicated in the CIE DE2000 model. The derived difference loci appear to specify physically reproducible experimental stimuli that could be used in the estimation of visual difference magnitude. Overall, the data derived by the new approach and presented in this paper increase the probability that a true vector model of the visual difference response may eventually be derived.     

The total colorant sensitivity of a color matching recipe: An approach to colorant weighting and tinctorial strength errors

The repeatability of the recipe color can be affected by several different types of inevitable inaccuracies in the coloration process. Two of the major causes of poor target‐color reproducibility are the (random) weighing and (proportional) strength errors. This article describes alternative definitions of colorant strength sensitivity and total colorant sensitivity of a dyeing recipe. The influences of the maximal colorant weighing and strength errors are taken into account in order to bring the magnitudes of the two treated types of sensitivity into a mutually realistic balance between each other. The quantifications of precision and accuracy of a color matching recipe are also developed and combined into a single‐number measure of recipe quality. The listed quantities are expected to be useful in selecting the most reliable one(s) among the different formulations for the same standard color. The methods are presented for calculating numerical estimates of the newly introduced quantities. The precision and accuracy of the coloration process are investigated in laboratory experiments involving repeated dyeings. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 300–306, 2008.     

Virtual colour: Communication standard and analytical tool

A number of problems have been identified with existing colour specification systems and their physical exemplifications: colorimetric conditions (illuminant, observer, spectrophotometer geometry, specular component inclusion/exclusion, etc.), restrictions of the notational system adopted, and limitations of the colour atlases themselves (sample size, sample error, sample range, etc.). Developments in computer science, and in particularly CRT displays and colour printing devices, now provide increasingly more affordable alternatives to traditional colour ranges and atlases. Techniques have been developed to increase the accuracy of printed colours relative to their CRT originals, and devices are manufactured to calibrate colour monitors. However, colour selection software developed to date is still primarily based on existing colour ranges and colour specification systems, or on a more general mechanism specific to an individual application. It is argued that basing such software on an existing model of colour specification inherits some of the problems of that system: notational methodology, means of representation, and atlas limitations. It is proposed that the restrictions imposed by such software be relaxed by providing a flexible method of constructing application-specific colour specification systems with conversion to a standardized notation for accurate colour communication. Furthermore, it is proposed that such a tool would be invaluable for the evaluation of human colour perception. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 204–211, May 1997.     

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.     

The role of gamut,intuition and engagement in colour management in a design context

Colour management is ubiquitous in the digital world. However, despite the many advances in colour management over the last couple of decades, it remains an imperfect process. In the art and design community there is often a level of dissatisfaction and deep cynicism about colour management that can lead to lac of engagement with the process. This research explores colour management in a design context though three issues: the gamut issue, the intuitive issue and the engagement issue; each relates to areas where colour management could better connect with tacit design knowhow. The work focusses on the selection of colour in a digital context since for many users this is the first touch point that they have with colour management. Psychophysical studies have been carried out in both laboratory and design‐studio settings. It is shown that users can better predict the results from subtractive colour mixing than from additive colour mixing. The performance of various types of colour picker are explored and consequences for the design of user interfaces are discussed.     

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 colour study approach through colour measurements on slides of art paintings

The present work deals with the problem of colorimetric fidelity of digitized colour slides representing fine art paintings. This consists of a comparison between colour measurements provided by spectrophotometry on fourteen original colour samples, which are uniformly painted square-shaped patches and those taken on digitized colour slides of the same colour samples. Digital acquisition of slides were performed by a 3CCD detector and a combination of colour correction filters. The obtained R_cam, G_cam, and B_cam values were then corrected and transformed into X_cam, Y_cam, and Z_cam values based on the CIE 1931 XYZ colorimetric system. The colour difference, , in the CMC uniform colour space is also calculated and found to be equal to seven, = 7, in average value, when colour compensating filters are used. This method is finally applied to four art works reproduced on different slides, and colour results were qualitatively compared as an indication to these resulting from Ostwald disk and saturation diagrams of previous research studies related to paintings that are created by the same artists. This comparison led to the same conclusions that were already proposed by the previous researchers. The results clearly demonstrate the contribution of the method to a computer-aided study of art works through image data banks that are realized by slides, taking into consideration, however, that this study consists of a first approach, offering a promising solution to the problem of colour accuracy of slides. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 326–334, 1997