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.     

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     

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     

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     

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 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     

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.     

Predicting the colour of trichromatic prints

The colour resulting from the partial overlap of tiny dots of cyan, magenta, and yellow inks in a matrix is difficult to predict. A method of simulating it on the macro-scale has been devised by measuring discs with coloured sectors using a spectrophotometer. Here the separate colours are mixed within the integrating sphere of the instrument. Although subtractive mixing occurs where colours overlap, the overall result to the eye/brain is interpreted by additive means. The result of mixing known areas of coloured surfaces additively was predicted successfully by Maxwell in the last century. His method, combined with the use of the CIE System, has been successfully used to predict the coordinates of the mixture of coloured sectors measured on the spectrophotometer. The theoretical model developed applies to trichromatic or polychromatic printing, whatever the substrate. © 1995 John Wiley & Sons, Inc.     

Logo colour and differentiation: A new application of environmental colour mapping

Creating a logo design is an important task for a new company wishing to gain entry in a particular industry sector. It requires an initial situation analysis that examines existing logos within the sector, and this information is then used to inform creation of a new logo design. Colour, one of a number of design elements used to create a new logo, is a key element in creating a unique logo and in terms of enabling a logo achieve differentiation in a competitive environment. This article discusses the application of the environmental colour mapping process during the initial situation analysis phase of logo design. The process, which has been applied in urban design studies in Japan, America, France, England, and Norway has recently been augmented with the addition of digital technology. Using a case study approach, the ‘environment’ for the purpose of this study represented the logo designs of organizations within a specific industry sector. The main outcome from the process (colour data presented in the form of a colour map) was examined for patterns of similarity and dissimilarity and an attempt was made to identify new options for logo colours within the sector based on colour differentiation. This study represents a new application of the environmental colour mapping process and a number of limitations and benefits are discussed. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

Requirements capture for colour information for design professionals

This article presents the results of a study that investigates the status of colour information use in the design process and generates ideas for a colour tool. Face‐to‐face interviews with senior designers and brand managers from the packaging and branding fields were conducted as the primary data collection method. The results are categorized into six topics: colour decision, types of colour information considered to be important in the design process, reasons for considering colour information important in the design process, current use of colour information, design professionals' preferences for existing colour tool types and data types and suggestions for a colour tool. It is concluded that there are problems with existing colour resources and tools regarding their availability and usefulness; there is a strong demand for a colour tool in the packaging design and branding processes. The insight from this work will help researchers, design professionals and colour tool developers to make informed decisions on the areas on which they should focus, how they should do so and why. This will facilitate better provisions and uptake of useful colour information for design professionals in the design process and strategy fields.     

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     

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 colour sensitivity of a colour matching recipe

The article examines the concepts of the following three quantities: partial colour sensitivity of a recipe to a particular colorant, colour balance of a recipe, and the overall colour sensitivity and the related property of colour robustness of a recipe. the way to calculate numerical estimates of the above quantities is extended from the case of CIE L *a*b* to the case CMC(l:c) colour difference formula. Results of a few numerical experiments are included for illustration and some possible practical consequences 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     

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     

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     

Evaluating colour models' performance between monitor and print images

The earlier experimental results (Color Res. Appl. 16, 166–180, 181–197; 18, 98–113, 191–209; 20, 18–28) have been further extended to include data obtained using complex images. Binocular memory and simultaneous matching techniques were used to assess the colour reproduction quality of displayed monitor images processed via eight colour models against a hardcopy (original) image illuminated in a viewing cabinet. The results from a panel of nine observers were used to compare different colour models' performance. It was found that the BFD chromatic adaptation transform outperformed the other models. The Hunt94 model, which gave a good fit to the earlier results, did not perform well. This indicates that there are differences in colour appearance between the complex and simple viewing fields. Other aspects were also investigated such as observer precision and repeatability, spatial uniformity of the monitor, image dependency, and the difference between the category judgment and paired comparison results. © 1996 John Wiley & Sons, Inc.     

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