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     

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     

What scene information is needed for models of colour appearance in the natural world?

Colorimetry is an essential tool in every part of colour. This article looks over our shoulders at the colour research of Maxwell, Wright, Land and Wyszecki to build a framework for colour. Then, the article looks forward to the needs of digital imaging's future. Colour is the fusion of today's imaging technology with our understanding of colour. Molecular physical chemistry describes the light–matter interactions, while human colour is controlled by neurons that compare light from the entire scene, covering a nearly 180° visual angle. This article's question asks about the information required by a future Model of Colour Appearance that is able to predict any scene: all natural scenes and any experimental display.     

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.     

A theory of unique hues and colour categories in the human colour vision

A novel approach to assessing colour appearance is described. It is based on a new technique—partial hue‐matching—which allows for measuring colour in terms of component hues objectively, without resorting to verbal definitions. The new method is believed to have a potential to be as exact as colorimetric techniques. In contrast to classical colour matching, which implies visual equivalence of lights, partial hue‐matching is based on judgements of whether two lights that are different in colour have some hue in common. The major difference between classical colour matching and partial hue‐matching is that the latter is intransitive, whereas the former is generally believed to be transitive (though see Logvinenko, Symposium on 75 years of the CIE Standard Colorimetric Observer, Vienna, Austria, 2006). Formally, partial hue‐matching can be described as a reflexive and symmetric binary relation (i.e., tolerance). The theoretical framework of tolerance spaces is used for developing a theory of partial hue‐matching. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

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

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     

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.     

Evaluating colour image difference metrics for gamut-mapped images

The quality of several colour image difference metrics, pixelwise CIELAB Δ E _ab , S-CIELAB, iCAM, Structural Similarity Index, Universal Image Quality and the hue-angle algorithm, have been investigated. These results were compared with the results from a psychophysical experiment in which the perceptual image difference was evaluated. Six original images were reproduced using six different colour gamut-mapping algorithms. The results of our experiment indicate that perceptual image difference cannot be directly related to colour image difference calculated by current metrics. Therefore, it is currently not possible to evaluate colour gamut mapping quality using colour image difference metrics.     

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     

Investigation of colour appearance models for illumination changes across media

The key to achieving successful cross‐media colour reproduction is a reliable colour appearance model, which is capable of predicting the colour appearance across a variety of imaging devices under different viewing conditions. The two most commonly used media, CRT displays (soft copy) and printed images (hard copy), were included in this study using four complex images. The original printed images were captured using a digital camera and processed using eight colour appearance models (CIELAB, RLAB, LLAB, ATD, Hunt96, Nayatani97, CIECAM97s, and CAM97s2) and two chromatic adaptation transforms (von Kries and CMCCAT97). Psychophysical experiments were carried out to assess colour model performance in terms of colour fidelity by comparing soft‐copy and hard‐copy images. By employing the memory‐matching method, observers categorized the reproductions displayed on a CRT and compared them to the original printed images viewed in a viewing cabinet. The experiment was divided into three phases according to the different colour temperatures between the CRT and light source, i.e., print (D50, A, and A) and CRT (D93, D93, and D50), respectively). It was found that the CIECAM97s‐type models performed better than the other models. In addition, input parameters for each model had a distinct impact on model performance. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 428–435, 2001     

Use of a predictive colour model for managing the colour appearance of two‐colour woven fabrics

The aim of this study was to implement a two‐dimensional colour appearance model for prediction of the colour values of weft threads when the optical mixing of a two‐colour woven structure had to match the colour appearance of a single‐colour reference woven fabric. Five single‐colour woven fabrics were woven from five threads of similar hue. One of the samples was chosen as a reference, for which the colour appearance was the goal to be achieved in the two‐colour woven fabrics prepared with the other available warp threads and newly dyed weft threads. The colour values of dyed weft threads were predicted by a two‐dimensional colour appearance model. With dyed weft threads, managing the colour appearance of the two‐colour woven fabric was enabled to achieve the colour values of the reference. In the results, colour deviations between the predicted and measured colour values of weft threads revealed some limitations to the colour appearance model and performance of the dyeing process. After the production of the two‐colour woven fabric, the colour appearance matched the appearance of the reference, resulting in deviations of ΔE_CMC(2:1) = 1.2‐7.8. Moreover, the differences between theoretically predicted and measured colour values of the two‐colour woven fabric were evaluated as small, ranging from ΔE_CMC(2:1) = 1.5‐1.9. The results demonstrated the efficiency of implementing the colour appearance model and the dyeing process of weft threads as an approach to achieve the defined colour appearance of two‐colour woven fabrics, which with small colour deviations matches the colour of a single‐colour reference.     

Changes in colour appearance of a large display in various surround ambient conditions

This article focuses on the change in colour appearance of a large display arising from various illumination conditions. Nine experimental phases were conducted according to different surround conditions including dark, indoor, and outdoor conditions. Each of the presented test colours was assessed by 10 observers using a magnitude estimation method. The surrounds used in all phases were divided into two groups: excluding and including veiling glare. Additionally, the effect due to different sizes of both stimuli and surround was investigated. Diverse visual effects were examined and reported. Finally, the visual colour appearance data were used to test the CIECAM02 colour appearance model. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

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     

Computational production of colour harmony. Part 1: A prototype colour harmonization tool

Although web page and computer interface developers generally have little experience in generating effective colour schemes, colour selection appears rarely in user interface design literature, and there are few tools available to assist in appropriate choice of colours. This article describes an algorithmic technique for applying colour harmony rules to the selection of colour schemes for computer interfaces and web pages. Our software implementation of this approach—which we term the Colour Harmoniser—adapts and extends classical colour harmony rules for graphical user interfaces, combining algorithmic techniques and personal taste. A companion article presents the experimental evaluation of the system presented here. Our technique applies a set of rules for colour harmony to specific features of the interface or web page to create abstract colour schemes; the user then modifies the overall colour cast, saturation, and light–dark distribution, producing colourings that are both harmonious and usable. We demonstrate experimentally that the software is relatively simple to use and produces colourings that are well‐received by humans. In this article, we define a fitness function that numerically evaluates the colour harmony of a user interface and underpins a genetic algorithm for creating harmonious schemes. We show how abstract, hue‐independent, colour schemes may be mapped to real colour schemes, leaving the abstract colour harmony unchanged, but accommodating the developer's personal preferences for overall colouring, light–dark contrast, and saturation. This abstract/concrete separation automates the creation of harmonious schemes and allows unskilled developers to express their aesthetic preferences using simple direct manipulation controls. © 2011 Wiley Periodicals, Inc. Col Res Appl, 38, 203–217, 2013.     

Quantifying colour appearance. Part I. Lutchi colour appearance data

The work described here forms part of a research project entitled Predictive Perceptual Colour Models. The aim of this project is to develop a colour appearance model capable of predicting changes of colour appearance under various different viewing conditions. This will provide industry with a quantitative measure for assessing the quality of colour reproduction and enable more rapid and accurate proofing simulations in the graphic art industry. A large-scale experiment has been carried out in which colour appearance was assessed under a wide range of viewing conditions. The parameters studied were (1) D65, D50, white fluorescent, and tungsten light sources, (2) luminance levels of about 40 and 240 cd/m², (3) five background conditions: white, grey, black, grey with white border, and grey with black border, and (4) two media: luminous colours (displayed on a high-resolution colour monitor) and nonluminous colours (presented in a viewing cabinet). Each colour was assessed by a panel of six or seven observers using a magnitude estimation method. In total, 43,332 estimations were made, and these form the LUTCHI Colour Appearance Data. Data analysis has been carried out to examine the reliability of the experimental results and to understand the effects of the various viewing parameters studied. (Part II of this article describes how the LUTCHI Colour Appearance Data has been used to test the performance of various colour spaces and models.