A colour-difference equation based on CIELAB is developed. It includes not only lightness, chroma, and hue weighting functions, but also an interactive term between chroma and hue differences for improving the performance for blue colours and a scaling factor for CIELAB a* scale for improving the performance for gray colours. Four reliable colour discrimination datasets based upon object colours were accumulated and combined. The equation was tested together with the other advanced CIELAB based equations using the combined dataset and each individual dataset. It outperformed CMC and CIE94 by a large margin, and predicted better than BFD and LCD. The equation has been officially adopted as the new CIE colour-difference equation. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 340–350, 2001

The development of the CIE 2000 colour‐difference formula: CIEDE2000

Defining Color. Describing Color. Measuring Color. Measuring Color Quality. Colorants. Producing Colors. Back to Principles. Appendix. Bibliography. Index.

Billmeyer and Saltzman's Principles of Color Technology

Images can affect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classification. Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification); and a set of peer rated abstract paintings to investigate the influence of the features and ratings on pictures without contextual content. Improved classification results are obtained on the International Affective Picture System (IAPS), compared to state of the art work.

Affective image classification using features inspired by psychology and art theory

In understanding how visual signals function, quantifying the components of those patterns is vital. With the ever- increasing power and availability of digital photography, many studies are utilizing this technique to study the con- tent of animal colour signals. Digital photography has many advantages over other techniques, such as spectrometry, for measuring chromatic information, particularly in terms of the speed of data acquisition and its relatively cheap cost. Not only do digital photographs provide a method of quantifying the chromatic and achromatic content of spa- tially complex markings, but also they can be incorporated into powerful models of animal vision. Unfortunately, many studies utilizing digital photography appear to be unaware of several crucial issues involved in the acquisition of images, notably the nonlinearity of many cameras' responses to light intensity, and biases in a camera's processing of the images towards particular wavebands. In the present study, we set out step-by-step guidelines for the use of digital photography to obtain accurate data, either independent of any particular visual system (such as reflection values), or for particular models of nonhuman visual processing (such as that of a passerine bird). These guidelines include how to: (1) linearize the camera's response to changes in light intensity; (2) equalize the different colour channels to obtain reflectance information; and (3) produce a mapping from camera colour space to that of another colour space (such as photon catches for the cone types of a specific animal species). © 2007 The Linnean Society of London, Biological Journal of the Linnean Society , 2007, 90 , 211-237. ADDITIONAL KEYWORDS: camera calibration - colour vision - colour measurement - digital cameras - imaging - radiance - reflection - signals.

http://www.cvc.uab.es/~aparraga/Publications/Stevens_et_al_BJLinnSoc()2007.pdf

Using digital photography to study animal coloration

Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the-art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods, and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available datasets. Finally, various freely available methods, of which some are considered to be state of the art, are evaluated on two datasets.

Computational Color Constancy: Survey and Experiments

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

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

The digital camera is a powerful tool to capture images for use in image processing and colour communication. However, the RGB signals generated by a digital camera are device-dependent, i.e., different digital cameras produce different RGB responses for the same scene. Furthermore, they are not colorimetric, i.e., the output RGB signals do not directly correspond to the device-independent tristimulus values based on the CIE standard colorimetric observer. One approach for deriving a colorimetric mapping between camera RGB signals and CIE tristimulus values uses polynomial modeling and is described here. The least squares fitting technique was used to derive the coefficients of 3 × n polynomial transfer matrices, yielding a modeling accuracy typically averaging 1 ΔE units in CMC(1:1) when a 3 × 11 matrix is used. Experiments were carried out to investigate the repeatability of the digitizing system, characterization performance when different polynomials were used, modeling accuracy when 8-bit and 12-bit RGB data were used for characterization, and the number of reference samples needed to achieve a reasonable degree of modeling accuracy. Choice of characterization target and media and their effect on metamerism have been examined. It is demonstrated that a model is dependent upon both media and colorant, and applying a model to other media/colorants can lead to serious eye–camera metamerism problems. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 76–84, 2001

A study of digital camera colorimetric characterization based on polynomial modeling

The CIE Technical Committee 8-01, color appearance models for color management applications, has recently proposed a single set of revisions to the CIECAM97s color appearance model This new model, called CIECAM02, is based on CIECAM97s but includes many revisions1-4 and some simplifications A partial list of revisions includes a linear chromatic adaptation transform, a new non-linear response compression function and modifications to the calculations for the perceptual attribute correlates The format of this paper is an annotated description of the forward equations for the model Introduction The CIECAM02 color appearance model builds upon the basic structure and form of the CIECAM97s5,6 color appearance model This document describes the single set of revisions to the CIECAM97s model that make up the CIECAM02 color appearance model There were many, often conflicting, considerations such as compatibility with CIECAM97s, prediction performance, computational complexity, invertibility and other factors The format for this paper will differ from previous papers introducing a color appearance model Often a general description of the model is provided, then discussion about its performance and finally the forward and inverse equations are listed separately in an appendix Performance of the CIECAM02 model will be described elsewhere7 and for the purposes of brevity this paper will focus on the forward model Specifically, this paper will attempt to document the decisions that went into the design of CIECAM02 For a complete description of the forward and inverse equations, as well as usage guidelines, interested readers are urged to refer to the TC 8-01 web site8 or to the CIE for the latest draft or final copy of the technical report This paper is not intended to provide a definitive reference for implementing CIECAM02 but as an introduction to the model and a summary of its structure Data Sets The CIECAM02 model, like CIECAM97s, is based primarily on a set corresponding colors experiments and a collection of color appearance experiments The corresponding color data sets9,10 were used for the optimization of the chromatic adaptation transform and the D factor The LUTCHI color appearance data11,12 was the basis for optimization of the perceptual attribute correlates Other data sets and spaces were also considered The NCS system was a reference for the e and hue fitting The chroma scaling was also compared to the Munsell Book of Color Finally, the saturation equation was based heavily on recent experimental data13 Summary of Forward Model A color appearance model14,15 provides a viewing condition specific means for transforming tristimulus values to or from perceptual attribute correlates The two major pieces of this model are a chromatic adaptation transform and equations for computing correlates of perceptual attributes, such as brightness, lightness, chroma, saturation, colorfulness and hue The chromatic adaptation transform takes into account changes in the chromaticity of the adopted white point In addition, the luminance of the adopted white point can influence the degree to which an observer adapts to that white point The degree of adaptation or D factor is therefore another aspect of the chromatic adaptation transform Generally, between the chromatic adaptation transform and computing perceptual attributes correlates there is also a non-linear response compression The chromatic adaptation transform and D factor was derived based on experimental data from corresponding colors data sets The non-linear response compression was derived based on physiological data and other considerations The perceptual attribute correlates was derived by comparing predictions to magnitude estimation experiments, such as various phases of the LUTCHI data, and other data sets, such as the Munsell Book of Color Finally the entire structure of the model is generally constrained to be invertible in closed form and to take into account a sub-set of color appearance phenomena Viewing Condition Parameters It is convenient to begin by computing viewing condition dependent constants First the surround is selected and then values for F, c and Nc can be read from Table 1 For intermediate surrounds these values can be linearly interpolated2 Table 1 Viewing condition parameters for different surrounds Surround F c Nc Average 10 069 10 Dim 09 059 095 Dark 08 0525 08 The value of FL can be computed using equations 1 and 2, where LA is the luminance of the adapting field in cd/m2 Note that this two piece formula quickly goes to very small values for mesopic and scotopic levels and while it may resemble a cube-root function there are considerable differences between this two-piece function and a cube-root as the luminance of the adapting field gets very small ! k =1/ 5L A +1 ( ) (1) ! F L = 02k 4 5L A ( ) + 01 1" k4 ( ) 2 5L A ( ) 1/ 3 (2) The value n is a function of the luminance factor of the background and provides a very limited model of spatial color appearance The value of n ranges from 0 for a background luminance factor of zero to 1 for a background luminance factor equal to the luminance factor of the adopted white point The n value can then be used to compute Nbb, Ncb and z, which are then used during the computation of several of the perceptual attribute correlates These calculations can be performed once for a given viewing condition

/pdf/the-ciecam02-color-appearance-model-as6tovjk05.pdf

The CIECAM02 color appearance model

Can a single colour model be used for all colorimetric applications? This article intends to answer that question. Colour appearance models have been developed to predict colour appearance under different viewing conditions. They are also capable of evaluating colour differences because of their embedded uniform colour spaces. This article first tests the performance of the CIE 2002 colour appearance model, CIECAM02, in predicting three types of colour discrimination data sets: large- and small-magnitude colour differences under daylight illuminants and small-magnitude colour differences under illuminant A. The results showed that CIECAM02 gave reasonable performance compared with the best available formulae and uniform colour spaces. It was further extended to give accurate predictions to all types of colour discrimination data. The results were very encouraging in that the CIECAM02 extensions performed second best among all the colour models tested and only slightly poorer than the models that were developed to fit a particular data set. One extension derived to fit all types of data can predict well for colour differences having a large range of difference magnitudes. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 320–330, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20227

Uniform colour spaces based on CIECAM02 colour appearance model

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

M. Ronnier Luo

Papers

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

A study of digital camera colorimetric characterization based on polynomial modeling

The CIECAM02 color appearance model

Uniform colour spaces based on CIECAM02 colour appearance model

A study of colour emotion and colour preference. Part III: Colour preference modeling