CMC 2000 chromatic adaptation transform: CMCCAT2000

Li, Changjun; Luo, M. Ronnier; Rigg, B.; Hunt, R. W. G.

doi:10.1002/col.10005

Cited by 99 publications

(76 citation statements)

References 21 publications

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“…The reference illuminant is selected as in CIE CRI. The tristimulus values of illuminated samples are corrected for chromatic adaptation by using CMCCAT2000 (Li, Luo, Rigg, & Hunt, 2002) and CIELAB colour space is used instead of CIE 1964 W*U*V* colour space. The saturation factor (Davis & Ohno, 2010) is introduced in calculating the colour difference of each reflective sample in CQS.…”

Section: Colour Quality Scalementioning

confidence: 99%

Performance of different metrics proposed to CIE TC 1-91

Bhusal

Dangol

2017

IJSL

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The main aim of the article is to find out the performance of different metrics proposed to CIE TC 1-91. Currently, six different indexes have been proposed to CIE TC 1-91: Colour Quality Scale (CQS), Feeling of Contrast Index (FCI), Memory colour rendering index (MCRI), Preference of skin (PS), Relative gamut area index (RGAI) and Illuminating Engineering society Method for evaluating light source colour rendition (IES TM-30). The evaluation and analysis are based on previously conducted experiment in lighting booth. The analysis showed the area based metric FCI was good subjective preference indicator. The subjective preference was measured in terms of naturalness of objects, colourfulness of colour checker chart, and the visual appearance of the lit scene in the booth.

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Section: Colour Quality Scalementioning

confidence: 99%

Performance of different metrics proposed to CIE TC 1-91

Bhusal

Dangol

2017

IJSL

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“…Eight data sets were used for deriving the CMCCAT2000 [8]. They are the data sets: the Colour Science Association of Japan (CSAJ), Kuo & Luo, Lam and Rigg, Helson et al, LUTCHI, Breneman, Braun & Fairchild, and McCann.…”

Section: Mathematical Approach For Searching a New Matrix For The Cat02mentioning

confidence: 99%

“…They are the data sets: the Colour Science Association of Japan (CSAJ), Kuo & Luo, Lam and Rigg, Helson et al, LUTCHI, Breneman, Braun & Fairchild, and McCann. For details about each of the data sets, see reference [8] and references there. However, there is a strong objection to using the McCann data because its viewing conditions, mainly using chromatic illuminants under low luminance level, are largely different from the other data sets.…”

Section: Mathematical Approach For Searching a New Matrix For The Cat02mentioning

confidence: 99%

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

Perales

Luo

et al. 2011

Color Research & Application

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It was reported that the CAT02 imbedded in the CIECAM02 suffers from predicting the corresponding colours with negative tristimulus values. To overcome this problem, a mathematical approach is proposed for modifying the CAT02. This approach combines the non-negativity constraint for the corresponding colours' tristimulus values with the minimisation of the colour differences between the tristimulus values of the corresponding colours obtained by visual observations and tristimulus values of the corresponding colours predicted by the model, which resulted in a constrained non-linear optimisation problem. A revised matrix is established using the MATLAB routine " fmincon ".The performances of the CAT02 with various matrices including the original CAT02 matrix and the new matrix are tested using the visual data sets and the optimum colours. Test results show that the CAT02 with the new matrix successfully predicted corresponding colours' tristimulus vales without negative values for all optimum colours and colour matching functions of two standard observers under the test illuminants considered. However, accuracy with the new matrix for predicting the visual data becomes about 1 CIELAB colour difference unit worse compared with the original CAT02. It seems that the accuracy has to be sacrificed in order to ensure the non-negativity constraint for the tristimulus values of the corresponding colours.

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“…CMCCAT2000 was developed to simplify CMCCAT97 and improve its performance in predicting corresponding colours across all available experimental data sets. 8 Therefore, it was derived by optimizing the transform to fit all the data sets, rather than one data set as for CMCCCAT97 (only fitting Lam and Rigg data set 8 ). Li et al 8 showed that the cone response functions across visible range obtained from the two transforms are very similar.…”

Section: Models' Performancementioning

confidence: 99%