Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples

Liu, Qiang; Huang, Zheng; Xiao, Kaida; Pointer, Michael R.; Westland, Stephen; Luo, M. Ronnier

doi:10.1364/oe.25.016378

Cited by 32 publications

(20 citation statements)

References 39 publications

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“…According to the current literature, the whiteness of lighting [1][2][3][4][5][6][7] and colour preference for lighting [8][9][10][11][12][13][14][15][16][17][18][19][20] are two of the most popular issues when evaluating the colour quality of light sources. Anecdotally, people prefer white illumination for general lighting applications although in some countries, some people are said to prefer yellowish lighting at home in the living room for relaxing in the evening.…”

Section: Introductionmentioning

confidence: 99%

“…34 In addition, an objective measure for such a colour quality dimension is also urgently desired. 9,10,12,13,17,18,35 In 2013, a relevant study that considered the correlation between colour preference and the whiteness of lighting was implemented by Rea and Freyssinier. 2 Their results indicated that people preferred whiter illumination for indoor applications.…”

Section: Introductionmentioning

confidence: 99%

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The whiteness of lighting and colour preference, Part 2: A meta-analysis of psychophysical data

Huang

Liu

Luo

et al. 2019

Lighting Research & Technology

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In this work, the correlation between the perceived whiteness of lighting and the corresponding colour preference of observers was comprehensively investigated based on the data of 19 groups of experiments collected from 14 studies. The dataset included 13 experiments with constant correlated colour temperature and 6 experiments with multiple correlated colour temperatures, with illuminance levels ranging from 200 lx to 500 lx. For the five studies implemented by the authors, the subjective ratings of colour preference and the whiteness of lighting were acquired. For other cases, only the preference ratings were collected while the whiteness of lighting was quantified by a measure named degree of neutrality ( Sneutral), which has been validated in our previous work. The meta-analysis results confirm our former statement that people prefer whiter illumination and further specify its prerequisite. That is, the whiteness of lighting generally correlates well with colour preference under the conditions where the candidate lights differed to a certain extent in white. In addition, it was further demonstrated that for the scenarios with multiple correlated colour temperatures ranging from 2500 K to 5500 K people indeed preferred perceptually whiter light chromaticities, while for correlated colour temperatures higher than 5500 K it seemed that they appeared too cold to be preferred.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The whiteness of lighting and colour preference, Part 2: A meta-analysis of psychophysical data

Huang

Liu

Luo

et al. 2019

Lighting Research & Technology

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“…However, the reverse is found not to be the case. Therefore, we separately select highly-saturated samples at the color gamut boundary of the printing color atlas [8], [20].…”

Section: Evaluation Methods Based On the Optimization Of Tcs A Wmentioning

confidence: 99%

“…Therefore, special consideration needs to be given to the effect of highly-saturated samples on the model. The color atlas samples are classified as highly-saturated sample set g at the color gamut boundary and the set f in the color gamut [17], [20].…”

Section: Evaluation Methods Based On the Optimization Of Tcs A Wmentioning

confidence: 99%

“…The OCS set consists of 1 and 2 . It is worth reiterating that considering the influence of the highly-saturated color samples on the color rendering evaluation of the light source, the clustering methods developed in this paper divide the color samples into two parts: TCS set with high saturation at the boundary and the set whose samples are within the bounds of the gamut [20]. The two parts are processed separately with the same process and algorithm.…”

Section: Ap Clustering Analysis Of Color Printing Atlas Samplesmentioning

confidence: 99%

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Study of Color Rendering Evaluation Method of Light Sources for Printing Matter

Lian

Liu

et al. 2020

IEEE Access

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Based on the optimization of color samples, we have developed a method to evaluate light sources' color rendering of printing matter. By performing Affinity Propagation (AP) Clustering Algorithm on the color appearance and spectra of the color printing atlas samples printed by a press, two typical color sample sets are obtained. The optimized color sample (OCS) set consists of these two typical color sample sets and it is presented by CMYK dot area coverage. The color rendering indexes (CRIs) CIE-R a , CRI2012, CIE-R f of 90 light sources are calculated by taking the OCS set, color printing atlas and standard color sample (SCS) set of CRI as the test color sample (TCS) set. By comparing the Absolute Difference (AD), Mean Absolute Difference (MAD), Coefficient of Variation (CV) and Spearman Correlation Coefficient (SCC) of the CRIs, we find that the indexes calculated by OCS set are closer to these indexes calculated by color printing atlas than by SCS set. Therefore, the OCS set is more suitable for printing applications. After output from a press and measured spectra, the OCS set with CMYK dot area coverage can be used as the TCS set to evaluate the color rendering of light sources for all printing matter output from this press. For proving the reliability of the results, that is, the universality of the OCS set to the presses, the OCS set with fixed CMYK dot area coverage is output from an another press and CRIs are calculated. The results show that OCS set we developed is better than the SCS set. This method can be used for color rendering evaluation of light sources for printing matter.

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CIELAB color space boundaries under theoretical spectra and 99 test color samples

Durmus

2020

Color Research & Application

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A color space is a three‐dimensional representation of all the possible color percepts. The CIE 1976 L*a*b* is one of the most widely used object color spaces. In CIELAB, lightness L* is limited between 0 and 100, while a* and b* coordinates have no fixed boundaries. The outer boundaries of CIELAB have been previously calculated using theoretical object spectral reflectance functions and the CIE 1931 and 1964 observers under the CIE standard illuminants D50 and D65. However, natural and manufactured objects reflect light smoothly as opposed to theoretical spectral reflectance functions. Here, data generated from a linear optimization method are analyzed to re‐evaluate the outer boundaries of the CIELAB. The color appearance of 99 test color samples under theoretical test spectra has been calculated in the CIELAB using CIE 1931 standard observer. The lightness L* boundary ranged between 6 and 97, redness‐greenness a* boundary ranged between −199 and 270, and yellowness‐blueness b* boundary ranged between −74 and 161. The boundary in the direction of positive b* (yellowness) was close to the previous findings. While the positive a* (redness) boundary exceeded previously known limits, the negative a* (greenness) and b* (blueness) boundaries were lower than the previously calculated CIELAB boundaries. The boundaries found here are dependent on the color samples used here and the spectral shape of the test light sources. Irregular spectral shapes and more saturated color samples can result in extended boundaries at the expense of computational time and power.

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Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples

Cited by 32 publications

References 39 publications

The whiteness of lighting and colour preference, Part 2: A meta-analysis of psychophysical data

The whiteness of lighting and colour preference, Part 2: A meta-analysis of psychophysical data

Study of Color Rendering Evaluation Method of Light Sources for Printing Matter

CIELAB color space boundaries under theoretical spectra and 99 test color samples

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