Performance measures of color-difference equations: correlation coefficient versus standardized residual sum of squares

Kirchner, Eric; Dekker, Niels

doi:10.1364/josaa.28.001841

Cited by 31 publications

(19 citation statements)

References 41 publications

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“…The non-null intercepts in Fig. 5 indicate that a systematic error or "false alarm" effect existed in our experiment, which was caused either by the characteristics of the display or by the observers' psychological effects, as reported also in previous literature [26][27][28]; i.e., even when there was no color difference between a pair of images, observers could still perceive some differences.…”

Section: B Performance Of Color-difference Formulassupporting

confidence: 80%

“…This is not a surprising result. In fact, it has been proved [26] that squared STRESS is equal to 1 − R 2 if R is the Pearson's linear correlation coefficient imposing the restriction that the regression line passes through the origin. In our case this restriction of null intercept is not true, as explained before, which justifies considering in our current analyses not just STRESS values, but also R values as well as ΔV − ΔE plots.…”

Section: B Performance Of Color-difference Formulasmentioning

confidence: 99%

“…Another potential cause of this fact is that the smallest intercept (1.52) or "false alarm" for CIELAB(1:1) introduced the smallest systematic error into STRESS. As mentioned in previous literature [26,29], the STRESS index assumes that there is a ratio relationship between two datasets ΔV i and ΔE i . A certain amount of systematic error may be introduced in STRESS if a nonzero intercept (b) exists between ΔV i and ΔE i ; i.e., ΔE i f ΔV i b.…”

Section: B Performance Of Color-difference Formulasmentioning

confidence: 99%

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Color-difference evaluation for digital images using a categorical judgment method

et al. 2013

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The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor k(L)=1 to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize k(L) in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor k(L)=1. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor k(L)=2 in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas.

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Section: B Performance Of Color-difference Formulassupporting

confidence: 80%

Section: B Performance Of Color-difference Formulasmentioning

confidence: 99%

Section: B Performance Of Color-difference Formulasmentioning

confidence: 99%

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Color-difference evaluation for digital images using a categorical judgment method

et al. 2013

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“…However, it was recently shown that STRESS should be used with caution [33]. The STRESS index is used as a tool to determine the statistical significance of improvements in color-difference formulas and to evaluate inter-and intra-subject variability in perceptual studies [34].…”

Section: Measure Of Fitmentioning

confidence: 99%

Performance of select color-difference formulas in the blue region

et al. 2014

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The main objective of this work was to test the performance of major formulas for assessment of small suprathreshold color differences in the blue region. The models examined include CIELAB color space based equations, including CIELAB, CIE94, CIEDE2000, CMC (l:c), BFD (l:c), and formulas based on more uniform color spaces, such as DIN99d, CAM02-SCD, CAM02-UCS, OSA-GP, and OSA-Eu in comparison against data obtained via visual assessments. For this purpose, a dataset around the CIE high-chroma blue color center, hereafter called NCSU-B2, was developed. The NCSU-B2 dataset comprised 65 textile substrates and a standard, with a mean ΔE(ab)* color difference of 2.72, ranging from 0.54-5.72. Samples were visually assessed by 26 subjects against the reference gray scale in three separate trials with at least 24 h between assessments. A total of 5070 assessments were obtained. The standardized residual sum of squares (STRESS) index was used to examine the performance of various formulas for this dataset, as well as a previously developed NCSU-B1 low-chroma blue dataset [Color Res. Appl. 36, 27, 2011], and blue centers from other established visual datasets. Results show that formulas based on more recent uniform color spaces provide better agreement with perceptual data compared with models based on CIELAB space.

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“…The instrumental values of the maximum sparkle grade of both anchors (not identical) for both geometries were SG_15 = 19.22 and SG_45 = 11.14. The STRESS Index (standardized residual sum of squares), which is usually applied for testing color difference formula [22][23][24], was also used herein to compute intra-and inter-observer variability. As a result, the lower the STRESS values, the better the correlation.…”

Section: Published Bymentioning

confidence: 99%

Visual and instrumental correlation of sparkle by the magnitude estimation method

et al. 2016

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Received XX Month XXXX; revised XX Month, XXXX; accepted XX Month XXXX; posted XX Month XXXX (Doc. ID XXXXX); published XX Month XX Most real surfaces and objects show variation in appearance with viewing and illumination directions. Besides angular dependency, they also show spatial variation in color; i.e. they exhibit some sort of texture. Of the surfaces we see, surfaces colored by special-effect pigments produce several complex visual effects, like change in color and lightness with viewing and illumination angles, and effects like sparkle and gloss on other textures.In the last two decades, different commercial devices have appeared to help ensure the proper characterization of materials with special-effect pigments. However, the instrumental characterization of sparkle is currently available only by a commercial device integrated into a multi-angle spectrophotometer. As it is difficult to find complete open original studies about the sparkle effect for designing and calibrating this commercial instrument, the main objective of this work was to check whether a good visual and instrumental correlation exists between the sparkle that the observer perceives and the sparkle value provided by the device using some subsets of goniochromatic samples with different types of special-effect pigments and colors. Visual assessments were made by a conventional magnitude estimation method in a directional lighting booth, which belonged to the same company owner of the sparkle instrument, in different geometries and at distinct illuminance levels.The results revealed that there was a good visual correlation of the sparkle-grade value. By separately analyzing the factors used in its instrument algorithm, such as sparkle intensity and sparkle-area values, it was clearly shown that the correlation was not good or simply did not exist. Consequently, and perhaps as regards choice of new special-effect pigments, such as synthetic mica and other future ones, we generated herein even more questions about current mathematical algorithms, and only recognized calculating this texture effect at the industrial level.

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Performance measures of color-difference equations: correlation coefficient versus standardized residual sum of squares

Cited by 31 publications

References 41 publications

Color-difference evaluation for digital images using a categorical judgment method

Color-difference evaluation for digital images using a categorical judgment method

Performance of select color-difference formulas in the blue region

Visual and instrumental correlation of sparkle by the magnitude estimation method

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