Study on Small Color Difference Evaluation Using Printed Samples with Different Gloss

Min, 黄敏 Huang; Lili, 王丽丽 Wang; Haoxue, 刘浩学 Liu; Ningfang, 廖宁放 Liao

doi:10.3788/aos20103006.1851

Cited by 5 publications

(4 citation statements)

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“…Gloss is an important perceptual factor that can strongly influence the visual perception of color differences [40,41]. The results for gloss given in Table 12 were the average of four pairs of phases (interested readers may find more detailed results in the STRESS values shown in Tables 3 and 10); they suggested that the color-difference formulas tested performed slightly worse (i.e., had higher STRESS values) for the matte samples.…”

Section: Parametric Effectsmentioning

confidence: 99%

Optimizing Color-Difference Formulas for 3D-Printed Objects

Huang

Gao

et al. 2022

Sensors

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Based on previous visual assessments of 440 color pairs of 3D-printed samples, we tested the performance of eight color-difference formulas (CIELAB, CIEDE2000, CAM02-LCD, CAM02-SCD, CAM02-UCS, CAM16-LCD, CAM16-SCD, and CAM16-UCS) using the standardized residual sum of squares (STRESS) index. For the whole set of 440 color pairs, the introduction of kL (lightness parametric factor), b (exponent in total color difference), and kL + b produced an average STRESS decrease of 2.6%, 26.9%, and 29.6%, respectively. In most cases, the CIELAB formula was significantly worse statistically than the remaining seven formulas, for which no statistically significant differences were found. Therefore, based on visual results using 3D-object colors with the specific shape, size, gloss, and magnitude of color differences considered here, we concluded that the CIEDE2000, CAM02-, and CAM16-based formulas were equivalent and thus cannot recommend only one of them. Disregarding CIELAB, the average STRESS decreases in the kL + b-optimized formulas from changes in each one of the four analyzed parametric factors were not statistically significant and had the following values: 6.2 units changing from color pairs with less to more than 5.0 CIELAB units; 2.9 units changing the shape of the samples (lowest STRESS values for cylinders); 0.7 units changing from nearly-matte to high-gloss samples; and 0.5 units changing from 4 cm to 2 cm samples.

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Section: Parametric Effectsmentioning

confidence: 99%

Optimizing Color-Difference Formulas for 3D-Printed Objects

Huang

Gao

et al. 2022

Sensors

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“…The SCDs set included most of the original datasets 4,7,15,16,[21][22][23][24] studied by TC 1-55. 11 All these sets had relatively small colour-differences, that is with a mean ΔE * ab colour difference of less than five.…”

Section: Testing the Performance Of Colour Models Using Available Dat...mentioning

confidence: 99%

A comprehensive test of colour‐difference formulae and uniform colour spaces using available visual datasets

Luo

Pointer

et al. 2023

Color Research & Application

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The paper describes a comprehensive test to evaluate the performance of current colour‐difference models using available experimental datasets. In total, 28 individual datasets were accumulated to test 17 colour‐difference formulae, 13 of them based on Uniform Colour Spaces (UCSs) in terms of the Standardized Residual Sum of Squares (STRESS) measure. The 28 datasets were divided into three groups: Large Colour‐Difference data (LCD), Small Colour‐Difference data for surface colours (SCDs), and Small Colour Difference data for display colours (SCDd). For each colour model, four versions were tested: the original model, and that including kL‐, Gamma‐ and kL/Gamma, which are the lightness parametric factor, the colour‐difference exponent factor, and the combination of both, respectively, optimized to fit particular dataset(s). The statistical F‐test was applied to test the difference between each pair of models. Furthermore, parametric effects between the large/small colour‐difference magnitudes, and between surface/display colours were investigated. The results showed that CAM16‐UCS significantly outperformed the other models for all groups. It accurately predicted all types of data and should be proposed for colour‐difference evaluation across all industries.

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“…In the experiment, 24 test images (8 standard test images were output by three kinds of paper, per set images judged two times) have been evaluated whether have color-difference by 15 observers, total 15×8×2×2=480 times of judgment. Considering distinguish rate 50% as Identify the threshold, that is, if there are 50% of the judgments identify the color-difference , then the color-difference was considered could be Distinguished [6] . The result of observe shown in table 5.…”

Section: Analysis Of Observer Accuracymentioning

confidence: 99%

The Influence of Paper Brightness on Color Reproduction of Printed Matter

Wang

Liu

2012

AMR

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Paper, as an important substrate of printed matter, the performance will impact on the color reproduction. Brightness, as a key index of paper performance, affects the apparent color of paper directly. It was investigated that the printed samples with different brightness have been evaluated the influence of paper brightness on color reproduction by the methods of subjective and objective color difference evaluation. Three kinds of paper with different brightness have been selected as substrate of printed samples. The standard test color blocks have been printed to do corresponding objective color difference evaluation to different paper by CIEDE2000 color-difference formulae methods. Meanwhile, the standard test images with difference contents have been printed on three kinds of paper, which was subjected to subjective color difference evaluation for visual evaluation of standard test images in standard observe conditions. The results of subjective and objective color difference evaluation have been combined to analyze the effect of paper brightness to color reproduction, and different visual influence to images of difference content, and could provide certain reference for selecting the brightness performance of paper in color reproduction.

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Study on Small Color Difference Evaluation Using Printed Samples with Different Gloss

Cited by 5 publications

References 0 publications

Optimizing Color-Difference Formulas for 3D-Printed Objects

Optimizing Color-Difference Formulas for 3D-Printed Objects

A comprehensive test of colour‐difference formulae and uniform colour spaces using available visual datasets

The Influence of Paper Brightness on Color Reproduction of Printed Matter

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