Haoxue Liu

Evaluation of threshold color differences using printed samples

The performances of uniform color spaces and color-difference formulae for predicting threshold color differences were investigated based on visual assessments of 893 pairs of printed color patches under a D65 source. The average ΔE(ab,10)* of the pairs was 1.1 units. A threshold psychophysical experiment was repeated three times by a panel of 16 observers with normal color vision. The experimental data were used to evaluate nine color-difference formulae and uniform color spaces using the standardized residual sum of squares (STRESS) measure. The results indicated that all formulae and spaces performed very similarly to each other, and outperformed CIELAB for threshold color differences. The chromaticity-discrimination ellipses were used to compare with previous results from small color differences [Color Res. Appl. (2011), doi:10.1002/col.20689], and they agreed with each other, except for the purple color center.

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

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.

Power functions improving the performance of color-difference formulas

Color-difference formulas modified by power functions provide results in better agreement with visually perceived color differences. Each of the modified color-difference formulas proposed here adds only one relevant parameter to the corresponding original color-difference formula. Results from 16 visual data sets and 11 color-difference formulas indicate that the modified formulas achieve an average decrease of 5.7 STRESS (Standardized Residual Sum of Squares) units with respect to the original formulas, signifying an improvement of 17.3%. In particular, for these 16 visual data sets, the average decrease for the current CIE/ISO recommended color-difference formula CIEDE2000 modified by an exponent 0.70 was 5.4 STRESS units (17.5%). The improvements of all modified color-difference formulas with respect to the original ones held for each of the 16 visual data sets and were statistically significant in most cases, particularly for all data sets with color differences close to the threshold. Results for 2 additional data sets with color pairs in the blue and black regions of the color space confirmed the usefulness of the proposed power functions. The main reason of the improvements found for the modified color-difference formulas with respect to the original color-difference formulas seems to be the compression provided by power functions.

Evaluation of Color Perception Among Different Aged Observers Based on Color Matching Experiments

In order to study the observer variability of color perception, 53 observers aging from 20 to 79 were organized to match the 17 color stimuli on the monitor. The spectral power distribution of the target and the matched colors were measured and used to evaluate the color difference threshold at different aged group of observers. The results indicated that the mean CIEDE2000 color difference of all the tested observers was 1.07. The experimental data were calculated by CIE1964 color matching function (CMF) and CIEPO06 CMF. Comparison of the two models showed little difference in color difference thresholds for different aged observers.

Testing the AUDI2000 colour-difference formula for solid colours using some visual datasets with usefulness to automotive industry

Colour-difference formulas are tools employed in colour industries for objective pass/fail decisions of manufactured products. These objective decisions are based on instrumental colour measurements which must reliably predict the subjective colour-difference evaluations performed by observers’ panels. In a previous paper we have tested the performance of different colour-difference formulas using the datasets employed at the development of the last CIErecommended colour-difference formula CIEDE2000, and we found that the AUDI2000 colour-difference formula for solid (homogeneous) colours performed reasonably well, despite the colour pairs in these datasets were not similar to those typically employed in the automotive industry (CIE Publication x038:2013, 465-469). Here we have tested again AUDI2000 together with 11 advanced colour-difference formulas (CIELUV, CIELAB, CMC, BFD, CIE94, CIEDE2000, CAM02-UCS, CAM02-SCD, DIN99d, DIN99b, OSA-GP-Euclidean) for three visual datasets we may consider particularly useful to the automotive industry because of different reasons: 1) 828 metallic colour pairs used to develop the highly reliable RIT-DuPont dataset (Color Res. Appl. 35, 274-283, 2010); 2) printed samples conforming 893 colour pairs with threshold colour differences (J. Opt. Soc. Am. A 29, 883-891, 2012); 3) 150 colour pairs in a tolerance dataset proposed by AUDI. To measure the relative merits of the different tested colour-difference formulas, we employed the STRESS index (J. Opt. Soc. Am. A 24, 1823-1829, 2007), assuming a 95% confidence level. For datasets 1) and 2), AUDI2000 was in the group of the best colour-difference formulas with no significant differences with respect to CIE94, CIEDE2000, CAM02-UCS, DIN99b and DIN99d formulas. For dataset 3) AUDI2000 provided the best results, being statistically significantly better than all other tested colour-difference formulas.

Ink feeding control based on measured ink density

Printing ink volume control is one of the key factors in the printing process, which decides the printing color and reproduction quality. Printing ink volume is controlled through the console by adjusting ink key opening degree. The only parameter that reflects printing ink volume and general used is solid ink density measurement. According to the ink feed principle of sheeted print machines, a special test sheet was designed and printing test experiments were carried out to search for the relations between ink density and printing ink volume. The function between the ink density and the ink key opening, as well as printing relative contrast and dot gain curve was established by the experiment simply. The results can be used as a mathematical model for automatic printing ink volume control and the experimental method is very useful in practice.

Evaluation of the Color-difference Formulae for Neutral Colors

In printing industry, the results of quality control of neutral prints are not consistent with the visual assessments. In order to solve this problem, 50 pairs of neutral color samples were prepared, and 29 observers with normal color vision were organized to carry out the color-difference experiments with the method of gray scale. In total, 1750 judgments were gathered. The visual results were used to test the performances of different color-difference formulae in terms of the standardized residual sum of square (STRESS) factor. The results indicate that the CIEDE2000 formulae have the best performance and all the tested formulae have the best performances for the evaluation of the color pairs only with the hue differences.

Measurement and Analysis of Colorimetric Values for Holographic Paper with Light Pillars

The holographic paper has the light rainbow visual effect but it will arouse the color measurement inaccuracy. In order to measure the colorimetric values of holographic paper accurately and stably, an automatic measuring platform was used to collect the colorimetric values of the standard and test samples. Three ‘moved methods’ were used to align the two groups, and then the CIELAB color difference values of each corresponding sampling point were compared one by one. The minimum averaged value was chosen as the real-value of the tested paper which can show the color difference of them accurately.

Study on Observers’ Categories Based on Color Matching Experiments

In order to study the observers’ categories based on different color matching functions, 28 observers were organized to match the 50 color stimuli on LED monitor. The spectral power distribution of the target and the matched colors were measured and 10 sets CMFs were used to calculate the corresponding chromatic values. The mean CIEDE2000 color difference for each of all the tested observers was calculated and the observers were assigned to different observer categories. The results indicated that most of the observers belonging to the S6, S2, CIE1964 color matching functions.

Test of primary channel independence of LCD and wavelength piecewise LCD color model

An experiment with EIZO CG 19, DELL 19, IBM 19 and HP 19 LCD was designed and carried out to test the interaction between RGB channels, and then to test the spectral additive property of LCDs. The results show that the interaction between channels is very weak and spectral additivity is held well. This result indicates that the manufacture technology of LCDs is improved greatly. But the computation results of tristimuli addition are not very accurate. A new calculation method based on spectral additivity, in which gamma is fitted by a cubic polynomial in each piece of wavelength, is proposed and discussed. The proposed method is proved simple and very few samples need to measure while the computation precision is very high.