CN114972549A - Color fiber color measuring and matching method based on global and local characteristics - Google Patents

Color fiber color measuring and matching method based on global and local characteristics Download PDF

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CN114972549A
CN114972549A CN202210521706.7A CN202210521706A CN114972549A CN 114972549 A CN114972549 A CN 114972549A CN 202210521706 A CN202210521706 A CN 202210521706A CN 114972549 A CN114972549 A CN 114972549A
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王妮
李瑶
易清珠
张艳茹
施金秀
陈超
王强
纵玉华
孔冬青
韩庆帅
任继杨
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Abstract

The invention discloses a color fiber color measuring and matching method based on global and local characteristics, which comprises the following steps: 1) collecting images of the color fiber mixed sample; 2) collecting the color of the color fiber mixed sample; 3) collecting the color of the color element; 4) carrying out image color separation treatment on the color fiber mixed sample; 5) establishing a color fiber mixed color prediction equation; 6) calculating the color difference between the color predicted value and the corresponding measured value of the color fiber mixed sample; 7) correcting the color fiber mixed sample color prediction equation by first-order linear regression; 8) and calculating the color difference between the predicted value and the corresponding measured value of the color fiber mixed sample after correction. Researches show that the color fiber mixed color prediction equation provided by the invention is a feasible color testing and matching method, can effectively describe the relationship between the overall color of a color fiber mixed sample and the color and distribution (area ratio) of local fibers of various colors, and provides reference for color testing and matching of color spun yarns of color spinning enterprises.

Description

Color fiber color measuring and matching method based on global and local characteristics
Technical Field
The invention belongs to the field of colored spun yarns, and relates to a colored fiber color measuring and matching method based on global and local characteristics.
Background
The textile with special color development effect and color style, which is made by mixing and processing two or more kinds of fibers with different colors through specific procedures, is called as 'color textile fabric'. The color spinning differentiation products such as variable color fabrics, bicolor plied yarns, colored silk yarns and the like can be produced by combining a specific spinning process technology, and the color spinning differentiation products have unique visual effects.
In the traditional processing of colored textile fabrics, different colors of primary color fibers are analyzed and prepared according to the sample from a customer, and then a target colored fabric is prepared by mixing a plurality of types of primary color fibers similar to the target color according to a certain proportion. With the continuous increase of yarn varieties and fabric colors, the variety and the number of the primary color fibers are increased, the inventory of the dyes and the primary color fibers of enterprises is increased, and the improvement of the production efficiency of the enterprises, the energy conservation and the emission reduction are not facilitated.
With the development of colorimetry, computers and color measuring and matching instruments, color spinning products which are mixed and matched with colors by using primary color fibers are widely concerned based on a color printing primary color mixing theory and a color spinning mixing technology, and the mixed color matching theory and optimization are one of the key contents in the current color spinning research field.
The Neugebauer (Neugebauer) equation in the color printing is used for predicting the quantity relation between the color of each ink dot on the surface of the color printed matter and the ink quantity (dot area rate) and the overall color of the color printed matter, and can effectively describe the conversion characteristics of the color in the color printing process. The color fiber mixed color generation is similar to the screen tone printing color generation in color printing, and is the color generation by the comprehensive action of subtractive color mixing and additive color mixing.
The research on the base color fiber mixing and matching theory and the optimization scheme thereof mainly relates to the relationship between the mixing ratio of the base color fibers and the overall color of a corresponding fiber mixing sample, namely the relationship between the color and mixing ratio of each base color fiber and the overall color of the fiber mixing sample, and the research on the color and distribution (area ratio) of each color fiber on the surface of the fiber mixing sample and the relationship between the color and distribution (area ratio) of each color fiber and the overall color of the fiber mixing sample.
Disclosure of Invention
The invention provides a color fiber color matching and measuring method based on global and local characteristics, which promotes the development of color matching and measuring of colored spun yarns and provides reference for color matching and measuring of colored spun yarns of colored spun enterprises.
The technical scheme adopted by the invention is as follows: a color fiber color matching and measuring method based on global and local characteristics comprises the following steps:
step S1: collecting images of the color fiber mixed sample;
step S2: collecting the color of the color fiber mixed sample;
step S3: collecting the color of the color element;
step S4: carrying out image color separation treatment on the color fiber mixed sample;
step S5: establishing a color fiber mixed color prediction equation;
step S6: calculating the color difference between a color predicted value and a corresponding measured value obtained by a color fiber color mixture prediction equation;
step S7: correcting the accuracy of the color fiber color mixture prediction equation by first-order linear regression;
step S8: and calculating the color difference between the color predicted value obtained by the corrected first-order linear regression color prediction equation and the corresponding measured value.
Preferably, the specific process of step S1 of the present invention is: uniformly mixing the color fiber mixed sample, spreading the color fiber mixed sample into a uniform and lightproof fiber web, and collecting TIFF format lossless storage image color images of 40mm multiplied by 40mm by a scanner with 3500ppi resolution under a black background; the scanning parameters are set as follows: the operation color space is professional (LCH), the file type is reflective draft, the image output type is RGB color, and other image correction control options are not selected or modified.
Preferably, the specific process of step S2 of the present invention is: opening the scanned image by using Photoshop software, selecting an image area, clicking a filter, selecting blurring, selecting average processing, selecting a color pick-up device to pick up colors and measure the brightness value L of the color fiber mixed sample image obtained in the step S1 in the Lab uniform color space * Color index a * And b * 。
Preferably, the specific process of step S3 of the present invention is: when the color fiber mixed sample is formed by randomly and uniformly mixing A, B, C three-color fibers, the probability of A, B, C three-color fibers appearing in each layer of the color fiber mixed sample is the same and is the corresponding mixing proportion; nine color elements of A + A, B + B, C + C, A + B, B + A, A + C, C + A, B + C, C + B can appear in the color fiber mixed sample; a. b and C are respectively the mixing proportion of the fiber of the color A, the fiber of the color B and the fiber of the color C in the three-color fiber mixed sample, and a + B + C is 1; considering A + B and B + A as a color element, A + C and C + A as a color element, and B + C and C + B as a color element;
uniformly spreading A, B, C three single-color fiber samples and a mixed sample of A + B, A + C, B + C three color fibers mixed in equal proportion into an opaque fiber web, and obtaining TIFF format lossless storage image color images of six basic color elements in the same operation process S1;
the operation process is the same as S2 to obtain the brightness value L of the color element image in the Lab uniform color space * Color index a * And b * 。
Priority ofSpecifically, the step S4 of the present invention includes: realizing fuzzy clustering algorithm with 7 clustering centers in Lab color space by Matlab software, performing clustering analysis on the scanned images of the color fiber mixed sample, and calculating the mixing percentage of the three-color fibers
Figure BDA0003643738090000031
I.e. area ratios a, b, c; is calculated by the formula
Figure BDA0003643738090000032
Wherein q is i For belonging to a colour q in an image j C is the number of clustering centers;
the deviation degree between the test result and the actual mixing proportion of the fibers is represented by a deviation ratio D, and the smaller the value D is, the better the clustering analysis effect is;
Figure BDA0003643738090000033
wherein Z is i To correspond to a color q j Actual percentage of color fibers mixed.
Preferably, the specific process of step S5 of the present invention is:
taking the neugebauer equation as a starting point, the relationship between the global overall color of the color fiber mixed sample and the color and the area rate of each local color element on the surface of the color fiber mixed sample is as follows:
Figure BDA0003643738090000034
in the formula: x, Y, Z shows the tristimulus value, f, of the overall color of the A, B, C three-color fiber blend sample i (i is not less than 1 and not more than n) is the area ratio of the color element A + A, B + B, C + C, A + B or B + A, A + C or C + A, B + C or C + B, X i 、Y i 、Z i Is the tristimulus value of the color element A + A, B + B, C + C, A + B or B + A, A + C or C + A, B + C or C + B.
Preferably, the inventionThe specific process of the step S6 is as follows: using CIEDE2000 (K) L ，K C ，K H ) Calculating the color difference between a color predicted value obtained by a color fiber mixed sample prediction equation and a corresponding measured value by a color difference formula;
Figure BDA0003643738090000035
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue, respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
Preferably, the specific process of step S7 of the present invention is: correcting the accuracy of a color prediction equation of a three-color fiber mixed sample by first-order linear regression;
let X in equation i ＝U i (X i )，Y i ＝V i (Y i )，Z i ＝W i (Z i ) The first-order linear regression correction equation of the color fiber mixed color prediction equation is as follows:
Figure BDA0003643738090000041
in the formula of U i 、V i 、Z i Indicates the tristimulus value X, Y, Z and the color element area ratio f i The regression coefficient of (2);
for any color sample j with the number of samples n, j is 1,2,3, …, n, and the measured value X of the X stimulus value mj And the calculated value X j The difference therebetween is Δ X j ；
Figure BDA0003643738090000042
Modified Neugebauer equation, Δ X j The following equation set is satisfied:
Figure BDA0003643738090000043
the iterative method is adopted to solve the equation set to obtain U i (i＝1,2,3,…,8)。
Preferably, the specific process of step S8 of the present invention is: using CIEDE2000 (K) L ，K C ，K H ) Calculating the color difference between a color predicted value obtained by a color fiber mixed sample prediction equation and a corresponding measured value by a color difference formula;
Figure BDA0003643738090000044
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue, respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
The invention has the following beneficial effects:
the invention establishes an equation between the global overall color of the colored fiber mixed sample and the color and distribution (area ratio) of the local various colors of fibers based on the Neugebauer equation in the color printing, and can effectively and stably predict the color and the color matching scheme of the colored fiber mixed sample.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a comparison of color differences between predicted values and measured values before and after equation correction in the present invention;
FIG. 2 is the comparison of the color difference between the predicted values and the measured values of the S-N model, the Friele model and the correction equation in the invention.
Detailed Description
A color fiber color matching and measuring method based on global and local characteristics comprises the following steps:
step S1: acquiring an image of a target color fiber mixed sample; determining a red, yellow and blue color fiber mixed sample needing color testing and matching, uniformly mixing and thinly paving the color fiber mixed sample, collecting a sample image by using a MICROTEK ArtixScan F2 color platform scanner, wherein the scanning background is black, the scanning resolution is 3500ppi, and the collection area is 40mm multiplied by 40mm, and after the scanning is finished, storing the image in a TIFF format in a lossless manner. The scanning parameters are set as follows, the running color space is professional (LCH), the type of the manuscript is reflective manuscript, the type of the image output is RGB color, and the other image correction control options are not selected or modified.
Step S2: collecting the color of the color fiber mixed sample; the color fiber mixed sample image obtained in the step S1 is subjected to space mixing processing through the image processing function of Photoshop software to obtain an image with uniform color, and a brightness value L of the image in Lab uniform color space is obtained by a color picking device * Color index a * And b * 。
Step S3: collecting the color of the color element; uniformly spreading a mixed sample of A, B, C three monochromatic fiber samples and A + B, A + C, B + C three color fibers mixed in equal proportion into an opaque fiber web, and obtaining TIFF format lossless storage image color images of six basic color elements in the same operation process of S1;
the operation process is the same as S2 to obtain the brightness value L of the color element image in the Lab uniform color space * Color index a * And b * 。
TABLE 1 color eigenvalues of color bins
Figure BDA0003643738090000051
Figure BDA0003643738090000061
Step S4: processing the image of the sample; realizing Fuzzy Clustering (FCM) algorithm with 7 clustering centers in Lab color space by Matlab software, performing clustering analysis on scanned images of color fiber mixed samples, and calculating the mixing percentage of three color fibers
Figure BDA0003643738090000062
I.e., area ratios a, b, c; is calculated by the formula
Figure BDA0003643738090000063
Wherein q is i For belonging to a colour q in an image j C is the number of clustering centers;
the deviation degree between the test result and the actual mixing proportion of the fibers is represented by a deviation ratio D, and the smaller the value D is, the better the clustering analysis effect is;
Figure BDA0003643738090000064
wherein, Z i To correspond to a color q j Actual percentage of color fibers mixed.
Namely, the area ratios a, b, c of the red, yellow and blue fibers.
Step S5: establishing a three-color fiber mixed color prediction equation; taking Neugebauer equation as a starting point, the relationship between the global overall color of the obtained color fiber mixed sample and the color and the area rate of local color elements on the surface of the color fiber mixed sample is as follows:
Figure BDA0003643738090000065
in the formula: x, Y, Z denotes red, yellow and blue fibersTristimulus value, f, of the overall color of the vitamin blend sample i (i is more than or equal to 1 and less than or equal to n) is the area ratio of basic color element red + red, yellow + yellow, blue + blue, red + yellow or yellow + red, red + blue or blue + red, yellow + blue or blue + yellow, X i 、Y i 、Z i Is the color element tristimulus value of basic color element red + red, yellow + yellow, blue + blue, red + yellow or yellow + red, red + blue or blue + red, yellow + blue or blue + yellow.
TABLE 2 area ratio and tristimulus values of elementary color elements
Figure BDA0003643738090000071
Step S6: the tristimulus values X, Y, Z in XYZ color space obtained by the color prediction equation were converted into lightness value L, chromaticity indices a and b in Lab uniform color space, in this example, CIEDE2000 (K) L ，K C ，K H ) Calculating the color difference between a color predicted value obtained by a color fiber mixed sample prediction equation and a corresponding measured value by a color difference formula;
Figure BDA0003643738090000072
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue, respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
TABLE 3 color difference between predicted value and measured value of color fiber mixture sample
Figure BDA0003643738090000073
Step S7: tricolor fiber mixed sample color pre-treatmentCorrecting the first-order linear regression of the accuracy of the measurement equation; let X in step S5 i ＝U i (X i )，Y i ＝V i (Y i )，Z i ＝W i (Z i ) The first-order linear regression correction equation of the color fiber mixed color prediction equation is as follows:
Figure BDA0003643738090000081
in the formula of U i 、V i 、Z i Indicates the tristimulus value X, Y, Z and the color element area ratio f i The regression coefficient of (2);
for any color sample j with the number of samples n, j is 1,2,3, …, n, and the measured value X of the X stimulus value mj And the calculated value X j The difference therebetween is DeltaX j ；
Figure BDA0003643738090000082
Modified Neugebauer equation, Δ X j The following equation set is satisfied:
Figure BDA0003643738090000083
the iterative method is adopted to solve the equation set to obtain U i (i＝1,2,3,…,8)。
Similarly, V can be solved i And W i Wherein (i ═ 1,2,3, …, 8).
TABLE 4 regression coefficients for this example
Figure BDA0003643738090000084
Step S8: converting the tristimulus value X, Y, Z of the XYZ color space obtained from the corrected first-order linear regression color prediction equation into a lightness value L in the Lab uniform color space * Color index a * And b * In this embodimentUsing CIEDE2000 (K) L ，K C ，K H ) The color difference formula calculates the color difference between a predicted value of the color obtained by the color fiber mixed sample prediction equation and a corresponding measured value;
Figure BDA0003643738090000091
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
TABLE 5 color difference between predicted value and measured value of color fiber mixture sample after correction
Figure BDA0003643738090000092
The correction of the color prediction equation by using the first-order linear regression method is feasible and effective, and the correction equation can better predict the color of the color fiber mixed sample and express the relationship between the global integral color of the color fiber mixed sample and the surface local color element color and the area ratio of the color fiber mixed sample.
Step S9: comparing and verifying a color prediction equation; as shown in FIG. 2, the correction equation is compared and analyzed with an S-N model and a Friele model in computer color matching, actual color difference is obtained by two ways of manufacturing a sample of a standard sample and a sample of an incoming sample, and the practicability of the correction equation in color fiber mixed color matching is verified.
Wherein, the parameter in the S-N model is M-0.09, and the empirical parameter of the Friele model is S-0.094.
TABLE 6 color difference between predicted value and measured value of S-N model, Friele model, and correction equation color fiber mixture sample
Figure BDA0003643738090000093
As shown in FIG. 2, the predicted values of the S-N model, the Friele model and the correction equation are compared with the measured values of the color difference, and the correction equation has the highest accuracy and the smallest fluctuation range in color mixing and matching of the color fibers.
The invention researches the color measuring and matching method of the colored spun yarn mixture based on the Neugebauer equation in the color printing to improve the accuracy of the color measuring and matching of the colored spun yarn, obtains a certain research result, lays a foundation theory for the research of the color matching field of the colored spun yarn, and provides a certain reference value for practical production and application.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.

Claims (9)

1. A color fiber color matching method based on global and local characteristics is characterized by comprising the following steps:
step S1: collecting images of the color fiber mixed sample;
step S2: collecting the color of the color fiber mixed sample;
step S3: collecting the color of the color element;
step S4: carrying out image color separation treatment on the color fiber mixed sample;
step S5: establishing a color fiber mixed color prediction equation;
step S6: calculating the color difference between a color predicted value and a corresponding measured value obtained by a color fiber color mixture prediction equation;
step S7: correcting the accuracy of the color fiber color mixture prediction equation by first-order linear regression;
step S8: and calculating the color difference between the color predicted value obtained by the corrected first-order linear regression color prediction equation and the corresponding measured value.
2. The color fiber matching method based on global and local features as claimed in claim 1, wherein the step S1 is to collect the image of the color fiber mixture sample by: uniformly mixing the color fiber mixed sample, spreading the color fiber mixed sample into a uniform and lightproof fiber web, and collecting TIFF format lossless storage image color images of 40mm multiplied by 40mm by a scanner with 3500ppi resolution under a black background; the scan parameters were set as follows: the operation color space is professional (LCH), the file type is reflection draft, the image output type is RGB color, and other image correction control options are not selected or modified.
3. The color fiber matching method based on global and local features as claimed in claim 2, wherein the step S2 is to collect the color of the color fiber mixture sample by: opening the scanned image by using Photoshop software, selecting an image area, clicking a filter, selecting blurring, selecting average processing, selecting a color pick-up device to pick up colors and measure the brightness value L of the color fiber mixed sample image obtained in the step S1 in the Lab uniform color space * Color index a * And b * 。
4. The color fiber matching method based on global and local features as claimed in claim 3, wherein the color collection of color elements in step S3 comprises the following steps: when the color fiber mixed sample is formed by randomly and uniformly mixing A, B, C three-color fibers, the probability of A, B, C three-color fibers appearing in each layer of the color fiber mixed sample is the same and is the corresponding mixing proportion; nine color elements of A + A, B + B, C + C, A + B, B + A, A + C, C + A, B + C, C + B can appear in the color fiber mixed sample; a. b and C are respectively the mixing proportion of the fiber of the color A, the fiber of the color B and the fiber of the color C in the three-color fiber mixed sample, and a + B + C is 1; considering A + B and B + A as a color element, A + C and C + A as a color element, and B + C and C + B as a color element;
uniformly spreading a mixed sample of A, B, C three monochromatic fiber samples and A + B, A + C, B + C three color fibers mixed in equal proportion into an opaque fiber web, and obtaining TIFF format lossless storage image color images of six basic color elements in the same operation process of S1;
the operation process is the same as S2 to obtain the brightness value L of the color element image in the Lab uniform color space * Color index a * And b * 。
5. The color fiber matching method based on global and local features as claimed in claim 4, wherein the step S4 is an image color separation process of the color fiber mixture sample, which comprises the following steps: realizing fuzzy clustering algorithm with 7 clustering centers in Lab color space by Matlab software, performing clustering analysis on the scanned images of the color fiber mixed sample, and calculating the mixing percentage P of three-color fibers Qi I.e. area ratios a, b, c; is calculated by the formula
Figure FDA0003643738080000021
Wherein q is i For belonging to a colour q in an image j C is the number of clustering centers;
the deviation degree between the test result and the actual mixing proportion of the fibers is represented by a deviation ratio D, and the smaller the value D is, the better the clustering analysis effect is;
Figure FDA0003643738080000022
wherein Z is i To correspond to a color q j Actual percentage of color fibers mixed.
6. The color fiber matching and color matching method based on global and local features of claim 5, wherein the step S5 of establishing the color fiber mixture color prediction equation comprises the following specific steps:
taking the Neugebauer (Neugebauer) equation as a starting point, the relationship between the global overall color of the color fiber mixed sample and the color and the area rate of each local color element on the surface of the color fiber mixed sample is as follows:
Figure FDA0003643738080000023
in the formula: x, Y, Z shows the tristimulus value, f, of the overall color of the A, B, C three-color fiber blend sample i (i is more than or equal to 1 and less than or equal to n) is the area ratio of the color element A + A, B + B, C + C, A + B or B + A, A + C or C + A, B + C or C + B, X i 、Y i 、Z i Is the tristimulus value of the color element A + A, B + B, C + C, A + B or B + A, A + C or C + A, B + C or C + B.
7. The color matching and matching method based on global and local features of claim 6, wherein the specific process of calculating the color difference between the predicted color value and the corresponding measured color value obtained from the color fiber color mixture prediction equation of step S6 is as follows: using CIEDE2000 (K) L ，K C ，K H ) The color difference formula calculates the color difference between a predicted value of the color obtained by the color fiber mixed sample prediction equation and a corresponding measured value;
Figure FDA0003643738080000031
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue, respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
8. The color fiber color matching and measuring method based on global and local features as claimed in claim 7, wherein the specific procedure of the first order linear regression correction of the accuracy of the color fiber color mixing prediction equation in the step S7 is as follows: correcting the accuracy of a color prediction equation of a three-color fiber mixed sample by first-order linear regression;
let X in equation i ＝U i (X i )，Y i ＝V i (Y i )，Z i ＝W i (Z i ) The first-order linear regression correction equation of the color fiber mixed color prediction equation is as follows:
Figure FDA0003643738080000032
in the formula of U i 、V i 、Z i Indicates the tristimulus value X, Y, Z and the color element area ratio f i The regression coefficient of (2);
for any color sample j with the number of samples n, j is 1,2,3, …, n, and the measured value X of the X stimulus value mj And the calculated value X j The difference therebetween is DeltaX j ；
Figure FDA0003643738080000033
Modified Neugebauer equation, Δ X j The following equation set is satisfied:
Figure FDA0003643738080000034
the iterative method is adopted to solve the equation set to obtain U i (i＝1,2,3,…,8)。
9. The method for color matching and matching of color fibers according to claim 8, wherein the step S8 is implemented by modifying the color prediction value obtained from the post-first order linear regression color prediction equationThe specific process of calculating the color difference between the corresponding measured values is as follows: using CIEDE2000 (K) L ，K C ，K H ) Calculating the color difference between a color predicted value obtained by a color fiber mixed sample prediction equation and a corresponding measured value by a color difference formula;
Figure FDA0003643738080000041
in the formula: k L ＝2，K C ＝K H 1, Δ L ', Δ C ', Δ H ' are lightness difference, chroma difference and hue difference, respectively, S L 、S C 、S H Are weight functions of lightness, chroma and hue, respectively, K L 、K C 、K H Correction factor for deviations of experimental conditions and visual evaluation criteria, R T As the interaction coefficient, can be represented by a luminance value L * Color index a * And b * And (4) calculating.
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Cited By (1)

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Publication number Priority date Publication date Assignee Title
CN115434054A (en) * 2022-10-28 2022-12-06 富尔美技术纺织(苏州)有限公司 Multicolor fiber uniform mixing processing technology

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115434054A (en) * 2022-10-28 2022-12-06 富尔美技术纺织(苏州)有限公司 Multicolor fiber uniform mixing processing technology

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