Lab prediction method for printing spot color screen hue

Abstract

The invention discloses a Lab prediction method for printing spot color screen hue, and relates to the technical field of package printing. Aiming at the problems that spot color full-tone hue cannot be predicted in advance and the effect after printing is not consistent in the prior art, the spot color is divided into six color systems, three characteristic spot colors are selected for each color system, Lab values of 100% and 50% tones of a printing sample and a digital sample and paper white are collected, a full-tone Lab value is calculated through a CIE Lab space linear interpolation method, and the spot color full-tone hue can be predicted in advance. And establishing a compensation model of the two by using a multiple linear regression method, popularizing and applying to all spot colors of corresponding color systems, and verifying that each tone E00 is less than or equal to 2.0. According to the method, the 0-100% tone hue of the spot color before printing is accurately predicted, the digital sample can accurately simulate the printing effect, brand owners can control the design effect in advance, rework waste is avoided, the applicability is wide, the precision is high, large-scale equipment transformation is not needed, and the popularization cost is low.

Description

Technical Field

[0001] This invention relates to the field of packaging and printing technology, specifically to a Lab prediction method for the halftone hue of a printing spot color, used to accurately predict the hue of each tone of a spot color before printing, and to accurately simulate the actual printing effect through digital proofing. Background Technology

[0002] In the packaging and printing industry, spot colors are widely used. The Pantone system alone contains over 2700 spot colors, and packaging designers also customize spot colors according to design requirements. Due to the vast number of spot colors, it's impossible to verify all 0-100% of their tones in actual printing. This presents significant challenges for brands when confirming the effect of new packaging designs: currently, the industry lacks effective methods for predicting the hue and tone of spot colors. Often, it's only after the solid hue of the spot color has been adjusted in the printing process that the hue and tone are found to be inconsistent with expectations. However, by then, the hue and tone cannot be changed, and brands can only passively accept unsatisfactory printing results. This not only affects the final presentation of the packaging design but may also lead to wasted printing costs and project delays. Therefore, there is an urgent need for a technical solution that can accurately predict the hue and tone of spot colors before printing and simulate the actual printing effect through digital proofing. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a Lab prediction method for the hue of spot color halftone printing. This method enables accurate prediction of the hue of each tone from 0 to 100% of the spot color before printing. Through digital proofing, it accurately simulates the hue effect of each tone of the actual printed spot color halftone printing, allowing brand owners to know the hue trend of the spot color halftone in advance, fully control the packaging design effect, and avoid losses caused by hue mismatch after printing.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A Lab prediction method for the hue of a printing spot color halftone screen includes the following steps: S1. Divide the spot colors into six major color families: red, orange, yellow, green, blue, and purple. S2. Select characteristic spot colors for each color system: Select three characteristic spot colors for each color system, namely a base color with high saturation and medium brightness, a light color with high brightness and low saturation, and a dark color with low brightness and medium-high saturation. The three characteristic spot colors form a triangular color gamut that surrounds most of the spot colors in the CIELab color space. S3. Collect Lab values ​​of printed samples: Collect the Lab values ​​of each sample from 100% tonality, 50% tonality, and paper white of three characteristic spot colors of each color system. S4. Calculate the Lab values ​​of the entire tonal range of the printed sample: Based on the Lab values ​​collected in step S3, the Lab values ​​of all tones from 0 to 100% of each feature spot color are calculated by using linear interpolation in the CIE Lab color space. S5. Collect Lab values ​​of digital samples: Digitally print 100% tone, 50% tone and paper white digital samples of three characteristic spot colors of each color system, and collect the Lab values ​​of each digital sample. S6. Calculate the Lab values ​​of the digital sample: Based on the Lab values ​​collected in step S5, calculate the Lab values ​​of all tones from 0 to 100% for each feature spot color by using linear interpolation in the CIE Lab color space. S7. Establish a compensation model: Using the multiple linear regression method, based on the full-tone Lab values ​​of the printed sample and the digital sample obtained in steps S4 and S6, establish a compensation model for the conversion of the Lab value of the printed sample to the Lab value of the digital sample. S8. Extend and apply the compensation model: Extend the compensation model of step S7 to all spot colors of the corresponding color system. Based on the Lab value of the printed sample of each spot color, calculate the corresponding Lab setting value in the digital proofing file through the compensation model. S9. Model Verification: Select multiple spot colors in each color system to produce digital samples, and compare them with the corresponding printed samples to verify the tonal values ​​ΔE. 00 ≤2.0, confirming the model is valid.

[0005] In this invention, in step S1, the red family includes warm red, cool red, and magenta; the orange family includes orange ranging from reddish to yellowish; the yellow family includes lemon yellow, bright yellow, and golden yellow; the green family includes yellowish-green ranging from yellowish-green to bluish-green; the blue family includes sky blue, diamond blue, ultramarine, and royal blue; and the purple family includes purple ranging from bluish-purple to reddish-purple and transitional colors back to red.

[0006] In this invention, the conversion compensation model in step S7 is: Model for predicting L value: L 数码样 = β 0L + β 1L ×L 印样 + β 2L × a 印样 + β 3L × b 印样 Model for predicting the value of a: a 数码样 = β0a + β1a × L 印样 + β2a × a 印样 + β3a× b 印样 Model for predicting the value of b: b 数码样 = β0b + β1b × L 印样 + β2b × a 印样 + β3b× b 印样 L 数码样 L value for digital sample spot color; L 印样 The L value for the spot color of the printed sample; a 数码样 a is the a value for the digital sample spot color; 印样 The a value is for the spot color of the printed sample; b 数码样 b value for digital sample spot color; b 印样 The b-value for the spot color of the printed sample; β 0L β0a and β0b are constant terms, representing the basic values ​​that the digital sample should possess when the L, a, and b values ​​of all printed samples are 0, respectively; β 1L , β1a, β1b, β 2L β2a, β2b, β 3L β3a and β3b are regression coefficients, representing the weight of each printed sample channel on the predicted digital sample value. 1L β1a and β1b represent the influence weights of the printed sample's brightness L on the digital samples L, a, and b. 2L β2a and β2b represent the influence weights of the saturation a of the printed sample on the digital samples L, a, and b. 3L β3a and β3b represent the influence weights of the saturation b of the printed sample on the digital samples L, a, and b.

[0007] In this invention, in steps S4 and S6, the 0-100% tones include 100%, 90%, 80%, 75%, 70%, 60%, 50%, 40%, 30%, 25%, 20%, 10%, 5%, and the tones corresponding to white paper.

[0008] In this invention, during step S9, at least two spot colors are selected for each color system during verification to output digital samples and compare them with printed samples.

[0009] By adopting the above technical solution, the present invention has the following advantages: 1. This invention enables accurate prediction of the hue of spot colors at all levels from 0 to 100% before printing. Brand owners can predict the hue trend of the spot color screen on the packaging in advance through digital samples, fully control the design effect, and avoid rework waste caused by hue mismatch after printing.

[0010] 2. By classifying six color systems and selecting characteristic spot colors, a prediction system covering most commonly used spot colors has been constructed. At the same time, it supports hue prediction of custom spot colors, making it widely applicable.

[0011] 3. The compensation model is based on linear regression and combined with linear interpolation calculations in the CIE Lab color space, resulting in high prediction accuracy and a precise color difference ΔE between the digital and printed samples. 00 ≤2.0, good visual consistency.

[0012] 4. The method and process are clear and easy to operate. It can be directly applied to the existing digital proofing process of packaging and printing without large-scale equipment modification. The promotion cost is low and it helps to improve the industry's production efficiency and product quality. Attached Figure Description

[0013] Figure 1 It displays color patches of 100% spot color and 50% halftone dots. Detailed Implementation

[0014] The present invention will be further described in detail below with reference to specific embodiments: Example 1: Lab Prediction and Compensation for Special Red Series 1. Color system classification and selection of characteristic spot colors The following reds were selected as the characteristic colors: a base red with high saturation and medium brightness, a light red with high brightness and low saturation, and a deep red with low brightness and medium-high saturation.

[0015] 2. Collection of Lab values ​​for printed samples Print 100% and 50% tones of each spot color, such as... Figure 1 As shown in the figure. Lab values ​​were collected for 100% tone, 50% tone, and paper white samples of three characteristic reds (i.e., the three characteristic reds highlighted in the figure), as shown in Table 1.

[0016] 3. Calculation of Lab values ​​for the entire tonal range of the printed sample The 0-100% Lab values ​​of the three characteristic special reds were calculated using linear interpolation in the CIE Lab color space, and the results are shown in Table 2 below:

[0017] 4. Digital Sample Lab Value Acquisition The Lab values ​​for 100% tone, 50% tone, and white paper samples of three characteristic reds printed digitally are shown in Table 3 below:

[0018] 5. Calculation of Lab values ​​for full-tone digital samples The Lab values ​​of each tone from 0 to 100% for the three characteristic red digital samples were calculated using linear interpolation. The results are shown in Table 4 below:

[0019] 6. Establishment of the compensation model By analyzing the Lab values ​​of printed and digital samples using linear regression, a conversion compensation model for the red color series was obtained: L 数码样 = 2.1234 + 0.9567×L 印样 + 0.1234×a 印样 - 0.0789×b 印样 a 数码样 = 1.5678 + 0.0345×L 印样 + 0.9876×a 印样 + 0.0456×b 印样 b 数码样 = 0.8765 - 0.0123×L 印样 + 0.0567×a 印样 + 0.9345×b 印样 7. Model Validation Two other spot colors from the red family were selected, and digital samples were output using the above model. These samples were then compared with the corresponding printed samples, and the tonal values ​​(ΔE) were analyzed. 00 ≤2.0, the model is valid.

[0020] Example 2: Application of other color schemes Following the method in Example 1, feature spot color selection, Lab value acquisition, full-tone calculation, compensation model establishment, and verification were performed for orange, yellow, green, blue, and purple color systems, respectively. The compensation models for each color system satisfied the ΔE values ​​for each tone. 00 With a requirement of ≤2.0, it can achieve halftone hue prediction and digital proofing simulation for all spot colors in the corresponding color system.

Claims

1. A Lab prediction method for the hue of a printing spot color halftone screen, characterized in that, Includes the following steps: S1. Divide the spot colors into six major color families: red, orange, yellow, green, blue, and purple. S2. Select characteristic spot colors for each color system: Select three characteristic spot colors for each color system, namely a base color with high saturation and medium brightness, a light color with high brightness and low saturation, and a dark color with low brightness and medium-high saturation. The three characteristic spot colors form a triangular color gamut that surrounds most of the spot colors in the CIE Lab color space. S3. Collect Lab values ​​of printed samples: Collect the Lab values ​​of each sample from 100% tonality, 50% tonality, and paper white of three characteristic spot colors of each color system. S4. Calculate the Lab values ​​of the entire tonal range of the printed sample: Based on the Lab values ​​collected in step S3, the Lab values ​​of all tones from 0 to 100% of each feature spot color are calculated by using linear interpolation in the CIE Lab color space. S5. Collect Lab values ​​of digital samples: Digitally print 100% tone, 50% tone and paper white digital samples of three characteristic spot colors of each color system, and collect the Lab values ​​of each digital sample. S6. Calculate the Lab values ​​of the digital sample: Based on the Lab values ​​collected in step S5, calculate the Lab values ​​of all tones from 0 to 100% for each feature spot color by using linear interpolation in the CIE Lab color space. S7. Establish a compensation model: Using the multiple linear regression method, based on the full-tone Lab values ​​of the printed sample and the digital sample obtained in steps S4 and S6, establish a compensation model for the conversion of the Lab value of the printed sample to the Lab value of the digital sample. S8. Extend and apply the compensation model: Extend the compensation model from step S7 to all spot colors of the corresponding color system. Based on the Lab value of the printed sample of each spot color, calculate the corresponding Lab setting value in the digital proofing file through the compensation model. S9. Model Verification: Select multiple spot colors in each color system to produce digital samples, and compare them with the corresponding printed samples to verify the tonal values ​​ΔE. 00 ≤2.0, confirming the model is valid.

2. The method for predicting the Lab color of a printing spot color using halftone screens according to claim 1, characterized in that, In step S1, the red family includes warm red, cool red, and magenta; the orange family includes orange ranging from reddish to yellowish; the yellow family includes lemon yellow, bright yellow, and golden yellow; the green family includes yellowish-green ranging from yellowish-green to bluish-green; the blue family includes sky blue, diamond blue, ultramarine, and royal blue; and the purple family includes purple ranging from bluish-purple to reddish-purple and transitional colors back to red.

3. The Lab prediction method for the hue of a printing spot color halftone screen according to claim 1, characterized in that, The conversion compensation model in step S7 is as follows: Model for predicting L value: L 数码样 = β 0L + β 1L ×L 印样 + β 2L × a 印样 + β 3L × b 印样 Model for predicting the value of a: a 数码样 = β0a + β1a × L 印样 + β2a × a 印样 + β3a× b 印样 Model for predicting the value of b: b 数码样 = β0b + β1b × L 印样 + β2b × a 印样 + β3b× b 印样 L 数码样 L value for digital sample spot color; L 印样 The L value for the spot color of the printed sample; a 数码样 a is the a value for the digital sample spot color; 印样 The a value is for the spot color of the printed sample; b 数码样 b value for digital sample spot color; b 印样 The b-value for the spot color of the printed sample; β 0L β0a and β0b are constant terms, representing the basic values ​​that the digital sample should possess when the L, a, and b values ​​of all printed samples are 0, respectively; β 1L , β1a, β1b, β 2L β2a, β2b, β 3L β3a and β3b are regression coefficients, representing the weight of each printed sample channel on the predicted digital sample value. 1L β1a and β1b represent the influence weights of the printed sample's brightness L on the digital samples' L, a, and b. 2L β2a and β2b represent the influence weights of the saturation a of the printed sample on the digital samples L, a, and b. 3L β3a and β3b represent the influence weights of the saturation b of the printed sample on the digital samples L, a, and b.

4. The Lab prediction method for the hue of a printing spot color halftone screen according to claim 1, characterized in that, In steps S4 and S6, the 0-100% tones include 100%, 90%, 80%, 75%, 70%, 60%, 50%, 40%, 30%, 25%, 20%, 10%, 5%, and the tones corresponding to white paper.

5. The Lab prediction method for the halftone hue of a printing spot color according to claim 1, characterized in that, In step S9, during verification, at least two spot colors for each color system are selected for digital sample output and comparison with the printed sample.

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