Color Compensation Calculation for Reprinted Prints Sample-matching Based on K-means Clustering Algorithm

FU Wenting, DENG Tijun

Packaging Engineering ›› 2026, Vol. 47 ›› Issue (3) : 161-167.

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (3) : 161-167. DOI: 10.19554/j.cnki.1001-3563.2026.03.017
Automatic and Intelligent Technology

Color Compensation Calculation for Reprinted Prints Sample-matching Based on K-means Clustering Algorithm

  • FU Wenting*, DENG Tijun
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Abstract

The work aims to introduce the K-means clustering algorithm to quantitatively assess the difference in dot area rate distribution between printed sheets and customer samples, utilize nonlinear fitting algorithms to determine the optimization adjustment parameters for theC/M/Y/K four-color channels, and achieve precise color compensation and restoration for reprinted prints. A scanner and machine printing ICC profile was used to convert scanned RGB files into CMYK files consistent with pre-press color separation standards. The K-means clustering algorithm model was introduced to conduct high-precision comparisons of the C/M/Y/K color-separated files of printed sheets and customer samples. Nonlinear fitting algorithms were used to determine the optimization adjustment nodes and parameters for the four-color channels. "Curve" adjustments were conducted for the C/M/Y/K four color channels in Photoshop. The dynamic compensation mechanism effectively corrected the defects of printed sheets being too blue or too dark, synchronously optimized the four primary colors, secondary overprint colors, and three-color overprint gray balance colors, and stabilized the color difference ΔE2000 of the compensated and corrected printed sheets within 2.5. This data-driven compensation method significantly outperforms traditional manual adjustments in efficiency, possesses fully replicable standardized characteristics, and provides key technical support for the digital upgrading of printing production.

Key words

K-means clustering algorithm / printing reorder sample-matching / color compensation / color management

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FU Wenting, DENG Tijun. Color Compensation Calculation for Reprinted Prints Sample-matching Based on K-means Clustering Algorithm[J]. Packaging Engineering. 2026, 47(3): 161-167 https://doi.org/10.19554/j.cnki.1001-3563.2026.03.017

References

[1] 潘哲. 印前图像色彩分析与校正[J]. 剑南文学(经典教苑), 2013(8): 175.
PAN Z.Color Analysis and Correction of Prepress Image[J]. Jiannan Literature (Classic Teaching Section), 2013(8): 175.
[2] 雷利香. Photoshop的印前技术实践研究[J]. 科技传播, 2013, 5(4): 196-197.
LEI L X.Practical Research on Photoshop Prepress Technology[J]. Public Communication of Science & Technology, 2013, 5(4): 196-197.
[3] 付文亭, 邓体俊. Photoshop中转换选项对印前分色的影响研究[J]. 包装工程, 2015, 36(5): 131-135.
FU W T, DENG T J.Influence of Conversion Options in Photoshop on Pre-Press Colour Separation[J]. Packaging Engineering, 2015, 36(5): 131-135.
[4] 李恒博, 刘静超, 吴珂彤. 基于改进K-means算法的图像分割[J]. 现代计算机, 2024, 30(2): 49-51.
LI H B, LIU J C, WU K T.Image Segmentation Based on K-Means Algorithm[J]. Modern Computer, 2024, 30(2): 49-51.
[5] 任恒怡, 贺松, 陈文亮. 一种改进的K-means聚类算法在图像分割中的应用[J]. 通信技术, 2017, 50(12): 2704-2707.
REN H Y, HE S, CHEN W L.Application of Modified K-Means Clustering Algorithm in Image Segmentation[J]. Communications Technology, 2017, 50(12): 2704-2707.
[6] 姜月秋, 牛硕, 高宏伟. 一种新的基于K均值聚类的色彩量化算法研究[J]. 计算机科学, 2012, 39(S3): 375-377.
JIANG Y Q, NIU S, GAO H W.New Color Quantization Algorithm Based on K-Means Clustering[J]. Computer Science, 2012, 39(S3): 375-377.
[7] 吴春法, 潘亚文, 王敬. 基于K-means颜色聚类分割与边缘检测的文字提取[J]. 电脑知识与技术, 2017, 13(28): 206-207.
WU C F, PAN Y W, WANG J.Based on K-Means Color Clustering Segmentation and Edge Detection of Text Extraction[J]. Computer Knowledge and Technology, 2017, 13(28): 206-207.
[8] 胡勋超, 杨勇. 基于多通道ICC特性文件的高保真分色模型的研究[J]. 印刷工业, 2025(1): 44-48.
HU X C, YANG Y.Research on High-Fidelity Color Separation Model Based on Multi-Channel ICC Profile[J]. Print China, 2025(1): 44-48.
[9] 王子琪, 方恩印. 图像聚类改进算法下的织锦纹样提取及数字化保护[J]. 网印工业, 2025(1): 10-12.
WANG Z Q, FANG E Y.Brocade Pattern Extraction and Digital Preservation under Improved Image Clustering Algorithm[J]. Screen Printing Industry, 2025(1): 10-12.
[10] 张朝, 郭秀娟, 张坤鹏. K-means算法聚类中心选取[J]. 吉林大学学报(信息科学版), 2019, 37(4): 437-441.
ZHANG C, GUO X J, ZHANG K P.Clustering Center Selection on K-Means Clustering Algorithm[J]. Journal of Jilin University (Information Science Edition), 2019, 37(4): 437-441.
[11] 贾瑞玉, 李玉功. 类簇数目和初始中心点自确定的K-means算法[J]. 计算机工程与应用, 2018, 54(7): 152-158.
JIA R Y, LI Y G.K-Means Algorithm of Clustering Number and Centers Self-Determination[J]. Computer Engineering and Applications, 2018, 54(7): 152-158.
[12] 王立英, 李嘉颖, 亓越. 改进的K-means聚类算法及在图像分割中的应用[J]. 中国信息界, 2024(6): 252-256.
WANG L Y, LI J Y, QI Y.Improved K-Means Clustering Algorithm and Its Application in Image Segmentation[J]. Information China, 2024(6): 252-256.
[13] 吴夙慧, 成颖, 郑彦宁, 等. K-means算法研究综述[J]. 现代图书情报技术, 2011(5): 28-35.
WU S H, CHENG Y, ZHENG Y N, et al.Survey on K-Means Algorithm[J]. New Technology of Library and Information Service, 2011(5): 28-35.
[14] REDMOND S J, HENEGHAN C.A Method for Initialising the K-Means Clustering Algorithm Using Kd-Trees[J]. Pattern Recognition Letters, 2007, 28(8): 965-973.
[15] LI H Y, HE H Z, WEN Y G.Dynamic Particle Swarm Optimization and K-Means Clustering Algorithm for Image Segmentation[J]. Optik, 2015, 126(24): 4817-4822.
[16] KAPOOR S, ZEYA I, SINGHAL C, et al.A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation[J]. Procedia Computer Science, 2017, 115: 415-422.
[17] KHRISSI L, EL AKKAD N, SATORI H, et al.Clustering Method and Sine Cosine Algorithm for Image Segmentation[J]. Evolutionary Intelligence, 2022, 15(1): 669-682.
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