Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (08): 74-80.doi: 10.13475/j.fzxb.20190702107

• Dyeing and Finishing & Chemicals • Previous Articles     Next Articles

Error optimization for measuring color chart data in textile color management

YING Shuangshuang, QIU Kebin, GUO Yufei, ZHOU Jiu, ZHOU Hua()   

  1. Key Laboratory of Advanced Textile Materials and Preparation Technology, Ministry of Education, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2019-07-03 Revised:2020-05-14 Online:2020-08-15 Published:2020-08-21
  • Contact: ZHOU Hua E-mail:hzzh@zstu.edu.cn

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Abstract:

In current textile color management, measurement errors are found to cause problems such as the color shade jump and inversion. In this research, a multi-directional curve fitting algorithm combined with the weight coefficient corresponding to each directional curve was proposed to optimize the bad points with excessive error in the measurement data. By analyzing the application effect of the optimization method through objective and subjective evaluation, it is found that the multi-directional curve fitting optimization algorithm works well with data having large errors, and it has little impact on other normal data, therefore effectively reducing the impact of the error from color chart measurement data. The overall average color difference of the International Color Consortium (ICC) Profile generated by the optimization is reduced by 12.30%. The average color difference of the difficult colors is reduced by 16.67%, and the average color difference of the neutral gray is reduced by 16.74%. During the soft proofing process, the proof color difference of the ICC Profile generated after optimization is also smaller than that before optimization.

Key words: textile color management, color measurement, multi-directional curve fitting, data optimization, average color difference, measurement error

CLC Number: 

  • TS194.4

Fig.1

Example for curve fitting"

Fig.2

Schematic diagram of all lines passing through one point in space. (a) Cube space; (b) Plan view; (c) Overall diagram"

Fig.3

Measurement data in Lab space"

Fig.4

Calculation of weight coefficient. (a) Same plane with normal vector angle of 0°; (b) Same plane with normal vector angle of 180°; (c) Different plane with same biased normal vectors of angle∈[0°,90°];(d) Different plane opposite normal vectors of angle∈(90°,180°]"

Fig.5

Effect of data optimization method based on multi-directional curve fitting. (a) Optimization of measurement data with large error; (b) Optimization of measurement data with less error"

Tab.1

Color difference of 25 ICC Profiles before and after optimization"

ICC Profile
序号		 整体平均色差 难打色平均色差 中性灰色平均色差
优化前 优化后 优化率/% 优化前 优化后 优化率/% 优化前 优化后 优化率/%
1 0.55 0.49 10.91 2.23 1.75 21.52 0.77 0.77 0.00
2 0.34 0.33 2.94 1.22 1.17 4.10 0.51 0.44 13.73
3 0.47 0.43 8.51 1.65 1.48 10.30 0.70 0.61 12.86
4 0.48 0.42 12.50 1.63 1.45 11.04 0.70 0.59 15.71
5 0.66 0.57 13.64 2.05 1.88 8.29 0.82 0.74 9.76
6 0.32 0.31 3.13 0.96 0.95 1.04 0.46 0.39 15.22
7 0.56 0.53 5.36 2.23 2.15 3.59 0.94 0.93 1.06
8 0.55 0.39 29.09 2.03 1.33 34.48 0.84 0.51 39.29
9 0.36 0.32 11.11 1.06 0.91 14.15 0.52 0.50 3.85
10 0.52 0.49 5.77 1.70 1.48 12.94 0.79 0.68 13.92
11 0.63 0.48 23.81 3.26 1.85 43.25 0.84 0.62 26.19
12 0.61 0.50 18.03 2.84 1.91 32.75 0.78 0.65 16.67
13 0.35 0.34 2.86 1.31 1.07 18.32 0.66 0.51 22.73
14 0.43 0.39 9.30 1.74 1.47 15.52 0.52 0.51 1.92
15 0.36 0.34 5.56 1.40 1.16 17.14 0.57 0.52 8.77
16 0.51 0.37 27.45 2.53 2.06 18.58 0.97 0.48 50.52
17 0.37 0.34 8.11 1.11 1.08 2.70 0.58 0.55 5.17
18 0.52 0.36 30.77 1.72 1.35 21.51 0.76 0.44 42.11
19 0.41 0.37 9.76 1.39 1.24 10.79 0.66 0.46 30.30
20 0.49 0.41 16.33 1.65 1.17 29.09 0.60 0.57 5.00
21 0.34 0.33 2.94 1.30 1.09 16.15 0.54 0.52 3.70
22 0.81 0.62 23.46 4.51 4.03 10.64 1.16 0.88 24.14
23 0.41 0.35 14.63 1.60 1.47 8.13 0.60 0.52 13.33
24 0.46 0.43 6.52 2.08 1.47 29.33 0.91 0.72 20.88
25 0.39 0.37 5.13 1.82 1.43 21.43 0.78 0.61 21.79
平均优化率/% 12.30 16.67 16.74

Fig.6

Color difference distribution map before and after optimization of ICC Profile. (a) Color difference distribution compared with original image before optimization; (b) Color difference distribution compared with original image after optimization; (c) Color difference value corresponding to each pixel before optimization; (d) Color difference value corresponding to each pixel after optimization"

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