纺织学报 ›› 2023, Vol. 44 ›› Issue (10): 104-112.doi: 10.13475/j.fzxb.20220905601

• 染整与化学品 • 上一篇    下一篇

基于色度特征化的乾隆色谱跨设备色彩再现

苏淼1,2(), 李赛权1,2,3, 杨丽梅1,2, 段怡婷4, 鲁佳亮1,2, 周凯丽1,2   

  1. 1.浙江理工大学 纺织科学与工程学院(国际丝绸学院), 浙江 杭州 310018
    2.浙江理工大学 国际丝绸与丝绸之路研究中心, 浙江 杭州 310018
    3.浙江理工大学嵊州创新研究院, 浙江 绍兴 311800
    4.利兹大学 设计学院, 英国 利兹 LS2 9JT
  • 收稿日期:2022-09-21 修回日期:2023-07-06 出版日期:2023-10-15 发布日期:2023-12-07
  • 作者简介:苏淼(1980—),女,教授,博士。主要研究方向为丝绸之路与传统丝绸技艺、纺织品设计与数字化。E-mail:sumiao2008@qq.com。
  • 基金资助:
    国家重点研发计划项目(2019YFC1521301);浙江省文物保护专项(2021016);国家社科基金项目(20WYSB006)

Cross-media reproduction of Qianlong palette color based on characterization model

SU Miao1,2(), LI Saiquan1,2,3, YANG Limei1,2, DUAN Yiting4, LU Jialiang1,2, ZHOU Kaili1,2   

  1. 1. College of Textile Science and Engineering(International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. International Center for Silk and Silk Road Studies, Hangzhou, Zhejiang 310018, China
    3. Zhejiang Sci-Tech University Shengzhou Innovation Research Institute, Shaoxing, Zhejiang 311800, China
    4. School of Design, University of Leeds, Leeds LS2 9JT, UK
  • Received:2022-09-21 Revised:2023-07-06 Published:2023-10-15 Online:2023-12-07

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摘要：

色彩失真现象普遍存在于图像在不同设备之间传输的过程中,为保证纺织文物图像跨设备的色彩真实再现,继而推进博物馆数字化发展,在乾隆色谱染色丝织物的基础上,分别对数码相机和显示屏进行色度特征化,并讨论了不同图像格式、不同训练数据集、多项式不同阶数对相机预测模型精度的影响,同时采用增益-偏置-伽马模型(GOG模型)对显示屏进行色度特征化,然后对乾隆色谱跨设备再现情况进行色差分析以确定最优特征化模型,最后采用中国丝绸博物馆馆藏纺织品文物对建立模型进行检验。结果表明:选择RAW格式图像和三阶多项式对相机进行色度特征化处理,并使用采集的纺织品颜色数据集训练模型,模型的精度可达到2.18($\Delta E_{\mathrm{ab}}^{*}$)(CIEDE2000色差值);显示器特征化模型精度为0.58($\Delta E_{\mathrm{ab}}^{*}$);乾隆色谱跨设备再现的色差均值为2.16($\Delta E_{\mathrm{ab}}^{*}$),真实丝绸文物跨设备再现的色差值为2.38($\Delta E_{\mathrm{ab}}^{*}$),表明该特征化模型具有优异的跨设备色彩再现效果。

关键词: 乾隆色谱, 色度特征化, 丝绸文物, 跨设备, 色彩再现

Abstract:

Objective Digital museums break through the barriers of space and time, expanding the range of collections in the form of video and images. However, in the process of museum relic image collection or online display, image reproduction across equipment is prone to color distortion phenomena such as eclipse, color deviation, affecting the visual effect of the image due to the correlation characteristics of color equipment and the lack of standard image collection conditions. In order to solve this problem, the Qianlong palette silk fabric was taken as the research objective to carry out the cross-device color reproduction of textile relic images, aiming to provide support for the establishment of digital museum.

Method On the basis of Qianlong palette silk fabric, the color characterization of digital camera and display screen were carried out respectively. The influences of different image formats, different training data sets and different order of polynomial on the accuracy of camera prediction model were discussed. At the same time, the Gain-Offset-Gamma(GOG) model was used to characterize the chrominance of display screen. Then color difference analysis was carried out to determine the optimal characteristic model of Qianlong chromatography reappearance across equipment. Finally, textile relics collected in the Silk Museum of China were used to test the model.

Results In this study, color information and image information were collected from 210 colors of Qianlong palette. The absence of Qianlong palette in blues (B) and blue-green tones (BG) may be due to the fact that indigo was the only natural blue dye selected in this study, and a stronger dye bath may have biased indigo stained samples towards purple tones (Fig. 6). The L* values of all 210 colors ranges from 17.85 to 78.13(Fig. 7), 210 colors are distributed in four quadrants of the a*-b* plane of CIELAB color space, and is widely distributed in the first quadrant (Fig. 8). The Qianlong palette covers different color series. Among all the colors, red and yellow are the main color series, which are the most important color representations of the royal costume in the Qing Dynasty. The color reproduction accuracy for the silk fabrics has been significantly influenced by the training datasets and the mathematical mapping methods and image format. Using both color charts, the RAW image format shows the better predictive accuracy, followed by the JPG format (Tab. 6), and the 3rd polynomial regression shows the best predictive accuracy (3.20 (ΔEab*)), followed by the 2nd (3.86 (ΔEab*)) and the 1st order polynomial regression (4.15 (ΔEab*)). Finally, the RAW image and third-order polynomial were selected to characterize the chrominance of the camera, and the collected textile color data set was used to train the model. The accuracy of the model can reach 2.18(ΔEab*), this is the highest precision camera characterization model in this study. The accuracy of display characterization model is 0.58 (ΔEab*). The mean color difference value of Qianlong palette and silk relics is 2.16 (ΔEab*), and 2.38 (ΔEab*), indicating that this characteristic model has excellent cross-equipment color reproduction effect.

Conclusion The Qianlong palette is mainly distributed in the yellow tones of the Munsell color system, with less color in the green and purple tones, and no distribution in the blues and blue-green tones. In the color space of L*a*b*, the brightness value of Qianlong chromatographic ranges from 17.85 to 78.13. The brightness value of yellow tone is above medium, and the brightness value of other tones is below medium. For museum image acquisition, it is suggested to standardize the lighting source, which mainly includes the selection of standard lighting body and corresponding color temperature to ensure the uniformity of lighting. At the same time, standardize shooting conditions: select and fix appropriate camera parameter settings, shooting distance, choose remote control shooting to avoid human operation error. For the characteristic prediction model of camera: the textile color data set of the same material and the same specification is used as the training data set, and the image modeling in high order polynomial and RAW format is adopted to obtain higher prediction accuracy and accurately predict the color after textile reduction.

Key words: Qianlong palette, color characterization, silk cultural relic, cross-media, color reproduction

中图分类号: 

  • O432.3

图1

照明装置示意图"

图2

光源亮度随时间的变化趋势"

图3

照明光源的相对光谱功率分布曲线"

表1

清代常见服饰主要用色"

服饰 主要颜色
袍 宝蓝、酱色、古铜、驼色、墨色(用得较少)
匹料、装饰 深蓝、宝蓝、部分用黄色系和绿色系
褂 红青色、元青色(少部分)
褡裢裙、套袖 蓝色系和黄色系
彩织、彩绣的绣纰、妆花绒 红色、绿色

图4

14张乾隆色卡之一"

表2

相机各参数设置"

参数 设置 参数 设置
色彩空间 sRGB 光圈值 5.6
焦距/mm 50 纵横比 3∶2
对焦方式 AF 分辨率/ppi 6 720×4 480
存储方式 RAW、JPG 拍摄模式 手动档
曝光时间/s 1/80 白平衡/K 6 500
闪光灯 关闭

表3

训练和测试数据集"

训练数据集 测试数据集
DC标准色卡 DC标准色卡
DC标准色卡 乾隆色卡
乾隆色卡 乾隆色卡

表4

EIZO显示器参数设置"

参数 设置 参数 设置
亮度/(cd·m-2) 120 节能模式 关
色温/K 6 500 模式 标准
伽马 2.2 色彩空间 sRGB

表5

显示设备空间均匀性色差"

颜色 平均色差(Δ E a b *)
白 0.42
灰 0.56
红 0.47
绿 0.66
蓝 0.51

图5

3个单通道色品坐标"

图6

色调分布图"

图7

乾隆色卡210色在L*a*b*色彩空间的分布"

图8

乾隆色卡210色在a*-b*坐标系的分布"

表6

不同训练数据集和预测数据集的建模结果"

数据集类型 平均色差(Δ E a b *)
训练数据集 预测数据集 JPG格式 RAW格式
DC色卡 乾隆色卡 3.31 1.30
DC色卡 乾隆色卡 8.83 6.68
乾隆色卡 乾隆色卡 4.20 3.20

表7

不同乾隆色卡数据子集建模结果"

训练数据集 测试数据集 平均色差(Δ E a b *)
乾隆色卡 (210) 乾隆色卡 (210) 3.20
乾隆色卡 (105, 2n-1) 乾隆色卡(105, 2n) 2.18
乾隆色卡 (106~210) 乾隆色卡 (1~105) 43.60

图9

乾隆色谱各色调跨设备的色差均值"

图10

测取文物的反射率过程"

图11

文物校正前后对比"

[1] HU Y T. Panoramic image acquisition method of the Tang dynasty based on virtual reality[C]// 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). Los Alamitos:IEEE Publishers Ltd, 2021: 287-294.
[2] WANG Y C, CHEN C L, DENG Y Y. Museum-authorization of digital rights: a sustainable and traceable cultural relics exhibition mechanism[J]. Sustainability, 2021, 13(4): 2046.
doi: 10.3390/su13042046
[3] SHI Y W. The digital protection of intangible cultural heritage: the construction of digital museum[C]// HAO J F, SUN S Q. 2008 9th International Conference on Computer-Aided Industrial Design and Conceptual Design. NY: EEE Publishers Ltd, 2008: 1196-1199.
[4] 苏一飞. 博物馆数字化展示技术与虚拟展示研究[J]. 时代报告(奔流), 2021(8): 70-71.
SU Yifei. Research on museum digital display technology and virtual display[J]. Time Report, 2021(8): 70-71.
[5] 李淋明. 数字技术在博物馆藏品陈列展示中的应用[J]. 收藏与投资, 2021, 12(11): 55-57.
LI Linming. The application of digital technology in the display of museum collections[J]. Collection & Investment, 2021, 12(11): 55-57.
[6] 尚玉平, 欧阳盼, 刁常宇, 等. 新疆尼雅墓地出土纺织品文物的数字化信息采集:以95MNIM8∶15“五星出东方利中国”织锦护臂为例[J]. 文物, 2020(5): 80-88.
SHANG Yuping, OUYANG Pan, DIAO Changyu, et al. Digitisation of textiles found in the Niya cemetery in Xinjiang: a case study of the five stars rising in the East Benefit China Arm Protector (95MNIM8:15)[J]. Cultural Relics, 2020(5): 80-88.
[7] 刘剑, 王业宏, 金鉴梅, 等. 乾隆色谱[M]. 杭州: 浙江大学出版社, 2020: 15-36.
LIU Jian, WANG Yehong, JIN Jianmei, et al. The Qianlong palette[M]. Hangzhou: Zhejiang University Press, 2020: 15-36.
[8] 王业宏, 刘剑, 金鉴梅. 从舒妃服装遗物看乾隆中期色彩时尚及染色工艺[J]. 艺术设计研究, 2018(4): 40-45.
WANG Yehong, LIU Jian, JIN Jianmei. A study on color fashion and dying techniques in the Middle Qianlong imperial regime according to the costumes of Shu Fei the imperial consort[J]. Art & Design Research, 2018(4): 40-45.
[9] 金鉴梅, 赵丰, 刘剑, 等. 清宫礼吉服中的黄色及槐子黄栌染色研究[J]. 丝绸, 2021, 58(5): 26-33.
JIN Jianmei, ZHAO Feng, LIU Jian, et al. A study on yellow and dyeing with sophora japonica and cotinus coggygria in the royal dress of Qing dynasty palace[J]. Journal of Silk, 2020, 58(5): 26-33.
[10] HUNT R W G. The reproduction of colour[M]. Hoboken, NJ: John Wiley & Sons, 2004: 163-178.
[11] CHEUNG T L V. Color camera characterization using artificial networks[C]// WESTLAND S. Proceedings of Color and Imaging Conference. Scottsdale: IEEE Publishers Ltd, 2002: 137-144.
[12] CHOU Y F, LUO M R, LI C J, et al. Methods for designing characterisation targets for digital cameras[J]. Coloration Technology, 2013, 129(3): 203-213.
doi: 10.1111/cote.2013.129.issue-3
[13] CHEUNG T L V, WESTLAND S, CONNAH D R, et al. A comparative study of the characterization of color cameras by means of neural networks and polynomial transforms[J]. Coloration Technology, 2004(120): 19-25.
[14] BERNS R S. Methods for characterizing CRT dis-plays[J]. Displays, 1996, 16(4): 173-182.
doi: 10.1016/0141-9382(96)01011-6
[15] BERNS R S. The science of digitizing paintings for color-accurate image archives: a review[J]. Journal of Imaging Science and Technology, 2001, 45(4): 305-325.
doi: 10.2352/J.ImagingSci.Technol.2001.45.4.art00002
[16] LEE C, LEE E, AHN S, et al. Color space conversion via gamut-based color samples of printer[J]. Journal of Imaging Science and Technology, 2001, 45(5): 427-435.
doi: 10.2352/J.ImagingSci.Technol.2001.45.5.art00003
[17] HE R, XIAO K D, POINTER M, et al. A novel camera colour characterisation model for the colour measurement of human skin[J]. Electronic Imaging, 2021, 33: 221-222.
doi: 10.2352/ISSN.2470-1173.2021.3.MOBMU-022
[18] 王业宏, 刘剑, 童永纪. 清代织染局染色方法及色彩[J]. 历史档案, 2011(2): 125-127.
WANG Yehong, LIU Jian, TONG Yongji. Dyeing methods and related colors used in Qing dynasty dyeing workshops[J]. Historical Archives, 2011(2): 125-127.
[19] MUSSAK R A M. Natural colorants in textile dyeing[M]// BECHTOLD T. Handbook of natural colorants. Hoboken, NJ: John Wiley & Sons, 2009: 314-337.
[20] PARK J. Engineered textile colour standards[J]. Coloration Technology, 2007, 123(1): 1-7.
doi: 10.1111/cte.2007.123.issue-1
[21] HONG G W, LUO M R, AHODES P A. A study of digital camera colorimetric characterization based on polynomial modeling[J]. Color Research and Application, 2001, 26(1): 76-84.
doi: 10.1002/(ISSN)1520-6378
[22] YANG L M. Color analysis of silk fabrics dyed based on "the Qianlong palette"[C]// SU M, DUAN Y T,ZHOU K L, et al. Proceedings 2021 14th International Symposium on Computational Intelligence and Design (ISCID). Scottsdale: IEEE Publishers Ltd, 2021: 69-72.
[23] HE R L, XIAO K D, POINTER M, et al. Development of an image-based measurement system for human facial skin colour[J]. Color Research and Application, 2022, 47(2): 288-300.
doi: 10.1002/col.v47.2
[1] 苏淼, 周凯丽, 段怡婷, 鲁佳亮, 杨丽梅. 基于色度测量的乾隆色谱色彩特征研究[J]. 纺织学报, 2023, 44(03): 132-138.
[2] 黄鹤玲;李霆;易积政;倪兵兵. 基于颜色空间与聚类分析相结合的袜带辨色分级方法[J]. 纺织学报, 2010, 31(11): 99-103.
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