{"title":"Artist Acrylic Paint Spectral, Colorimetric, and Image Dataset","authors":"R. Berns","doi":"10.2352/issn.2168-3204.2022.19.1.10","DOIUrl":null,"url":null,"abstract":"Spectral datasets, and subsequent colorimetric and image data, have a variety of uses in cultural heritage imaging including sensor design, lighting design, synthetic target generation, spectral accuracy assessment of multispectral and hyperspectral cameras, color accuracy assessment of digital cameras, and encoding errors. Spectral data for 58 Golden Artist Colors Heavy Body Acrylics were used to calculate the spectra of 831 varnished tints, tones, and masstones, based on the two-constant opaque form of Kubelka Munk turbid-media theory. The data were used to calculate a synthetic target that was used to quantify encoding errors using AdobeRGB (1998), commonly used in cultural heritage imaging, and sRGB, commonly used in documents and consumer imaging. 22% and 31% of the target colors were out of gamut, respectively. Principal component analysis was performed and the first three eigenvectors used to extract spectra similar to cyan, magenta, and yellow. These PCA-based primaries poorly approximated the 58 pigments.","PeriodicalId":89080,"journal":{"name":"Archiving : final program and proceedings. IS & T's Archiving Conference","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archiving : final program and proceedings. IS & T's Archiving Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2352/issn.2168-3204.2022.19.1.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Spectral datasets, and subsequent colorimetric and image data, have a variety of uses in cultural heritage imaging including sensor design, lighting design, synthetic target generation, spectral accuracy assessment of multispectral and hyperspectral cameras, color accuracy assessment of digital cameras, and encoding errors. Spectral data for 58 Golden Artist Colors Heavy Body Acrylics were used to calculate the spectra of 831 varnished tints, tones, and masstones, based on the two-constant opaque form of Kubelka Munk turbid-media theory. The data were used to calculate a synthetic target that was used to quantify encoding errors using AdobeRGB (1998), commonly used in cultural heritage imaging, and sRGB, commonly used in documents and consumer imaging. 22% and 31% of the target colors were out of gamut, respectively. Principal component analysis was performed and the first three eigenvectors used to extract spectra similar to cyan, magenta, and yellow. These PCA-based primaries poorly approximated the 58 pigments.
光谱数据集以及随后的色度和图像数据在文化遗产成像中有多种用途,包括传感器设计、照明设计、合成目标生成、多光谱和高光谱相机的光谱精度评估、数码相机的颜色精度评估和编码错误。基于Kubelka-Munk混浊介质理论的两种恒定不透明形式,使用58种Golden Artist Colors Heavy Body Acrylics的光谱数据来计算831种清漆色调、色调和质量色调的光谱。这些数据用于计算合成目标,该目标用于使用AdobeRGB(1998)和sRGB量化编码误差,AdobeRGB通常用于文化遗产成像,sRGB通常用于文档和消费者成像。22%和31%的目标颜色分别在色域之外。进行主成分分析,前三个特征向量用于提取类似于青色、品红色和黄色的光谱。这些基于PCA的原色很难接近58种颜料。