Ming Ronnier Luo, Qiang Xu, Michael Pointer, Manuel Melgosa, Guihua Cui, Changjun Li, Kaida Xiao, Min Huang
{"title":"A comprehensive test of colour-difference formulae and uniform colour spaces using available visual datasets","authors":"Ming Ronnier Luo, Qiang Xu, Michael Pointer, Manuel Melgosa, Guihua Cui, Changjun Li, Kaida Xiao, Min Huang","doi":"10.1002/col.22844","DOIUrl":null,"url":null,"abstract":"<p>The paper describes a comprehensive test to evaluate the performance of current colour-difference models using available experimental datasets. In total, 28 individual datasets were accumulated to test 17 colour-difference formulae, 13 of them based on Uniform Colour Spaces (UCSs) in terms of the Standardized Residual Sum of Squares (<i>STRESS</i>) measure. The 28 datasets were divided into three groups: Large Colour-Difference data (LCD), Small Colour-Difference data for surface colours (SCDs), and Small Colour Difference data for display colours (SCDd). For each colour model, four versions were tested: the original model, and that including <i>k</i><sub>L</sub><i>-</i>, Gamma- and <i>k</i><sub>L</sub>/Gamma, which are the lightness parametric factor, the colour-difference exponent factor, and the combination of both, respectively, optimized to fit particular dataset(s). The statistical <i>F</i>-test was applied to test the difference between each pair of models. Furthermore, parametric effects between the large/small colour-difference magnitudes, and between surface/display colours were investigated. The results showed that CAM16-UCS significantly outperformed the other models for all groups. It accurately predicted all types of data and should be proposed for colour-difference evaluation across all industries.</p>","PeriodicalId":10459,"journal":{"name":"Color Research and Application","volume":"48 3","pages":"267-282"},"PeriodicalIF":1.2000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/col.22844","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Color Research and Application","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/col.22844","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 2
Abstract
The paper describes a comprehensive test to evaluate the performance of current colour-difference models using available experimental datasets. In total, 28 individual datasets were accumulated to test 17 colour-difference formulae, 13 of them based on Uniform Colour Spaces (UCSs) in terms of the Standardized Residual Sum of Squares (STRESS) measure. The 28 datasets were divided into three groups: Large Colour-Difference data (LCD), Small Colour-Difference data for surface colours (SCDs), and Small Colour Difference data for display colours (SCDd). For each colour model, four versions were tested: the original model, and that including kL-, Gamma- and kL/Gamma, which are the lightness parametric factor, the colour-difference exponent factor, and the combination of both, respectively, optimized to fit particular dataset(s). The statistical F-test was applied to test the difference between each pair of models. Furthermore, parametric effects between the large/small colour-difference magnitudes, and between surface/display colours were investigated. The results showed that CAM16-UCS significantly outperformed the other models for all groups. It accurately predicted all types of data and should be proposed for colour-difference evaluation across all industries.
期刊介绍:
Color Research and Application provides a forum for the publication of peer-reviewed research reviews, original research articles, and editorials of the highest quality on the science, technology, and application of color in multiple disciplines. Due to the highly interdisciplinary influence of color, the readership of the journal is similarly widespread and includes those in business, art, design, education, as well as various industries.