RAS PhysiologyСенсорные системы Sensory Systems

  • ISSN (Print) 0235-0092
  • ISSN (Online) 3034-5936

Computationally efficient adaptive color correction

PII
10.31857/S0235009224040077-1
DOI
10.31857/S0235009224040077
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume 38 / Issue number 4
Pages
78-84
Abstract
To obtain a photo that reproduces the original scene as accurately as possible, it is necessary to solve the problem of color correction, that is, to find a mapping that translates the coordinates of the camera color space (RGB) into the coordinates of the human color space (CIE XYZ). In this article, we consider color correction using lookup tables, pre-built for various lighting conditions. This approach allows you to achieve high speed and accuracy when applying color correction on the device, but requires large amounts of RAM, which, for example, mobile phones do not have. We propose a method for automatic thinning of a set of search tables without loss of accuracy of color correction. The method is based on clustering of the mappings that specify the color correction. To compare the mappings, we propose a criterion for their similarity based on the maximum difference of the generated colors in the target space of a standard CIE XYZ observer. For the proposed criterion, the article provides an effective calculation method and, together with a theorem justifying the correctness of the method.
Keywords
адаптивная цветовая коррекция критерий схожести отображений таблица поиска математическое программирование кластеризация
Date of publication
14.09.2025
Year of publication
2025
Number of purchasers
0
Views
6

References

  1. 1. Коноваленко И. А. Критерии и алгоритмы вычисления точности проективной нормализации изображений. Дисс. канд. физ-мат. наук. М., 2021. 136 с.
  2. 2. Николаев Д.П., Николаев П.П., Гладилин С.А., Божкова В.П. Основы цветовой теории в техническом зрении. I. Введение в цветовую теорию. М.: Мир науки, 2021. 40 с.
  3. 3. Николаев П.П., Николаев Д.П., Гладилин С.А., Басова О.А., Ярыкина М.С. Сборник задач по обработке изображений и техническому зрению. М.: 2023. 78 с.
  4. 4. Шашлов А.Б. Основы светотехники. М.: Логос, 2016. 256 с.
  5. 5. Fernando R., Matt P. GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation. Addison-Wesley Professional, 2005. 814 p.
  6. 6. Finlayson G.D., Michal M., Anya H. Root-polynomial colour correction. Color and Imaging Conference. Society of Imaging Science and Technology, 2011. V. 19. P. 115–119.
  7. 7. Gasparini F., Schettini R. Color correction for digital photographs. 12th International Conference on Image Analysis and Processing, 2003. Proceedings. IEEE, 2003. P. 646–651. DOI: 10.1109/ICIAP.2003.1234123
  8. 8. Han D. Real-time color gamut mapping method for digital TV display quality enhancement. IEEE Transactions on consumer Electronics. IEEE, 2004. V. 50. P. 691–698. DOI: 10.1109/TCE.2004.1309450
  9. 9. Ives H.E. The transformation of color-mixture equations from one system to another. Journal of the Franklin Institute. 1915. V. 180. P. 673–701.
  10. 10. Kim Y.T., Cho Y.H., Lee C.H., Ka Y.H. Color look-up table design for gamut mapping and color space conversion. International Conference on Digital Production Printing and Industrial Applications. 2003. P. 28–29.
  11. 11. Luther R. Aus dem gebiet der farbreizmetrik, Zeitschrift fur Technishe Physik. 1927. V. 12. P. 540–558.
  12. 12. Mantiuk R., Mantiuk R., Tomaszewska A., Heidrich W. Color correction for tone mapping. Computer graphics forum. Oxford. Blackwell Publishing Ltd, 2009. V. 50. P. 193–202.
  13. 13. Moroney N. Local color correction using non-linear masking. Color and Imaging conference. California. Society of Imaging Science and Technology, 2000. V. 8. P. 108–111.
  14. 14. Morovic J., Luo M.R. The fundamentals of gamut mapping: A survey. Journal of Imaging Science and Technology. Derby. The Society for Imaging Science and Technology, 2001. V. 45. P. 283–290.
  15. 15. Sari Y.A., Ginardi R.V. H., Suciati N. Color correction using improved linear regression algorithm. 2015 International Conference on Information & Communication Technology and Systems (ICTS). IEEE, 2015. P. 73–78. DOI: 10.1109/ICTS.2015.7379874.
  16. 16. Solomatov G., Akkaynak D. Spectral Sensitivity Estimation Without a Camera. 2023 IEEE International Conference on Computational Photography (ICCP). IEEE, 2023. P. 1–12. DOI: 10.1109/ICCP56744.2023.10233713
  17. 17. Soshin K.V., Nikolaev D.P., Ershov E.I., Tchobanou M.K. A scalable rational color correction for an image. Patent RF. № WO2023121500A1. 2023.
QR
Translate

Индексирование

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library