Analisis Performa Metode Perceptual Color Transfer Dalam Peningkatan Kualitas Citra

Osmanila Tamo Ina(1*), AR Himamunanto(2), Haeni Budiati(3),

(1) Universitas Kristen Immanuel Yogyakarta, Indonesia
(2) Universitas Kristen Immanuel Yogyakarta, Indonesia
(3) Universitas Kristen Immanuel Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


The eye, as the sense of human vision, not only serves to see objects but also builds perceptions of the objects seen so that, in this case, it can judge images from different perspectives. Improved image quality is required because images often experience decreased quality caused by many factors, including being too dark, blurred, less sharp, too bright, and other factors. Perceptual Color Transfer is one of the most popular methods used in research. This method changes the color of an image to match the characteristics of another image, while maintaining the visual quality and naturality of the image. By considering the way humans perceive color, this method produces visual and consistent color adjustments that can contribute to improving the overall image quality. The color spaces used in this study are the lαβ and HSV color spaces using the MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) parameters. The results of the study show that the Perceptual Color Transfer method can be a good alternative to image processing techniques in light and dim light conditions, with the best average MSE and PSNR results in dark source image color transfer in the HSV color space of 0.0678021 and 21.43221, as well as the best mean results in light source image color transfer in Lαβ spaces of 0.0608865 and 20.03709.

Full Text:

PDF

References


R. A. Cepeda-Negrete, J., Sanchez-Yanez, R. E., Correa-Tome, F. E., & Lizarraga-Morales, “Ro of of,” Dark image Enhanc. using Percept. Color Transf., vol. 5, no. IEEE Access, pp. 1–1, 2017, doi: https://n2t.net/ark:/13683/pa8v/sVM Esta.

C. Gu, X. Lu, and C. Zhang, “Example-based color transfer with Gaussian mixture modeling,” Pattern Recognit., vol. 129, pp. 1–9, 2022, doi: 10.1016/j.patcog.2022.108716.

P. boedi dessyanto yanu f. mangaras, yuwono bambang, dasar pengolahan citra digital edisi 2022, vol. 11, no. 1. 2022.

K. M. Suliestiyanti S.R., Setyawan FX ., “Pengolahan Citra Dasar Dan Contoh Penerapannya,” in Teknosain, vol. 11, no. 1, 2016, pp. 1–5.

D. Kotovenko, A. Sanakoyeu, P. Ma, S. Lang, and B. Ommer, “A content transformation block for image style transfer,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 10024–10033, 2019, doi: 10.1109/CVPR.2019.01027.

M. He, J. Liao, D. Chen, L. Yuan, and P. V. Sander, “Progressive color transfer with dense semantic correspondences,” ACM Trans. Graph., vol. 38, no. 2, 2019, doi: 10.1145/3292482.

C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and M. Sbetr, “Color Channel Transfer for Image Dehazing,” IEEE Signal Process. Lett., vol. 26, no. 9, pp. 1413–1417, 2019, doi: 10.1109/lsp.2019.2932189.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Comput. Graph. Appl., vol. 21, no. 5, pp. 34–41, 2001, doi: 10.1109/38.946629.

C. Ruderman, D.L.,Cronin, T.W., chiao, “Statistics of cone responses to natural images: impilcations for visual coding.,” J. Opt. Soc. Am. Opt. Image Sci. Vis., vol. 15, no. 2036–2045, 1998.

A. Usman, Pengolahan Citra Digital dan Teknik Pemrogramannya, Pertama. Yogyakarta: Graha Ilmu, 2005.




DOI: https://doi.org/10.30645/kesatria.v5i3.420

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.420.g416

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: