Charcoal Drawing Style and Color Effect of Children Face Images based on Structural Similarity Index and Color Image Scale

Main Article Content

Kanakarn Ruxpaitoon
Thitiporn Lertrusdachakul
Kasem Thiptarajan


Many people love to capture and view children pictures to catch their cute moments. The smile and innocence of children’s faces are very impressive. Therefore, this paper proposes an alternative method to create charcoal drawing style and color effect for children face images. The method applies the structural similarity index (SSIM) of image quality assessment to generate rich black tones automatically. The image is blurred with Gaussian filter to the appropriate level and compared with the original image to obtain the local SSIM values. The blurred level and the weight of neighborhood pixels for estimating local statistics in SSIM quality assessment is controlled by the threshold of the average SSIM value of image detail acquired from the preliminary experiment. The color effect is based on SSIM values and the knowledge of color image scale. The results reveal that the sophisticated intensity of lightness from SSIM map has the power to convey this expressive drawing style emotionally and naturally.


Download data is not yet available.

Article Details

Research Article


Blue Lightning TV Photoshop. Photoshop Tutorial: How To Transform Photos into Gorgeous, Pencil Crawings. (May 11, 2013). Accessed: Mar. 28, 2020. [Online Video]. Available:

Photoshopessentials. “Photo to color pencil sketch with photoshop CC.” (accessed Mar. 28, 2020).

Photofunny. “Online pencil drawing effect for your photo.” (accessed Mar. 28, 2020).

Rojdark. “Charcoal art - realistic charcoal photoshop action.” (accessed Mar. 28, 2020).

M. Nieves. “How to create a charcoal drawing from a photo (with a Photoshop Action).” (accessed Mar. 28, 2020).

Adobe. “Sketch filters.” (accessed Mar. 28, 2020).

P. Tresset and F. F. Leymarie, “Generative portrait sketching,” in Proc. VSMM’05, Oct. 2005, pp. 1-10.

D. R. Martin, C. C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530-549, May 2004.

Miinu Limited. “My sketch.” (accessed May 8, 2020).

Pixel Force Private Limited. “Photo to sketch.” (accessed May 8, 2020).

L. Zhang. “Sketch master.” (accessed May 8, 2020).

U. Sara, M. Akter, and M. S. Uddin, “Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study,” Journal of Computer and Communications, vol. 7, pp. 8-18, 2019.

G. P. Renieblas, A. T. Nogués, A. M. González, N. G. Leon, and E. G. D. Castillo, “Structural similarity index family for image quality assessment in radiological images,” Journal of Medical Imaging, vol. 4, no. 3, pp. 1-11, Jul. 2017, doi: 10.1117/1.JMI.4.3.035501.

J. Snell, K. Ridgeway, R. Liao, B. D. Roads, M. C. Mozer, and R. S. Zemel, “Learning to generate images with perceptual similarity metrics,” in 2017 IEEE International Conf. on Image Processing (ICIP), Sep. 2017, pp. 4277-4281.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.

The MathWorks, Inc. “SSIM.” (accessed May 8, 2020).

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979.

S. L. Bangare, A. Dubal, P. S. Bangare, and S. T. Patil, “Reviewing otsu’s method for image thresholding,” International Journal of Applied Engineering Research, vol. 10 no. 9, pp. 21777-21783, 2015.

S. Kobayashi, Color Image Scale. Tokyo, Japan: Kodansha, 1990.

S. Kobayashi, Colorist. Tokyo, Japan: Kodansha, 1997.

H. Nagumo, New Color Image Chart. Tokyo, Japan: Graphic-sha, 2016.

S. Kobayashi, “The aim and method of the Color Image Scale,” Color Research & Application, vol. 6, pp. 93-107, 2009.