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An art-oriented pixelation method for cartoon images

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Abstract

Pixel art has evolved from a primitive computer image presentation to an independent digital art style. It is widely used on the internet, for graphic user interface (GUI) design, and game industries. Existing pixelation tools and algorithms generate pixel images with artifacts, color clutter, blurring, and a lack of aesthetics. Generally, aesthetics are the dominant concern for pixel art. In this paper, an art-oriented pixelation (AOP) algorithm is proposed to effectively retain the main features of the original image content and the integrity of essential details with the artistic and aesthetic styles. At the same time, the AOP algorithm enables high-quality pixel image generation of arbitrary size without paired datasets and model training effort. The experimental results demonstrate that the pixel image generated by the AOP algorithm outperforms existing algorithms and tools in terms of aesthetics.

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References

  1. Qian et al.: Pixel Design-Designers Talk about Pixel Art Creation. Electronic Industry Press (2004)

  2. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5h Berkeley symposium on mathemati-cal statistics and probability, pp. 281–297 (1967)

  3. Kazuya Saito: PixelMe: Convert your photo into pixelart. https://pixel-me.tokyo/en/ (2020). Accessed 10 Jan 2022

  4. RonenNess: Pixelator. http://pixelatorapp.com/index.html (2021). Accessed 10 Jan 2022

  5. Kabka007: Photoshop quickly converts illustrations to pixel-style. https://www.bilibili.com/video/BV1Tx411q7Wb?share_source=copy_web (2017). Accessed 10 Jan 2022

  6. Allebach, J., Wong, P.W.: Edge-directed interpolation. In: Proceedings of 3rd IEEE International Conference on Image Processing, vol. 3. IEEE (1996)

  7. Carlson, R.E., Fritsch, F.N.: Monotone piecewise bicubic interpolation. SIAM J. Numer. Anal. 22(2), 386–400 (1985)

    Article  MathSciNet  Google Scholar 

  8. Shang, Y., Wong, H.-C.: Automatic portrait image pixelization. Comput. Graph. 95, 47–59 (2021)

    Article  Google Scholar 

  9. Zhu, J.-Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

  10. Yi, Z., et al.: Dualgan: unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

  11. Kim, T., et al.: Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017)

  12. Han, C., et al.: Deep unsupervised pixelization. ACM Trans. Graph. (TOG) 37(6), 1–11 (2018)

    Article  Google Scholar 

  13. Mao, X.: Introduction to OpenCV3 Programming, pp. 248–249. Electronic Industry Press (2015)

  14. One pixel: {Pixel painting teaching}- Modeling 03- manual anti-aliasing. https://www.bilibili.com/video/BV1CE411B7bD?share_source=copy_web (2019). Accessed 10 Jan 2022

  15. Pedro Medeiros: Start drawing pixel paintings. https://indienova.com/indie-game-development/saint11-making-pixel-art-1/ (2020). Accessed 10 Jan 2022

  16. Kopf, J., Shamir, A., Peers, P.: Content-adaptive image downscaling. ACM Trans. Graph. 32(6), 173:1-173:8 (2013)

    Article  Google Scholar 

  17. Öztireli, A.C., Gross, M.: Perceptually based downscaling of images. ACM Trans. Graph 34(4), 1–10 (2015)

    Article  Google Scholar 

  18. Gerstner, T., DeCarlo, D., Alexa, M., Finkelstein, A., Gingold, Y.I., Nealen, A.: Pixelated image abstraction. In: Expressive, pp. 29–36 (2012)

  19. Gerstner, T., DeCarlo, D., Alexa, M., Finkelstein, A., Gingold, Y., Nealen, A.: Pix-elated image abstraction with integrated user constraints. Comput. Graph. 37(5), 333–347 (2013)

    Article  Google Scholar 

  20. Kuang, H., Huang, N., Xu, S., Du, S.: A pixel image generation algorithm based on CycleGAN. In: 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 476-480 (2021). https://doi.org/10.1109/IMCEC51613.2021.9482118

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Acknowledgments

The work is supported by Beijing Dailybread Co., Ltd., and partly supported by the Soft Science Key Research Project of Zhejiang Province (No. 2022C25033).

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Peng Lei & Sanyuan Zhang

  2. School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China

    Shuchang Xu

  3. Institute of Big data and Information Technology, Wenzhou University, Wenzhou, China

    Shuchang Xu

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  1. Peng Lei
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  2. Shuchang Xu
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  3. Sanyuan Zhang
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Correspondence to Shuchang Xu.

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Lei, P., Xu, S. & Zhang, S. An art-oriented pixelation method for cartoon images. Vis Comput 40, 27–39 (2024). https://doi.org/10.1007/s00371-022-02763-0

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  • DOI: https://doi.org/10.1007/s00371-022-02763-0

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