The Visual Computer

, Volume 31, Issue 6–8, pp 1123–1133 | Cite as

Structure-aware QR Code abstraction

  • Siyuan Qiao
  • Xiaoxin Fang
  • Bin Sheng
  • Wen Wu
  • Enhua Wu
Original Article

Abstract

Quick Response Codes (QR Codes) have proved to be a great success in both automotive industry and general commercial use. However, in most cases, QR Codes are not identified as aesthetical, since the original motivation to invent QR Codes is to achieve high speed and high readability in scanning. This paper proposes a solution framework for QR Code abstraction, which produces machine readable QR Code that is visually similar to the input image. Unlike the existing schemes for QR Code abstraction, which produces QR Codes of low resolution, the framework aims to resolve the high computation complexity of producing halftone QR Codes of high resolution with both similarity and readability preserved. The solution framework has been implemented and tested on various input texts and images, and a user study was conducted to evaluate its performance in preserving similarity. The experimental results show that the framework can produce QR Codes of high resolution and high similarity without compromising readability.

Keywords

QR Code abstraction Structure-aware halftoning Parallel computing 

References

  1. 1.
    Analoui, M., Allebach, J.P.: Model-based halftoning using direct binary search. Proc. SPIE 1666, 96–108 (1992)Google Scholar
  2. 2.
    Bayer, B.: An optimum method for two-level rendition of continuous-tone pictures. In: IEEE International Conference on Communications, pp. 11–15. IEEE (1973)Google Scholar
  3. 3.
    Chang, J., Alain, B., Ostromoukhov, V.: Structure-aware error diffusion. ACM Trans. Graph. 28(5), 162:1–162:8 (2009)CrossRefGoogle Scholar
  4. 4.
    Chu, H.K., Chang, C.S., Lee, R.R., Mitra, N.J.: Halftone qr codes. ACM Trans. Graph. 32(6), 217:1–217:8 (2013)CrossRefGoogle Scholar
  5. 5.
    Cox, R.: Qart codes. http://research.swtch.com/qart. Accessed 08 Feb 2015
  6. 6.
    Eschbach, R., Knox, K.T.: Error-diffusion algorithm with edge enhancement. J. Optic. Soc. Am. A Optic. Image Sci. Vis. 8(12), 1844–1850 (1991)CrossRefGoogle Scholar
  7. 7.
    Floyd, R., Steinberg, L.: An adaptive algorithm for spatial grey scale. In: SID International Symposium Digest of Technical Papers, pp. 36–37. Society for Information Display (1974)Google Scholar
  8. 8.
    Garateguy, G., Arce, G., Lau, D., Villarreal, O.: Qr images: optimized image embedding in qr codes. IEEE Trans. Image Process. 23(7), 2842–2853 (2014)Google Scholar
  9. 9.
    Geist, R., Reynolds, R., Suggs, D.: A Markovian framework for digital halftoning. ACM Trans. Graph. 12(2), 136–159 (1993)MATHCrossRefGoogle Scholar
  10. 10.
    Hara, M., Watabe, M., Nojiri, T., Nagaya, T., Uchiyama, Y.: Optically readable two-dimensional code and method and apparatus using the same. http://www.google.com/patents/US5726435 (1998). US Patent 5,726,435
  11. 11.
    Hwang, B.W., Kang, T.H., Lee, T.S.: Improved edge enhanced error diffusion based on first-order gradient shaping filter. In: Orchard, B., Yang, C., Ali, M. (eds.) Innovations in Applied Artificial Intelligence. Lecture Notes in Computer Science, vol. 3029, pp. 473–482. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Knuth, D.E.: Digital halftones by dot diffusion. ACM Trans. Graph. 6(4), 245–273 (1987)MATHCrossRefGoogle Scholar
  13. 13.
    Kwak, N.J., Ryu, S.P., Ahn, J.H.: Edge-enhanced error diffusion halftoning using human visual properties. In: International Conference on Hybrid Information Technology, vol. 1, pp. 499–504 (2006)Google Scholar
  14. 14.
    Lee, C., Allebach, J.: The hybrid screen—improving the breed. IEEE Trans. Image Process. 19(2), 435–450 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lee, H.S., Kong, K.K., Hong, K.S.: Laplacian based structure-aware error diffusion. In: IEEE International Conference on Image Processing, pp. 525–528 (2010)Google Scholar
  16. 16.
    Li, H., Mould, D.: Contrast-aware halftoning. Eurographics 29(2), 273–280 (2010)Google Scholar
  17. 17.
    Neuhoff, D., Pappas, T., Seshadri, N.: One-dimensional least-squares model-based halftoning. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 189–192 (1992)Google Scholar
  18. 18.
    Ono, S., Morinaga, K., Nakayama, S.: Two-dimensional barcode decoration based on real-coded genetic algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1068–1073 (2008)Google Scholar
  19. 19.
    Ostromoukhov, V.: A simple and efficient error-diffusion algorithm. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01, pp. 567–572. ACM (2001)Google Scholar
  20. 20.
    Pang, W.M., Qu, Y., Wong, T.T., Cohen-Or, D., Heng, P.A.: Structure-aware halftoning. ACM Trans. Graph. (SIGGRAPH 2008 issue) 27(3), 89:1–89:8 (2008)Google Scholar
  21. 21.
    Pappas, T., Allebach, J., Neuhoff, D.: Model-based digital halftoning. IEEE Signal Process. Mag. 20(4), 14–27 (2003)CrossRefGoogle Scholar
  22. 22.
    Pelet, U.: Visualead. http://www.visualead.com/ (2012)
  23. 23.
    Samretwit, D., Wakahara, T.: Measurement of reading characteristics of multiplexed image in qr code. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 552–557. IEEE (2011)Google Scholar
  24. 24.
    A T COMMUNICATIONS: Logoqnet. http://logoq.net/logoq/index.html (2007)
  25. 25.
    Ulichney, R.: Dithering with blue noise. Proc. IEEE 76(1), 56–79 (1988)CrossRefGoogle Scholar
  26. 26.
    Wang, S., Cai, K., Lu, J., Liu, X., Wu, E.: Real-time coherent stylization for augmented reality. Vis. Comput. 26(6–8), 445–455 (2010)CrossRefGoogle Scholar
  27. 27.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  28. 28.
    Zang, Y., Huang, H., Li, C.F.: Artistic preprocessing for painterly rendering and image stylization. Vis. Comput. 30(9), 969–979 (2014)CrossRefGoogle Scholar
  29. 29.
    Zhou, B., Fang, X.: Improving mid-tone quality of variable-coefficient error diffusion using threshold modulation. ACM Trans. Graph. 22(3), 437–444 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Siyuan Qiao
    • 1
  • Xiaoxin Fang
    • 1
  • Bin Sheng
    • 1
    • 2
  • Wen Wu
    • 3
  • Enhua Wu
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.State Key Laboratory of Computer ScienceInstitute of Software, Chinese Academy of SciencesBeijingChina
  3. 3.Department of Computer and Information Science, Faculty of Science and TechnologyUniversity of MacauMacaoChina

Personalised recommendations