DOI QR코드

DOI QR Code

Comparison of Recognition Performance of Color QR Codes for Inserted Pattern Information

칼라 QR코드의 패턴 종류에 따른 인식 성능 비교

  • 김진수 (국립한밭대학교 지능미디어공학과)
  • Received : 2022.04.27
  • Accepted : 2022.06.02
  • Published : 2022.06.30

Abstract

Currently, the black-white QR (Quick Response) codes have been used widely in consumer advertising fields and the study of color QR codes have received a growing demand because of much higher data encoding capacity. Color QR codes can be reproduced by the printing and scanning processes, however, these encounter colors distortion caused by insufficient lighting, low resolution of camera and geometric deformation during the capturing processes. In order to overcome these problems, this paper proposes an efficient decoding algorithm for color QR codes with inserted patterns, which are dealt with conventional studies. These are evaluated in view of the recognition rate under different noise conditions, for example, Gaussian noises/blurring and geometric deformation. Experimental results demonstrate that the color QR codes with simple pattern can resist the distortion of Gaussian noises/blurrings.

현재 광고 분야 등에 널리 사용되고 있는 흑백 QR코드의 정보 저장 용량을 증가시키기 위해 칼라 QR코드에 대한 연구가 활발히 진행되고 있다. 그러나 칼라 QR코드는 프린팅 또는 스캐닝 과정에 의해 복재가 될 수 있으며, 이 과정에서 불충분한 조도에 의한 색상 왜곡과 잡음, 카메라의 낮은 해상도와 기하학적 변형의 가능성이 있다. 이러한 일련의 복합적인 과정들은 품질 저하와 인식률 저하를 초래한다. 이러한 문제점을 극복하기 위해 본 논문에서는 칼라 QR코드에 패턴 삽입을 고려하고, 이를 위한 효과적인 인식 방법을 제안한다. 또한, 제안한 방법을 통해 기존에 다루어진 대표적인 패턴을 도입하고, 인식률 측면에서 실험을 수행하여 그 결과를 비교 분석한다. 즉, 인식과정에 있어서 쉽게 초래되는 가우시안 잡음과 블러링, 기하학적 변형 등의 잡음을 고려하여 성능을 비교 분석한다. 다양한 실험을 통해 가우시안 잡음과 블러 측면에서 단순한 패턴의 칼라 QR코드가 우수한 성능을 보이는 것을 확인할 수 있다.

Keywords

References

  1. Andre, P., and Ferreria, R. (2014). Colour Multiplexing of Quick-Response (QR) Codes, Electronics Letters (IET), 50(24), 1828-1830. https://doi.org/10.1049/el.2014.2501
  2. Choi, D., and Kim, J. (2018). A Code Authentication System of Counterfeit Printed Image Using Multiple Comparison Measures, Journal of the Korea Industrial Information Systems Research, 23(4), 1-12. https://doi.org/10.9723/JKSIIS.2018.23.4.001
  3. Choi, D., and Kim, J. (2020). An Authentic Certification System of a Printed Color QR Code based on Convolutional Neural Network, Journal of the Korea Industrial Information Systems Research, 25(3), 21-30. https://doi.org/10.9723/JKSIIS.2020.25.3.021
  4. EI-Sheref, I., Asad, A., and EI-Licy, F. (2018). Improving Storage Capacity of QR Code: A Survey, The 53rd Annual Conference on Statistics, Computer Science and Operation Research, 227-248.
  5. EI-Sheref, I., EI-Licy, F., and Asad, A. (2020). Improving Performance of the Multiplexed Colored QR Codes, International Journal of Advanced Computer Science and Applications, 11(3), 202-206.
  6. Escriva, D., Mendonca, V., and Joshi, P. (2016), Learn OpenCV 4 by Building Projects, U.K. Packt.
  7. Grillo, A., Lentini, A., Querini, M., and Italiano, G. (2010), High Capacity Colored Two Dimensional Codes, Proceedings of the International Multiconference on Computer Science and Information Technology, 709-716.
  8. Kaehler, A., and Bradski, G. (2021). Learning OpenCV, Computer Vision in C++ with the OpenCV Library, CA, O'Reilly.
  9. Kim, J. (2018). Performance Improvement of Distributed Compressive Video Sensing Using Reliability Estimation, Journal of the Korea Industrial Information Systems Research, 23(6), 47-58. https://doi.org/10.9723/JKSIIS.2018.23.6.047
  10. Kim, J. (2021). Improvement of Bit Recognition Rate for Color QR Codes By Multiplexing Color and Pattern Information, Journal of Korea Multimedia Society, Vol. 24, No. 8, 1012-1019. https://doi.org/10.9717/KMMS.2021.24.8.1012
  11. Liao, Z., Huang, T., Wang, R., and Zhou, X. (2010), A Method of Image Analysis for QR Code Recognition, 2010 International Conference on Intelligent Computing and Integrated Systems, 250-253.
  12. Melgar, M., Zaghetto, A., Macchiavello, B., and Nascimento, A. (2012). CQR Codes: Colored Quick-Response Codes, 2012 IEEE Second International Conference on Consumer Electronics(ICCE), Berlin, Germany.
  13. Melgar, M., Farias, M., Vidal, F., and Zaghetto, A. (2016), A High Density Colored 2D-Barcode: CQR Code-9, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, 329-334.
  14. Querini, M., and Italiano, G. (2014). Reliability and Data Density in High Capacity Color Barcodes, Computer Science and Information Systems, 11(4), 1595-1615. https://doi.org/10.2298/CSIS131218054Q
  15. Sabri, P., Abas, A., and Din, R. (2020). Enhancing Data Storage of Colored QR Code Using C3M Technique, European Journal of Molecular & Clinical Medicine, 07(08), 3805-3813.
  16. Tkachenko, I., Puech, W., Strauss, O., and Destruel, C. (2015) Rich QR Code for Multimedia Management Applications, International Conference on Image Analysis and Processing, 383-393.
  17. Tsai, M., and Hsieh, C. (2020). The Human Visual System Based Color QR Codes, 2020 Workshop on Computing, Networking and Communications (CNC), 46-50.
  18. Xu, M., Li, Q., Niu, J., Su, H., Liu, X., Xu, W., Lv, P., Zhou, B., and Yang, Y. (2021). Art-Up: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes, ACM Trans. Multimedia Comput. Commun. Appl., 17(1), 1-23