Skip to main content

Advertisement

SpringerLink
Log in

The visual color QR code algorithm (DWT-QR) based on wavelet transform and human vision system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A novel visual Quick Response (QR) code algorithm based on Wavelet transform and Human Visual System (HVS) approach is presented in this study and named DWT-QR. Unlike other QR codes are generally embedded in the spatial domain, the composite coefficients using global and local characteristics of the host image are considered in the discrete wavelet transform (DWT) domain for the visual QR codes. In order to get the best perceptual embedding capability of visual QR codes, the collaboration of the perceptual model of contrast-sensitive function (CSF) with the noise reduction of the visibility thresholds for HVS in DWT domain, achieves the goal of fine tuning of the perceptual weights according to the basis function amplitudes for the best quality of perceptual visibility. In addition, the computation of Noise Visibility Function (NVF) characterizes the local image properties to determine the optimal QR code strength during the QR codes embedding stage. After the detection pattern embedded for the visual QR codes, different distortion attacks have been performed for the proposed method. The experimental results demonstrate that the proposed DWT-QR approach outperforms the known techniques and not only improves the visual quality of the images but also the robustness against various attacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. A T COMMUNICATIONS CO., L. (2007) LogoQnet. [Online]: http://logoq.net/. Accessed 12 June 2017

  2. Armstrong P (2017) Apple just made QR codes a must have for your strategy. Forbes. [Online]: https://www.forbes.com/sites/paularmstrongtech/2017/09/22/apple-just-made-qr-codes-a-must-have-for-your-strategy/#43e0da7150dd. Accessed 27 Jan 2018

  3. Beegan AP, Iyer LR, Bell AE (2002) Design and evaluation of perceptual masks for wavelet image compression. In: Proceedings 10th IEEE digital signal processing workshop. IEEE CS Press, pp 88–93

  4. Bekkat N, Saadane A (2004) Coded image quality assessment based on a new contrast masking model. J Electronic Imaging 13:341–348

    Article  Google Scholar 

  5. Braudaway GW, Magerlein KA, Mintzer FC (1996) Protecting publicly available images with a visible image watermark. In: Proc. SPIE, Int. conf. Electronic imaging, vol 2659, pp 126–132

  6. Brooks AC, Zhao XN, Pappas TN (2008) Structural similarity quality metrics in a coding context: exploring the space of realistic distortions. IEEE Trans Image Process 17(8):1261–1273

    Article  MathSciNet  Google Scholar 

  7. Chang J, Alain B (2009) Structure-aware error diffusion. ACM Trans Graph (TOG) 28(5):162:1–162:8. Proceedings of ACM SIGGRAPH

  8. Chu H, Chang C, Lee R, Mitra N (2013) Halftone QR codes. ACM Trans Graph (TOG) 32(6):no. 217. Proceedings of ACM SIGGRAPH. http://doi.acm.org/10.1145/2508363.2508408

  9. Cox R QArt coder. Retrieved 8 May 2015. [Online]: http://research.swtch.com/qr/draw

  10. Duda J Embedding gray scale halftone pictures in QR codes using correction trees. [Online]: https://arxiv.org/abs/1211.1572. Accessed 8 Aug 2017

  11. Garateguy GJ, Arce GR, Lau DL, Villarreal OP (2014) QR images: optimized image embedding in QR codes. IEEE Multimedia 23(7):2842–2853

    MathSciNet  MATH  Google Scholar 

  12. Hu Y, Kwong S (2001) Wavelet domain adaptive visible watermarking. Electron Lett 37(20):1219–1220

    Article  Google Scholar 

  13. Huang BB, Tang SX (2006) A contrast-sensitive visible watermarking scheme. IEEE Multimedia 13(2):60–66

    Article  Google Scholar 

  14. Huang CH, Wu JL (2004) Attacking visible watermarking schemes. IEEE Trans Multimedia 6(1):16–30

    Article  Google Scholar 

  15. Kyprianidis JE et al (2008) Image abstraction by structure adaptive filtering. In: Proceedings EG UKTheory and Practice of Computer Graphics, pp 51–58

  16. Levický D, Foriš P (2004) Human visual system models in digital image watermarking. Radioengineering 13(4):38–43

    Google Scholar 

  17. Lin SS, Hu MC, Lee CH, Lee TY (2015) Efficient QR code beautification with high quality visual content. IEEE Multimedia 17(9):1515–1524

    Article  Google Scholar 

  18. Mannos JL, Sakrison DJ (1974) The effects of a visual fidelity criterion on the encoding of images. IEEE Trans Inf Theory 20(4):525–536

    Article  MATH  Google Scholar 

  19. Peled U (2012) Visualead. [Online]: http://www.visualead.com/. Accessed 20 June 2017

  20. QR code-PRO intuitive and creative. [Online]: http://en.qrcode-pro.com. Accessed 5 Sep 2017

  21. Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  22. Structure of a QR code. [Online]: https://en.wikipedia.org/wiki/QR_code. Accessed 8 Sep 2017

  23. Teo PC, Heeger DJ (1994) Perceptual image distortion. Proc SPIE 2179:27–141

    Google Scholar 

  24. Tong S, Koller D Support vector machine active learning with applications to text classification. J Mach Learn Res:45–66. http://www.jmlr.org/papers/volume2/tong01a/tong01a.pdf. Accessed Nov 2017

  25. Tsai M-J (2009) A visible watermarking algorithm based on the content and contrast aware (COCOA) technique. J Vis Commun Image Represent 20(5):323–338

    Article  MathSciNet  Google Scholar 

  26. Tsai M et al Deep learning for printed document source identification. https://doi.org/10.1016/j.image.2018.09.006. Accessed 16 Oct 2018

  27. USC SIPI–The USC-SIPI image database. [Online]: http://sipi.usc.edu/services/database/Database.html. Accessed 3 Mar 2017

  28. Villasenor JD, Belzer B, Liao J (1995) Wavelet filter evaluation for image compression. IEEE Trans Image Process 4(8):1053–1060

    Article  Google Scholar 

  29. Voloshynovskiy S et al (1999) A stochastic approach to content adaptive digital image watermarking. In: Proc. 3rd Int. workshop information hiding, Dresden, Germany, pp 211–236

  30. Wang Z, Simoncelli EP (2005) An adaptive linear system framework for image distortion analysis. In: IEEE International Conference on Image Processing, vol 3, pp 1160–1163

  31. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  32. Watson AB (1993) DCT quantization matrices visually optimized for individual images. Proc SPIE 1913:202–216

    Article  Google Scholar 

  33. Watson AB (1998) Toward a perceptual video quality metric. In: HVEI 1998 proceedings, pp 139–147

  34. Watson AB, Yang GY, Solomon JA, Villasenor J (1997) Visibility of wavelet quantization noise. EEE Trans Image Process 6(8):1164–1175

    Article  Google Scholar 

  35. Winter M (2011) Scan me: Everybody’s guide to the magical world of QR codes. Westsong Publishing

  36. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature SIMilarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Chiao Tung University, Institute of Information Management, 1001 Ta-Hsueh Road, Hsin-Chu, 300, Taiwan, Republic of China

    Min-Jen Tsai & Chin-Yu Hsieh

Authors
  1. Min-Jen Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chin-Yu Hsieh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min-Jen Tsai.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsai, MJ., Hsieh, CY. The visual color QR code algorithm (DWT-QR) based on wavelet transform and human vision system. Multimed Tools Appl 78, 21423–21454 (2019). https://doi.org/10.1007/s11042-019-7308-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7308-y

Keywords

Search

Navigation