Auto ROI & mask R-CNN model for QR code beautification (ARM-QR)
The development of the Internet has enabled the QR code to become the most frequently applied two-dimensional barcode in daily life and in commercial advertisements, and its application continues to be more diversified to include warehouse management, electronic tickets, mobile payments, etc. The st...
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| Published in | Multimedia systems Vol. 29; no. 3; pp. 1245 - 1276 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0942-4962 1432-1882 |
| DOI | 10.1007/s00530-022-01046-x |
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| Abstract | The development of the Internet has enabled the QR code to become the most frequently applied two-dimensional barcode in daily life and in commercial advertisements, and its application continues to be more diversified to include warehouse management, electronic tickets, mobile payments, etc. The standard QR code consists of black and white modules, which display a monotonous visual effect. Since graph patterns are much easier to understand than text characters, showing the subject by patterns inside the QR code is the easiest way to understand implicit content.
This research involves the development of a methodology called ARM-QR, in which the QR code is integrated with full-color images, and deep learning technology is used to beautify it. First, the region of interest (ROI) of the color image is automatically identified using Mask R-CNN. The QR code’s visual beautification is further adjusted by the content of the object. Discrete wavelet transform and contrast sensitivity functions are also used to strengthen the visual perception of the QR code and reduce the impact of a low print resolution on the graphic legibility. The ARM-QR code’s visual quality is intensively verified by visual quality indices, which include the Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), Structural Similarity Index Metric (SSIM), and Gradient Magnitude Similarity Deviation (GMSD) based on evaluating the experimental data. The results of the experiment confirm that the visual beautification of the QR code generated in this research is of higher quality than that in other QR code beautification studies. |
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| AbstractList | The development of the Internet has enabled the QR code to become the most frequently applied two-dimensional barcode in daily life and in commercial advertisements, and its application continues to be more diversified to include warehouse management, electronic tickets, mobile payments, etc. The standard QR code consists of black and white modules, which display a monotonous visual effect. Since graph patterns are much easier to understand than text characters, showing the subject by patterns inside the QR code is the easiest way to understand implicit content.This research involves the development of a methodology called ARM-QR, in which the QR code is integrated with full-color images, and deep learning technology is used to beautify it. First, the region of interest (ROI) of the color image is automatically identified using Mask R-CNN. The QR code’s visual beautification is further adjusted by the content of the object. Discrete wavelet transform and contrast sensitivity functions are also used to strengthen the visual perception of the QR code and reduce the impact of a low print resolution on the graphic legibility. The ARM-QR code’s visual quality is intensively verified by visual quality indices, which include the Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), Structural Similarity Index Metric (SSIM), and Gradient Magnitude Similarity Deviation (GMSD) based on evaluating the experimental data. The results of the experiment confirm that the visual beautification of the QR code generated in this research is of higher quality than that in other QR code beautification studies. The development of the Internet has enabled the QR code to become the most frequently applied two-dimensional barcode in daily life and in commercial advertisements, and its application continues to be more diversified to include warehouse management, electronic tickets, mobile payments, etc. The standard QR code consists of black and white modules, which display a monotonous visual effect. Since graph patterns are much easier to understand than text characters, showing the subject by patterns inside the QR code is the easiest way to understand implicit content. This research involves the development of a methodology called ARM-QR, in which the QR code is integrated with full-color images, and deep learning technology is used to beautify it. First, the region of interest (ROI) of the color image is automatically identified using Mask R-CNN. The QR code’s visual beautification is further adjusted by the content of the object. Discrete wavelet transform and contrast sensitivity functions are also used to strengthen the visual perception of the QR code and reduce the impact of a low print resolution on the graphic legibility. The ARM-QR code’s visual quality is intensively verified by visual quality indices, which include the Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), Structural Similarity Index Metric (SSIM), and Gradient Magnitude Similarity Deviation (GMSD) based on evaluating the experimental data. The results of the experiment confirm that the visual beautification of the QR code generated in this research is of higher quality than that in other QR code beautification studies. |
| Author | Lin, Di-Ting Tsai, Min-Jen Wu, Hung-Yu |
| Author_xml | – sequence: 1 givenname: Min-Jen surname: Tsai fullname: Tsai, Min-Jen email: mjtsai@cc.nctu.edu.tw organization: Institute of Information Management, National Yang Ming Chiao Tung University – sequence: 2 givenname: Hung-Yu surname: Wu fullname: Wu, Hung-Yu organization: Institute of Information Management, National Yang Ming Chiao Tung University – sequence: 3 givenname: Di-Ting surname: Lin fullname: Lin, Di-Ting organization: Institute of Information Management, National Yang Ming Chiao Tung University |
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| Cites_doi | 10.1109/MMUL.2006.23 10.1016/j.jvcir.2009.03.011 10.1109/TIP.2014.2321501 10.1109/TIT.1974.1055250 10.1109/TIP.2011.2109730 10.1007/s11042-019-7308-y 10.1145/2508363.2508408 10.1109/DSPWS.2002.1231082 10.1109/ICCV.2017.322 10.3390/sym10110543 10.1109/TIP.2013.2293423 10.1145/1618452.1618508 10.1109/CVPR.2016.91 10.1109/CVPR.2017.106 10.1145/3355056.3364574 10.1016/j.patrec.2008.04.005 10.1109/TMM.2015.2437711 10.1117/12.2280282 10.1111/cgf.12221 10.1109/83605413 10.1109/TIP.2003.819861 10.1117/12.2502054 10.1109/CVPR42600.2020.00860 10.1016/j.dsp.2022.103887 10.1109/CEC.2008.4630929 10.1007/s00371-015-1107-x 10.1109/TMM.2019.2891420 10.1007/978-3-030-01234-2_49 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| References | BrostowGJFauqueurJCipollaRSemantic object classes in video: a high-definition ground truth databasePattern Recogn. Lett.2009302889710.1016/j.patrec.2008.04.005 LinYSLuoSJChenBYArtistic QR code embellishmentComputer. Graph. Forum201332713714610.1111/cgf.12221 Kyprianidis J.E., and Döllner, J. “Image abstraction by structure adaptive filtering,” in Proc. EG UKTheory and Practice of Computer Graphics, pp 51–58, 2008. Chu, H.K., Chang, C.S., Lee, R.R. et al., “Halftone QR codes,” in Proc. ACM Trans Graph (TOG) 32(6): no. 217. ACM SIGGRAPH ASIA 2013, https://doi.org/10.1145/2508363.2508408 QiaoSFangXShengBStructure-aware QR code abstractionVis Comp201510.1007/s00371-015-1107-x USC SIPI–The USC-SIPI image database [Online]. Available: http://sipi.usc.edu/services/database/Database.html (accessed 3 Jan, 2021) GarateguyGJArceGRLauDLQR images: optimized image embedding in QR codesIEEE Transact. Image Process2014322646510.1109/TIP.2014.23215011374.94117 LevickyDForisPHuman Visual System Models in Digital Image WatermarkingRadioengineering20041343843 Chen, L.C., Zhu, Y., Papandreou, G. et al., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” in Conf. Computer Vision – ECCV 2018, pp. 833–851. LiLWangBLuJZhangSA new aesthetic QR code algorithm based on salient region detection and SPBVMJ. Int Technol2019203935946 He, K., Gkioxari, G., Dollár, P., et al. “Mask R-CNN,” in Conf. IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969, Mar 2017. Available: https://doi.org/10.1109/ICCV.2017.322 WatsonABYangGYSolomonJAVisibility of wavelet quantization noiseIEEE Trans Image Proc.199710.1109/83605413 MannosJSakrisonDThe effects of a visual fidelity criterion on the encoding of imagesIEEE Trans Inf Theorem197410.1109/TIT.1974.10552500295.94044 Shelhamer, E., Long, J. and Darrell, T. “Fully Convolutional Networks for Semantic Segmentation,” in Conf. IEEE Transactions on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, June 2015. Visualead Company, “Visual QR code” [Online]. Available online: http://www.visualead.com/ (accessed on 16 November 2022). LinSSHuMCLeeCHEfficient QR code beautification with high quality visual contentIEEE Transact Multimed201510.1109/TMM.2015.2437711 TsaiMJHsiehCYThe visual color QR code algorithm (DWT-QR) based on wavelet transform and human vision systemMultimed Tools App201910.1007/s11042-019-7308-y XuMSuHLiYStylized aesthetic QR codeIEEE Trans. Multimed.20182181960197010.1109/TMM.2019.2891420 XueWZhangLMouXGradient magnitude similarity deviation: a highly efficient perceptual image quality indexIEEE Trans. Image Process.2014232684695315953210.1109/TIP.2013.22934231374.94418 LiLLiYWangB“A new aesthetic QR code algorithm based on salient region detection and SPBVM”, in Conf2017Springer, ChamSecurity with Intelligent Computing and Big-data Services2032 Chen, H., Sun, K., Tian, Z., Shen, C. et al., “BlendMask: Top-down meets bottom-up for instance segmentation,” in Conf. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 8573–8581, Jan 2020. Lin, L., Zou, X., He, L. et al. “Aesthetic QR code generation with background contrast enhancement and user interaction,” in Conf. Third International Workshop on Pattern Recognition, July 2018. 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IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117–2125, Dec 2016. Available: https://doi.org/10.1109/CVPR.2017.106 Lin, L., Wu, S., Liu, S., et al., “Interactive QR code beautification with full background image embedding,” in Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044317, June 2017. Available: https://doi.org/10.1117/12.2280282 Lin, S.S., Chang, Y.F., Le, T.N.H. et al. Generation of Photorealistic QR codes,” in Conf. SIGGRAPH Asia 2019 Posters, Nov 2019. ZhangLZhangLMouXFSIM: a feature similarity index for image quality assessmentIEEE Trans Image Proc.200110.1109/TIP.2011.21097301373.62333 Beegan, A.P., Iyer, L.R., Bell, A.E., et al. “Design and Evaluation of Perceptual Masks for Wavelet Image Compression,” in Proc. 10th IEEE Digital Signal Processing Workshop, IEEE CS Press, pp. 88–93, Oct 2002. Available: https://doi.org/10.1109/DSPWS.2002.1231082 Chang, J., Alain B., and Ostromoukhov, V. “Structure-aware error diffusion,” in Proc. ACM Trans Graph (TOG) 28(5): no. 162:1–162:8, Dec 2009. RathiJGrewalSKAesthetic QR: approaches for beautified, fast decoding, and secured QR codes”IJ Inform. Eng. Elect. Bus.202231018 Falcon, A. (2017) 40 Gorgeous QR code Artworks That Rock [Online]. Available: http://www.hongkiat.com/blog/qr-code-artworks/ (accessed on 16 November 2022) TsaiMJPengSLQR code beautification by instance segmentation (IS-QR)Dig Signal Process202310.1016/j.dsp.2022.103887 TsaiMJA visible watermarking algorithm based on the content and contrast aware (COCOA) techniqueJ. Visual Commun. Image Represent.200910.1016/j.jvcir.2009.03.011 LuJChengWZhangSQA novel aesthetic QR code algorithm based on hybrid basis vector matricesSymmetry201810.3390/sym10110543 W Xue (1046_CR38) 2014; 23 1046_CR28 J Redmon (1046_CR26) 2015 1046_CR27 YS Lin (1046_CR19) 2013; 32 MJ Tsai (1046_CR31) 2023 J Mannos (1046_CR22) 1974 MJ Tsai (1046_CR29) 2009 GJ Brostow (1046_CR2) 2009; 30 L Li (1046_CR13) 2017 1046_CR23 M Xu (1046_CR37) 2018; 21 D Levicky (1046_CR12) 2004; 13 Z Wang (1046_CR35) 2004 AB Watson (1046_CR36) 1997 1046_CR1 1046_CR18 1046_CR4 1046_CR3 BB Huang (1046_CR10) 2006 1046_CR15 J Lu (1046_CR21) 2018 1046_CR17 L Zhang (1046_CR39) 2001 1046_CR16 1046_CR9 S Qiao (1046_CR24) 2015 1046_CR6 1046_CR5 GJ Garateguy (1046_CR8) 2014 L Li (1046_CR14) 2019; 20 J Rathi (1046_CR25) 2022; 3 1046_CR7 MJ Tsai (1046_CR30) 2019 1046_CR11 1046_CR32 1046_CR34 SS Lin (1046_CR20) 2015 P Viola (1046_CR33) 2001; 1 |
| References_xml | – reference: Chen, L.C., Zhu, Y., Papandreou, G. et al., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” in Conf. Computer Vision – ECCV 2018, pp. 833–851. – reference: QiaoSFangXShengBStructure-aware QR code abstractionVis Comp201510.1007/s00371-015-1107-x – reference: LevickyDForisPHuman Visual System Models in Digital Image WatermarkingRadioengineering20041343843 – reference: LiLWangBLuJZhangSA new aesthetic QR code algorithm based on salient region detection and SPBVMJ. Int Technol2019203935946 – reference: XuMSuHLiYStylized aesthetic QR codeIEEE Trans. Multimed.20182181960197010.1109/TMM.2019.2891420 – reference: Chang, J., Alain B., and Ostromoukhov, V. “Structure-aware error diffusion,” in Proc. ACM Trans Graph (TOG) 28(5): no. 162:1–162:8, Dec 2009. – reference: Chu, H.K., Chang, C.S., Lee, R.R. et al., “Halftone QR codes,” in Proc. ACM Trans Graph (TOG) 32(6): no. 217. ACM SIGGRAPH ASIA 2013, https://doi.org/10.1145/2508363.2508408 – reference: XueWZhangLMouXGradient magnitude similarity deviation: a highly efficient perceptual image quality indexIEEE Trans. Image Process.2014232684695315953210.1109/TIP.2013.22934231374.94418 – reference: TsaiMJPengSLQR code beautification by instance segmentation (IS-QR)Dig Signal Process202310.1016/j.dsp.2022.103887 – reference: ZhangLZhangLMouXFSIM: a feature similarity index for image quality assessmentIEEE Trans Image Proc.200110.1109/TIP.2011.21097301373.62333 – reference: Falcon, A. (2017) 40 Gorgeous QR code Artworks That Rock [Online]. Available: http://www.hongkiat.com/blog/qr-code-artworks/ (accessed on 16 November 2022) – reference: TsaiMJA visible watermarking algorithm based on the content and contrast aware (COCOA) techniqueJ. Visual Commun. Image Represent.200910.1016/j.jvcir.2009.03.011 – reference: Visualead Company, “Visual QR code” [Online]. Available online: http://www.visualead.com/ (accessed on 16 November 2022). – reference: LinYSLuoSJChenBYArtistic QR code embellishmentComputer. Graph. Forum201332713714610.1111/cgf.12221 – reference: LuJChengWZhangSQA novel aesthetic QR code algorithm based on hybrid basis vector matricesSymmetry201810.3390/sym10110543 – reference: GarateguyGJArceGRLauDLQR images: optimized image embedding in QR codesIEEE Transact. Image Process2014322646510.1109/TIP.2014.23215011374.94117 – reference: Kyprianidis J.E., and Döllner, J. “Image abstraction by structure adaptive filtering,” in Proc. EG UKTheory and Practice of Computer Graphics, pp 51–58, 2008. – reference: Russ Cox's method, (2012, April 12) QArt Codes [Online]. Available: https://research.swtch.com/qart – reference: LinSSHuMCLeeCHEfficient QR code beautification with high quality visual contentIEEE Transact Multimed201510.1109/TMM.2015.2437711 – reference: He, K., Gkioxari, G., Dollár, P., et al. “Mask R-CNN,” in Conf. IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969, Mar 2017. Available: https://doi.org/10.1109/ICCV.2017.322 – reference: WangZBovikACSheikhHRImage quality assessment: from error visibility to structural similarityIEEE Trans Image Proc.200410.1109/TIP.2003.819861 – reference: LiLLiYWangB“A new aesthetic QR code algorithm based on salient region detection and SPBVM”, in Conf2017Springer, ChamSecurity with Intelligent Computing and Big-data Services2032 – reference: ViolaPJonesMJ“Rapid object detection using a boosted cascade of simple features”, in ConfIEEE Comp Soc Conf Comp Vis Patt Recogn20011511518 – reference: Lin, L., Zou, X., He, L. et al. “Aesthetic QR code generation with background contrast enhancement and user interaction,” in Conf. Third International Workshop on Pattern Recognition, July 2018. Available: https://doi.org/10.1117/12.2502054 – reference: TsaiMJHsiehCYThe visual color QR code algorithm (DWT-QR) based on wavelet transform and human vision systemMultimed Tools App201910.1007/s11042-019-7308-y – reference: Chen, H., Sun, K., Tian, Z., Shen, C. et al., “BlendMask: Top-down meets bottom-up for instance segmentation,” in Conf. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 8573–8581, Jan 2020. – reference: RedmonJDivvalaSGirshickR“You only look once: unified, real-time object detection”, in ConfIEEE Conf Comp Vis Patt Recogn.201510.1109/CVPR.2016.91 – reference: Lin, L., Wu, S., Liu, S., et al., “Interactive QR code beautification with full background image embedding,” in Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044317, June 2017. Available: https://doi.org/10.1117/12.2280282 – reference: USC SIPI–The USC-SIPI image database [Online]. 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| SubjectTerms | Color imagery Computer Communication Networks Computer Graphics Computer Science Cryptology Data Storage Representation Discrete Wavelet Transform Legibility Mobile commerce Multimedia Information Systems Operating Systems Regular Paper Signal to noise ratio Similarity Visual effects Visual perception Visual signals Warehousing management Wavelet transforms |
| Title | Auto ROI & mask R-CNN model for QR code beautification (ARM-QR) |
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