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研究生: 林育峯
Lin, Yu-Feng
論文名稱: 圖像化QR Code輸出條件與辨識率分析
Recognition of Printed Graphic QR Codes at different Output Conditions
指導教授: 王希俊
Wang, Hsi-Chun
學位類別: 碩士
Master
系所名稱: 圖文傳播學系
Department of Graphic Arts and Communications
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 49
中文關鍵詞: 圖像化QR CodeCodeword雷射印表機數位印刷機
英文關鍵詞: Graphic QR Code, Codeword, Laser printer, Digital printer
DOI URL: http://doi.org/10.6345/THE.NTNU.DGAC.012.2019.F05
論文種類: 學術論文
相關次數: 點閱:135下載:0
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  • 科技發展伴隨著行動裝置與網路的普及,二維條碼中的QR Code(Quick Response Code)已成為串聯網路與實體世界的主要媒介,如今QR Code被廣泛地應用於行銷或廣告用途,所以美化發展也越來越受重視,其中圖像化QR Code因具備視覺有意義的外觀,漸漸成為相關研究的重點。QR Code常出現於物聯網「一物一碼」的應用,然而若圖像化QR Code要配合一物一碼之實體輸出,則輸出條件能否使得圖像化QR Code被手機掃描,將會是重要關鍵,但鮮少研究針對此問題進行探討,故此為本研究之目的。本研究使用版本六之QR Code(41x41個模組, module),容錯率為H等級(容許30%錯誤)。首先選擇4張不同的影像產生圖像化QR Code,再使用雷射印表機與數位印刷機各輸出3個不同尺寸,得24張影像。輸出品透過600PPI掃描器取像,透過軟體分析24張圖像化QR Code影像中的module以及codeword之錯誤數據。為了降低錯誤以提升辨識率,進行5種不同數值的階調處理,以及調整8個不同參數之加密強度來得到圖像化QR Code,最後再測試印刷之穩定度。經實驗結果顯示,使用數位印刷機輸出的圖像化QR Code辨識效果優於雷射印表機,另一方面發現多數情況下資訊點錯誤情況之False-black(資訊點白點誤判黑點)多於False-white(資訊點黑點誤判白點)。經過階調調整之後,兩種錯誤情況皆得到改善,而加密強度的提升更能有效降低錯誤率。最後,本實驗找出圖像化QR Code錯誤情況,並透過調整圖像化QR Code使其能被手機掃描。本研究找出圖像化QR Code使用雷射印表機與數位印刷機之下辨識率的差異,並且透過調整圖像化QR Code之後使辨識率提升,可供QR Code與印刷相關研究作為參考。

    With the popularity of mobile devices and network, QR Code (Quick Response Code) have flourished and have been widely used as the main medium which can connected the virtual as well as the physical world. Today, QR Code is widely used for marketing or advertising purposes, so the development of beautification is also receiving attention. Among the developments, the Graphic QR Code has gradually become important because of its visual appearance. Also, QR Code often used in the application of the IoT (Interment of Things). However, the printing conditions could affect the Graphic QR Code’s recognition, which can caused some inconveniences in practical uses, but much less research had discussed on this issue. Therefore, this research focused on those output condition problem. In this research, the 6th version QR Code with H level (which with 30% fault tolerance) was adopted. First, there were four images had been formed to 4 Graphic QR Codes. Second, they were printed out with the laser printer and the digital printer with three sizes, 24 graphic QR Codes in total. Next, all Graphic QR Codes were scanned by 600 PPI (Pixels Per Inch) and then tested the module and codeword error rate. Finally, the tone-adjustment and different encode-strength were used to eliminate those error rate. According to the result, the recognition of printed Graphic QR Code by the digital printer was superior to the laser printer. In the other hand, through the adjustment of tone-adjustment and encode-strength, the recognition rate could enhanced. This research can be provided as the reference of QR Code and printing related studies.

    摘要 i Abstract ii 目錄 iii 表目錄 v 圖目錄 vi 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 研究問題 2 第四節 研究範圍與限制 2 第五節 研究流程 3 第六節 名詞解釋 4 第二章 文獻探討 5 第一節 二維條碼 5 第二節 圖像化QR Code 10 第三節 印刷與輸出 11 第四節 文獻探討小結 13 第三章 研究方法 14 第一節 研究架構 14 第二節 研究工具 15 第三節 研究方法 15 第四章 實驗結果與討論 22 第一節 雷射印表機輸出之結果 22 第二節 數位印刷機輸出之結果 25 第三節 QR Code資訊點錯誤情況 27 第四節 QR Code錯誤位置與原始影像對比 30 第五節 階調調整測試結果 33 第六節 穩定度測試結果 41 第五章 結論與建議 47 參考文獻 48 一、中文文獻 48 二、英文文獻 48 三、網路文獻 49

    一、中文文獻
    王育梅(2018)。以紅外線浮水印技術於圖像化二維條碼中隱藏訊息之研究(未出版之碩士論文)。國立臺灣師範大學,台北市。

    二、英文文獻
    Chu, H.K., Chang, C.S., Lee, R.R., & Mitra, N.J. (2013). Halftone QR Codes. ACM Transactions on Graphics, 32(6), 217:1-8.
    Espejel-Trujillo, A., Castillo-Camacho, I. & Nakano-Miyatake, M. (2012). Identity document authentication based on VSS and QR Codes. Procedia Technology 3, 241-250.
    EUIPO (2018). EUR 60 billion lost every year across the EU due to counterfeiting in 13 key economic sectors. European Union Intellectual Property Office.
    Gaubatz, M.D. & Simske, S.J. (2009). Printer-scanner identification via analysis of structured security deterrents. IEEE International Workshop on Information Forensics and Security, London, UK.
    Kim D.G. & Lee, H.K. (2015). Color laser printer identification using halftone texture fingerprint. Electronic Letters, 51(13), 918-983.
    Kim, D.G., Hou, J. U. & Lee, H. K. (2017). Learning deep features for source color laser printer identification based on cascaded learning. Signal Processing: Image Communication, arXiv preprint:1711.00207.
    Lin, L., Wu, S., Liu, S. & Jiang, B. (2017). Interactive QR Code beautification with full background image embedding. Second International Workshop on Pattern Recognition, 10443, 1044317. DOI: 10.1117/12.2280282
    Lin, S.S., Hu, M.C., Lee, C.H., & Lee, T.Y. (2015). Efficient QR Code beautification with high quality visual content. IEEE Transactions on Multimedia, 17(9), 1515-1524.
    Lin, Y. H., Chang, Y. P. & Wu, J.L. (2013). Appearance-based QR Code beautifier. IEEE Transactions on Multimedia, 15(8), 2198-2207.
    Richter, T., Escher, S., Schönfeld, D. & Strufe, T. (2018). Forensic Analysis and Anonymisation of Printed Documents. ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec’18), Innsbruck, Austria.

    三、網路文獻
    Visualead company, Herzelia, Israel. (2013). Free Visual QR Code Generator [Online]. Available: http://www.visualead.com/

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