Comparative Robustness Evaluation of Code 128 and QR Codes under Controlled Degradation
DOI:
https://doi.org/10.59061/repit.v4i2.1548Keywords:
Barcode Robustness, Code 128, Image Degradation, QR Code, TraceabilityAbstract
Barcode robustness is determined jointly by symbol configuration, image quality, degradation geometry, and decoder behavior. This study presents a reproducible matched computational benchmark comparing Code 128 and QR Code under Gaussian blur, synthetic low-light noise, glare, perspective and rotational distortion, and simulated physical damage. Twelve deterministic alphanumeric payloads were encoded in both formats and evaluated at five ordered severity levels with three generated replicates per cell, producing 1,800 decoding trials or 900 matched pairs using the same ZBar engine. QR Code achieved a higher overall correct-decoding rate than Code 128 (72.3% vs 68.8%; paired risk difference 3.56 percentage points, 95% CI 0.32 to 6.79; McNemar p=0.037). The difference increased under predefined severe conditions, while performance remained degradation-specific: QR Code was more tolerant of blur, low-light noise, and moderate geometric distortion, whereas Code 128 performed better under the tested glare and distributed-damage models. Two Code 128 misdecodes under extreme distortion illustrate the importance of treating false decoding separately from no-read outcomes. The principal contribution is a severity-dependent benchmark framework rather than a new decoding algorithm or a claim of universal symbology superiority. The findings apply to the tested software-generated configurations and require validation with printed labels, measured lighting, and physical scanners before operational deployment.
References
Chen, R., Yu, Y., Xu, X., Wang, L., Zhao, H., & Tan, H.-Z. (2021a). Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing. IEEE Sensors Journal, 21, 18222–18236. https://doi.org/10.1109/JSEN.2021.3085568
Chen, R., Yu, Y., Xu, X., Wang, L., Zhao, H., & Tan, H.-Z. (2021b). Fast blind deblurring of QR code images based on adaptive scale control. Mobile Networks and Applications, 26, 2472–2487. https://doi.org/10.1007/s11036-021-01780-y
Dong, H., Liu, H., Li, M., Ren, F., & Xie, F. (2024). An algorithm for the recognition of motion-blurred QR codes based on generative adversarial networks and attention mechanisms. International Journal of Computational Intelligence Systems, 17, Article 450. https://doi.org/10.1007/s44196-024-00450-7
GS1 US. (n.d.). What is GS1 Sunrise 2027? Retrieved August 20, 2026, from https://www.gs1us.org/industries-and-insights/by-topic/sunrise-2027
GS1. (n.d.). 2D barcodes at retail point-of-sale implementation guidance. Retrieved August 20, 2026, from https://ref.gs1.org/guidelines/2d-in-retail/
Gu, W., Sun, K., Jiang, Z., & Sun, L. (2024). Gs-DeblurGANv2: A QR code deblurring algorithm based on lightweight network structure. Multimedia Systems, 30, 87. https://doi.org/10.1007/s00530-024-01292-1
Ishikawa, Y., Okazaki, S., & Ohta, M. (2023). A method for removing shadows and white-outs on QR code images by deep learning. In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) (pp. 126–127). https://doi.org/10.1109/GCCE59613.2023.10315668
ISO/IEC. (2007). ISO/IEC 15417:2007 Information technology Automatic identification and data capture techniques Code 128 bar code symbology specification. International Organization for Standardization.
ISO/IEC. (2024a). ISO/IEC 15415:2024 Information technology Automatic identification and data capture techniques Bar code symbol print quality test specification Two-dimensional symbols. International Organization for Standardization.
ISO/IEC. (2024b). ISO/IEC 18004:2024 Information technology Automatic identification and data capture techniques QR code bar code symbology specification. International Organization for Standardization.
ISO/IEC. (2025). ISO/IEC 15416:2025 Automatic identification and data capture techniques Bar code symbol print quality test specification Linear symbols. International Organization for Standardization.
Latha, Y. M., & Rao, B. S. (2023). Advanced denoising model for QR code images using Hough transformation and convolutional neural networks. Traitement du Signal, 40, 1243–1249. https://doi.org/10.18280/ts.400342
Maulana, M. S., Pratiwi, D., & Prayogi, A. (2026a). MolecuTrace: Development of a web-based molecular oncology dashboard for integrating sample metadata, SNV profiles, and cancer stage classification. Journal of Intelligent Systems and Information Technology, 3(2).
Maulana, M. S., Pratiwi, D., & Prayogi, A. (2026b). Vaccination programs and infectious disease burden among military personnel: A systematic review with pathogen-specific quantitative synthesis. The ASEAN Journal of Military and Preventive Medicine, 3(2).
Muallim, T., Kucuk, H., Bareket, M., & Kahraman, M. (2025). Lightweight deep learning model and novel dataset for restoring damaged barcodes and QR codes in logistics applications. Computer Modeling in Engineering & Sciences, 143(3), 3557–3581. https://doi.org/10.32604/cmes.2025.064733
Rioux, G., Scarvelis, C., Choksi, R., Hoheisel, T., & Marechal, P. (2019). Blind deblurring of barcodes via Kullback-Leibler divergence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 77–88. https://doi.org/10.1109/TPAMI.2019.2927311
Sancar, Y. (2025). Reconstructing unreadable QR codes: A deep learning based super resolution strategy. PeerJ Computer Science, 11, e2841. https://doi.org/10.7717/peerj-cs.2841
Xie, S., & Tan, H.-Z. (2021). Blur-readable two-dimensional barcode based on blur-invariant shape and geometric features. International Journal of Advanced Robotic Systems, 18. https://doi.org/10.1177/1729881421999589
Xu, B., Jin, R., Li, J., Zhang, B., & Liu, K. (2024). Robust and fast QR code images deblurring via local maximum and minimum intensity prior. The Visual Computer, 40, 8809–8823. https://doi.org/10.1007/s00371-024-03272-y
Yi, J., & Chen, J. (2024). Enhancement of two-dimensional barcode restoration based on recurrent feature reasoning and structural fusion attention mechanism. Electronics, 13, 1873. https://doi.org/10.3390/electronics13101873
Zakaria, M. R. (2025). A comparative study on QR code and bar code detection techniques in modern systems. International Journal of Synergy in Engineering and Technology, 6(1), 14–22.
Zhang, H., Zhao, C., Jiang, S., Zhang, J., Zhang, J., & Chen, H. (2026). A lightweight causal Mamba network for blurred QR code image restoration. Scientific Reports, 16, 18932. https://doi.org/10.1038/s41598-026-49128-4
Zheng, H., Guo, Z., Liu, C., Li, X., Wang, T., & You, C. (2023). Blind deblurring of QR code using intensity and gradient prior of positioning patterns. The Visual Computer, 40, 441–455. https://doi.org/10.1007/s00371-023-02792-3
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