QRnet: fast learning-based QR code image embedding
Date
2022-02-16
Journal Title
Journal ISSN
Volume Title
Publisher
Multimedia Tools and Applications
Abstract
Quick Response (QR) codes usage in e-commerce is on the rise due to their versatility and ability to connect offline and online content, taking over almost every aspect of a business from posters to payments. Thus, many efforts have aimed at improving the visual quality of QR codes to be easily included in publicity designs in billboards and magazines. The most successful approaches, however, are slow since optimization algorithms are required for the generation of each beautified QR code, hindering its online customization. The aim of this paper is the fast generation of visually pleasant and robust QR codes. The proposed framework leverages state-of-the-art deep-learning algorithms to embed a color image into a baseline QR code in seconds while keeping a maximum probability of error during the decoding procedure. Halftoning techniques that exploit the human visual system (HVS) are used to smooth the embedding of the QR code structure in the final QR code image while reinforcing the decoding robustness. Compared to optimization-based methods, our framework provides similar qualitative results but is 3 orders of magnitude faster.
Description
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record first published in Multimedia Tools and Applications is available online at: https://doi.org/10.1007/s11042-022-12357-6. This article will be embargoed until 02/16/2023.
Keywords
QR codes, machine learning, optimization-free
Citation
Pena-Pena, K., Lau, D.L., Arce, A.J. et al. QRnet: fast learning-based QR code image embedding. Multimed Tools Appl 81, 10653–10672 (2022). https://doi.org/10.1007/s11042-022-12357-6