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A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions
Color QR Codes are often generated to encode digital information, but one also could use colors or to allocate colors in a QR Code to act as a color calibration chart. In this dataset, we present several thousand QR Codes images generated with two different colorization algorithms (random and back-c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720510/ https://www.ncbi.nlm.nih.gov/pubmed/36478685 http://dx.doi.org/10.1016/j.dib.2022.108780 |
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author | Benito-Altamirano, Ismael Martínez-Carpena, David Casals, Olga Fàbrega, Cristian Waag, Andreas Prades, Joan Daniel |
author_facet | Benito-Altamirano, Ismael Martínez-Carpena, David Casals, Olga Fàbrega, Cristian Waag, Andreas Prades, Joan Daniel |
author_sort | Benito-Altamirano, Ismael |
collection | PubMed |
description | Color QR Codes are often generated to encode digital information, but one also could use colors or to allocate colors in a QR Code to act as a color calibration chart. In this dataset, we present several thousand QR Codes images generated with two different colorization algorithms (random and back-compatible) and several tuning variables in these color encoding. The QR Codes were also exposed to three different channel conditions (empty, augmentation and real-life). Also, we derive the SNR and BER computations for these QR Code in comparison with their black and white versions. Finally, we also show if ZBar, a commercial QR Code scanner, is able to read them. |
format | Online Article Text |
id | pubmed-9720510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97205102022-12-06 A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions Benito-Altamirano, Ismael Martínez-Carpena, David Casals, Olga Fàbrega, Cristian Waag, Andreas Prades, Joan Daniel Data Brief Data Article Color QR Codes are often generated to encode digital information, but one also could use colors or to allocate colors in a QR Code to act as a color calibration chart. In this dataset, we present several thousand QR Codes images generated with two different colorization algorithms (random and back-compatible) and several tuning variables in these color encoding. The QR Codes were also exposed to three different channel conditions (empty, augmentation and real-life). Also, we derive the SNR and BER computations for these QR Code in comparison with their black and white versions. Finally, we also show if ZBar, a commercial QR Code scanner, is able to read them. Elsevier 2022-11-24 /pmc/articles/PMC9720510/ /pubmed/36478685 http://dx.doi.org/10.1016/j.dib.2022.108780 Text en © 2022 Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Benito-Altamirano, Ismael Martínez-Carpena, David Casals, Olga Fàbrega, Cristian Waag, Andreas Prades, Joan Daniel A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title | A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title_full | A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title_fullStr | A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title_full_unstemmed | A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title_short | A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
title_sort | dataset of color qr codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720510/ https://www.ncbi.nlm.nih.gov/pubmed/36478685 http://dx.doi.org/10.1016/j.dib.2022.108780 |
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