A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions

Ismael Benito-Altamirano; David Martínez-Carpena; Olga Casals; Cristian Fàbrega; Andreas Waag; Joan Daniel Prades

doi:10.1016/j.dib.2022.108780

A dataset of color QR codes generated using back-compatible and random colorization algorithms exposed to different illumination-capture channel conditions

Data Brief. 2022 Nov 24:46:108780. doi: 10.1016/j.dib.2022.108780. eCollection 2023 Feb.

Authors

Ismael Benito-Altamirano^{1

2}, David Martínez-Carpena^{2

3}, Olga Casals¹, Cristian Fàbrega¹, Andreas Waag⁴, Joan Daniel Prades^{1

2}

Affiliations

¹ MIND/IN2UB, Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Carrer de Martí i Franquès, 1, Barcelona, 08028, Barcelona, Spain.
² ColorSensing SL, Carrer Morales, 21, 1L, Barcelona, 08029, Barcelona, Spain.
³ Department of Mathematics and Computer Science, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, Barcelona, 08007, Barcelona, Spain.
⁴ Institute for Semiconductor Technology, Braunschweig University of Technology, Universitätspl. 2, Braunschweig, 38106, Lower Saxony, Germany.

Abstract

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.

Keywords: Barcodes; Color calibration; Color correction; Colorchecker; Colorimetry; QR Codes.