Reliability and Data Density in High Capacity Color Barcodes

Marco Querini1 and Giuseppe F. Italiano1

  1. University of Rome “Tor Vergata”
    via del Politecnico 1, 00133 Rome, Italy
    marco.querini@uniroma2.it, italiano@disp.uniroma2.it

Abstract

2D color barcodes have been introduced to obtain larger storage capabilities than traditional black and white barcodes. Unfortunately, the data density of color barcodes is substantially limited by the redundancy needed for correcting errors, which are due not only to geometric but also to chromatic distortions introduced by the printing and scanning process. The higher the expected error rate, the more redundancy is needed for avoiding failures in barcode reading, and thus, the lower the actual data density. Our work addresses this trade-off between reliability and data density in 2D color barcodes and aims at identifying the most effective algorithms, in terms of byte error rate and computational overhead, for decoding 2D color barcodes. In particular, we perform a thorough experimental study to identify the most suitable color classifiers for converting analog barcode cells to digital bit streams. To accomplish this task, we implemented a prototype capable of decoding 2D color barcodes by using different methods, including clustering algorithms and machine learning classifiers. We show that, even if state-of-art methods for color classification could be successfully used for decoding color barcodes in the desktop scenario, there is an emerging need for new color classification methods in the mobile scenario. In desktop scenarios, our experimental findings show that complex techniques, such as support vector machines, does not seem to pay off, as they do not achieve better accuracy in classifying color barcode cells. The lowest error rates are indeed obtained by means of clustering algorithms and probabilistic classifiers. From the computational viewpoint, classification with clustering seems to be the method of choice. In mobile scenarios, simple and efficient methods (in terms of computational time) such as the Euclidean and the K-means classifiers are not effective (in terms of error rate), while, more complex methods are effective but not efficient. Even if a few color barcode designs have been proposed in recent studies, to the best of our knowledge, there is no previous research that addresses a comparative and experimental analysis of clustering and machine learning methods for color classification in 2D color barcodes.

Key words

color, classification, barcode

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS131218054Q

Publication information

Volume 11, Issue 4 (October 2014)
Special Issue on Advances in Systems, Modeling, Languages and Agents
Year of Publication: 2014
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Querini, M., Italiano, G. F.: Reliability and Data Density in High Capacity Color Barcodes. Computer Science and Information Systems, Vol. 11, No. 4, 1595–1615. (2014)