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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.

Reliability and data density in high capacity color barcodes