High Information Rate and Efficient Color Barcode Decoding

Abstract

The necessity of increasing information density in a given space motivates the use of more colors in color barcodes. A popular system, Microsoft’s HCCB technology, uses four or eight colors per patch. This system displays a color palette of four or eight colors in the color barcode to solve the problem with the dependency of the surface color on the illuminant spectrum, viewing parameters, and other sources. Since the displayed colors cannot be used to encode information, this solution comes at the cost of reduced information rate. In this contribution, we introduce a new approach to color barcode decoding that uses 24 colors per patch and requires a small number of reference colors to display in a barcode. Our algorithm builds groups of colors from each color patch and a small number of reference color patches, and models their evolution due to changing illuminant using a linear subspace. Therefore, each group of colors is represented by one such subspace. Our experimental results show that our barcode decoding algorithm achieves higher information rate with a very low probability of decoding error compared to systems that do display a color palette. The computational complexity of our algorithm is relatively low due to searching for the nearest subspace among 24 subspaces only.