Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications
  The use of color in QR codes brings extra data capacity, but also inflicts
tremendous challenges on the decoding process due to chromatic distortion,
cross-channel color interference and illumination variation. Particularly, we
further discover a new type of chromatic distortion in high-density color QR
codes, cross-module color interference, caused by the high density which also
makes the geometric distortion correction more challenging. To address these
problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which
jointly model these different types of chromatic distortion. Extended from SVM
and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular
objective function to learn a color classifier. Furthermore, a robust geometric
transformation method and several pipeline refinements are proposed to boost
the decoding performance for mobile applications. We put forth and implement a
framework for high-capacity color QR codes equipped with our methods, called
HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale
color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR
code samples. The comparison with the baseline method [2] on CUHK-CQRC shows
that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in
bit error rate. Our implementation of HiQ in iOS and Android also demonstrates
the effectiveness of our framework in real-world applications.
