  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.
