{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-and-fast-decoding-of-high-capacity","title":"Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications","arxiv_id":"1704.06447","date":"2017-04-21","proceeding":null,"authors":["Zhibo Yang","Huanle Xu","Jianyuan Deng","Chen Change Loy","Wing Cheong Lau"],"abstract":"The use of color in QR codes brings extra data capacity, but also inflicts\ntremendous challenges on the decoding process due to chromatic distortion,\ncross-channel color interference and illumination variation. Particularly, we\nfurther discover a new type of chromatic distortion in high-density color QR\ncodes, cross-module color interference, caused by the high density which also\nmakes the geometric distortion correction more challenging. To address these\nproblems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which\njointly model these different types of chromatic distortion. Extended from SVM\nand QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular\nobjective function to learn a color classifier. Furthermore, a robust geometric\ntransformation method and several pipeline refinements are proposed to boost\nthe decoding performance for mobile applications. We put forth and implement a\nframework for high-capacity color QR codes equipped with our methods, called\nHiQ. To evaluate the performance of HiQ, we collect a challenging large-scale\ncolor QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR\ncode samples. The comparison with the baseline method [2] on CUHK-CQRC shows\nthat HiQ at least outperforms [2] by 188% in decoding success rate and 60% in\nbit error rate. Our implementation of HiQ in iOS and Android also demonstrates\nthe effectiveness of our framework in real-world applications.","url_abs":"http://arxiv.org/abs/1704.06447v3","url_pdf":"http://arxiv.org/pdf/1704.06447v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-and-fast-decoding-of-high-capacity","repo_url":"https://github.com/ouyangzhibo/HiQ-Robust-and-Fast-Decoding-of-High-Capacity-Color-QR-Codes","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"distortion-correction","task_name":"distortion correction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}