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.
Recent projects like COBRA @cite_10 , Strata @cite_8 and FOCUS @cite_14 support visual light communications by streaming a sequence of 2D barcodes from a display to the camera of the receiving smartphone. However, the scope of their work is different from ours. They focus on designing new 2D (color or monochrome) barcode systems that are robust for message streaming (via video sequences) between relatively large smartphone screens (or other displays) and the capturing camera. In contrast, our work focuses on tackling the critical challenges such as CMI and CCI to support fast and robust decoding when dense color QR codes are printed on paper substrates with maximal data-capacity-per-unit-area ratio.
@cite_14 @cite_10 @cite_8
2021767041 2050755131 2104952406 2388390164
This paper presents COBRA - a visible light communication (VLC) system for off-the-shelf smartphones. COBRA encodes information into specially designed 2D color barcodes and streams them between screen and camera of smartphones. Due to the directionality and short range of visible light, COBRA can preserve user privacy and security in many near field communication scenarios such as opportunistic data exchange between smartphones. We develop a new 2D color barcode for COBRA that is optimized for streaming between small-size screen and low-speed camera of smartphones. COBRA adapts the size and layout of code blocks in streamed barcodes to deal with the significant image blur in mobile environments, and adopts new image processing techniques to achieve real-time barcode stream decoding. Our approach is evaluated through extensive experiments on Android smartphones. Existing code designs for display-camera based visual communication all have an all-or-nothing behavior, i.e., they assume the entire code must be decoded. However, diverse operational conditions due to device hardware diversity (in camera resolution and frame rate) and distance range motivate more scalable designs. In this paper, we borrow the notion of hierarchical modulation from traditional RF communication, and design Strata, a layered coding scheme for visual communication. Strata can support a range of frame capture resolutions and rates, and deliver information rates correspondingly. Strata embeds information at multiple granularity into the same code area spatially or the same frame interval temporally. It ensures all layers are decodable independently, by controlling the amount of interference between adjacent layers. Further, our design is recursive and extends readily to generate more layers. Compared with existing codes, it significantly extends the operational range, though at the expense of less capacity than a single-layer code. Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 × 480, JPEG compressed) in an average time of 400-500 ms. Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the reader's channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.