5 research outputs found
Taming Reversible Halftoning via Predictive Luminance
Traditional halftoning usually drops colors when dithering images with binary
dots, which makes it difficult to recover the original color information. We
proposed a novel halftoning technique that converts a color image into a binary
halftone with full restorability to its original version. Our novel base
halftoning technique consists of two convolutional neural networks (CNNs) to
produce the reversible halftone patterns, and a noise incentive block (NIB) to
mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the
conflicts between the blue-noise quality and restoration accuracy in our novel
base method, we proposed a predictor-embedded approach to offload predictable
information from the network, which in our case is the luminance information
resembling from the halftone pattern. Such an approach allows the network to
gain more flexibility to produce halftones with better blue-noise quality
without compromising the restoration quality. Detailed studies on the
multiple-stage training method and loss weightings have been conducted. We have
compared our predictor-embedded method and our novel method regarding spectrum
analysis on halftone, halftone accuracy, restoration accuracy, and the data
embedding studies. Our entropy evaluation evidences our halftone contains less
encoding information than our novel base method. The experiments show our
predictor-embedded method gains more flexibility to improve the blue-noise
quality of halftones and maintains a comparable restoration quality with a
higher tolerance for disturbances.Comment: to be published in IEEE Transactions on Visualization and Computer
Graphic
Orientation Modulation for Data Hiding in Chrominance Channels of Direct Binary Search Halftone Prints
In this article, we propose a joint halftoning and data hiding technique for color images. To ensure high quality of the printed image, the color direct binary search (CDBS) iterative halftoning algorithm is used. The proposed approach uses the commonly available cyan, magenta and yellow colorants to hide data in the chrominance channels. Orientation modulation is used for data embedding during the iterative CDBS halftoning stage. The detector is using PCA-learned components to extract the embedded data from the scanned image. Experimental results show that this proposed CDBS-based data hiding method offers both higher data hiding capacity and higher robustness to the print-and-scan channel when compared to the state-of-the-art grayscale counterpart method. The relatively high correct detection rate make this approach suitable for applications which require exact extraction of embedded data in prints
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp