A Data Hiding Method Based on Partition Variable Block Size with Exclusive-or Operation on Binary Image

In this paper, we propose a high capacity data hiding method applying in binary images. Since a binary image has only two colors, black or white, it is hard to hide data imperceptible. The capacities and imperception are always in a trade-off problem. Before embedding we shuffle the secret data by a pseudo-random number generator to keep more secure. We divide the host image into several non-overlapping (2n+1) by (2n+1) sub-blocks in an M by N host image as many as possible, where n can equal 1, 2, 3 , …, or min(M,N). Then we partition each sub-block into four overlapping (n+1) by (n+1) sub-blocks. We skip the all blacks or all whites in each (2n+1) by (2n+1) sub-blocks. We consider all four (n+1) by (n+1) sub-blocks to check the XOR between the non overlapping parts and center pixel of the (2n+1) by (2n+1) sub-block, it embed n 2 bits in each (n+1) by (n+1) sub-block, totally are 4*n 2 . The entire host image can be embedded 4×n 2×M/(2n+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with their non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The adaptive means the partitioning sub-block may affect the capacities and imperception that we want to select. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless

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

Spectrally stable ink variability in a multi-primary printer

It was shown previously that a multi-ink printer can reproduce spectral reflectances within a specified tolerance range using many distinct ink combinations. An algorithm was developed to systematically analyze a printer to determine the amount of multi-ink variability throughout its spectral gamut. The advantage of this algorithm is that any spectral difference metric can be used as the objective function. Based on the results of the analysis for one spectral difference metric, six-dimensional density map displays were constructed to illustrate the amount of spectral redundancy throughout the ink space. One CMYKGO ink-jet printer was analyzed using spectral reflectance factor RMS as the spectral difference metric and selecting 0.02 RMS as the tolerance limit. For these parameters, the degree of spectral matching freedom for the printer reduced to five inks because the chromatic inks were able to reproduce spectra within the 0.02 tolerance limit throughout the printer\u27s gamut. Experiments were designed to exploit spectrally stable multi-ink variability within the analyzed printer. The first experiment used spectral redundancy to visually evaluate spectral difference metrics. Using the developed database of spectrally similar samples allows any spectral difference metric to be compared to a visual response. The second experiment demonstrated the impact of spectral redundancy on spectral color management. Typical color image processing techniques use profiles consisting of sparse multi-dimensional lookup tables that interpolate between adjacent nodes to prepare an image for rendering. It was shown that colorimetric error resulted when interpolating between lookup table nodes that were inconsistent in digital count space although spectrally similar. Finally, the analysis was used to enable spectral watermarking of images. To illustrate the significance of this watermarking technique, information was embedded into three images with varying levels of complexity. Prints were made verifying that information could be hidden while preserving the visual and spectral integrity of the original image

Data hiding for halftone images

With the ease of distribution of digital images, there is a growing concern for copyright control and authentication. While there are many existing watermarking and data hiding methods for natural images, almost none can be applied to halftone images. In this paper, we proposed two novel data hiding methods for halftone images. The proposed Data Hiding Pair-Toggling (DHPT) hides data by forced complementary toggling at pseudo-random locations within a halftone image. It is found to be very effective for halftone images with relatively coarse textures. For halftone images with fine textures (such as error diffusion with Steinberg kernel), the proposed Data Hiding Error Diffusion (DHED) gives significantly better visual quality by integrating the data hiding into the error diffusion operation. Both DHPT and DHED are computationally very simple and yet effective in hiding a relatively large amount of data. Both algorithms yield halftone images with good visual quality

A new inverse halftoning method using reversible data hiding for halftone images

[[abstract]]A new inverse halftoning algorithm based on reversible data hiding techniques for halftone images is proposed in this paper. The proposed scheme has the advantages of two commonly used methods, the lookup table (LUT) and Gaussian filtering methods. We embed a part of important LUT templates into a halftone image and restore the lossless image after these templates have been extracted. Then a hybrid method is performed to reconstruct a grayscale image from the halftone image. In the image reconstruction process, the halftone image is scanned pixel by pixel. If the scanned pattern surrounding a pixel appeared in the LUT templates, a gray value is directly predicted using the LUT value; otherwise, it is predicted using Gaussian filtering. Experimental results show that the reconstructed grayscale images using the proposed scheme own better quality than both the LUT and Gaussian filtering methods