Digital Halftone Database (DHD): A Comprehensive Analysis on Halftone Types

Guo, Jing; Sankarasrinivasan, S

doi:10.23919/apsipa.2018.8659732

Cited by 21 publications

(18 citation statements)

References 13 publications

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“…M B and W M B refer to the mean block and the current watermark image block. Concurrently a stochastic parameters termed radially averaged power spectral density (RAPSD) is computed and its inner product is added with I wt [18]. This helps to accurately differentiate the DD patterns and improvise the decoding accuracy.…”

Section: Fig7: Watermark Embeddingmentioning

confidence: 99%

Watermarking in dot-diffusion halftones using adaptive class-matrix and error diffusion

Guo

Seshathiri²

2019

ECTI-CIT

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Digital halftoning deals with transforming a gray or color image into its binary version which is useful in printing applications. Dot diffusion is one of the prominent halftone methods which can yield superior image quality with parallel processing capabilities. In this paper, a rapid watermarking algorithm is proposed for dot-diffusion halftone images using adaptive class-matrix selection and modified error diffusion kernels. To process the image using an adaptive class matrix, the processing order of the class matrix is reversed and transposed, and for error diffusion the coefficients are replaced with different weights. For decoding, an effective strategy is proposed based on a correlation analysis and halftone statistics. The proposed strategy can successfully embed and decode the binary watermark from a single dot-diffused halftone image. From the experimental results, the proposed method is found to be effective in terms of good decoding accuracy, imperceptibility and robustness against various printed distortions.

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Section: Fig7: Watermark Embeddingmentioning

confidence: 99%

Watermarking in dot-diffusion halftones using adaptive class-matrix and error diffusion

Guo

Seshathiri²

2019

ECTI-CIT

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“…In practical application, there are many different types of halftone images, which are called multi-modal halftone images. For example, there are 23 types of halftone images in reference [44]. Thus, the means of restoring a high quality image by taking into consideration of these multi-modal halftone images is a challenging problem.…”

Section: Multi-modal Halftone Images Recoverymentioning

confidence: 99%

Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

Zhang

Wang

et al. 2020

Applied Sciences

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Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.

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“…The other algorithms against which we will compare for the numerical experiments are: Atkinson, Burkes, Fan, Sieraa, Filter Lite, Jarvis, Judice and Ninke (JJN), Jarvis, Stucki and Shio Fan [2].…”

Section: Algorithm 1 Dithering Algorithmmentioning

confidence: 99%

“…The purpose of image dithering, or halftoning, is the procedure that generates a pattern of quantized pixels able to create a continuoustone image illusion. A comprehensive analysis about halftoning techniques can be found in [2]. Dithering is necessary for showing gray scale images on printed surfaces when the only available tones are black and white.…”

Section: Background and Preliminariesmentioning

confidence: 99%

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Automatic Stochastic Dithering Techniques on GPU: Image Quality and Processing Time Improved

Franchini¹,

Cavicchioli²,

Hu³

2020

Adv. sci. technol. eng. syst. j.

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Dithering or error diffusion is a technique used to obtain a binary image, suitable for printing, from a grayscale one. At each step, the algorithm computes an allowed value of a pixel from a grayscale one, applying a threshold and, therefore, causing a conversion error. To obtain the optical illusion of a continuous tone, the obtained error is distributed to adjacent pixels. In literature there are many algorithms of this type, to cite some Jarvis, Judice and Ninke (JJN), Stucki, Atkinson, Burkes, Sierra but the most known and used is the Floyd-Steinberg. We compared various types of dithering, which differ from each other for the weights and number of pixels involved in the error diffusion scheme. All these algorithms suffer from two problems: artifacts and slowness. First, we address the artifacts, which are undesired texture patterns generated by the dithering algorithm, leading to a less appealing visual results. To address this problem, we developed a stochastic version of Floyd-Steinberg's algorithm. The Weighted Signal to Noise Ratio (WSNR) is adopted to evaluate the outcome of the procedure, an error measure based on human visual perception that also takes into account artifacts. This measure behaves similarly to a low-pass filter and, in particular, exploits a contrast sensitivity function to compare the algorithm's result and the original image in terms of similarity. We will show that the new stochastic algorithm is better in terms of both WSNR measurement and visual analysis. Secondly, we address the method's inherent computational slowness: we implemented a parallel version of the Floyd-Steinberg algorithm that takes advantage of GPGPU (General Purtose Graphics Processing Unit) computing, drastically reducing the execution time. Specifically, we observed a quadratic time complexity with respect to the input size for the serial case, whereas the computational time required for our parallel implementation increased linearly. We then evaluated both image quality and the performance of the parallel algorithm on a exhaustive image database. Finally, to make the method fully automatic, an empirical technique is presented to choose the best degree of stochasticity.

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Digital Halftone Database (DHD): A Comprehensive Analysis on Halftone Types

Cited by 21 publications

References 13 publications

Watermarking in dot-diffusion halftones using adaptive class-matrix and error diffusion

Watermarking in dot-diffusion halftones using adaptive class-matrix and error diffusion

Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

Automatic Stochastic Dithering Techniques on GPU: Image Quality and Processing Time Improved

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