Efficient Halftoning via Deep Reinforcement Learning

Halftoning aims to reproduce a continuous-tone image with pixels whose intensities are constrained to two discrete levels. This technique has been deployed on every printer, and the majority of them adopt fast methods (e.g., ordered dithering, error diffusion) that fail to render structural details, which determine halftone's quality. Other prior methods of pursuing visual pleasure by searching for the optimal halftone solution, on the contrary, suffer from their high computational cost. In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach. Specifically, we formulate halftoning as a reinforcement learning problem, in which each binary pixel's value is regarded as an action chosen by a virtual agent with a shared fully convolutional neural network (CNN) policy. In the offline phase, an effective gradient estimator is utilized to train the agents in producing high-quality halftones in one action step. Then, halftones can be generated online by one fast CNN inference. Besides, we propose a novel anisotropy suppressing loss function, which brings the desirable blue-noise property. Finally, we find that optimizing SSIM could result in holes in flat areas, which can be avoided by weighting the metric with the contone's contrast map. Experiments show that our framework can effectively train a light-weight CNN, which is 15x faster than previous structure-aware methods, to generate blue-noise halftones with satisfactory visual quality. We also present a prototype of deep multitoning to demonstrate the extensibility of our method

Analysis of random halftone dithering using second order statistics

An analytical approach is proposed to explain the appearance of unwanted low frequency artifacts during the random dithering halftoning process. The solution uses a theorem which relates the correlation of the input gray level (continuous) signal to the correlation of the output (halftone) binary signal. The numerical solution of the above relationship suggests that: 1. Introduction of low frequency artifacts is inevitable. 2. The effect is enhanced for mean gray levels farther from mid-gray. 3. High frequency information in the input signal is attenuated more than low frequency information

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Threshold modulation in 1-D error diffusion

Error diffusion (ED) is widely used in digital imaging as a binarization process which preserves fine detail and results in pleasant images. The process resembles the human visual system in that it exhibits an intrinsic edge enhancement An additional extrinsic edge enhancement can be controlled by varying the threshold. None of these characteristics has yet been fully explained due to the lack of a suitable mathematical model of the algorithm. The extrinsic sharpening introduced in 1-D error diffusion is the subject of this thesis. An available pulse density modulation(PDM) model generated from a frequency modulation is used to gain a better understanding of variables in ED. As a result, threshold variation fits the model as an additional phase modulation. Equivalent images are obtained by applying ED with threshold modulation or by preprocessing an image with an appropriate convolution mask and subsequently running standard ED

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Laser scanner jitter characterization, page content analysis for optimal rendering, and understanding image graininess

In Chapter 1, the electrophotographic (EP) process is widely used in imaging systems such as laser printers and office copiers. In the EP process, laser scanner jitter is a common artifact that mainly appears along the scan direction due to the condition of polygon facets. Prior studies have not focused on the periodic characteristic of laser scanner jitter in terms of the modeling and analysis. This chapter addresses the periodic characteristic of laser scanner jitter in the mathematical model. In the Fourier domain, we derive an analytic expression for laser scanner jitter in general, and extend the expression assuming a sinusoidal displacement. This leads to a simple closed-form expression in terms of Bessel functions of the first kind. We further examine the relationship between the continuous-space halftone image and the periodic laser scanner jitter. The simulation results show that our proposed mathematical model predicts the phenomenon of laser scanner jitter effectively, when compared to the characterization using a test pattern, which consists of a flat field with 25% dot coverage However, there is some mismatches between the analytical spectrum and spectrum of the processed scanned test target. We improve experimental results by directly estimating the displacement instead of assuming a sinusoidal displacement. This gives a better prediction of the phenomenon of laser scanner jitter. ^ In Chapter 2, we describe a segmentation-based object map correction algorithm, which can be integrated in a new imaging pipeline for laser electrophotographic (EP) printers. This new imaging pipeline incorporates the idea of object-oriented halftoning, which applies different halftone screens to different regions of the page, to improve the overall print quality. In particular, smooth areas are halftoned with a low-frequency screen to provide more stable printing; whereas detail areas are halftoned with a high-frequency screen, since this will better reproduce the object detail. In this case, the object detail also serves to mask any print defects that arise from the use of a high frequency screen. These regions are defined by the initial object map, which is translated from the page description language (PDL). However, the information of object type obtained from the PDL may be incorrect. Some smooth areas may be labeled as raster causing them to be halftoned with a high frequency screen, rather than being labeled as vector, which would result in them being rendered with a low frequency screen. To correct the misclassification, we propose an object map correction algorithm that combines information from the incorrect object map with information obtained by segmentation of the continuous-tone RGB rasterized page image. Finally, the rendered image can be halftoned by the object-oriented halftoning approach, based on the corrected object map. Preliminary experimental results indicate the benefits of our algorithm combined with the new imaging pipeline, in terms of correction of misclassification errors. ^ In Chapter 3, we describe a study to understand image graininess. With the emergence of the high-end digital printing technologies, it is of interest to analyze the nature and causes of image graininess in order to understand the factors that prevent high-end digital presses from achieving the same print quality as commercial offset presses. We want to understand how image graininess relates to the halftoning technology and marking technology. This chapter provides three different approaches to understand image graininess. First, we perform a Fourier-based analysis of regular and irregular periodic, clustered-dot halftone textures. With high-end digital printing technology, irregular screens can be considered since they can achieve a better approximation to the screen sets used for commercial offset presses. This is due to the fact that the elements of the periodicity matrix of an irregular screen are rational numbers, rather than integers, which would be the case for a regular screen. From the analytical results, we show that irregular halftone textures generate new frequency components near the spectrum origin; and these frequency components are low enough to be visible to the human viewer. However, regular halftone textures do not have these frequency components. In addition, we provide a metric to measure the nonuniformity of a given halftone texture. The metric indicates that the nonuniformity of irregular halftone textures is higher than the nonuniformity of regular halftone textures. Furthermore, a method to visualize the nonuniformity of given halftone textures is described. The analysis shows that irregular halftone textures are grainier than regular halftone textures. Second, we analyze the regular and irregular periodic, clustered-dot halftone textures by calculating three spatial statistics. First, the disparity between lattice points generated by the periodicity matrix, and centroids of dot clusters are considered. Next, the area of dot clusters in regular and irregular halftone textures is considered. Third, the compactness of dot clusters in the regular and irregular halftone textures is calculated. The disparity of between centroids of irregular dot clusters and lattices points generated by the irregular screen is larger than the disparity of between centroids of regular dot clusters and lattices points generated by the regular screen. Irregular halftone textures have higher variance in the histogram of dot-cluster area. In addition, the compactness measurement shows that irregular dot clusters are less compact than regular dot clusters. But, a clustered-dot halftone algorithm wants to produce clustered-dot as compact as possible. Lastly, we exam the current marking technology by printing the same halftone pattern on different substrates, glossy and polyester media. The experimental results show that the current marking technology provides better print quality on glossy media than on polyester media. With above three different approaches, we conclude that the current halftoning technology introduces image graininess in the spatial domain because of the non-integer elements in the periodicity matrix of the irregular screen and the finite addressability of the marking engine. In addition, the geometric characteristics of irregular dot clusters is more irregular than the geometric characteristics of regular dot clusters. Finally, the marking technology provides inconsistency of print quality between substrates

A New framework for an electrophotographic printer model

Digital halftoning is a printing technology that creates the illusion of continuous tone images for printing devices such as electrophotographic printers that can only produce a limited number of tone levels. Digital halftoning works because the human visual system has limited spatial resolution which blurs the printed dots of the halftone image, creating the gray sensation of a continuous tone image. Because the printing process is imperfect it introduces distortions to the halftone image. The quality of the printed image depends, among other factors, on the complex interactions between the halftone image, the printer characteristics, the colorant, and the printing substrate. Printer models are used to assist in the development of new types of halftone algorithms that are designed to withstand the effects of printer distortions. For example, model-based halftone algorithms optimize the halftone image through an iterative process that integrates a printer model within the algorithm. The two main goals of a printer model are to provide accurate estimates of the tone and of the spatial characteristics of the printed halftone pattern. Various classes of printer models, from simple tone calibrations, to complex mechanistic models, have been reported in the literature. Existing models have one or more of the following limiting factors: they only predict tone reproduction, they depend on the halftone pattern, they require complex calibrations or complex calculations, they are printer specific, they reproduce unrealistic dot structures, and they are unable to adapt responses to new data. The two research objectives of this dissertation are (1) to introduce a new framework for printer modeling and (2) to demonstrate the feasibility of such a framework in building an electrophotographic printer model. The proposed framework introduces the concept of modeling a printer as a texture transformation machine. The basic premise is that modeling the texture differences between the output printed images and the input images encompasses all printing distortions. The feasibility of the framework was tested with a case study modeling a monotone electrophotographic printer. The printer model was implemented as a bank of feed-forward neural networks, each one specialized in modeling a group of textural features of the printed halftone pattern. The textural features were obtained using a parametric representation of texture developed from a multiresolution decomposition proposed by other researchers. The textural properties of halftone patterns were analyzed and the key texture parameters to be modeled by the bank were identified. Guidelines for the multiresolution texture decomposition and the model operational parameters and operational limits were established. A method for the selection of training sets based on the morphological properties of the halftone patterns was also developed. The model is fast and has the capability to continue to learn with additional training. The model can be easily implemented because it only requires a calibrated scanner. The model was tested with halftone patterns representing a range of spatial characteristics found in halftoning. Results show that the model provides accurate predictions for the tone and the spatial characteristics when modeling halftone patterns individually and it provides close approximations when modeling multiple halftone patterns simultaneously. The success of the model justifies continued research of this new printer model framework

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

ID Photograph hashing : a global approach

This thesis addresses the question of the authenticity of identity photographs, part of the documents required in controlled access. Since sophisticated means of reproduction are publicly available, new methods / techniques should prevent tampering and unauthorized reproduction of the photograph. This thesis proposes a hashing method for the authentication of the identity photographs, robust to print-and-scan. This study focuses also on the effects of digitization at hash level. The developed algorithm performs a dimension reduction, based on independent component analysis (ICA). In the learning stage, the subspace projection is obtained by applying ICA and then reduced according to an original entropic selection strategy. In the extraction stage, the coefficients obtained after projecting the identity image on the subspace are quantified and binarized to obtain the hash value. The study reveals the effects of the scanning noise on the hash values of the identity photographs and shows that the proposed method is robust to the print-and-scan attack. The approach focusing on robust hashing of a restricted class of images (identity) differs from classical approaches that address any imageCette thèse traite de la question de l’authenticité des photographies d’identité, partie intégrante des documents nécessaires lors d’un contrôle d’accès. Alors que les moyens de reproduction sophistiqués sont accessibles au grand public, de nouvelles méthodes / techniques doivent empêcher toute falsification / reproduction non autorisée de la photographie d’identité. Cette thèse propose une méthode de hachage pour l’authentification de photographies d’identité, robuste à l’impression-lecture. Ce travail met ainsi l’accent sur les effets de la numérisation au niveau de hachage. L’algorithme mis au point procède à une réduction de dimension, basée sur l’analyse en composantes indépendantes (ICA). Dans la phase d’apprentissage, le sous-espace de projection est obtenu en appliquant l’ICA puis réduit selon une stratégie de sélection entropique originale. Dans l’étape d’extraction, les coefficients obtenus après projection de l’image d’identité sur le sous-espace sont quantifiés et binarisés pour obtenir la valeur de hachage. L’étude révèle les effets du bruit de balayage intervenant lors de la numérisation des photographies d’identité sur les valeurs de hachage et montre que la méthode proposée est robuste à l’attaque d’impression-lecture. L’approche suivie en se focalisant sur le hachage robuste d’une classe restreinte d’images (d’identité) se distingue des approches classiques qui adressent une image quelconqu