New methods for digital halftoning and inverse halftoning

Halftoning is the rendition of continuous-tone pictures on bi-level displays. Here we first review some of the halftoning algorithms which have a direct bearing on our paper and then describe some of the more recent advances in the field. Dot diffusion halftoning has the advantage of pixel-level parallelism, unlike the popular error diffusion halftoning method. We first review the dot diffusion algorithm and describe a recent method to improve its image quality by taking advantage of the Human Visual System function. Then we discuss the inverse halftoning problem: The reconstruction of a continuous tone image from its halftone. We briefly review the methods for inverse halftoning, and discuss the advantages of a recent algorithm, namely, the Look Up Table (LUT)Method. This method is extremely fast and achieves image quality comparable to that of the best known methods. It can be applied to any halftoning scheme. We then introduce LUT based halftoning and tree-structured LUT (TLUT)halftoning. We demonstrate how halftone image quality in between that of error diffusion and Direct Binary Search (DBS)can be achieved depending on the size of tree structure in TLUT algorithm while keeping the complexity of the algorithm much lower than that of DBS

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

Clustered-dot periodic halftone screen design and ICC profile color table compression

This dissertation studies image quality problems associated with rendering images in devices like printing or displaying. It mainly includes two parts: clustered-dot periodic halftone screen design, and color table compression. Screening is a widely used halftoning method. As a consequence of the lower resolution of digital presses and printers, the number of printer-addressable dots or holes in each microcell may be too few to provide the requisite number of tone lev- els between paper white and full colorant coverage. To address this limitation, the microcells can be grouped into supercells. The challenge is then to determine the desired supercell shape and the order in which dots are added to the microcell. Using DBS to determine this order results in a very homogeneous halftone pattern. To simplify the design and implementation of supercell halftone screens, it is common to repeat the supercell to yield a periodically repeating rectangular block called the basic screen block (BSB). While applying DBS to design a dot-cluster growth order- ing for the entire BSB is simpler to implement than is the application of DBS to the single non-rectangular supercell, it is computationally very inefficient. To achieve a more efficient way to apply DBS to determine the microcell sequence, we describe a procedure for design of high-quality regular screens using the non-rectangular super- cell. A novel concept the Elementary Periodicity Set is proposed to characterize how a supercell is developed. After a supercell is set, we use DBS to determine the micro-cell sequence within the supercell. We derive the DBS equations for this situation, and show that it is more efficient to implement. Then, we mainly focus on the regular and irregular screen design. With digital printing systems, the achievable screen angles and frequencies are limited by the finite addressability of the marking engine. In order for such screens to generate dot clusters in which each cluster is identical, the elements of the periodicity matrix must be integer-valued, when expressed in units of printer-addressable pixels. Good approximation of the screen sets result in better printing quality. So to achieve a better approximation to the screen sets used for commercial offset printing, irregular screens can be used. With an irregular screen, the elements of the periodicity matrix are rational numbers. In this section, first we propose a procedure to generate regular screens starting from midtone level. And then we describe a procedure for design of high-quality irregular screens based on the regular screen design method. We then propose an algorithm to determine how to add dots from midtone to shadow and how to remove dots from midtone to highlight. We present experimental results illustrating the quality of the halftones resulting from our design procedure by comparing images halftoned with irregular screens using our approach and a template-based approach. We also present the evaluation of the smoothness and improvement of the proposed methods. In the next part, we study another quality problem: ICC profile color table compression. ICC profiles are widely used to provide transformations between different color spaces in different devices. The color look-up tables (CLUTs) in the profiles will increase the file sizes when embedded in color documents. In this chapter, we discuss compression methods that decrease the storage cost of the CLUTs. For DCT compression method, a compressed color table includes quantized DCT coefficients for the color table, the additional nodes with large color difference, and the coefficients bit assignment table. For wavelet-based compression method, a compressed color table includes output of the wavelet encoding method, and the additional nodes with large color difference. These methods support lossy table compression to minimize the network traffic and delay, and also achieves relatively small maximum color difference

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

Black-box printer models and their applications

In the electrophotographic printing process, the deposition of toner within the area of a given printer addressable pixel is strongly influenced by the values of its neighboring pixels. The interaction between neighboring pixels, which is commonly referred to as dot-gain, is complicated. The printer models which are developed according to a pre-designed test page can either be embedded in the halftoning algorithm, or used to predict the printed halftone image at the input to an algorithm being used to assess print quality. In our research, we examine the potential influence of a larger neighborhood (45?45) of the digital halftone image on the measured value of a printed pixel at the center of that neighborhood by introducing a feasible strategy for the contribution. We developed a series of six models with different accuracy and computational complexity to account for local neighborhood effects and the influence of a 45?45 neighborhood of pixels on the central printer-addressable pixel tone development. All these models are referred to as Black Box Model (BBM) since they are based solely on measuring what is on the printed page, and do not incorporate any information about the marking process itself. We developed two different types of printer models Standard Definition (SD) BBM and High Definition (HD) BBM with capture device Epson Expression 10000XL (Epson America, Inc., Long Beach, CA, USA) flatbed scanner operated at 2400 dpi under different analysis resolutions. The experiment results show that the larger neighborhood models yield a significant improvement in the accuracy of the prediction of the pixel values of the printed halftone image. The sample function generation black box model (SFG-BBM) is an extension of SD-BBM that adds the printing variation to the mean prediction to improve the prediction by more accurately matching the characteristics of the actual printed image. We also followed a structure similar to that used to develop our series of BBMs to develop a two-stage toner usage predictor for electrophotographic printers. We first obtained on a pixel-by-pixel basis, the predicted absorptance of printed and scanned page with the digital input using BBM. We then form a weighted sum of these predicted pixel values to predict overall toner usage on the printed page. Our two-stage predictor significantly outperforms existing method that is based on a simple pixel counting strategy, in terms of both accuracy and robustness of the prediction

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

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