Modeling and Halftoning for Multichannel Printers: A Spectral Approach

Printing has been has been the major communication medium for many centuries. In the last twenty years, multichannel printing has brought new opportunities and challenges. Beside of extended colour gamut of the multichannel printer, the opportunity was presented to use a multichannel printer for ‘spectral printing’. The aim of spectral printing is typically the same as for colour printing; that is, to match input signal with printing specific ink combinations. In order to control printers so that the combination or mixture of inks results in specific colour or spectra requires a spectral reflectance printer model that estimates reflectance spectra from nominal dot coverage. The printer models have one of the key roles in accurate communication of colour to the printed media. Accordingly, this has been one of the most active research areas in printing. The research direction was toward improvement of the model accuracy, model simplicity and toward minimal resources used by the model in terms of computational power and usage of material. The contribution of the work included in the thesis is also directed toward improvement of the printer models but for the multichannel printing. The thesis is focused primarily on improving existing spectral printer models and developing a new model. In addition, the aim was to develop and implement a multichannel halftoning method which should provide with high image quality. Therefore, the research goals of the thesis were: maximal accuracy of printer models, optimal resource usage and maximal image quality of halftoning and whole spectral reproduction system. Maximal colour accuracy of a model but with the least resources used is achieved by optimizing printer model calibration process. First, estimation of the physical and optical dot gain is performed with newly proposed method and model. Second, a custom training target is estimated using the proposed new method. These two proposed methods and one proposed model were at the same time the means of optimal resource usage, both in computational time and material. The third goal was satisfied with newly proposed halftoning method for multichannel printing. This method also satisfies the goal of optimal computational time but with maintaining high image quality. When applied in spectral reproduction workflow, this halftoning reduces noise induced in an inversion of the printer model. Finally, a case study was conducted on the practical use of multichannel printers and spectral reproduction workflow. In addition to a gamut comparison in colour space, it is shown that otherwise limited reach of spectral printing could potentially be used to simulate spectra and colour of textile fabrics

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

Perceptual Error Optimization for {Monte Carlo} Rendering

Realistic image synthesis involves computing high-dimensional light transport integrals which in practice are numerically estimated using Monte Carlo integration. The error of this estimation manifests itself in the image as visually displeasing aliasing or noise. To ameliorate this, we develop a theoretical framework for optimizing screen-space error distribution. Our model is flexible and works for arbitrary target error power spectra. We focus on perceptual error optimization by leveraging models of the human visual system's (HVS) point spread function (PSF) from halftoning literature. This results in a specific optimization problem whose solution distributes the error as visually pleasing blue noise in image space. We develop a set of algorithms that provide a trade-off between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using both quantitative and perceptual error metrics to support our analysis, and provide extensive supplemental material to help evaluate the perceptual improvements achieved by our methods

Effects of texture on color difference evaluation of surface color

The parametric effects of texture on supratheshold color tolerance thresholds were investigated in two psychophysical experiments using simulated textures presented on a CRT. Textured images were created from scanned photographs of physical texture samples with semi-random textured pattern. Differences in appearance were created by varying the illumination geometry during the image capture stage. Two conditions were simulated: diffuse illumination of a standard light booth and directional lighting which accentuates texture relief. In the first experiment observers matched average perceived lightness of grayscale textured images by adjusting the lightness of a uniform gray field. Images varied in their average L*. The results showed that, on average, there was no statistically significant difference between the observer match and the average L of the image. The only exception was found for darker images of coarse texture. In the second experiment, an array of color images was created from three texture patterns: one simulating diffuse lighting conditions and two simulating directional illumination. The CTELAB coordinates of the images were centered around the five CEE color centers recommended for color tolerance research. Color differences were varied in the lightness, chroma, and hue dimensions. Color tolerance thresholds were measured in each dimension for each texture type and uniform patches. An adaptive psychophysical technique, QUEST, was utilized to determine color tolerances in a greater than/less than task using test pairs in comparison to a fixed anchor pair of 1 unit AE*94. The results indicated that the presence of texture increases tolerance thresholds for hue irrespective of the texture pattern. The chroma dimension remained unaffected. Less conclusive results were found for lightness dimension with a strong trend toward increased tolerance thresholds for textured stimuli. When the different textures were compared, it was found that the L* thresholds were significantly higher for the images simulating directional lighting compared to the images of diffusely illuminated surface. No differences in tolerances for chroma and hue were found in that case

Digital halftoning and the physical reconstruction function

Originally presented as author's thesis (Ph. D.--Massachusetts Institute of Technology), 1986.Bibliography: p. 397-405."This work has been supported by the Digital Equipement Corporation."by Robert A. Ulichney

Bayesian Dictionary Learning for Single and Coupled Feature Spaces

Over-complete bases offer the flexibility to represent much wider range of signals with more elementary basis atoms than signal dimension. The use of over-complete dictionaries for sparse representation has been a new trend recently and has increasingly become recognized as providing high performance for applications such as denoise, image super-resolution, inpaiting, compression, blind source separation and linear unmixing. This dissertation studies the dictionary learning for single or coupled feature spaces and its application in image restoration tasks. A Bayesian strategy using a beta process prior is applied to solve both problems. Firstly, we illustrate how to generalize the existing beta process dictionary learning method (BP) to learn dictionary for single feature space. The advantage of this approach is that the number of dictionary atoms and their relative importance may be inferred non-parametrically. Next, we propose a new beta process joint dictionary learning method (BP-JDL) for coupled feature spaces, where the learned dictionaries also reflect the relationship between the two spaces. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dictionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Two applications, single image super-resolution and inverse halftoning, are chosen to evaluate the performance of the proposed Bayesian approach. In both cases, the Bayesian approach, either for single feature space or coupled feature spaces, outperforms state-of-the-art methods in comparative domains