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

Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials

Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this paper, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure

Basic mathematics of color space invariants

Color management in printing processes has been traditionally based on an analysis of the behavior of tone reproduction curves (TRC) calculated for the initial color channels. The tone curves, as well as, the color channels, are considered separately. This approach does not take into account the mutual influence of colorants when they overlap. We propose replacing two-dimensional tone reproduction curves with three-dimensional gradation trajectories in the CIE Lab metric space. When two colors overlap, one considers the space between two gradation trajectories that forms a gradation surface. These objects are described using the apparatus of differential geometry of spatial curves and surfaces, respectively, and are also invariants of color spaces. In this paper, we offer their analytical description. © 2020 World Scientific and Engineering Academy and Society. All rights reserved

Deep learning for characterizing full-color 3D printers: accuracy, robustness, and data-efficiency

High-fidelity color and appearance reproduction via multi-material-jetting full-color 3D printing has seen increasing applications, including art and cultural artifacts preservation, product prototypes, game character figurines, stop-motion animated movie, and 3D-printed prostheses such as dental restorations or prosthetic eyes. To achieve high-quality appearance reproduction via full-color 3D printing, a prerequisite is an accurate optical printer model that is a predicting function from an arrangement or ratio of printing materials to the optical/visual properties (e.g. spectral reflectance, color, and translucency) of the resulting print. For appearance 3D printing, the model needs to be inverted to determine the printing material arrangement that reproduces distinct optical/visual properties such as color. Therefore, the accuracy of optical printer models plays a crucial role for the final print quality. The process of fitting an optical printer model's parameters for a printing system is called optical characterization, which requires test prints and optical measurements. The objective of developing a printer model is to maximize prediction performance such as accuracy, while minimizing optical characterization efforts including printing, post-processing, and measuring. In this thesis, I aim at leveraging deep learning to achieve holistically-performant optical printer models, in terms of three different performance aspects of optical printer models: 1) accuracy, 2) robustness, and 3) data efficiency. First, for model accuracy, we propose two deep learning-based printer models that both achieve high accuracies with only a moderate number of required training samples. Experiments show that both models outperform the traditional cellular Neugebauer model by large margins: up to 6 times higher accuracy, or, up to 10 times less data for a similar accuracy. The high accuracy could enhance or even enable color- and translucency-critical applications of 3D printing such as dental restorations or prosthetic eyes. Second, for model robustness, we propose a methodology to induce physically-plausible constraints and smoothness into deep learning-based optical printer models. Experiments show that the model not only almost always corrects implausible relationships between material arrangement and the resulting optical/visual properties, but also ensures significantly smoother predictions. The robustness and smoothness improvements are important to alleviate or avoid unacceptable banding artifacts on textures of the final printouts, particularly for applications where texture details must be preserved, such as for reproducing prosthetic eyes whose texture must match the companion (healthy) eye. Finally, for data efficiency, we propose a learning framework that significantly improves printer models' data efficiency by employing existing characterization data from other printers. We also propose a contrastive learning-based approach to learn dataset embeddings that are extra inputs required by the aforementioned learning framework. Experiments show that the learning framework can drastically reduce the number of required samples for achieving an application-specific prediction accuracy. For some printers, it requires only 10% of the samples to achieve a similar accuracy as the state-of-the-art model. The significant improvement in data efficiency makes it economically possible to frequently characterize 3D printers to achieve more consistent output across different printers over time, which is crucial for color- and translucency-critical individualized mass production. With these proposed deep learning-based methodologies significantly improving the three performance aspects (i.e. accuracy, robustness, and data efficiency), a holistically-performant optical printer model can be achieved, which is particularly important for color- and translucency-critical applications such as dental restorations or prosthetic eyes

Automatic multi-resolution spatio-frequency mottle metric (sfmm) for evaluation of macrouniformity

Evaluation of mottle is an area of on-going research in print quality assessment. We propose an unsupervised evaluation technique and a metric that measures mottle in a hard-copy laser print. The proposed algorithm uses a scanned image to quantify the low frequency variation or mottle in what is supposed to be a uniform field. `Banding\u27 and `Streaking\u27 effects are explicitly ignored and the proposed algorithm scales the test targets from Flat print (Good) to Noisy print (Bad) based on mottle only. The evaluation procedure is modeled as feature computation in different combinations of spatial, frequency and wavelet domains. The model is primarily independent of the nature of the input test target, i.e. whether it is chromatic or achromatic. The algorithm adapts accordingly and provides a mottle metric for any test target. The evaluation process is done using three major modules: (1) Pre-processing Stage, which includes acquisition of the test target and preparing it for processing; (2) Spatio-frequency Parameter Estimation where different features characterizing mottle are calculated in spatial and frequency domains; (3) Invalid Feature Removal Stage, where the invalid or insignificant features (in context to mottle) are eliminated and the dataset is ranked relatively. The algorithm was demonstrated successfully on a collection of 60 K-Only printed images spread over 2 datasets printed on 3 different faulty printers and 4 different media Also, it was tested on 5 color targets for the color version of the algorithm printed using 2 different printers and 5 different media, provided by Hewlett Packard Company

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

Navigating the roadblocks to spectral color reproduction: data-efficient multi-channel imaging and spectral color management

Commercialization of spectral imaging for color reproduction will require the identification and traversal of roadblocks to its success. Among the drawbacks associated with spectral reproduction is a tremendous increase in data capture bandwidth and processing throughput. Methods are proposed for attenuating these increases with data-efficient methods based on adaptive multi-channel visible-spectrum capture and with low-dimensional approaches to spectral color management. First, concepts of adaptive spectral capture are explored. Current spectral imaging approaches require tens of camera channels although previous research has shown that five to nine channels can be sufficient for scenes limited to pre-characterized spectra. New camera systems are proposed and evaluated that incorporate adaptive features reducing capture demands to a similar few channels with the advantage that a priori information about expected scenes is not needed at the time of system design. Second, proposals are made to address problems arising from the significant increase in dimensionality within the image processing stage of a spectral image workflow. An Interim Connection Space (ICS) is proposed as a reduced dimensionality bottleneck in the processing workflow allowing support of spectral color management. In combination these investigations into data-efficient approaches improve two critical points in the spectral reproduction workflow: capture and processing. The progress reported here should help the color reproduction community appreciate that the route to data-efficient multi-channel visible spectrum imaging is passable and can be considered for many imaging modalities

Characterization of color, gloss and mechanical performance of 3D printed structures

The demand for customized products is on a rise. As such, there is a great need for methods to efficiently satisfy this need for customization. Rapid prototyping, also known as 3D printing, is a technology that enables economic production of customized low volume products and enables expedited product development cycles. Given the importance of color as a means to differentiate products generated through 3D printing processes, accurate color reproduction is essential for broad market acceptance. Color reproduction in 2D document printing is itself a complex science; achieving similar results in 3D printing will require significant research. This research work explores the various factors that affect accurate color reproduction in rapid prototyping. More specifically, it studies the effect of process parameters and post-processing techniques on the color reproduction achieved in powder based layer deposition process with a selective binder delivery enabled by an ink-jet print head. A systematic study of a subset of these factors was conducted using a ZCorp Z510 3D printer. The process parameters that were included in the study were color, hue, coverage, layer thickness, and binder saturation. The post-processing technique focused on the use of an infiltrant, which is commonly used to increase the mechanical strength of the printed 3D structure. The response variables of interest for this study included color response, gloss and mechanical strength. A full factorial experiment was designed in order to characterize the effect. Gloss, which contributes to the visual perception, was studied as a qualitative response. Of particular interest was the effect of process parameters and the type of post processing on the tensile strength of the specimen to identify trade-offs between quality of color reproduction and the mechanical strength required for structural integrity. Analysis of the experimental data indicates that the standard process settings, on average, generate samples that have greater color lightness and lower chroma, representing an opportunity for improvement. In addition, layer thickness was found to have a significant effect on the tensile strength as well. The process of infiltration improved both the color reproduction and mechanical properties of the 3D printed samples. The choice of infiltrant did not have a significant effect on the color reproduction but had a significant effect on the tensile strength of the part. In this research, the effect of layer thickness on color reproduction in ink jet based 3D printing has been studied for the first time. Also, adding to the body of current research, the interaction effects of the various factors have been studied. The research intends to serve as a platform to enable color scientists to collaborate with rapid prototyping experts towards achieving full selective color throughout 3D printing in the future