Search

In the field of digital printing, accurately reproducing the color of computer images is a prerequisite for high-quality printing, where color characterization is a key step. Traditional polynomial regression models tend to amplify outliers in the characterization sample set due to high-order terms, causing model oscillation and affecting the accuracy of color characterization. Color characterization algorithms based on neural network have higher precision but significantly increase in algorithmic complexity, making them unsuitable for the efficiency requirements in industrial production. To address these issues, this paper proposed a color characterization method based on weighted polynomial regression algorithm with residual correction. This algorithm employed the Huber loss function, known for its strong robustness against outliers, as a substitute for mean squared error. It determined the weight of each sample through an adaptive mechanism and iteratively optimizes the residual values to obtain the optimal weight matrix, effectively reducing the impact of outlier samples on the system. Additionally, the correction module captured nonlinear scenarios that the initial model might miss, significantly improving the adjustment of the transformation results and thereby enhanced characterization precision. The results show that compared to conventional polynomial regression, this algorithm reduces the average color difference by 1.2. It achieves a precision close to that of deep belief network algorithms but with more than 99.37% reduction in inference time. [ABSTRACT FROM AUTHOR]

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study illustrates at‐line application of hyperspectral imaging in the visible range for quality control of large‐scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding L*, a*, and b* values. The printing process was described by hyperspectral images (in visible range) of selected regions from template color fields acquired at 17 sampling occasions. Spectra constituting hyperspectral images were visualized and evaluated in the space of significant principal components obtained from the principal component analysis. Furthermore, confidence ellipses were constructed for each set of spectra characterizing a specific moment of the printing process. Comparing their mutual locations, shapes, orientations, and sizes enabled effective visualization of process variability and was more comprehensive regarding the classic approach based on information provided by desktop and handheld spectrometers. This study demonstrates the effectiveness of hyperspectral imaging for real‐time quality control in large‐scale offset printing, in particular by studying sampling distributions. By assessing measurement stability and comparing competitive devices with traditional spectrophotometers, it reveals the advantages of hyperspectral imaging in measurement speed, spatial dimension inclusion, and process variability visualization. Chemometric analysis aids in effective color assessment, while local segmentation can offer insights into color perception, crucial for process monitoring and detection of various sources of variation. [ABSTRACT FROM AUTHOR]

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry. [ABSTRACT FROM AUTHOR]

In textile products, color plays an influential role in changing fashion trends. The main challenge in dyeing processes is the achievement of the desired color output (CIELab value) through various recipe matching algorithms and programs. A color matching approach in this context uses the type of dyestuff, the color of pre-treated fabric, and the owf% value of dyestuff as input sets, and the recipe for dyeing and CIELab values as outputs. The fiber, yarn, and finishing parameters that could influence dyeing processes, however, are excluded from these approaches (based on Kubelka-Munk theory). This study aims to develop a model that can predict the CIELab values (L*, a*, and b*) of dyed yarns after package dyeing. In order to predict CIELab values, artificial neural network-based prediction models have been developed using the properties of cotton fiber used in yarn production (HVI-AFIS etc.), yarn spinning, dyeing and finishing process parameters as input sets. A study has also been carried out on how input sets are to be related to the optimization of a prediction model. In order to account for the variability of the input sets, two different prediction models have been developed. Both models yield low prediction errors for three outputs: L*, a*, and b*. Based on the predictions by the models, the difference between the actual values and the predicted values is found to be within acceptable tolerances for L*, a* and b*, and a large part of the 498 predicted test data have an absolute error deviation of less than 0.5. [ABSTRACT FROM AUTHOR]

In pigment ink digital printing, the color difference between the original and the printed product can be visible due to the varied color gamut of devices and the impact of fabric characteristics on the color-presenting effect. To improve the color reproduction accuracy of digital printing, two methods, the full-process optimized (FPO) method and the neural network aided original adjustment (NNAOA) method, were proposed to reduce printing color difference during the color-transferring process. The FPO method, based on accounting for the fabric type and the characteristics of the printing machine, was proposed by optimizing the printing parameter settings in the color management process. Here, the optimal single-channel ink limit was determined and dot gain was compensated using the inverse function correction method, and then the International Color Consortium profile was generated. After using the FPO method, the color difference between the printed sample and the original was reduced from 20.8 to 5.254 compared with performing the original color management. To reduce the reliance on personal experience in color adjustment, the NNAOA method was proposed and implemented for 20 color patches with the largest color differences in the FPO method, allowing the color of the originals to be adjusted with the establishment of the general regression neural network model. This method predicted the input CMYK values of the original using measured L*a*b* values for the printed sample. The results showed that the color difference of the 20 color patches was reduced from 8.54 to 6.60. Consequently, both methods offered distinguishable improvements in the color reproduction effect. [ABSTRACT FROM AUTHOR]

Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry.

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The growing importance of publishing art collections online has led to increasingly strict tolerances on digital photography of art objects. There are two internationally recognized sets of guidelines for creating high-quality digital images, Metamorfoze and FADGI. These guidelines require using sets of standardized color patches in museal photography. The X-Rite ColorChecker SG chart with 140 color patches is often used. Recent studies showed that even in standardized conditions it is often difficult to satisfy the strictest guidelines on color accuracy for camera profiling, with no indications for improvements. We report results of our investigation into the bottlenecks in achieving high color accuracy. We show that a large part of the color deviations originates from the 15 black color patches of the ColorChecker SG chart. These patches have a large impact on the average color deviation and the maximum color deviation that are the performance measures for color accuracy in the FADGI and Metamorfoze guidelines. We show that spectrophotometer measurements for the black patches produce color deviations dE(CIE 1976) ranging from 3.7 to 5.2 with respect to reference data, making it impossible to meet the strictest Metamorfoze guidelines. The black patches push the average color difference CIEDE2000 from 0.59 to 0.82 with respect to reference data already when using spectrophotometer data. Since the strictest FADGI guidelines prescribe an average CIEDE2000 = 2.0, this leaves little tolerance for errors due to lighting and camera profile. Our results indicate that the common practice of manually tweaking camera profiles until software suggests sufficient color accuracy is obtained with respect to suppliers’ reference data often does not improve color representation but makes it worse, especially for representing dark nuances. This is unfortunate for example for the digital photography of seventeenth century Dutch paintings, where dark passages occupy large areas of the art works. We show that the key step in achieving color accurate digital photography is to use customer reference data rather than commonly used generic reference data. We explain the results by investigating not only the color properties of the ColorChecker SG chart, but also its glossiness.

Accurate color is very important in the printing profession. Clients expect their brand colors to be reproduced consistently and to specification. This can be challenging when printing across a variety of substrates, and in particular, substrates with high reflectivity such as gloss varnishes and metalized materials. Effective color management throughout the workflow is necessary. The use of spectrophotometers to read color values is commonly used. This study examines how varying gloss levels in print impact color measurement. The experiment uses two different devices to measure two different color samples with varying gloss levels. The X-Rite 500 is a conventional spectrophotometer; and the X-Rite SP60 is a sphere spectrophotometer. Mean comparison of values by device was conducted using Tukey-Kramer's HSD and mean L* to determine the accuracy of the color value with differing gloss levels. Two paint samples from the local home improvement store with varying gloss levels were measured in this experiment--a lighter beige and a dark blue. Data showed there was a significant difference in L* values read as gloss level increased, though to a lesser degree in when using the specular included (SPIN) mode of the spherical instrument. Interestingly, it was more evident in the darker blue color than the lighter beige, perhaps as a result of greater spectral reflectivity from dark backgrounds. The use of a sphere spectrophotometer in this experiment allowed the measurement of color while compensating for the gloss appearance in the specular included (SPIN) measurement geometry. Though the color values measured across gloss values were much closer, they still produced significantly different results. [ABSTRACT FROM AUTHOR]

The growing importance of publishing art collections online has led to increasingly strict tolerances on digital photography of art objects. There are two internationally recognized sets of guidelines for creating high-quality digital images, Metamorfoze and FADGI. These guidelines require using sets of standardized color patches in museal photography. The X-Rite ColorChecker SG chart with 140 color patches is often used. Recent studies showed that even in standardized conditions it is often difficult to satisfy the strictest guidelines on color accuracy for camera profiling, with no indications for improvements. We report results of our investigation into the bottlenecks in achieving high color accuracy. We show that a large part of the color deviations originates from the 15 black color patches of the ColorChecker SG chart. These patches have a large impact on the average color deviation and the maximum color deviation that are the performance measures for color accuracy in the FADGI and Metamorfoze guidelines. We show that spectrophotometer measurements for the black patches produce color deviations dE(CIE 1976) ranging from 3.7 to 5.2 with respect to reference data, making it impossible to meet the strictest Metamorfoze guidelines. The black patches push the average color difference CIEDE2000 from 0.59 to 0.82 with respect to reference data already when using spectrophotometer data. Since the strictest FADGI guidelines prescribe an average CIEDE2000 = 2.0, this leaves little tolerance for errors due to lighting and camera profile. Our results indicate that the common practice of manually tweaking camera profiles until software suggests sufficient color accuracy is obtained with respect to suppliers' reference data often does not improve color representation but makes it worse, especially for representing dark nuances. This is unfortunate for example for the digital photography of seventeenth century Dutch paintings, where dark passages occupy large areas of the art works. We show that the key step in achieving color accurate digital photography is to use customer reference data rather than commonly used generic reference data. We explain the results by investigating not only the color properties of the ColorChecker SG chart, but also its glossiness. [ABSTRACT FROM AUTHOR]

To improve the accuracy of color reproduction for camouflage printing, a new color matching method, named monitor-paper-fabric, was proposed by importing color management into textiles, which matched the printing pastes according to the color of the paper card printed by the ink-jet printer after color management. Not like the traditional color matching method, it matched the printing pastes according to the color from computer monitor after color correction. Two color matching methods were analyzed by comparing the color difference. It was found that the "monitor-paper-fabric" color matching method could be considered as a convenient and feasible color matching method. Most color differences between the monitor and the fabric in the camouflage pattern were reduced to lower than 4, except for color blocks 5 and 6. In addition, the color differences of six color blocks between the paper and the fabric were all less than 3.5, and were lower than those between the monitor and the fabric. The color consistency between the paper and the fabric was better than that between the monitor and the fabric. [ABSTRACT FROM AUTHOR]

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using color management systems in order to obtain uniform color production along all stages of printing production which allows: Increasing the quality of the print production. Increasing quality consistency. Time/money savings in prepress through decreased retouching of images and remaking of proofs. Time/money savings in printing through faster color ok in make-ready. The study is to find The impact of using color management systems which can be applied through all print production stages from prepress till the press stage. Adjusting ink zones on the printing machine is considered to be one of the most important factors that affect the quality of the printed color which can be an affecting factor in producing the dot gain phenomenon, and also decrease the print waste percentage. The research have been established through a number of experiments and measurements recorded in each of the extent of change. Research problem: Deficiencies in the use of the color management system in order to improve colorimetric properties of the printed product during the print production on the litho offset sheet feed printing machine. Aim of the research: Studying the impact of using color management system to adjust the ink zones of the sheet feed litho offset printing machine during the printing process.

A whole-slide imaging (WSI) system is a digital color imaging system used in digital pathology with the potential to substitute the conventional light microscope. A WSI system digitalizes a glass slide by converting the optical image to digital data with a scanner and then converting the digital data back to the optical image with a display. During the digital-to-optical or optical-to-digital conversion, a color space is required to define the mapping between the digital domain and the optical domain so that the numerical data of each color pixel can be interpreted meaningfully. Unfortunately, many current WSI products do not specify the designated color space clearly, which leaves the user using the universally default color space, sRGB. sRGB is a legacy color space that has a limited color gamut, which is known to be unable to reproduce all color shades present in histology slides. In this work, experiments were conducted to quantitatively investigate the limitation of the sRGB color space used in WSI systems. Eight hematoxylin and eosin (H and E)-stained tissue samples, including human bladder, brain, breast, colon, kidney, liver, lung, and uterus, were measured with a multispectral imaging system to obtain the true colors at the pixel level. The measured color truth of each pixel was converted into the standard CIELAB color space to test whether it was within the color gamut of the sRGB color space. Experiment results show that all the eight images have a portion of pixels outside the sRGB color gamut. In the worst-case scenario, the bladder sample, about 35% of the image exceeded the sRGB color gamut. The results suggest that the sRGB color space is inadequate for WSI scanners to encode H and E-stained whole-slide images, and an sRGB display may have insufficient color gamut for displaying H and E-stained histology images. [ABSTRACT FROM AUTHOR]

When watching sports on screen, fans recognize inconsistencies in their team colors. ColorNet is an artificial intelligence (AI) technology that ingests live video, adjusts each frame pixel‐by‐pixel, and outputs color corrected video in real‐time ensuring accurate brand colors. In this study, ColorNet is demonstrated using Clemson University's orange, Pantone 165. [ABSTRACT FROM AUTHOR]

Expanded gamut printing is an approach in color reproduction that expands the color gamut of conventional CMYK printing processes via the use of additional colorants, such as Orange, Green, and Violet inks. This study evaluates the ability of commercial color management software to create an accurate solution for an expanded gamut printing system. In this study, two printing processes were used, an Epson SureColor P9000 inkjet printer/proofer and an HP Indigo 7900 digital production press, both with 7‐color expanded gamut ink sets. Software solutions from Alwan, CGS ORIS, ColorLogic, GMG Color, Heidelberg, and Kodak were evaluated. The systems were tested to see how well they could reproduce the colors in the entire PANTONE+ Solid Coated spot color library. It is shown that the solutions are able to reproduce 89% to 94% of the spot colors on the Epson P9000 inkjet printer and 77% to 87% of the library on the Indigo 7900, both to less than two CIEDE2000 (a typical tolerance in label and packaging work). The number of color patches in expanded gamut characterization test charts was noted, as this is still an area of proprietary, nonstandardized working practice. There are many different colorant combinations that can make the same color in expanded gamut printing. The ink build created by the different software solutions was studied, as it relates to press stability through appropriate choice of colorants. Pantone and Adobe provide everyday commercial tools for expanded color workflows. The study identified some issues with products from these companies that could confuse a less‐skilled user in a busy production environment. The conclusion of the study is that expanded gamut solutions for spot color printing produce totally acceptable results for digital printing processes; expanded gamut printing is ready, here and now. The findings show that expanded gamut printing can replace cumbersome conventional spot color workflows creating considerable savings and advantages, especially for label and packaging printers. [ABSTRACT FROM AUTHOR]

On the occasion of the recent restoration of the sixteenth century Neptune’s Fountain in Bologna, promoted by the municipality in 2015 to preserve one of the major town’s landmark, high-quality imagery has been acquired for documenting the current state of preservation of the marble and bronze surfaces before and during the restoration phases. Starting from this available detailed photographic campaign and using new tools developed to solve problems linked with some limitations of the captured imagery, authors produced a photogrammetry-based 3D model of the monumental sculpture group, reliable for both geometric features and colour reproduction consistency, in order to be used to support the supplementary activities not foreseen in the original project. The paper presents methods and techniques implemented to produce the 3D model of the fountain, besides its main actual applications as a basic tool to support different restoration activities: (i) reliable documentation of the actual state; (ii) high-quality visualization and rendering; (iii) technical outcomes and graphical representation extraction; (iv) study of original and current water supply system and simulation of the new project for the jets system and watergames; (v) new lighting design simulation; (vi) structural analysis; (vii) support to preliminary analysis and design studies related to hardly accessible areas; (viii) surface cleaning analysis; (ix) base for filling gaps or missing elements through 3D printing; (x) support for data modelling and semantic-based diagrams. [ABSTRACT FROM AUTHOR]

Offset lithography is one of the most common ways of creating printed materials. Compared to other printing methods, offset printing is best suited for economically producing large volumes of high quality prints in a manner that requires little maintenance. Because of the high speed and the high volume of the printing press, we have to rely on automation for press control and not just to the printer’s eye. When printing an image that has more than one color, it is necessary to print each color separately and ensure each color overlaps the others precisely. If this is not done, the finished image will look fuzzy, blurred or "out of register". To help line the colors up correctly, a system of registration is necessary. Therefore, the use of an automated real time quality control system will result in a more consistent color for the customer and less waste for the printer.

This paper proposes the optimal white point settings of consumer televisions observed in different viewing conditions. The ratings from visual experiments were modeled with a bivariate Gaussian distribution to predict the optimal white points. Under dark surround conditions, where there is no effect of lighting chromaticity, the averaged optimal white point was located at a correlated color temperature of 7346 K and a Duv of −0.0015. The optimal white point was shifted toward a higher color temperature compared to the current standards. The results also indicated that current white point settings of major television manufacturers are too bluish. Neither televisions that meet the current standards nor televisions in the market would appear as true white under the viewing conditions employed in this paper. Moreover, the optimal white point depended largely on viewing conditions. Image content and ambient lighting conditions were observed to be important factors that influence the perception of the white point. The research therefore proposed to automatically change the white point of the display based on the image content and the real-time measurements of the surrounding ambient light falling on the screen. Ultimately, this paper might be of interest to the industry as a basis for future deliberations on target white points for consumer televisions designed to provide a better color appearance. [ABSTRACT FROM AUTHOR]