Colors of skin, green plant, and blue sky of digital photographic images were studied for modeling and detection of these
three important memory color regions. The color modeling of these three regions in CIELAB and CAM02-UCS was
presented, and the properties of these three color groups were investigated.
The CMYK to CMYK mapping preserving the black channel is a method to solve the problem in standard ICC color
management that lacks the capability of preserving the K channel for printing CMYK contents. While the method has
been successfully used for digital commercial printing, limitations and areas for improvement are found. To address
these problems in generating CMYK re-rendering tables, an alternative method is developed. The K usage and total ink
usage are optimized in a color separation step. Instead of preserving the K channel globally, it preserves K-only gray
contents and maps other colors by optimizing the print quality and ink usage. Experiments verify that the method
significantly improves the print quality.
Skin tone is the most important color category in memory colors. Reproducing it pleasingly is an important factor in
photographic color reproduction. Moving skin colors toward their preferred skin color center improves the skin color
preference on photographic color reproduction. Two key factors to successfully enhance skin colors are: a method to
detect original skin colors effectively even if they are shifted far away from the regular skin color region, and a method
to morph skin colors toward a preferred skin color region properly without introducing artifacts. A method for skin
color enhancement presented by the authors in the same conference last year applies a static skin color model for skin
color detection, which may miss to detect skin colors that are far away from regular skin tones. In this paper, a new
method using the combination of face detection and statistical skin color modeling is proposed to effectively detect skin
pixels and to enhance skin colors more effectively.
Although anaglyphs have a big advantage that they can be presented using traditional single channel media such as print,
film, display, etc., a media type must be determined as a pair of views is combined into a single image to minimize
retinal rivalry and stereo crosstalk. Most of anaglyph maps and map tools are optimized for display and assumed using
red-cyan filtered glasses for viewing. Due to the large difference between a display gamut and a printer gamut, red and
cyan colors that are used to separate the left view and the right view are changed considerably as they are mapped from a
display color space to a printer color space for printing and results in serious retinal rivalry. A solution using a special
gamut mapping method to preserve the relative relationship of cyanish and reddish colors was developed to gamut map
colors from display to printer. And the color characterization to balance neutral colors for specific red/cyan glasses is
applied to further improve the color appearance.
Skin tones are the most important colors among the memory color category. Reproducing skin colors pleasingly is an
important factor in photographic color reproduction. Moving skin colors toward their preferred skin color center
improves the color preference of skin color reproduction. Several methods to morph skin colors to a smaller preferred
skin color region has been reported in the past. In this paper, a new approach is proposed to further improve the result of
skin color enhancement. An ellipsoid skin color model is applied to compute skin color probabilities for skin color
detection and to determine a weight for skin color adjustment. Preferred skin color centers determined through
psychophysical experiments were applied for color adjustment. Preferred skin color centers for dark, medium, and light
skin colors are applied to adjust skin colors differently. Skin colors are morphed toward their preferred color centers. A
special processing is applied to avoid contrast loss in highlight. A 3-D interpolation method is applied to fix a potential
contouring problem and to improve color processing efficiency. An psychophysical experiment validates that the
method of preferred skin color enhancement effectively identifies skin colors, improves the skin color preference, and
does not objectionably affect preferred skin colors in original images.
Color schemes have been used in maps to visually distinguish different regions or to approximately represent the
magnitude of a property. Since human eyes are not able to translate a color to a numerical scale, colors on a traditional
map can only be used to visually estimate magnitudes. As maps are represented more and more digitally, a properly
designed color scheme may be able to use color to encode numbers and to accurately translate colors into numerical
scales of a property. As a mouse (or other pointers) points to a location, the color of the location can be translated into
the original encoded number and therefore the numerical property of the location may be displayed. In this paper,
method to encode information in digital maps using color schemes is investigated. A hue-based color scheme was
developed to encode and decode numerical scales for digital maps. Color gamut issues between display and print are
investigated as well.
Colour preference adjustment is an essential step for colour image enhancement and perceptual gamut mapping. In
colour reproduction for pictorial images, properly shifting colours away from their colorimetric originals may produce
more preferred colour reproduction result. Memory colours, as a portion of the colour regions for colour preference
adjustment, are especially important for preference colour reproduction. Identifying memory colours or modelling the
memory colour region is a basic step to study preferred memory colour enhancement. In this study, we first created
gamut for each memory colour region represented as a convex hull, and then used the convex hull to guide mathematical
modelling to formulate the colour region for colour enhancement.
Traditionally, the AToB0 tag of an ICC printer profile, containing the perceptual transform from device specific to PCS
Lab or XYZ values, has been generated directly from measurement data without considering the color re-rendering
included in the PCS to device-specific transform found in the corresponding BToA0 tag. In this case, the AToB0 color
conversion will often not be the inverse of the color conversion in the BToA0 tag. However, with ICC version 4, the
AToBn and BToAn transforms are in general supposed to be inverses of each other, to the extent possible. This feature
supports the re-purposing of color data within an ICC color management workflow. This inversion is a challenge for
profile generation due to issues with either directly inverting 3-D interpolations or inverting every step applied in
generating the BToA0 tag. Directly inverting a 3-D LUT may not be feasible because the forward mapping is usually
not a one-to-one mapping. Mathematically, inverting every operation for generating a BToA0 tag may also be
extremely difficult if not impossible. Consequently, a closed-loop method has been developed which iteratively adjusts
AToBn tags to improve the invertibility of ICC profile transforms. The test results are very encouraging.
Copier color mapping is the color modeling and mapping from the scanner RGB color space to the printer device color
space. It may be aimed for exact/closest color matching or for preference color matching from the source hardcopy to
the reproduced hardcopy. It is not simply the linking of a scanner color characterization and a printer color
characterization. Unlike other cross-color reproduction systems in which both the source color space and the
destination color space are well-defined, copy source types are not controllable in general; thus, the source color space
(e.g. the black and white points, color gamut, and the scanner response to the source) cannot be well-defined. Thus no
exact color characterization can be made for general copy. A trade-off for the balance of overall copy reproduction or a
fuzzy characterization is important for the color characterization. In this paper, we present a method for scanner
characterization preserving the neutral balance, a method to characterize scanners with controllable extrapolation,
selecting a gamut mapping method for optimized system color reproduction, and overall characterization methods for
different source types and different print modes for the trade-off for overall copy color reproduction is achieved.
A gamut mapping method, spring-primary gamut mapping, was developed for device to device color mapping. Instead of performing gamut mapping point-wisely, it selects a small number of points for gamut mapping and determines the color mapping of other points by interpolation using color similarity information. It incorporates primary adjustment and gamut mapping into a single step. Using the color similarity information of neighbor colors for color mapping, it well preserves the color to color relationship. Applying interpolation instead of gamut mapping for majority of colors, it tremendously increases the speed of gamut mapping process.
Monitor oriented RGB color spaces (e.g. sRGB) are widely applied for digital image representation for the simplicity in displaying images on monitor displays. However, the physical gamut limits its ability to encode colors accurately for color images that are not limited to the display RGB gamut. To extend the encoding gamut, non-physical RGB primaries may be used to define the color space, or the RGB tone ranges may be extended beyond the physical range.
An out-of-gamut color has at least one of the R, G, and B channels that are smaller than 0 or higher than 100%. Instead of using wide-gamut RGB primaries for gamut expansion, we may extend the tone ranges to expand the encoding gamut. Negative tone values and tone values over 100% are allowed. Methods to efficiently and accurately encode out-of-gamut colors are discussed in this paper. Interpretation bits are added to interpret the range of color values or to encode color values with a higher bit-depth. The interpretation bits of R, G, and B primaries can be packed and stored in an alpha channel in some image formats (e.g. TIFF) or stored in a data tag (e.g. in JEPG format). If a color image does not have colors that are out of a regular RGB gamut, a regular program (e.g. Photoshop) is able to manipulate the data correctly.
Due to the drop size variation of the print heads in inkjet printers, consistent color reproduction becomes challenge for high quality color printing. To improve the color consistency, we developed a method and system to characterize a pair of printers using a colorimeter or a color scanner. Different from prior known approaches that simply try to match colors of one printer to the other without considering the gamut differences, we first constructed an overlapped gamut in which colors can be produced by both printers, and then characterized both printers using a pair of 3-D or 4-D lookup tables (LUT) to produce same colors limited to the overlapped gamut. Each LUT converts nominal device color values into engine-dependent device color values limited to the overlapped gamut. Compared to traditional approaches, the color calibration accuracy is significantly improved.
This method can be simply extended to calibrate more than two engines. In a color imaging system that includes a scanner and more than one print engine, this method improves the color consistency very effectively without increasing hardware costs. A few examples for applying this method are: 1) one-pass bi-directional inkjet printing; 2) a printer with two or more sets of pens for printing; and 3) a system embedded with a pair of printers (the number of printers could be easily incremented).
A 3-D lookup table (LUT) approach has been developed for color separation for color inkjet printers to better control the color separation and ink limit. This method starts from the color separation for critical points, followed by 1-D interpolation to determine the color separation for grid points in critical lines, followed by 2-D interpolations to determine the color separation for grid points in critical planes, followed by 3-D interpolation to determine the color separation for the remaining grid points in the 3-D LUT. To control the spreading of K and high-density ink to some regions, such as the highlight region and the flesh tone region, we start from controlling the ink propagation in line interpolation, which is a fairly easy step, then control the ink propagation in planes, and finally control the ink propagation in 3-D interpolation. With this process, a 3-D LUT for the conversion from a printer RGB space or a virtual CMY space to an n-colorant space is built to maximize the printer gamut and to minimize grain. This paper describes the details of special interpolations to fill the grids in a 3-D color separation LUT, which includes controlling ink propagation in grids on selected lines, followed by special interpolations to minimize grain in plane-interpolation, followed by special interpolations to fill the remaining grids in the 3-D LUT.
In the linking step of the standard ICC color management workflow for CMYK to CMYK conversion, a CMM takes an AToBn tag (n = 0, 1, or 2) from a source ICC profile to convert a color from the source color space to PCS (profile connection space), and then takes a BToAn tag from the destination ICC profile to convert the color from PCS to the
destination color space. This approach may give satisfactory result perceptually or colorimetrically. However, it does not preserve the K channel for CMYK to CMYK conversion, which is often required in graphic art’s market. The problem is that the structure of a BtoAn tag is designed to convert colors from PCS to a device color space ignoring the K values from the source color space. Different approaches have been developed to control K in CMYK to CMYK
printing, yet none of them well fits into the "Profile - PCS - Profile" model in the ICC color management system. A traditional approach is to transform the source CMYK to the destination CMYK by 1-D TRC curves and GCR/UCR tables. This method is so simple that it cannot accurately transform colors perceptually or colorimetrically. Another method is to build a 4-D CMYK to CMYK closed-loop lookup table (LUT) (or a deviceLink ICC profile) for the color transformation. However, this approach does not fit into opened color management workflows for it ties the source and the destination color spaces in the color characterization step. A specialized CMM may preserve K for a limit number of colors by mapping those CMYK colors to some carefully chosen PCS colors in both the AToBi tag and the BToAi tag. A more complete solution is to move to smart linking in which gamut mapping is performed in the real-time linking at a CMM. This method seems to solve all problems existed in the CMYK to CMYK conversion. However, it introduces new problems: 1) gamut mapping at real-time linking is often unacceptable slow; 2) gamut mapping may not
be optimized or may be unreliable; 3) manual adjustment for building high quality maps does not fit to the smart CMM workflow.
A new approach is described in this paper to solve these problems. Instead of using a BtoAn tag from the destination profile for color transformation, a new tag is created to map colors in PCS (L*a*b* or XYZ) with different K values to different CMY values. A set of 3-D LUTs for different K values are created for the conversion from PCS to CMY, and 1-D LUTs are created for the conversion from luminance to K and to guide a CMM to perform the interpolation from KPCS (K plus PCS) to CMYK. The gamut mapping is performed in the step to create the profile, thus avoiding realtime gamut mapping in a CMM. With this approach, the black channel is preserved; the "Profile - PCS - Profile" approach is still valid; and the gamut mapping is not performed during linking in a CMM. Therefore, gamut mapping can be manually adjusted for high quality color mapping, the linking is almost as easy and fast as the standard linking, and the black channel is preserved.
Neutral gray balance is very important in printing black and white images and printing images that have gray or near gray contents. Accurate gray balance is often difficult to achieve by ICC profiling software packages, especially by commercial software packages designed for users with little knowledge in color science. Furthermore, neutral gray may be judged differently by different individuals. In order to solve these problems, we developed different mechanisms to improve and to modify the neutral gray balance in printer ICC profiles. In this paper, a method to modify the transformation of the neutral gray balance for printer ICC profiles is presented. A colorimetric table, which is derived from the AToB1 tag and the white point tag or derived from a colorimetric data set measured from a near-gray target, is applied to recreate the neutral gray points in BToAi tags. The neutral gray point in a* and b* is determined by color appearance modeling or by a user for personal preference. Different a* and b* values for different lightness can be supplied to compensate the inaccuracy in AToB1 tag or color shift of the printer. To achieve highly accurate neutral gray balance, a target surrounds the neutral gray from white to black is printed for neutral gray balance calibration. Black adaptation is also taken into account to compensate the chrominance difference between the white point and the black point.
Besides having CMY colorants, most of color printers include at lease one extra colorant, black (K), to increase the density for shadow colors and to reduce the colorants required for printing shadow colors. In recent years, CMYKcm, CMYKcmk (Cyan, Magenta, Yellow, blacK, light-cyan, light-magenta, and light-black), and CMYKOG (O and G stand for Orange, and Green) or CMYKOV (V stands for Violet) ink-sets have been used in printers to reduce graininess or to extend printer color gamut. No matter how many colorants are used, a printer is often configured as a three-channel printer to simplify the color mapping process. The traditional GCR/UCR approach has been widely applied for CMY to CMYK color separation. However, this approach is not flexible for controlling K usage locally; it does not guarantee reasonable gamut usage; and it does not work very well for more than CMYK colorants.
In order to solve the problems existed in traditional GCR approaches, a color separation method based on 3-D interpolation was developed. In this process, we first determine the color conversion for some important node points, which include primary colors, neutral colors, and other color ramps in the gamut surface. Then different interpolation approaches are applied to fill the entire 3-D lookup table. This approach solves the problem existed in traditional GCR that a lot of high-chroma shadow colors may be lost in the color separation step. It controls K usage globally as well as locally. It well controls ink limit in the entire gamut. It also works for the color separation for more than CMYK four colorants. Because it performs automatically without human interaction, it can be applied to general printer color calibration as well as ICC profile recreation and smart CMM implementation.
A color calibration process and system for CMYK printing with black preservation is described in this paper. This approach is used to create a 4-D lookup table (LUT) off time for a closed-loop workflow or a deviceLink ICC profile at real-time by a smart color management module (CMM) for ICC color management workflow. The output K' in the 4-D LUT or the deviceLink profile is decided by the lightness or density mapping between the input K and the output K' and the black usage of the output printer, and K' is proportional to K. The calibration processes are: 1) to convert the input CMYK (e.g. SWOP CMYK) into a device-independent color space (e.g. CIE CAM97s Jab, CIE L*a*b*, or MLab); 2) gamut mapping; and 3) to convert the in-gamut device-independent color into the output CMYK color space. A major difference of this approach over existing methods is that the input K is carried to the second and the third steps to determine the amount of the output K. Therefore the input K information is not lost during the color transformation. This approach can be applied to both closed-loop color architecture and ICC color management.
The saturation rendering intent in ICC color management system has never been practiced successfully. One of the reasons is that it is expected to have primary matching for this rendering intent, but the current ICC workflow makes this impossible. In this paper, three approaches to achieve primary matching for printing applications will be presented. We start with building a printer ICC profile that converts selected first and/or secondary primaries of the default monitor RGB color space to the corresponding printer primaries. If a source monitor RGB color space is different from the default RGB color space, a simple approach is to apply the default RGB color space for the saturation rendering intent for primary preservation. This is because the primary matching is more important than color accuracy for the saturation intent, and different source monitor RGB color spaces are not very different. For more accurate color transformation and still to preserve the primary matching, the source monitor RGB color space is adjusted so that the selected primaries are fully adapted to those of the default RGB color space and the adaptation is decreased gradually as the hue of a source color moves off the selected primaries. In a smart CMM environment, the primary matching can be achieved by hue rotation and gamut adaptation during the real-time linking.
Ink limit is an important parameter for printer color calibration, especially for inkjet printers. A GCR approach is often used to control the total ink amount for CMYK printers. However, a tradition GCR approach has the following limitations: 1) it can not reduce the total ink amount to less than 200 percent for CMYK printers; 2) it can not be applied to reduce ink for CMY printers; 3) to achieve highest image quality, ink amount may be limited to different values in different regions, in which the GCR approach fails. In this paper, a new approach is presented to control ink limit. It controls ink limit globally as well as locally. An algorithm was developed to construct a gamut boundary for gamut mapping that guarantees that the constructed gamut surface covers only colors within the ink limit. If the ink limit needs to be modified, the gamut surface is reconstructed based on the original measured data. Therefore redoing and remeasuring a target is avoided. It greatly simplifies the ink limit control and color calibration.
In the process of gamut mapping from monitor display into printer hardcopy in CIE L*a*b* color space, blue is tend to map to purple. This paper presents a new approach to solve the perceived blue hue shift problem. By this approach, the entire color gamut is divided into four regions: a non-blue region, a blue-region, and two in-between regions. The segmentation of the four regions is based on the hue angle in CIE L*a*b* color space. Different color spaces are applied to different regions for gamut mapping. In the non- blue region, CIE L*a*b* color space is applied for gamut mapping. In the blue region, CIE L*u*v* color space is applied to eliminate the perceived blue shift. In the two in-between regions, both color spaces are used for gamut mapping, and a weighting function is applied for smooth transaction.
A new gray component replacement approach for four-color printing process is developed to directly convert CIE XYZ values into CMYK values. We start with building a colorimetric density lookup table (LUT) for black channel from 0 to 255 (for 8-bit per-channel). A color in CIE XYZ color space is converted into colorimetric density, then the colorimetric density is compared with colorimetric densities in the black densities LUT to find maximum black. The actual black is determined based on the maximum black that has been found. The remaining of the total colorimetric density subtracted from the colorimetric density of the actual black is converted into CIE XYZ value, and finally the CIEXYZ value is converted into CMY by a predictive printer color mixing model. A close-up correction algorithm is implemented to reduce color errors coming from both the CIE XYZ to CMYK inversion and the assumption that the colorimetric density is additive.
An approach to optimize dot area coverage is presented. It improves the color prediction accuracy for printer color modeling using Neugebauer narrow-band color mixing model. The Neugebauer colorimetric quality factor (CQF) is applied to integrate dot areas calculated from Neugebauer narrow- band color mixing model. However, simply using CQF to optimize dot area often results in very noisy dot areas. The noise comes from some spectral bands where measured spectral reflectance of the color patch is higher than the measured spectra reflectance of the paper white. To eliminate this kind of noise, the CQF weighting filter approach is modified. The simplest approach is to use Neugebauer CQF weighting but not to use dot areas in the bands where the spectral reflectances of color patches are very close to the spectral reflectance of the paper white. Another approach is to use the spectral reflectance differences between the paper white and the 100% ink coverage as the weighting of this ink channel. The third approach (most effective one) is to combine (multiply) the weightings generated from the first and the second approaches.
A model to predict colorimetric value for color printers is presented. The Neugebauer narrow-band color mixing model was applied with modifications. While sixteen primaries are used for four-color printing process in Neugebauer mode, we used two data sets in our model, one with eighty-one CMYK primaries and the other with one hundred twenty-five CMY primaries. Two Yule-Nielsen factors were applied to optimize the CMYK set and the CMY set separately. The Yule-Nielsen factors were optimized by minimizing (Delta) E*L*a*b* or (Delta) E*94. The Neugebauer calorimetric quality factor was applied as a weighting function to optimize dot areas. By optimizing primaries and applying the CQF weighting function, the average color error and the maximum color error decrease significantly.