Source: https://patents.google.com/patent/US9667829B2/en
Timestamp: 2019-08-21 20:55:10
Document Index: 475661379

Matched Legal Cases: ['Application No. 62', 'Application No. 62', 'application No. 61', 'Application No. 61', 'Application No. 62', 'Application No. 62']

US9667829B2 - System and methods for encoding information for printed articles - Google Patents
System and methods for encoding information for printed articles Download PDF
US9667829B2
US9667829B2 US14/932,645 US201514932645A US9667829B2 US 9667829 B2 US9667829 B2 US 9667829B2 US 201514932645 A US201514932645 A US 201514932645A US 9667829 B2 US9667829 B2 US 9667829B2
US14/932,645
US20160198064A1 (en
2014-08-12 Priority to US201462036444P priority Critical
2015-05-20 Priority to US201562164479P priority
2015-11-04 Priority to US14/932,645 priority patent/US9667829B2/en
2015-11-04 Application filed by Digimarc Corp filed Critical Digimarc Corp
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2017-04-10 Assigned to DIGIMARC CORPORATION reassignment DIGIMARC CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: REED, ALASTAIR M., FALKENSTERN, KRISTYN R., BAI, yang
2017-05-30 Publication of US9667829B2 publication Critical patent/US9667829B2/en
The present disclosure relates generally to digital watermarking for spot colors. In one implementation a substitute spot color+CMY tint is selected to replace an original spot color. The CMY tint can be transformed to carry a digital watermark signal. Of course, other features, combinations and technology are described herein.
This application claims the benefit of U.S. Provisional Patent Application No. 62/164,479, filed May 20, 2015, which is hereby incorporated herein by reference in its entirety.
This application is a continuation in part of U.S. patent application Ser. No. 14/616,686, filed Feb. 7, 2015 (published as US 2015-0156369 A1, now U.S. Pat. No. 9,380,186), which claims the benefit of U.S. Patent Application No. 62/102,247, filed Jan. 12, 2015, 62/063,790, filed Oct. 14, 2014, 62/063,360, filed Oct. 13, 2014, and 62/036,444, filed Aug. 12, 2014. Each of the above patent documents is hereby incorporated herein by reference in its entirety.
This application is related to International Patent Application No. PCT/US15/44904 filed Aug. 12, 2015 (published as WO 2016/025631 A1), which are each hereby incorporated herein by reference in its entirety.
This application is also related to U.S. patent application Ser. No. 14/588,636, filed Jan. 2, 2015 (published as US 2015-0187039 A1, issued as U.S. Pat. No. 9,401,001), which claims the benefit of U.S. Provisional application No. 61/923,060, filed Jan. 2, 2014. This application is also related to U.S. patent application Ser. No. 13/975,919, filed Aug. 26, 2013 (issued as U.S. Pat. No. 9,449,357), which claims the benefit of U.S. Provisional Application No. 61/749,767, filed Jan. 7, 2013 and 61/693,106, filed Aug. 24, 2012. This application is also related to U.S. Provisional Patent Application No. 62/152,745, filed Apr. 24, 2015, and 62/136,146, filed Mar. 20, 2015. This application is also related to U.S. Pat. No. 8,199,969, US Published Patent Application Nos. US 2010-0150434 A1 and US 2013-0329006 A1; and U.S. Provisional Application No. 62/106,685, filed Jan. 22, 2015, 62/102,547, filed Jan. 12, 2015, 61/693,106, filed Aug. 24, 2012, 61/716,591, filed Oct. 21, 2012, and 61/719,920, filed Oct. 29, 2012. Each of the above patent documents is hereby incorporated herein by reference in its entirety.
Some of the present assignee's work in steganography, data hiding and digital watermarking is reflected, e.g., in U.S. Pat. Nos. 6,947,571; 6,912,295; 6,891,959, 6,763,123; 6,718,046; 6,614,914; 6,590,996; 6,408,082; 6,122,403 and 5,862,260, and in published specifications WO 9953428 and WO 0007356 (corresponding to U.S. Pat. Nos. 6,449,377 and 6,345,104). Each of these patent documents is hereby incorporated by reference herein in its entirety. Of course, a great many other approaches are familiar to those skilled in the art. The artisan is presumed to be familiar with a full range of literature concerning steganography, data hiding and digital watermarking.
A digital watermark may contain signal energy, e.g., over the spatial resolutions shown by the gray box in FIG. 2a . If the luminance and chrominance contrast sensitivity functions are integrated over this gray box region, the resultant energy ratios calculate the uniform perceptual scaling for CIELAB L*, a* and b*. Thus the watermark perceptual error ΔEWM can be calculated as:
R(λ)=∝o R o(λ)+∝s R s(λ)+∝c R c(λ)+∝M R M(λ)+∝R SC(λ)+∝R SM(λ)+∝R CM(λ)+∝SCM R SCM(λ) (2)
∝ o = ( 1 - ⁢ ∝ s ) ⁢ ( 1 - ⁢ ∝ c ) ⁢ ( 1 - ⁢ ∝ M ) ⁢ ⁢ ∝ M = ( 1 - ⁢ ∝ s ) ⁢ ( 1 - ⁢ ∝ c ) ⁢ ∝ M ⁢ ⁢ ∝ CM = ( 1 - ⁢ ∝ S ) ⁢ ∝ C ⁢ ∝ M ⁢ ⁢ ∝ S ⁢ = ∝ S ⁢ ( 1 - ⁢ ∝ C ) ⁢ ( 1 - ⁢ ∝ M ) ⁢ ⁢ ∝ SC = ∝ S ⁢ ∝ C ⁢ ( 1 - ⁢ ∝ M ) ⁢ ⁢ ∝ SCM = ∝ S ⁢ ∝ C ∝ ⁢ ∝ c = ( 1 - ⁢ ∝ S ) ⁢ ∝ C ⁢ ( 1 - ⁢ ∝ M ) ⁢ ⁢ ∝ SM = ∝ S ⁢ ( 1 - ⁢ ∝ c ) ⁢ ∝ M ( 3 )
R SC ⁡ ( λ ) = R 0 ⁡ ( λ ) = R S ⁡ ( λ ) R 0 ⁡ ( λ ) ⁢ R C ⁡ ( λ ) R 0 ⁡ ( λ ) . ( 4 )
Coefficients ∝i in Spectral Neugebauer model are linear ink percentages before any dot gain correct ion. Demichel equation (3), linear ramp in ∝i results in a linear change of reflectance and thus linear change of CIE XYZ. To correct for any single-ink non-linearity caused by the press (often called dot gain), we substitute ∝i in the above model with gain corrected values gi −1({circumflex over (∝)}i). Function gi −1 inverts the dot—gain effect such that linear ramp in {circumflex over (∝)}i. leads back to linear increase of reflectance. Several patches of single screened ink can be used to estimate gi −1 for i-th ink.
FIG. 28b corresponds to Appendix B's FIG. 2, which shows thumbnails of the color patch samples with a watermark applied.
4. Decompose CMY tint into Min and Max Tweaks. For example, a gradient search process or least squares distance process can be conducted to find optimum tweaks.
These processes may consider other factors as well. For example, an optimization process may consider the original spot color, visibility constraints, robustness requirements at a particular spectral response (e.g., at 660 nm), a k (black) channel, other spot colors, etc. For the illustrated example, Min Tweak (%): 75, 27, 42, 16; and Max Tweak (%): 75, 0, 73, 0 were determined. In FIG. 7, EWM=watermark error, which can be a weighted sum of ΔL*, Δa* and Δb* where CIE ΔL* can be weighted heaviest to account for greater visibility of the watermark signal in lightness, followed by Δa* (red-green) and then Δb* (yellow-blue). Some goals of the Min and Max CMY Tweak may include, e.g., i) similar perceived visibility to original spot color when overprinted with screened spot, ii) maximum tweak difference at 660 nm (corresponding to red LED scanner), which maximizes scanner visibility; iii) minimum luminance (CIE L*) difference, recall that human visual system is most sensitive to luminance changes and color difference (CIE a* and b*) should be kept under control.
C i ′=C i+σωC W i ,M i ′=M i+σωM W i ,Y i ′=Y i+σωY W i, (7)
Color difference between min and max tweaks overprinted with 75% spot, denoted EWM, is called Watermark Error. A final watermark can be produced by overprinting 75% screened spot and the modulated CMY tint. In this process, both color errors are interconnected. In order to keep luminance changes minimal more space for CMY tweaks can be used and thus, possibly, increasing the color match error, ECM. A spot screen of 75% is also a parameter that could be changed. Difference of spectral reflectance at 660 nm, denoted as Δ660 serves as a measure of watermark signal strength similar to parameter σ in Eq. (7). Given a value of Δ660 spectral ink overprint models can be used to find optimal value of spot screen and min and max tweak ink percentages minimizing weighted sum of both color errors:
min α min , α max ⁢ E CM + p · E WM = Δ ⁢ ⁢ E 76 ⁡ ( R S , R max + R min 2 ) 2 + p · Δ ⁢ ⁢ E WM ⁡ ( R min , R max ) 2 , ⁢ ⁢ s . t . ⁢ R max ⁡ ( 660 ) - R min ⁡ ( 660 ) ≥ Δ 660 0 ≤ α min , α max ≤ 1 ( 9 )
The optimization problem in Eq. (9) can be solved numerically with, e.g., the IPOPT library, using the underlying technology detailed in A. Wachter and L. T. Biegler, “On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming.” Mathematical Programming, Vol. 106, issue (1): pages 25-57, 2006, which is hereby incorporated herein by reference in its entirety. IPOPT code is available as open source, e.g., at http://www.coin-or.org/Ipopt.
One alternative but related embedding technology uses a blend model taking, e.g., a 4 color SWOP profile, and creates a 5 color profile (4 SWOP colors+S1 a) to create a 5 color search space. The search space can be searched to find an optimized solution of robustness, readability, and minimized visibility changes. (Even if a black color is not used, it can be advantageous to search across a 4 color space.)
A first approximation of a combined color (e.g., S1 a+CMY) may use the following process:
1. a) Reduce spot color (S1) percentage to yield a screened back spot color (S1 a). This can aid in watermark detectability by a POS scanner, and b) estimate process color percentages (e.g., a CMY combination to overlay the spot color).
2. Estimate colorimetric coefficients for composite color, e.g., % S1 a+xC+yM+zY, where % is the spot color screening percentage, and x, y and z are weighting or percentage coefficients for their respective process colors.
We left the FIG. 13 flow diagram discussion at screening back spot color S1 if the CMYK equivalent is not less than or equal to 75%. Recall that we are going to combine the screen backed spot color S1 a with process color equivalents, with the watermark signal preferably being carried in the process colors. And, if using a red color laser or LED, we want to match the red with tweaks in Cyan so that they can be more readily seen by the capture device. So, if the spot color S1 a is Cyan heavy, it may risk washing out the printed CMY. That is, the Cyan heavy S1 a introduces noise such that the watermark tweaks in the underlying process colors are difficult to detect. Depending on the application and tolerance for noise, a Cyan trigger in the range of, e.g., 60-85%, can be used to decide whether to screen spot color S1.
An exhaustive search may be carried out over an entire spot color library to find close candidates. For example, the 2014 version of PANTONE+ coated color book includes 1,755 spot colors, and if one of them is the first spot color Sa, then the other 1,754 can be evaluated relative to Sa (e.g., a distance metric or color error metric for each of the 1,754 spot colors relative to Sa). The shortest distance or lowest error metric spot colors can be included in the set of candidates. (Since the PANTONE color book is not very well organized (e.g., similar colors are not always labeled with consecutive indices), an exhaustive search can likely find potential close candidates.
Once a CMY tint is selected for each Sbi, one (1) or more final candidates are selected. For example, a digital simulation of the Sbi+overprinted CMY tint can be analyzed and compared to the first spot color Sa. Final candidates may include those with the smallest Lab distance or Chroma distance. For the Lab distance, and for (L1*, a1*, b1*) and (L2*, a2*, b2*), two colors in L*a*b*:
ΔEab*=√{square root over ((L2*−L1*)2+(a2*−a1*)2+(b2*−b1*)2)}. A chronia distance may look similar, but without the first (L2−L1)2 term. Of course, other distance metrics can be used, e.g., ΔE94, ΔE2000.
2. In step 32, the Pantone spot color universe is examined to find 1-i (where i is an integer) candidate spot color substitutes Sbi having: i) a low color error (or shortest distance) between the candidate substitute spot color Sbi and the screened back version Ss of the original spot color, and also ii) a candidate Sbi with a color value that is brighter than the screened backed version Ss of the original spot color. Color error or color distance metrics can be determined using, e.g., Lab distance, Chroma distance, ΔE94, ΔE2000 or CIEDE2000, etc. “Low” can be determined relative to a predetermined threshold value or by a relative evaluation, e.g., the “lowest” 1-5 substitute spot colors are selected for further evaluation. The second prong, a color value that is brighter than the screened back version Ss, can be viewed from a scanner's perspective, e.g., what is the substitute spot color's spectral reflectance at or around 660 nm. Generally, the bigger the brightness value is, the brighter the color is. In step 32, if the original spot color is brighter than paper white (e.g., like the florescent colors such as Pantone 804, 805 and 806) or other threshold, the process can be optionally configured to not enforce the 2nd prong constraint of “brighter than” when searching for substitute spot color candidates.
5. As an optional step 35, repeat steps 32-34 for additional substitute spot color candidates Sb2-Sbi. While this step is optional, practice has shown that designers like to make choices.
Using (L1*, a1*, b1*) and (L2*, a2*, b2*), colors in L*a*b*, the error between two corresponding pixel values is:
ΔEnb*=√{square root over ((L2*−L1*)2+(a2*−a1*)2+(b2*−b1*)2)}, where ΔEAB*≈2.3 corresponds to a JND (just noticeable difference). Other comparisons may utilize, e.g., ΔE94 or ΔE2000.
With reference to FIG. 27a , one objective may include embedding digital watermarking into images with equal visibility. That is, the image includes watermarking embedded therein at different signal strength values to achieve uniform or equal visibility. During the embedding stage, the visibility model can predict the visibility of the watermark signal and then adjust the embedding strength. The result will be an embedded image with a uniform watermark signal visibility, with the embedding strength varying locally across the image depending on characteristics of the cover image's content. For example, a visibility map generated from the FIG. 26 system is used to reshape (e.g., locally scale according to an error map and/or mask embedding or avoidance areas according to a visibility map) a watermark signal. The original signal is then embedded with the reshaped watermark signal to create an equal visibility embedded (EVE) image. In such a case, the watermark signal locally varies to achieve an overall equal visibility.
Some visibility advantages of EVE vs. uniform strength embedding (USE) are shown in FIG. 27b . The visibility of the USE varies from area to area, as see in the bottom left image. In comparison, when embedding the same image area with EVE (bottom right image), the watermark visibility appears equal. The bottom left and right images represent the same image area highlighted in blue in the upper right image.
f ~ ⁡ ( x , y ) = h ⁡ ( x , y ) * f ⁡ ( x , y ) = ∑ m ⁢ ∑ n ⁢ h ⁡ ( m , n ) ⁢ f ⁡ ( x - m , y - n ) , ( 10 )
f 0 ⁡ ( x , y ) = f ⁡ ( x , y ) ⁢ ⁢ and ( 12 ) f l ⁡ ( x , y ) = ∑ m ⁢ ∑ n ⁢ h 0 ⁡ ( m , n ) ⁢ f l - 1 ⁡ ( 2 ⁢ x - m , 2 ⁢ y - n ) ( 13 )
where αl is the coefficient of the basis function {tilde over (f)}l(x,y) obtained by up-sampling the corresponding pyramid level fl(x,y) back to the base resolution.
f ~ l ⁡ ( x , y ) = 4 l ∑ m ⁢ ∑ n ⁢ h l ⁡ ( x - 2 l ⁢ m , y - 2 l ⁢ n ) ⁢ f l ⁡ ( m , n ) , ( 16 )
{tilde over (f)} l(x,y)={tilde over (h)} l(x,y)*f l(x,y), (17)
where {tilde over (h)}l(x,y)=hl(x,y)*hl(x,y). Thus, considering Eq. (15), we assert that the optimal representation is obtained by minimizing the sum of the squared error between the desired CSF and the Gaussian representation; e.g.,
a = arg ⁢ ⁢ min a ⁢ E , ⁢ where ( 18 ) E = ∑ x ⁢ ∑ y ⁢ ( h ⁡ ( x , y ) - ∑ l ⁢ a l ⁢ h ~ l ⁡ ( x , y ) ) 2 , ( 19 )
and a=[α1, α2, . . . ]. A linear least-squares problem, which can be solved using software packages such as, e.g., Matlab® or GNU Octave, can be utilized to solve equation 18. Further, the optimization can be pre-calculated for each local luminance of interest and stored in a look-up table, noting that for one example application each coefficient αl is spatially varying according to the local luminance level Lf=Lf(x,y) of f(x,y), i.e., αl=al(Lf)=al(Lf(x,y)).
The luminance and chrominance CSF of the human visual system has been measured for various retinal illumination levels. The luminance CSF variation was measured by Floris L. Van Nes and Maarten Bouman, “Spatial modulation transfer in the human eye,” Journal of Optical Society of America, vol. 57, issue 3, pp. 401-406, 1967 and the chrominance CSF variation by G J Van der Horst and Maarten Bouman, “Spatiotemporal chromaticity discrimination,” Journal of Optical Society of America, vol. 59, issue 11, 1969. These measurements show a variation in peak sensitivity of about a factor of 8 for luminance and 5 for chrominance over retinal illumination levels which change by about a factor of 100.
Since the retinal illumination can change by about a factor of 100 between the lightest to darkest area on a page, the CSF peak sensitivity and shape can change significantly. The function is estimated by the average local luminance on the page, and a spatially dependent CSF is applied to the image. This correction is similar to the luminance masking in adaptive image dependent compression. See G J Van der Horst and Maarten Bouman, “Spatiotemporal chromaticity discrimination,” Journal of Optical Society of America, vol. 59, issue 11, 1969.
A set of observers were asked to rate their perception of the image degradation of 20 color patch samples using a quality ruler. The quality ruler (illustrated in [FIG. 13a ]) increases in watermark strength from left (B) to right (F). The color samples were viewed one at a time at a viewing distance of approximately 12 inches. The samples were presented using the Latin square design (see Geoffrey Keppel and Thomas Wickens, “Design and analysis: A researcher's handbook.” Prentice Hall, pp. 381-386, 2004) to ensure a unique viewing order for each observer.
See [FIG. 28a ] Quality ruler increasing in degradation from B (slight) to F (strong).
Thumbnails of the 20 color patches are illustrated in See [FIG. 28b ]. The color samples were chosen largely based on the results of a previous experiment; where it was observed that the visibility model had difficulty accurately predicting the observer response with darker color patches. Additionally, one color patch had a much higher perceived and predicted degradation. Ten of the original samples were included in the second experiment. Dark patches, patches which were expected to have a higher perception of degradation and memory colors were added to complete the set of 20 patches. The experiment and the quality ruler patches were all printed with an Epson Stylus 4880 on Epson professional photo semi-gloss 16 inch paper.
See [FIG. 28b ] Thumbnails of the 20 color patch samples with the watermark applied.
Pearson and R2 correlation between the observers'
mean responses and the objective metrics. For both tests,
the proposed full-color visibility model with the luminance
adjustment shows the highest correlation.
f ~ ⁡ ( x , y ) = h ⁡ ( x , y ) * f ⁡ ( x , y ) = ∑ m ⁢ ∑ n ⁢ h ⁡ ( m , n ) ⁢ f ⁡ ( x - m , y - n ) , ( 1 ⁢ a )
f 0 ⁡ ( x , y ) = f ⁡ ( x , y ) ⁢ ⁢ and ( 3 ⁢ a ) f l ⁡ ( x , y ) = ∑ m ⁢ ∑ n ⁢ h 0 ⁡ ( m , n ) ⁢ f l - 1 ⁡ ( 2 ⁢ x - m , 2 ⁢ y - n ) ( 4 ⁢ a )
for l>0 and generating kernel h0 (m,n). It is easily shown from this definition that each level fl(x,y) of an image pyramid can also be constructed iteratively by convolving the input image with a corresponding effective kernel hl(m,n) and down-sampling directly to the resolution of the level, as follows:
f ~ ⁡ ( x , y ) = ∑ l ⁢ a l ⁢ f ~ l ⁡ ( x , y ) , ( 6 ⁢ a )
{tilde over (f)} l(x,y)={tilde over (h)} l(x,y)*f(x,y), (8a)
where {tilde over (h)}l(x,y)=hl(x,y)*hl(x,y). Thus, considering Eq. (6a), we assert that the optimal representation is obtained by minimizing the sum of the squared error between the desired CSF and the Gaussian representation; i.e.,
a = arg ⁢ ⁢ min a ⁢ E , ⁢ where ( 8 ⁢ a ) E = ∑ x ⁢ ∑ y ⁢ ( h ⁡ ( x , y ) - ∑ l ⁢ a l ⁢ h ~ l ⁡ ( x , y ) ) 2 , ( 9 ⁢ a )
and a=[α1, α2, . . . ]. This is a standard linear least-squares problem and can be solved using standard software packages, like Matlab® or GNU Octave. Further, the optimization can be pre-calculated for each local luminance of interest and stored in a look-up table, noting that for our application each coefficient αl is spatially varying according to the local luminance level Lf=Lf(x,y) of f(x,y), i.e., αl=al(Lf)=al(Lf(x,y)).
[FIG. 33] shows an example from a cover image mimicking a package design. The design has two embedding schemes: on the left the watermark signal strength is uniform across the whole image, and on the right the watermark signal strength is adjusted based on the prediction from the visibility model. Since the human visual system is approximately a peak error detector, the image degradation caused by the watermark signal is determined by the most noticeable area. In this example, the hilly area in the background has the most noticeable degradation, as shown in the magnified insets. The visibility model is used to find this severe degradation. The signal strength in this area is reduced which improves the overall visibility of the embedded image, making it more acceptable. The total watermark signal on the right is 40% more than that on the left, but visually, the marked image on the right is preferable to the left one, because the degradation in the most noticeable area is reduced significantly.
[FIG. 34] shows the calculated visibility for the uniform signal strength embedding (left) and the visibility model adjusted embedding (right). Notice that the visibility map is smoother on the right than on the left.
Standard deviation of the visibility maps on
the 4 images from the two embedding schemes.
Detection rate on 4 images from the two embedding
schemes, out of 1000 captures each image/embedding.
1. A method for generating a color design for a printed article comprising:
determining a plurality of substitute spot color candidates Sb1-Sbi, where i is an integer, by evaluating—for each substitute spot color candidate—color distance metrics between data associated with the substitute candidate spot color and the data associated with the first spot color Sa;
determining a Cyan (C), Magenta (M) and Yellow (Y) tint for each of the plurality of substitute spot color candidates Sb1-Sbi;
using one or more electronic processors, simulating an overprint of each of the plurality of substitute spot color candidates Sb1-Sbi with its respective CMY tint, and for each of the overprinted substitute spot color candidates, generating an Lab or Chroma distance metric relative to the first spot color Sa;
for at least one of the final spot color candidates, and using one or more electronic processors, transforming its respective CMY tint with a digital watermark signal; and
substituting the at least one of the final spot color candidates plus its transformed CMY tint for the first spot color Sa in the color design.
2. The method of claim 1 in which the data associated with the substitute candidate spot color comprises Lab data.
3. The method of claim 1 in which the data associated with the substitute candidate spot color comprises RGB or CMYK data.
4. The method of claim 1 in which said determining final spot color candidates comprises determining Lab distance values relative to the first spot color Sa for each of the plurality of substitute spot color candidates Sb1-Sbi.
5. The method of claim 1 in which said determining final spot color candidates comprises determining Chroma distance values relative to the first spot color Sa for each of the plurality of substitute spot color candidates Sb1-Sbi.
6. A system for encoding information for a printed article comprising:
determining a color error between S1 and a combination of S2 and S3, the combination including the max and min tweaks;
7. The system of claim 6 in which the optimizing includes determining color weights and a global signal strength, the color weights to be applied to the data representing third color data (S3) and the global signal strength for regulating the modulating.
8. The system of claim 7 in which the optimizing is constrained by spectral reflectance between 630 nm to 680 nm.
9. The system of claim 7 in which the optimizing is constrained by spectral reflectance between 655 nm to 670 nm.
10. The system of claim 7 in which the data representing third color data (S3) comprises data representing two (2) or more process colors, and the data representing second color data (S2) represents a second color.
11. The system of claim 10 in which the second color comprises a screened-back version of the first color.
12. The system of claim 6 in which the max and min tweaks correspond to a 2-dimensional image digital watermark tile.
13. The system of claim 6 further comprising a display, in which said one or more processors are configured for providing a graphical user interface through which a user can select embedding options.
14. A system for transforming color data to include an information signal embedded therein, comprising:
one or more processors configured as an optimizer bounded by the system constraint information, the optimizer generates C (Cyan), M (Magenta), Y (Yellow) process color data to be combined with a screen of the spot color data to yield a minimized color error approximation of the spot color data, the optimizer generates tweak values in terms of at least ΔC, ΔM, ΔY for modulating the C, M, Y process color data to carry the information signal, the tweak values optimized based on an error metric; and
15. A method for generating a design for a printed article comprising:
minimizing: i) a color match error between a first color (S1) and a combination of data representing second color data (S2) and data representing third color data (S3), the combination including auxiliary encoded information in the data representing third color data, and ii) an information modulating error associated with modulations of the data representing third color data (S3) that carry the auxiliary encoded information,
16. The method of claim 15 in which spectral component comprises a metric associated with a spectral reflectance between 630 nm to 680 nm.
17. The method of claim 15 in which the modulations correspond to a 2-dimensional image digital watermark tile.
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