JP5152515B2 - Color correction coefficient generation apparatus and color correction coefficient generation program - Google Patents

Description

  The present invention relates to a color correction coefficient generation device and a color correction coefficient generation program.

  Currently, devices that handle color, such as digital cameras, color scanners, color printers, and color displays, are widespread and widely used. At the same time, improvement in color reproducibility is required from the market. In the system used, for example, particularly DTP (Desk Top Publishing), etc., CMS (Color Management System) in each device is indispensable. Currently, table values for color correction are determined based on a color profile typified by ICC (International Color Consortium), and color correction is performed by various invented interpolation processes.

  Various methods for performing color conversion, calibration, color calibration and the like have been invented so far. In these, target values for each device are set, and color conversion is performed to values in a device-dependent color space that reproduces the target values. In the case of calibration, most of the correction methods currently implemented using a one-dimensional table are single-color calibration for correcting a single color, and gray balance for correcting process black of CMY three-color superposition. There are two main techniques for calibration. However, each location to be corrected is specified, and the correction accuracy in a portion where a plurality of color materials such as a secondary color and a tertiary color are overlapped is not guaranteed in the single color calibration. For example, particularly in the electrophotographic system, even if the primary colors are combined due to the variation in the multiple transfer rate, the color balance is lost for the secondary colors or more. Even with gray balance calibration, accuracy is not guaranteed for colors other than the process gray portion.

  In recent years, a calibration method using a three-dimensional table has been developed. As a result, correction can be performed with high accuracy over the entire color region reproduced by the apparatus. In the calibration method using this three-dimensional table, the value of each color component is required. Therefore, it is more suitable for point-sequential processing before color separation than processing by frame-sequential after color components are separated.

  For example, Patent Document 1 describes that when correcting in-plane unevenness, not only the primary color but also multi-order color unevenness is improved over the entire color gamut. In this technique, an average of values of a correction table created from a certain color component single color (primary color) and a correction table created by separating the color component from multi-order colors is obtained, and the correction table for the color component is obtained. Is to be generated. As a result, average color correction is performed for the entire color range.

JP 2006-343682 A

  An object of the present invention is to provide a color correction coefficient generation device and a color correction coefficient generation program capable of ensuring the correction accuracy of the entire color gamut while improving the correction accuracy of a specific correction target color. Is.

  The invention according to claim 1 is a correction value acquisition unit that acquires a correction color value corresponding to a target color value to be corrected, a weighting factor setting unit that sets a weighting factor for the target color value, A generation unit that generates a color correction coefficient for correcting the target color value for each color component according to the target color value, the correction color value, and the weighting factor, and the color correction coefficient generated by the generation unit satisfies a convergence condition. A convergence condition determining means for determining whether or not the condition is satisfied; and an evaluation value of a correction result using the color correction coefficient generated by the generating means when the convergence condition determining means determines that the convergence condition is not satisfied. Evaluation value acquisition means for acquiring, wherein the weighting coefficient setting means changes the weighting coefficient based on the evaluation value acquired by the evaluation value acquisition means and re-applies the color correction coefficient to the color correction coefficient generation means. To generate A color correction coefficient generation device according to claim.

  The invention according to claim 2 of the present application is a correction value acquisition unit that acquires a correction color value corresponding to a target color value to be corrected, a weighting factor setting unit that sets a weighting factor for the target color value, A generation unit that generates a color correction coefficient for correcting the target color value for each color component according to the target color value, the correction color value, and the weighting factor, and the color correction coefficient generated by the generation unit are used. Evaluation value acquisition means for acquiring an evaluation value of a correction result, and convergence condition determination means for determining whether or not the evaluation value acquired by the evaluation value acquisition means satisfies a convergence condition, and the weighting factor setting means When the convergence condition determination unit determines that the convergence condition is not satisfied, the weight correction coefficient is changed based on the evaluation value acquired by the evaluation value acquisition unit, and the color correction coefficient generation unit is updated. Generate color correction factor A color correction coefficient generating device for causing.

  According to a third aspect of the present invention, in the color correction coefficient generation device according to the first or second aspect of the present invention, the evaluation value acquisition unit performs the correction result by simulation of color correction processing using the color correction coefficient. And obtaining an evaluation value based on the color difference before and after the correction.

  The invention described in claim 4 is a color obtained by the evaluation value acquisition unit of the color correction coefficient generation device according to claim 1 or 2 outputting an image using the color correction coefficient. The color correction coefficient generation apparatus is characterized in that the correction result is obtained based on a value and an evaluation value is acquired based on a color difference before and after the correction.

  The invention according to claim 5 of the present application causes a computer to execute the function of the color correction coefficient generation device according to any one of claims 1 to 4. It is a program.

  According to the first and second aspects of the present invention, it is possible to ensure the correction accuracy for the entire color gamut while improving the correction accuracy for a specific color to be corrected.

  According to the third aspect of the present invention, the color correction coefficient can be obtained without using an actual output device.

  According to the fourth aspect of the present invention, a color correction coefficient suitable for an actual output device can be obtained.

  According to the invention described in claim 5 of the present application, the effect of any one of claims 1 to 4 can be obtained.

  FIG. 1 is a block diagram showing a first embodiment of the present invention. In the figure, 1 is a correction value acquisition unit, 2 is a weight coefficient setting unit, 3 is a color correction coefficient generation unit, 4 is a convergence condition determination unit, 5 is an evaluation value acquisition unit, 6 is a color correction coefficient storage unit, and 11 is a first color correction coefficient storage unit. One color prediction unit, 12 a second color prediction unit, 21 a balance parameter setting unit, and 22 a weighting factor calculation unit. In the following description, color values having C (cyan), M (magenta), Y (yellow), and K (black) as color components are targeted, but it goes without saying that the present invention is not limited to this.

  The correction value acquisition unit 1 acquires a correction color value corresponding to a target color value to be corrected. The target color value preferably includes color information that is important for correction. For example, C, M, Y, and K values such as C, M, Y, and K single colors and three colors of gray are used. The target color value can be considered. If correction is necessary for the entire color gamut, color values inside the color gamut such as secondary color, tertiary color, and quaternary color are also set as target color values. For example, a combination of single colors in increments set in advance for each color component may be used as the target color value. The correction color value is a color value to be input to the output device in order to obtain the color originally desired when the target color value is output by the output device that is the correction target. That is, if the target color value is converted into a correction color value and input to the output device, the output color can be obtained originally.

  In the example illustrated in FIG. 1, the correction value acquisition unit 1 includes a first color prediction unit 11 and a second color prediction unit 12. The first color predicting unit 11 predicts a color value originally intended to be obtained from the target color value. For example, a color conversion model is created based on raw data that is a pair of CMYK values given to an output device that does not require correction and colorimetric values of an image output by the output device based on the CMYK values. The target color value may be converted into the predicted value using the color conversion model. Here, the predicted value is a value Lab in the LAB color space that is a color space independent of the apparatus.

  The second color prediction unit 12 predicts a color value to be input to the output device in order to output the color predicted by the first color prediction unit 11 by the output device that needs to be corrected. For example, a colorimetric value (here, a CMYK value given to an output device that needs to be corrected) and a raw data obtained by pairing a colorimetric value of an image output by the output device based on the CMYK value. A color conversion model from Lab values) to CMYK values may be created, and the color values predicted by the first color prediction unit 11 may be converted to correction color values using the color conversion model.

  The correction value acquisition unit 1 is not limited to the above-described configuration as long as the correction color value corresponding to the target color value is acquired, and the correction color value may be acquired by another method. Further, if the set of the target color value and the correction color value is given, the correction value acquisition unit 1 may not be provided.

  The weighting factor setting unit 2 sets a weighting factor indicating the importance of each target color value for each target color value. This weighting factor is based on the evaluation value acquired by the evaluation value acquisition unit 5 when it is determined by the convergence condition determination unit 4 that the color correction coefficient generated by the color correction coefficient generation unit 3 does not satisfy the convergence condition. The color correction coefficient generation unit 3 is caused to generate a color correction coefficient using the changed weighting coefficient.

  The weighting factor setting unit 2 includes a balance parameter setting unit 21 and a weighting factor calculation unit 22 in this example. The balance parameter setting unit 21 sets the degree of importance for each of a plurality of important color areas (important color areas). As the important color area, a specific color is designated, and various color areas such as a single color axis, a gray axis, a high density area or a low density area, a high saturation area, and a low saturation area are candidates for setting. Set the weighting function weight decreases with increasing distance from these important color regions, further, it sets a coefficient (balance parameter) indicating the degree of emphasis on the weighting function.

  The weighting coefficient calculation unit 22 receives the target color value and the correction color value corresponding to the target color value acquired by the correction value acquisition unit 1, and according to each weight function and balance parameter set by the balance parameter setting unit 21 The weighting coefficient for each target color value is generated. More specifically, when the weight function is set for each of the single color and the gray axis as the important color region and the balance parameter is set to the single color: gray axis = 3: 7 as described above, the target color value is set to the distance from the single color. depending the weight was, and a weight corresponding to the distance from the gray axis, 3: 7 weight synthesized in a proportion of it may be a weighting coefficient for the target color values.

  Of course, the method of setting the weighting factor is not limited to this example, and the weighting factor may be set using various setting methods. As described above, since the weighting coefficient is changed based on the evaluation value acquired by the evaluation value acquisition unit 5, any setting method for the weighting coefficient corresponding to the change may be used. In the illustrated configuration, when the convergence condition determination unit 4 determines that the convergence condition is not satisfied, the balance parameter set by the balance parameter setting unit 21 is changed to change the weighting coefficient. Of course, the weighting coefficient may be changed by another configuration such as changing each weighting function.

  The color correction coefficient generation unit 3 corrects the target color value for each color component in accordance with the correction color value acquired by the target color value and correction value acquisition unit 1 and the weighting coefficient set by the weighting coefficient setting unit 2. Generate color correction coefficients. For example, in the case of CMYK, the color correction coefficient may be generated in a one-dimensional table format in which values before correction and values after correction are associated with each other for C, M, Y, and K color components. Of course, a configuration for generating a function for performing color correction as a color correction coefficient, or generating a parameter for a function for color correction that is set in advance may be used.

  Convergence condition determination section 4 determines the color correction coefficient generated by the color correction coefficient generation unit 3, whether the convergence conditions are satisfied or not, which is set in advance. As the convergence condition, it is conceivable to set a range of difference from the color correction coefficient that was generated last time or previously set for the first time. When the convergence condition is satisfied, the generated color correction coefficient is stored in the color correction coefficient storage unit 6 and the color correction coefficient generation process is terminated. If the convergence condition is not satisfied, the color correction coefficient is transferred to the evaluation value acquisition unit 5, the evaluation value of the color correction coefficient is acquired, and the weighting coefficient is redesigned. Also in this case, the color correction coefficient is stored in the color correction coefficient storage unit 6 and is used when the convergence condition is determined next time.

  The evaluation value acquisition unit 5 generates the color correction coefficient generated by the color correction coefficient generation unit 3 when the color correction coefficient generated by the color correction coefficient generation unit 3 determines that the convergence condition determination unit 4 does not satisfy the convergence condition. The evaluation value of the correction result using is acquired. The correction result is obtained by, for example, simulating color correction processing using the color correction coefficient for the color value of the color chart for evaluation, and by causing the output device to actually output an image using the color correction coefficient. A correction result may be obtained based on the color value. An evaluation value is acquired based on the difference between the color values before and after correction (color difference) based on the correction result for the color value of each color chart obtained. For example, the average color difference, the median value, the maximum color difference, the RMS, or the color difference corresponding to a preset position (for example, 95% position) when the color differences are arranged in descending order for the color obtained as the correction result is used as the evaluation value. That's fine. Of course, other values may be used as evaluation values. Note that the evaluation color chart used when obtaining the correction result and what value is used as the evaluation value may be configured to be selected and set in advance by the user. Further, the correction value acquisition unit 1 may be used to acquire the correction result.

  The color correction coefficient storage unit 6 stores the color correction coefficient generated by the color correction coefficient generation unit 3 for each color component. When the convergence condition determination unit 4 satisfies the convergence condition, the color correction coefficient stored at that time is the final generation result.

  FIG. 2 is a flowchart showing an example of operation in the first exemplary embodiment of the present invention. When the operation starts, first, in S41, the target color value is set evenly over the entire color gamut, the correction color value corresponding to the target color value is acquired by the correction value acquisition unit 1, and the target color value and the correction color value are determined. Get a pair. In S42, the weighting coefficient setting unit 2 sets a weighting coefficient for each of the obtained target color value and correction color value pairs. This weighting factor is preferably set so as to improve the reproducibility of a color gamut that is important, such as a single color, gray, secondary color, or specific color. Also, by setting a weighting factor for each specific color area such as low density or high density, it is possible to set a color area that is important for each density area. A weighting factor may be set so that reproduction such as emphasis on gray is performed in the region.

  In S43, the color correction coefficient generation unit 3 generates a color correction coefficient for each color component, based on the target color value and the correction color value for which the weight is set in S42. In S44, it is determined whether or not the color correction coefficient generated in S43 satisfies a preset convergence condition. If the convergence condition is satisfied, the process ends.

  If it is determined in S44 that the convergence condition is not satisfied, in S45, the evaluation value acquisition unit 5 obtains a correction result using the color correction coefficient generated in S43, and acquires an evaluation value from the color difference before and after correction. To do. Then, returning to S42, the weighting factor setting unit 2 recalculates the weighting factor based on the evaluation value. In S43, a color correction coefficient is newly generated, and in S44, it is determined whether or not the convergence condition is satisfied. By performing a series of steps such as recalculation of weighting factors based on evaluation values, generation of color correction factors, and determination of convergence conditions, a color that performs correction in a specific color region and correction of the entire color gamut in a balanced manner A correction factor is obtained.

  An example of the above operation will be further described using a specific example. 3 is an explanatory diagram of an example of a pair of a target color value and a correction color value, FIG. 4 is an explanatory diagram of an example of raw data of a color conversion model used by the first color prediction unit 11, and FIG. It is explanatory drawing of an example of the raw data of the color conversion model which the 2nd color estimation part 12 uses. First, the target color value is prepared. The target color value may be set evenly over the entire color gamut as described above. For example, in the example shown in the column “Target color value” in FIG. 3, the range from 0 to 100 is set to 25 for C, M, and Y, and the range from 0 to 100 is set to 10 for K. The combination of values is the target color value.

  The correction value acquisition unit 1 acquires a correction color value corresponding to the target color value. In the configuration shown in FIG. 1, the first color prediction unit 11 converts the CMYK value that is the target color value into a value in the LAB color space that is a device-independent color space, and the value depends on the output device to be corrected. The correction color value is obtained by predicting the CMYK values by the second color prediction unit 12.

  The first color prediction unit 11 predicts a color (Lab value) to be obtained when the target color value is input to the output device, and here performs conversion from CMYK to Lab. For the conversion, for example, a color conversion model may be created and used. For example, CMYK is given to an output device that does not require correction, or another target output device, and the output color is measured to obtain a pair of CMYK and Lab. For example, FIG. 4 shows CMYK values that are combined in units of 25 for each color component, and Lab values that correspond to the respective CMYK values. A pair of CMYK values and Lab values may be used as raw data to create a color conversion model. Of course, for example, it may be data expressed by Neugebauer, Kubelka-Munk, Lambert Bale, etc. as a polynomial approximation or a physical model expression, or may be data converted by an ICC profile or the like. Any method can be used as long as the value is obtained.

  Further, the second color prediction unit 12 predicts a correction color value from the color predicted by the first color prediction unit 11. As described above, the correction color value is a color value that must be input to the output device in order to obtain the Lab value, which is the color desired to be obtained, predicted by the first color prediction unit 11 as the output result of the output device. is there. In this case as well, CMYK is given to an output device that requires correction or an output device that is to be actually output to measure the output color, and a pair of CMYK and Lab is obtained. For example, FIG. 5 shows CMYK values combined for each color component in increments of 25, and Lab values corresponding to the respective CMYK values. A color conversion model for converting from a Lab value to a CMYK value may be created using the pair of the CMYK value and the Lab value as raw data. Mathematically, three to four elements are not uniquely determined, but may be predicted using a known color prediction method. For example, although it is good to predict using the method of Unexamined-Japanese-Patent No. 10-262157, it is not limited to this prediction method.

  Here, the color value between the first color prediction unit 11 and the second color prediction unit 12 is the Lab value, but the present invention is not limited to this. For example, a color system such as tristimulus value XYZ or uniform color space Luv may be used.

  For example, the Lab value obtained by inputting the color value shown in the target color value column in FIG. 3 to the first color prediction unit 11 is input to the second color prediction unit 12, and is obtained by the second color prediction unit 12. The CMYK values corresponding to the Lab values are shown in the correction color value column in FIG. For example, when the target color values are C = 25, M = 50, Y = 75, and K = 0, C ′ = 24 and M ′ = 49 in order to cause the output device to output the desired color. , Y ′ = 70 and K ′ = 0 to be corrected to the correction color value and input to the output device. Basically, it is only necessary to correct the target color value to the correction color value. However, when correcting for each color component assumed in this embodiment, each color is corrected with a certain target color value and the correction color value. Even if the color correction coefficient in the component is determined, the target color value is not necessarily corrected to the correction color value in other color values. Therefore, in this embodiment, a weighting coefficient setting unit 2, a color correction coefficient generation unit 3, a convergence condition determination unit 4, an evaluation value acquisition unit 5, and the like are provided.

  When a pair of the target color value and the correction color value is obtained, the weighting coefficient setting unit 2 sets a weighting coefficient for the pair. In the weighting coefficient setting unit 2, a weighting function for important colors is set in advance, and when a plurality of weighting functions are set, initial values of balance parameters between them are set.

  FIG. 6 is an explanatory diagram of an example of the weight function. As the weight function, it is preferable to set a function in which the weight value is large in the important color region and the weight becomes smaller as the distance from the important color region increases. For example, FIG. 6A shows an example of a weighting function when importance is attached to any one of C, M, and Y. The value of the weighting factor w decreases as the distance from the monochromatic axis increases. FIG. 6B shows an example of a weight function when gray is important. The greater the distance from the gray axis, the smaller the value of the weighting factor w. In the example shown in FIG. 6C, an example of a weighting function when emphasizing a specific color is shown. As the distance from the specific color increases, the value of the weight coefficient w decreases. The specific color may be any color such as sky blue, skin color, and green.

  The example of the weight function shown in FIGS. 6D and 6E is an example of the weight function according to the density, and FIG. 6D is an example of the weight function that places importance on the high density area. E) shows an example of a weight function that places importance on the low density region. Here, the total amount of color materials used for image formation is the density, and in the example of the weighting function emphasizing the high density region shown in FIG. 6D, the value of the weight coefficient w decreases as the total amount of color material increases. Thus, in the example of the weighting function emphasizing the low density region shown in FIG. 6E, the weighting factor w is set to be smaller as the total amount of the color material is smaller. Note that the example of the weighting function emphasizing the high density region shown in FIG. 6D apparently appears to have a tendency opposite to that of the other examples of the weighting function. This is because the value of the total amount is larger.

  FIG. 7 is an explanatory diagram of an example of combinations of weight functions. By using the above weight functions in combination, the weight coefficient is set more finely. In the example shown in FIG. 7A, the weighting function emphasizing the low density region shown in FIG. 6E and the weighting function emphasizing single color shown in FIG. 6A are used in combination. Is shown. In this case, a large weighting coefficient is set for a single color and low density region. Conversely, even for a single color, the weighting coefficient is small for a high density region.

  In the example shown in FIG. 7B, the weighting function emphasizing the high density region shown in FIG. 6D and the weighting function emphasizing gray shown in FIG. 6B are used in combination. Is shown. In this case, a large weighting factor is set for a gray and high density region. Conversely, even in the case of gray, the weighting coefficient is small for a low density region.

  Of course, three or more weight functions may be combined. For example, when the four weighting functions shown in FIGS. 7A and 7B are used in combination, a large weighting factor is set in a low density and monochromatic area, while high density is set. A large weight coefficient is set in the gray area. That is, the weighting coefficient is set so that the color area to be emphasized changes according to the target density.

  When combining several weight functions, the importance of each weight function is determined by the balance parameter. Even when there is one weight function, how much the weight function is involved may be determined by the balance parameter. More specifically, when using two weight functions having a certain single color and the gray axis as an important color region, the balance parameter may be set such that the single color: gray axis is 3: 7.

The weighting factor calculation unit 22 determines the weighting factor for the pair of the target color value and the correction color value according to the setting of the weighting function corresponding to the important color region and the balance parameter by the balance parameter setting unit 21. For example, if the target color value is D, the weighting functions are f1 and f2, and the balance parameter is a: b, the weighting factor W is
W = (a / (a + b)) · f1 (D) × (b / (a + b)) · f2 (D)
And so on. Of course, this weighting factor calculation method is merely an example, and it goes without saying that the weighting factor may be calculated using other calculation methods.

  FIG. 8 is an explanatory diagram of an example of the weighting factor added to the pair of the target color value and the correction color value. In the example shown in FIG. 8, an example is shown in which a balance parameter is set so as to place importance on a single color, and a weighting coefficient is calculated using a weight function that places importance on a single color. The weighting factor W is 0 ≦ W ≦ 1. As an example, the target color value is (C, M, Y, K) = Y single color such as (0, 0, 10, 0), (0, 0, 20, 0), or (C, M, Y, K). ) = (0,0,0,10), (0,0,0,20), etc., the weight coefficient “1.00” is set for the K single color. Since the target color value such as (C, M, Y, K) = (50, 50, 10, 0) is far from a single color, a weighting coefficient smaller than “1.00” is set by the weight function. and which is further set a small weighting coefficient is more distance from the monochrome leaves (C, M, Y, K) = (100,100,80,0) or the like.

  When the weight coefficient is set for the pair of the target color value and the correction color value in this way, the color correction coefficient generation unit 3 performs color correction coefficient correction for each color component according to the weight coefficient. Generate in one-dimensional tabular format.

  The color correction coefficient generation unit 3 receives, for example, the target color value / correction color value pair and the weight coefficient shown in FIG. 8 from the weight coefficient calculation unit 22, and sets the target color value and the correction color value for each color component. And the weighting factor. FIG. 9 is an explanatory diagram of an example of the extracted target color value, correction color value, and weighting coefficient of the single color component. FIG. 9 shows an example in which the target color value (Y), the correction color value (Y ′), and the weighting coefficient (W) are extracted for the Y component. Referring to FIG. 9, even if the Y component value of the target color value is 10, the correction color value is 12 or 15, and the corrected color value for the Y component value 10 of the target color value is corrected. Cannot be determined uniquely. Therefore, as a simple method, for each Y component value of the target color value, for example, a weighted average is calculated using a weighting coefficient, thereby obtaining a corrected value for each Y component.

  However, in this method, it is necessary that a plurality of correction color values exist for a certain Y component value, and when the Y component values are scattered, variations in the correction color values are reflected as they are. In this case, another problem such as the occurrence of a gradation step occurs.

  Therefore, it is preferable to calculate a correction value corresponding to the target color value by weighted regression calculation. That is, a correction value is calculated by weighted local regression based on the data shown in FIG. Incidentally, this is the calculation of the correction value may be used the method described in JP aforementioned JP-A 10-262157, but briefly mention specific calculation methods. In this method, in determining one correction value for the target color value of interest, not only the correction value and the weight coefficient for the target color value but also the correction value and the weight coefficient for other target color values are used. In this case, the maximum weighting is performed on the correction value and the weighting coefficient for the target color value of interest, and the correction value and the weighting coefficient for other target color values increase as the difference from the target color value of interest increases. Apply local regression with a smaller weighting. Thus, by calculating the correction value by the regression calculation, it is preferable to obtain a color correction coefficient in which the target color value and the correction value for a certain color component are associated with each other. Although the Y component has been described in the above example, color correction coefficients are also obtained for the C, M, and K components.

  Once this way generates a color correction coefficient for each color component, determined by the convergence condition determination unit 4 whether the color correction coefficient meets the convergence condition. For example, whether or not the difference between the previously generated color correction coefficient stored in the color correction coefficient storage unit 6 and the color correction coefficient generated this time in the color correction coefficient generation unit 3 is within a predetermined range. Can be determined. More specifically, the difference between the previous correction value and the current correction value is examined, and if there is a correction value that has a difference that deviates from a predetermined range, it may be determined that it has not converged. Of course, the convergence may be determined based on other convergence conditions. When the convergence condition is satisfied, it is assumed that the color correction coefficient generated this time should be finally obtained and stored in the color correction coefficient storage unit 6, and the color correction coefficient generation process is terminated.

  When the convergence condition determination unit 4 determines that the convergence condition is not satisfied, the color correction coefficient is sent to the evaluation value acquisition unit 5, and a correction result is obtained using the generated color correction coefficient for each color component. The method of acquiring the correction result includes a method of generating an image that has been actually corrected by the color correction coefficient, outputting, measuring, and acquiring, or a method of acquiring by simulation using a color prediction model. Conceivable. Of course, other methods may be used as long as the correction result can be acquired. The color chart used for obtaining the correction result may be a predetermined one or may be selected by the user.

  Further, the evaluation value acquisition unit 5 obtains the color difference from the color values before and after correction, and acquires the evaluation value. For example, an average color difference, median value, maximum color difference, RMS, or a color difference corresponding to a preset position (for example, a 95% position) when the color differences are arranged in descending order for the correction result is used as the evaluation value. Good. Of course, the evaluation value is not limited to this example. Also, the user may select which of several evaluation values to use.

  The balance parameter setting unit 21 of the weighting factor setting unit 2 corrects the balance parameter setting according to the evaluation value acquired by the evaluation value acquisition unit 5. For example, if the evaluation value is an average color difference as described above, the balance parameter may be corrected so that the evaluation value decreases. For example, the balance parameter may be corrected in a direction in which the evaluation value decreases by using a method such as binary search or Newton method.

  After that, the weighting coefficient calculation unit 22 recalculates the weighting coefficient corresponding to each target color value and correction color value pair according to the modified balance parameter, and the color correction coefficient generation unit 3 regenerates the color correction coefficient. become. Such processing is repeated until the convergence condition is satisfied, and the color correction coefficient generated when the convergence condition is satisfied may be used as the processing result.

  The above-described repetitive processing will be described using a case where binary search is used as an example. FIG. 10 is an explanatory diagram of an example of the balance parameter and the convergence state, and FIG. 11 is an explanatory diagram of an example of the search direction in the binary search. Here, a weight function of a single color and emphasis gray Balanced calculates a weighting factor synthesized according balance parameters, it is assumed to generate a color correction coefficient. In the example shown in FIG. 10, the horizontal axis indicates the balance parameter, 0.0 indicates the emphasis on single color, and 1.0 indicates the case where the emphasis is on gray. Synthesize a weight function that emphasizes balance. The vertical axis represents an evaluation value used in the balance parameter setting unit 21, and here, an average value of color differences is used as the evaluation value.

  When performing a binary search, three points are required to determine where the convergence point exists. In FIG. 10, indicated as first (1), point balance parameter is between the 0.0, 1.0, determined, for example, the evaluation value at 0.5 (average color difference in this case). Therefore, advance or balance parameters previously seeking evaluation value and the color correction coefficient in the case of 0.0 to 1.0, or about 2 times of the processing of the balance parameter is 0.0 and 1.0 convergence condition determination unit It is good to repeat without performing determination of 4.

  In the state where the evaluation value and color correction coefficient when the balance parameter is 0.0 and 1.0 are obtained, the initial balance parameter is set to 0.5 which is an intermediate value between 0.0 and 1.0. , calculation of weighting coefficients by the weighting factor calculation unit 22 performs the generation of the color correction coefficient by the color correction coefficient generation unit 3, the determination of the convergence conditions by the convergence condition determination unit 4 is performed. In this case, since the color correction coefficient generated when the balance parameter is 0.0 or 1.0 is stored in the color correction coefficient storage unit 6, if this is compared with the newly generated color correction coefficient, Good. Here, if the convergence condition is satisfied, a newly generated color correction coefficient may be used as the processing result.

  If the convergence condition is not satisfied, an evaluation value (here, average color difference) is acquired from the correction result when the color correction coefficient generated by the color correction coefficient generation unit 3 is used, and the balance parameter is obtained. It is passed to the setting unit 21. The balance parameter setting unit 21 corrects the balance parameter from the evaluation value passed from the evaluation value acquisition unit 5 and the evaluation value when the balance parameter is 0.0 and 1.0.

  When three evaluation values are obtained, the evaluation value changes in any of the three cases shown in FIG. In FIG. 11, the evaluation values are a, b, and c from the smaller balance parameter. FIG. 11A shows a case where a ≧ b ≧ c. In this case, there is a convergence point between b and c, or there is no convergence point between a, b, and c. In the former case, it is sufficient to further search between b and c. In the latter case, c may be a convergence point.

  FIG. 11B shows a case where a ≦ b ≦ c, and the reverse case of FIG. In this case, there is a convergence point between a and b, or there is no convergence point between a, b and c. In the former case, it is sufficient to further search between a and b. In the latter case, a may be a convergence point.

  FIG. 11C shows a case where a ≧ b ≦ c. In this case, a convergence point exists between a and b, or a convergence point exists between b and c. In this case, it is not known whether the convergence point exists between a and b or between b and c, but the range may be narrowed by dividing and examining each section. In other words, the division point between ab is d and the division point between bc is e, and adb and bec are used to determine which of the cases shown in FIG. If any one of the relations shown in FIG. 11C, the search may be continued for the section having the relation. Further, in the case of the relationship between FIGS. 11A and 11B, dbe has the relationship of FIG. 11C in that case, and the range may be searched.

  In the example shown in FIG. 10, the relationship of the average color difference when the balance parameter shown as (1) is 0.0, 0.5, 1.0 is the relationship shown in FIG. . Therefore, the balance parameter is further searched between 0.5 and 1.0. Divide between 0.5 and 1.0 and set the weighting coefficient for the balance parameter of 0.75 ((2) in the figure), generate the color correction coefficient, and meet the convergence condition Judge whether or not. For example, if the convergence condition is not satisfied, a correction result is obtained using a color correction coefficient when the balance parameter is 0.75, and an average color difference is calculated as an evaluation value. In the example shown in FIG. 10, the three points of the balance parameter is 0.5 to 0.75 and 1.0, since a relationship shown in FIG. 11 (C), the balance parameter is 0.5 to 0.75 Whether the weighting coefficient is set and the color correction coefficient is generated for 0.625 that bisects the distance between the two and 0.875 that divides the balance parameter between 0.75 and 1.0, and the convergence condition is satisfied Judge whether or not. For example, assuming that the convergence condition is not satisfied, a correction result is obtained using each color correction coefficient, and for example, an average color difference is calculated and used as an evaluation value. In this case, when the balance parameter is three points of 0.5, 0.625, and 0.75, the relationship shown in FIG. 11A is obtained, and when the balance parameter is three points of 0.75, 0.875, and 1.0, FIG. 11B shows the relationship. Therefore, the balance parameters continue to explore further between 0.625 and 0.75 and three of 0.875 (this is the case in the relationship of FIG. 11 (C)).

  As such repeated processing continues, the balance parameter search range narrows and approaches a convergence point where the color difference is small. At the same time, the generated color correction coefficient does not change so much, and the difference between the generated color correction coefficient and the previous color correction coefficient is within a predetermined range so that the convergence condition is satisfied. A color correction coefficient that satisfies the convergence condition may be used as the processing result.

  In this example, the obtained color correction coefficient is compatible with both the correction that maintains the reproducibility of the single color with emphasis on the single color and the correction that maintains the reproducibility of the gray with the emphasis on the gray balance, and there is no color difference as a whole. The coefficient is small and well balanced. A simple correction with emphasis on single color or a correction with emphasis on gray balance will result in good correction for single color and gray balance, but good correction will not always be made in other color areas. By using the color correction coefficient obtained in the form, the correction accuracy is improved as a whole together with the single color and the gray balance as described above.

  In this embodiment, the important color region is assumed to be a single color and gray, and a weight function emphasizing single color and gray balance is used. However, it goes without saying that the present invention is not limited to this example. In addition to the binary search, other search methods such as Newton's method may be used.

  Since the color correction coefficient obtained in this way is generated for each color component by the color correction coefficient generation unit 3, for example, after color separation into C, M, Y, and K color components, Color correction may be performed using the color correction coefficient, and even in the correction for each color component, the correction in the important color region and the entire correction are performed in a balanced manner as described above.

  FIG. 12 is a block diagram showing a second embodiment of the present invention. In the above-described first embodiment, the convergence condition determination unit 4 determines the convergence condition based on the color correction coefficient generated by the color correction coefficient generation unit 3, but in this second embodiment, In the example, the convergence condition is determined based on the evaluation value acquired by the evaluation value acquisition unit 5.

  The convergence condition determination unit 4 in the second embodiment receives the evaluation value from the evaluation value acquisition unit 5 and determines whether or not the convergence condition is satisfied. For example, if the evaluation value is based on a color difference such as an average color difference, it may be determined whether or not the evaluation value is within a preset range. In this case, a color correction coefficient is obtained in which the color difference due to correction falls within a preset range. Of course, it goes without saying that convergence may be determined by other methods using evaluation values.

  FIG. 13 is a block diagram showing a third embodiment of the present invention. In the third embodiment, an example is shown in which the convergence condition is determined based on the balance parameter. The convergence condition determination unit 4 in the third embodiment receives a balance parameter to be changed according to the evaluation value acquired by the evaluation value acquisition unit 5 from the balance parameter setting unit 21 of the weighting factor setting unit 2, and satisfies the convergence condition. It is determined whether or not. For example a balance parameter during previous determination, the difference between the balance parameters received from the balance parameter setting unit 21 may determine whether or not within a predetermined range. For example, in the above-described binary search, the convergence may be determined based on the balance parameter division width when performing the search. Of course, needless to say, convergence may be determined by other methods using balance parameters.

  FIG. 14 is an explanatory diagram of an example of a computer program, a storage medium storing the computer program, and a computer when the functions described in the embodiments of the present invention are realized by the computer program. In the figure, 71 is a program, 72 is a computer, 81 is a magneto-optical disk, 82 is an optical disk, 83 is a magnetic disk, 84 is a memory, 91 is a CPU, 92 is an internal memory, 93 is a reading unit, 94 is a hard disk, and 95 is An interface 96 is a communication unit.

  You may implement | achieve all or a part of function of each part demonstrated by each embodiment of the above-mentioned this invention with the program 71 executable by a computer. In that case, the program 71 and data used by the program may be stored in a computer-readable storage medium. The storage medium causes the reading unit 93 provided in the hardware resource of the computer to cause a change state of energy such as magnetism, light, electricity, etc. according to the description content of the program, and the signal corresponding thereto In this format, the program description is transmitted to the reading unit 93. For example, a magneto-optical disk 81, an optical disk 82 (including a CD and a DVD), a magnetic disk 83, a memory 84 (including an IC card and a memory card), and the like. Of course, these storage media are not limited to portable types.

  The program 71 is stored in these storage media, and the program 71 is read from the computer and stored in the internal memory 92 or the hard disk 94, for example, by mounting these storage media on the reading unit 93 or the interface 95 of the computer 72. By executing the program 71 by the CPU 91, the functions described in the above embodiments of the present invention are realized in whole or in part. Alternatively, the program 71 is transferred to the computer 72 via the communication path, and the computer 72 receives the program 71 by the communication unit 96 and stores it in the internal memory 92 or the hard disk 94, and the program 71 is executed by the CPU 91. May be.

  In addition, the computer 72 may be connected to various devices via an interface 95. For example, display means for displaying information, reception means for receiving information from the user, and the like may be connected. Further, for example, an image forming apparatus may be connected via the interface 95, and a color signal corrected using the obtained color correction coefficient may be sent to the image forming apparatus to form an image.

It is a block diagram which shows the 1st Embodiment of this invention. It is a flowchart which shows an example of the operation | movement in the 1st Embodiment of this invention. It is explanatory drawing of an example of the pair of a target color value and a correction color value. It is explanatory drawing of an example of the raw data of the color conversion model which the 1st color estimation part uses. It is explanatory drawing of an example of the raw data of the color conversion model which the 2nd color estimation part 12 uses. It is explanatory drawing of an example of a weight function. It is explanatory drawing of an example of the combination of a weight function. It is explanatory drawing of an example of the weighting coefficient added to the pair of a target color value and a correction color value. It is explanatory drawing of an example of the object color value of the extracted single color component, the color value for correction | amendment, and a weighting coefficient. It is explanatory drawing of an example of a balance parameter and a convergence state. It is explanatory drawing of an example of the search direction in the case of a binary search. It is a block diagram which shows the 2nd Embodiment of this invention. It is a block diagram which shows the 3rd Embodiment of this invention. The functions described in the embodiments of the present invention is an explanatory diagram showing an example of a storage medium and a computer which stores the computer program and the computer program in case of realizing by a computer program.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Correction value acquisition part, 2 ... Weight coefficient setting part, 3 ... Color correction coefficient production | generation part, 4 ... Convergence condition determination part, 5 ... Evaluation value acquisition part, 6 ... Color correction coefficient memory | storage part, 11 ... 1st color prediction , 12 ... second color prediction unit, 21 ... balance parameter setting unit, 22 ... weight coefficient calculation unit, 71 ... program, 72 ... computer, 81 ... magneto-optical disk, 82 ... optical disk, 83 ... magnetic disk, 84 ... memory 91 ... CPU, 92 ... internal memory, 93 ... reading unit, 94 ... hard disk, 95 ... interface, 96 ... communication unit.

Claims (5)

  Correction value acquisition means for acquiring a correction color value corresponding to a target color value to be corrected; weight coefficient setting means for setting a weight coefficient for the target color value; the target color value and the correction color value; Generating means for generating a color correction coefficient for correcting the target color value for each color component according to the weighting coefficient, and a convergence condition determination for determining whether the color correction coefficient generated by the generating means satisfies a convergence condition And an evaluation value acquisition means for acquiring an evaluation value of a correction result using the color correction coefficient generated by the generation means when the convergence condition determination means determines that the convergence condition is not satisfied, The weight coefficient setting means changes the weight coefficient based on the evaluation value acquired by the evaluation value acquisition means, and causes the color correction coefficient generation means to generate a color correction coefficient again. Coefficient generation Location.   Correction value acquisition means for acquiring a correction color value corresponding to a target color value to be corrected; weight coefficient setting means for setting a weight coefficient for the target color value; the target color value and the correction color value; A generation unit that generates a color correction coefficient for correcting the target color value for each color component according to the weighting factor, and an evaluation value that acquires an evaluation value of a correction result using the color correction coefficient generated by the generation unit And a convergence condition determining unit that determines whether or not the evaluation value acquired by the evaluation value acquiring unit satisfies a convergence condition, and the weighting factor setting unit is the convergence condition determining unit. If it is determined that the condition is not satisfied, the weight correction coefficient is changed based on the evaluation value acquired by the evaluation value acquisition unit, and the color correction coefficient generation unit generates a color correction coefficient again. Color correction Number generating device.   2. The evaluation value acquisition unit according to claim 1, wherein the evaluation value acquisition unit acquires the correction result by simulation of color correction processing using the color correction coefficient, and acquires an evaluation value based on a color difference before and after correction. 2. The color correction coefficient generation device according to 2.   The evaluation value acquisition means obtains the correction result based on a color value obtained by outputting an image using the color correction coefficient, and acquires an evaluation value based on a color difference before and after correction. The color correction coefficient generation device according to claim 1 or 2.   A computer, a color correction coefficient generation program characterized by claim 1 is intended to perform the functions of a color correction coefficient generation device according to any one of claims 4.

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