JP2614379B2 - How to set the reference density point - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for setting a highlight point and / or a shadow point as a reference density point in a gradation conversion apparatus used in the field of image processing such as a color scanner for plate making.

[0002]

2. Description of the Related Art A method for automatically setting highlight density and shadow density in a color separation apparatus such as a color scanner for plate making is disclosed in Japanese Patent Application Laid-Open No. 2-157775 by the present applicant.
No. 8 discloses a technique disclosed in Japanese Patent Publication No. The method disclosed in this conventional publication obtains a highlight density value and a shadow density value on the input side of a gradation conversion curve by a procedure as shown in FIG.

First, in step S501, an original image to be copied is prescanned, and a density value for each color component of the original image is obtained for each pixel.

Next, in step S502, an average density value is obtained by averaging the density values for each color component for each pixel, and an average density histogram is obtained.

Next, in step S503, a cumulative density value for each class is obtained for each color component, and a cumulative density value histogram as shown in FIG. 20 is obtained (however, FIG.
Is shown only for the cumulative density value histogram for

Next, in step S504, the relative frequency of the pixel obtained by cumulatively adding the pixels from the lower density class is obtained.
As shown in FIG. 19, a cumulative relative frequency histogram that changes from 0 to 100% in the range of the minimum and maximum generated density values is obtained.

Next, in step S505, predetermined cumulative density appearance rates RNH and RNH corresponding to highlight points and shadow points that provide the optimum gradation conversion characteristics empirically obtained.
By applying NS to the cumulative relative frequency histogram, a provisional highlight average density value DMH and a shadow average density value DMS corresponding to the cumulative relative frequency are obtained.

Finally, in step S506, the provisional highlight average density value DMH and the shadow average density value D
The MS is applied to the cumulative density value histogram for each color component shown in FIG. 20 to determine the input-side highlight density value and the input-side shadow density value for each color component. The gradation conversion curve should pass through the input-side highlight density value and the input-side shadow density value obtained as described above, and the output-side highlight density value and the output-side shadow density value arbitrarily set in advance. A highlight point and a shadow point, that is, a reference density point are set.

[0009]

However, according to the method disclosed in Japanese Patent Application Laid-Open No. 2-157758, the original image occupies a region having a very low density and a certain amount or more, such as a shining portion of a metal. If there is a part that
The input-side highlight density value was set lower than a desired value, and the duplicated image sometimes had a dark finish or had an impression that the entire color was cloudy. Conversely, the original image has a very high density and occupies a certain area or more,
For example, when the image has a dark background, the input-side shadow density value is set to a high value, resulting in a whitish finish.

This is due to the following reasons. That is, for example, when the original image has a portion having a very low density and occupying a certain area or more, the cumulative density value histogram shown in FIG. A high histogram results. Therefore, the provisional highlight average density value obtained from the cumulative relative frequency histogram of FIG.
The input-side highlight density value obtained at 506 also becomes low. When the input-side highlight density value decreases in this way, the gradation conversion curve set based on the input-side highlight density value shifts the highlight area to the output-side shadow density value, resulting in a duplicate image. The result is a dark finish.

If the original image has a dark background, the cumulative density value histogram in FIG. 20 is a histogram in which the frequency of the class having a relatively high density is high, and the input side shadow density value obtained in step S506 is Get higher. Therefore, the gradation conversion curve is obtained by shifting the shadow area to the output side highlight density side, and the reproduced image gradation-converted according to the gradation conversion curve has a whitish finish.

According to the present invention, there is provided a method for setting a reference density point of a gradation conversion curve which can obtain a desired finish of a reproduced image without being affected by the presence or absence of a local low-density area or high-density area in an original image. The purpose is to provide.

[0013]

According to a first aspect of the present invention, there is provided a method of setting a reference density point, comprising the steps of: obtaining a gradation based on image data obtained by reading a duplication original image having a gradation; A method for setting a reference density point through which a gradation conversion curve to be set for performing conversion is to pass, wherein a first step of dividing an original image into a plurality of blocks, and A second step of obtaining a density histogram for each block indicating the relationship between the density value for each block and the number of pixels giving this density value, and, based on the image data, each density is uniform and each density is continuous. A third step of detecting a block group composed of blocks adjacent to each other, a fourth step of counting the number of blocks included in each of the block groups detected in the third step, and The number of pixels for each rank of the blocks by the concentration histogram in block contained in,
A fifth step of obtaining the number of corrected pixels by multiplying a coefficient inversely proportional to the number of blocks of the block group including the block, and after the fifth step, all blocks not included in the block group for each class A sixth step of obtaining a density histogram of the entire original image by adding the number of pixels of the original image and the number of corrected pixels of all the blocks included in the block group, and accumulating based on the density histogram of the entire original image. A seventh step of obtaining a density value histogram, and an eighth step of setting a reference density point based on the cumulative density value histogram.
And a step of:

According to a second aspect of the present invention, in the method for setting a reference density point according to the first aspect, the third step comprises the steps of:
When the difference between the average density values of blocks that are close to each other and have uniform densities is equal to or smaller than a predetermined value, the densities of these blocks are assumed to be continuous.

According to a third aspect of the present invention, there is provided a method for setting a reference density point.
3. The method for setting a reference density point according to claim 1, wherein (1) the density value is an average density value obtained by averaging density values of respective color components for each pixel, and (2) the density value is selected. Density value for each pixel of the color component
(3) The density value is a density value of each color component for each pixel.

In the above description, the coefficient inversely proportional to the number of blocks may be, for example, the reciprocal of the number of blocks or the square root of the number of blocks.

[0017]

According to the method of setting a reference density point of the first aspect, in the first to third steps, a block group consisting of blocks close to each other in which each density is uniform and each density is continuous is detected. And in the fourth and fifth steps,
The number of corrected pixels is obtained by multiplying the number of pixels of each class in the density histogram for each block in the blocks included in the detected block group by a coefficient inversely proportional to the number of blocks in the block group. By using the number of corrected pixels obtained in this way, in the density histogram of the whole original image obtained in the sixth step, the density value of the density histogram corresponding to the density area of the block included in the block group increases. Is suppressed. For this reason, for example, in the case of an original image having a block group composed of blocks with low density, the present invention mainly has a larger input-side highlight density value than the case where the number of corrected pixels is not used, so that the gradation conversion curve Indicates that the highlight area shifts to the output highlight density side. Also, in the case where the original image has a block group composed of high-density blocks, the present invention mainly reduces the input side shadow density value in comparison with the case where the number of corrected pixels is not used, so that the tone conversion curve The area shifts to the output side shadow density side.

In the method of setting a reference density point according to a second aspect of the present invention, the difference between the uniformly bright blocks or uniformly dark blocks having uniform and close densities is equal to or less than a predetermined value. When, the block is a block having a continuous density.

According to the method of setting the reference density point according to the third aspect, the influence of the pixels in the portion where the density is continuous is reduced according to the ratio of the color components near the reference density.

According to the method of setting the reference density point according to the fourth aspect, when the portion where the density is continuous is a pixel occupying most of a specific color component, the influence of the pixel of the background portion on the color component is reduced. .

[0021] method for setting a reference density points according to claim 5, the influence of the pixels of the part density is continuous is mitigated <br/> for each color component.

[0022]

FIG. 11 is a schematic block diagram of a plate making scanner to which an embodiment of the present invention is applied. In the figure,
The image of the original image 100 is read for each pixel by the scanning reading device 200, and the image signal thus obtained is transferred to the image processing device 300. The image processing device 300
Highlight / shadow point setting unit 4 with functions described later
00 to perform processing such as setting a highlight / shadow point on an input image signal. Then, the processed image signal is provided to the scanning recording device 500. The scanning recording apparatus 500 converts an image signal into a dot signal, and, based on the converted signal, exposes and records a dot image on the photosensitive film 600.

FIG. 12 shows an image processing apparatus 300 including the highlight / shadow point setting section 400 described above. Here, first, the image data obtained by the scanning reading device 200 is read into the image memory 401 constituting the highlight / shadow point setting unit 400. This image memory 4
No. 01 decomposes the original image OG into a large number of pixels, for example, 262144 pixels of 512 in length and 512 in width and stores them. C constituting the highlight / shadow point setting unit 400
The PU 402 divides the original image OG having a large number of pixels into N blocks Tn of vertical V and horizontal H. The original image having 262144 pixels as described above has V = 3
2. The block is divided into N = 1024 blocks Tn where H = 32. In this case, one block Tn is 16 in length and 1 in width.
6, which is a total of 256 pixels. Also,
The CPU 402 calculates a highlight point and a shadow point of a gradation conversion curve based on the original image data read into the image memory 401 in a procedure described later.

The highlight point / shadow point calculated by the CPU 402 is given to a storage unit such as a look-up table provided in the gradation conversion device 301. Thereafter, the uncorrected digital density signal given from the scanning reading device 200 to the tone conversion device 301 is converted into a normalized digital density signal according to a tone conversion curve set based on the highlight point / shadow point. Is converted. This normalized digital density signal is subjected to a predetermined color operation in a color operation block 302 and then output to the scanning recording device 500. The color calculation block 302 performs a predetermined color calculation according to an instruction from an input / output unit (not shown) including a CRT, a keyboard, and the like.

The highlight / shadow point setting section 400
Sets a highlight point and a shadow point of the gradation conversion curve as follows. In addition, as shown in FIG.
In the following description, it is assumed that the original image OG for which the gradation conversion curve is set has a catchlight portion HA due to the reflected light of the metal in a part thereof. In FIG. 13, only the catchlight portion HA is surrounded by a dashed line, and the original image OG is shown.
The other parts of the image are omitted. FIG. 1 shows a schematic setting procedure of a highlight point and a shadow point.

As shown in FIG. 1, first, in step S1, the highlight / shadow point setting section 400 executes the processing shown in FIG.
Blocks constituting the catchlight portion HA of the original image OG shown in FIG. 3 are detected.

Next, in step S2, a coefficient inversely proportional to the number of blocks included in the block group is added to the number of pixels for each class of the density histogram for each block in the blocks included in the block group detected in step S1. The number of corrected pixels is obtained by multiplication. Then, a cumulative density value histogram of the entire original image is created by adding the number of pixels of the blocks not included in the block group and the number of corrected pixels for each of the classes, and the cumulative relative value histogram is further determined based on the cumulative density value histogram. Create a frequency histogram.

Next, in step 3, a cumulative density value histogram for each color component is created.

In step S4, based on the cumulative relative frequency histogram of the entire original image OG created in step S2 and the cumulative density value histogram created in step S3, the reference density point, that is, the highlight point of the gradation conversion curve is obtained. And a shadow point are calculated.

The following steps S1 to S4
Will be described in detail.

In the step S1, FIGS.
The catchlight portion HA is detected by the procedure shown in FIG.

First, in step S101 of FIG.
As shown in FIG. 13, the original image OG is divided into N (H × V) blocks Tn, which are V vertically and H horizontally. n is the block number of the block Tn and is an integer of 1 to N.

Next, steps S102 to S10
5, the density histogram hnj of each block Tn,
The density average value Dn and the density variance value Sn are sequentially obtained in the range of n = 1 to N. Here, j is a class number of the density histogram and is an integer of 0 to J. When the maximum density value is Dmax and the density step is ΔD, the final class number J is J
= Dmax / △ D. Hereinafter, the density histogram hnj, the density average value Dn, and the density variance value Sn will be described in more detail.

The density histogram hnj in this embodiment is a density histogram of an average density value obtained by averaging the density values of each color component of each pixel, and is created by the following procedure, for example. That is, the density values DR, DG, and DB for each color component obtained by pre-scanning the original image OG are averaged by the equation (1) to obtain an average density value DM for each pixel. Ask for.

[0035]

(Equation 1)

Then, for each block Tn, the average density value DM of the pixels belonging to the class having a predetermined width and this average density value D
The density histogram hnj is created as a histogram indicating the relationship with the number of pixels giving M.

The average density value Dn is an average value of the average density values DMni of the pixels included in each block Tn. That is, the density average value Dn of each block Tn can be obtained by the following equation (2). In the equation (2), I is the number of pixels in the block Tn.

[0038]

(Equation 2)

Next, in this embodiment, the density variance value Sn is determined by the following equation as a sample standard deviation using the average density value DMni of each pixel as a sample random variable.

[0040]

(Equation 3)

As described above, the density histogram hn
After obtaining the j, the density average value Dn, and the density variance value Sn, in steps S106 to S111 shown in FIG. 2, a block having a uniform density is detected from the blocks T1 to TN. In this embodiment, first, in step S107, the variance value Sn of the block Tn is compared with a predetermined value ε.
Here, a block having a variance value Sn smaller than a predetermined value ε is a block having a uniform density. Blocks having a variance value Sn equal to or larger than a predetermined value ε are blocks having uneven density.

Then, for a block having a uniform density, the flag FSn is set to "1" in step S108.
Steps S1 and B2 are performed for blocks having uneven density.
In step 09, the flag FSn is set to "0". Steps S107 to S111 from block numbers n = 1 to n = N
By repeating this routine, all blocks Tn
Is set to "1" or "0".

Next, step S112 shown in FIGS.
In step S137, the continuity of the density of the block having the uniform density with the adjacent block is evaluated. The evaluation of the continuity of the density is performed using a scanning mask as shown in FIG. The scan mask shown in FIG. 14 includes a block Tn to be evaluated, a block Tn-H already evaluated above the evaluation block Tn, a block Tn-1 evaluated before the evaluation block Tn, and The block Tn-H-1 evaluated before the block Tn-H is covered.

In step S112, FIG.
Prior to performing the evaluation using the scanning mask, the block numbers n of the blocks T1 to TN, the table numbers k of the later-described active tables ETk0 and ETk1, and the block T
All the label values Lc to be assigned to the label Ln of n are "1".
Has been initialized.

Next, in steps S113 to S114, it is determined whether or not the evaluation block Tn and the block Tn-H-1 covered by the scanning mask in FIG. 14 are blocks having uniform densities. If the blocks Tn and Tn-H-1 are both blocks having uniform densities, the difference between the average density Dn of the block Tn and the average density Dn-H-1 of the block Tn-H-1 is determined in step S115. Is compared with a predetermined value d. If the absolute value is smaller than the predetermined value d, the label Ln of the block Tn is determined in step S116.
Is the same as the label value of the label Ln-H-1 of the block Tn-H-1. When the value of the label Ln is made the same as the value of the label Ln-H-1, the process proceeds to step S136 described later.

In step S114, the block Tn-H-
If it is determined that the density in Step 1 is not uniform, Step S1
When it is determined in 15 that the absolute value of the difference between the average density value Dn and the average density value Dn-H-1 is equal to or greater than the predetermined value d,
Proceed to step S117.

In step S117, it is determined whether or not the block Tn-H is a block having a uniform density. If the block Tn-H is a block having a uniform density, in step S118, the absolute value of the difference between the average density Dn of the block Tn and the average density Dn-H of the block Tn-H is compared with a predetermined value d. . The density average value Dn and the density average value Dn−
If the absolute value of the difference from H is smaller than the predetermined value d, the label value of the label Ln of the block Tn is made the same as the label value of the label Ln-H of the block Tn-H in step S119.

As described above, when the value of the label Ln of the block Tn to be evaluated is made the same as the value of the label Ln-H of the block Tn-H above it, one of the evaluation blocks Tn is determined in the next step S120. It is determined whether or not the preceding block Tn-1 is a block having a uniform density. This block T
If n-1 is a block having a uniform density, it is determined in step S121 whether the value of the label Ln-H of the block Tn-H is equal to the value of the label Ln-1 of the block Tn-1.
If they are not equal, the process proceeds to step S122.

In step S122, block T
The absolute value of the difference between the average density Dn-H of nH and the average density Dn-1 of the block Tn-1 is compared with a predetermined value d. If the absolute value of the difference between the average density value Dn-H and the average density value Dn-1 is smaller than a predetermined value d, in step S123, an equitable table ET for recording labels that can be integrated with each other.
The label value Lc of the label Ln-1 and the label Ln-
The label value Lc of H is recorded.

This means that the blocks Tn and Tn-
1, the blocks Tn-H are all blocks with uniform density, the values of the labels Ln-H and Ln are the same,
In addition, when the density of the block Tn-H and the density of the block TN-1 are continuous, the value of the label Ln-1 can be made the same as the value of the label Ln-H and the label Ln. As described above, the equitable tables ETk0 and ETk1
After the label Ln-1 and the label Ln-H are recorded in step S124, the table number of the EQUIV table is advanced by one in step S124, and then the process proceeds to step S136.

In step S120, the block Tn-1
Is not a block with a uniform density, step S121
If the label Ln-1 and the label Ln-H are already the same value at this stage, and if the density average value D
If the absolute value of the difference between nH and the average density value Dn-1 is equal to or greater than the predetermined value d, the process directly proceeds to step S136 without recording anything in the equitable tables ETk0 and ETk1.

When it is determined in step S117 that the block Tn-H is not a block having a uniform density, and in step S118, the absolute value of the difference between the density average value Dn and the density average value Dn-H is determined to be a predetermined value. If it is determined that the value is equal to or larger than the value d, the process proceeds to step S125.

In step S125, block T
It is determined whether or not n-1 is a block having a uniform density.
If the block Tn-1 is a block having a uniform density, in step S126, the absolute value of the difference between the density average Dn-1 of the block Tn-1 and the density average Dn of the evaluation block Tn is set to a predetermined value d. It is determined whether it is smaller than. If the absolute value is smaller than the predetermined value d, the value of the label Ln of the block Tn is made equal to the value of the label Ln-1 of the block Tn-1 in step S127. After setting the label Ln to the same value as the label Ln-1, step S136
Proceed to.

In step S125, block Tn-1
Is determined not to be a block having a uniform density, or when it is determined in step S126 that the absolute value of the difference between the average density value Dn-1 and the average density value Dn is equal to or greater than a predetermined value d,
That is, although the evaluation block Tn is a block having a uniform density, the continuity of the density of any of the other blocks Tn-H-1, Tn-H, and TN-1 covered by the scanning mask shown in FIG. If it is determined that there is no
Proceed to 128.

In step S128, a new label value Lc that has not been used is assigned to the label Ln attached to the evaluation block Tn. As described above, when the label value Lc is used, the label value Lc is advanced by one in step S129. Thus, the label value Lc
Is updated, the process proceeds to step S136.

In step S113, the evaluation block Tn
If it is determined that is not a block having a uniform density, the process proceeds to step S130. In steps S130 to S133, when the evaluation block Tn is not a block having a uniform density, the label Ln-1 of the block Tn-1 before the evaluation block Tn and the label of the block Tn-H above the evaluation block Tn It is determined whether or not Ln-H can be integrated.

That is, in steps S130 and S131, both the blocks Tn-H and Tn-1 are blocks having uniform densities, and in step S132 the values of the labels Ln-H and Ln-1 are not the same. And step S13
In step 3, the density average value Dn-1 of the block Tn-1 and the block Tn-
When the absolute value of the difference from the density average value Dn-H of H is smaller than the predetermined value d, the label Ln-
1 and the label Ln-H are recorded in the equitable tables ETk0 and ETk1 as labels that can be integrated with each other. Then, in step S135, the numbers of the equitable tables ETk0 and ETk1 are updated by one, and the flow advances to step S136.

In steps S130 and S131, block T
If one of the block n-1 and the block Tn-H is not a block having a uniform density, the label L is determined in step S132.
If the values of n-1 and Ln-H are already the same, or in step S133, the density average value Dn-1 and the density average value Dn
If the absolute value of the difference from nH is equal to or greater than the predetermined value d, the process proceeds to step S136 at that time.

In step S136, the block number of the evaluation block Tn covered by the scanning mask of FIG. 14 is advanced by one. If the updated block number n is equal to or less than N in step S137, the process returns to step S113 to return to step S113. The routine from S113 to S137 is repeated. By the above routine, labels are applied to all blocks having a uniform density. In step S137, the number n of the evaluation block Tn
If N exceeds N, the process proceeds to step S138.

In steps S138 to S158 shown in FIGS. 5 and 6, sorting of the equitable tables ETk0 and ETk1 is performed based on the size of the recorded label Lc.

First, in steps S138 to S139, the table number k of the equitable tables ETk0 and ETk1 is returned to the previous number. In step S140, it is determined whether or not the table number k of the immediately preceding EQUI tables ETk0 and ETk1 is 1 or more. If the table number k is smaller than 1 in step S140, that is, "0", there is no remaining Eve table ETk0 or ETk1 in which the label value is recorded, and the process proceeds to step S144.

If the number k is equal to or greater than 1 in step S140, the EIVE table ETk recording the label value
0, ETk1 exists. In this case, the label values recorded in the active tables ETk0 and ETk1 are compared in step S141, and if the label value recorded in the active table ETk0 is larger than the label value recorded in the active table ETk1, the process proceeds to step S141. In S142, the Equiv table ETk0 and the Equit table ETk1 are exchanged.

Next, in step S143, the table numbers of the equitable tables ETk0 and ETk1 are returned to the previous numbers, and thereafter, the above-mentioned steps S140 to S143 are performed.
repeat. As a result, in all of the EIVE tables ETk0 and ETk1 in which the label values are recorded, the label value recorded in the EIVE table ETk1 is the same as the label value recorded in the EIVE table ETk1, or the EIVE table ETk1 Becomes smaller than the label value recorded in. Steps S141 to S143 for all the EQUIB tables ETk0 and ETk1 recording the label values
Is completed, k = 0, and the process proceeds from step S140 to step S144 as described above.

In steps S144 to S152, the EIVE tables ETk0 and ETk1 are stored in the EIVE table ETk1.
Are arranged in ascending order of the label values recorded in the EQUIV table ETk1, and when the EIVE table ETk1 records the same label value, the label values recorded in the EIVE table ETk0 are arranged in ascending order.

First, in step S144, the last EQUI tables ETk0 and ETk1 in which the label values are recorded are called. In step S145, it is determined whether or not the number k of the last active table ETk0, ETk1 is 2 or more, that is, whether or not the active table ETk0, ETk1 in which the label value is recorded is 2 or more. ing. If k <2 in step S145, there is no need for sorting, and the flow advances to the next step S153. If k ≧ 2 in step S145, the process proceeds to step S146.

In step S146, the EQUIB tables ETq0 and ETq1 which are one immediately before the last EIVE tables ETk0 and ETk1 in which the label values are recorded are called. In step S147, it is determined whether or not the number q is 1 or more, that is, whether or not the equitable tables ETq0 and ETq1 record label values. q <1
Is satisfied, that is, if there are no EQUI tables ETq0 and ETq1 that record label values, step S
Proceed to 152. If q ≧ 1, the process proceeds to step S148.

In step S148, the label value recorded in the active table ETq1 is stored in the active table ETk1.
Is determined to be greater than the label value recorded in the.
If the recorded label value is ETk1 ≧ ETq1, the process proceeds to step S149, and if ETk1 <ETq1, the process proceeds to step S1.
Go to 50.

In step S149, the label value recorded in the active table ETq1 is stored in the active table ETk1.
It is determined whether or not the label value recorded in the EIVE table ETq0 is greater than the label value recorded in the EIVE table ETk0. The recorded label value is ETk1 = E
If Tq1 and ETk0 <ETq0, the process proceeds to step S150. If ETk1 ≠ ETq1 and ETk0 ≧ ETq0, the process proceeds to step S151. In step S150, the equitable tables ETk0 and ETk1 are converted into the equitable tables ETq0 and ETq0.
Replace with Tq1. In step S151, the number q of the equitable tables ETq0 and ETq1 is returned by one, and then the process returns to step S147.

The routine of steps S148 to S151 is repeated until q <1 in step S147. As a result, the equitable tables ETk0 and ETk1 in which the largest label value is recorded are arranged at the end. Further, when there are two or more of the eave tables ETk0 and ETk1 in which the largest label value is recorded, the size of the labels recorded in the eave table ETk0 from the last one is changed. They are arranged in descending order.

If q <1 in step S147, the process proceeds to step S152. In step S152, the number k of the equitable tables ETk0, ETk1 is returned to the previous number, and then the process returns to step S145. Then, the routine of steps S145 to S152 is repeated until k <2 in step S145. As a result, the equitable tables ETk0 and ETk1 are
The label values recorded in k1 are arranged in ascending order, and when the label values recorded in the eave table ETk1 are equal, the label values recorded in the eave table ETk0 are arranged in ascending order. If k <2 in step S145, the process proceeds to step S153 shown in FIG.

In step S153, step S138
The last EIVE tables ETk0 and ETk1 sorted in the routine of S152 to S152 are called. In step S154, the last Equity table ET
It is determined whether or not the number k of k0 and ETk1 is 2 or more, that is, whether or not there are two or more of the equitable tables ETk0 and ETk1 on which the labels are recorded. If k <2 in step S154, the process proceeds to step S159, and if k ≧ 2, the process proceeds to step S155. In step S155, the last EQUI table ETk0, Ek
Equiv tables ETq0 and ETq1 immediately before Tk1 are called.

In step S156, in the last EQUIB tables ETk0 and ETk1 and the EQUIB tables ETq0 and ETq1 arranged immediately before the last EQUI tables, the label values recorded in the EQUIB tables ETk0 and ETq0 are equal and the Equiv tables are equal. It is determined whether or not the label values recorded in the tables ETk1 and ETq1 are equal. This step S156
If ETk0 = ETq0 and ETk1 = ETq1, the process proceeds to step S157, where ETk0 ≠ ETq0 or E
If Tk1 ≠ ETq1, the process proceeds to step S158.

In step S157, the EIVE tables ETq0 and ETq1 are replaced with new EIVE tables ETk0 and ETk.
Here, k1 is used to delete the original EIVE tables ETk0 and ETk1. When the processing in step S157 is completed, step S
Proceed to 158. In step S158, the table number k of the equitable tables ETk0 and ETk1 is returned by one, and the process returns to step S154. Thereafter, steps S154 to S154 are performed until k <1 in step S154.
The routine of 158 is repeated. In step S154, k <
When it becomes 1, the process proceeds to step S159.

In steps S159 to S166, labels are integrated with reference to the equitable tables ETk0 and ETk1 sorted in the routine of steps S138 to S158.

In step S159, the equivalency tables ETk0 and ETk1 arranged at the end by sorting are displayed.
Is calling. In step S160, step S
The last Equitable ETk0 called at 159,
It is determined whether the number k of ETk1 is 1 or more.
If k <1 in step S160, it is determined that there are no EQUI tables ETk0 and ETk1 in which labels to be integrated are recorded, and the process proceeds to step S167. Step S1
If k ≧ 1 at 60, the process proceeds to step S161. In step S161, the number n of the block Tn is initialized to 1, and
Next, the process proceeds to step S162.

In steps S162 to S165,
First, in step S162, the label Ln of the block Tn
Is compared with one of the equitable tables ETk1. Only when the label value stored in the EIVE table ETk1 is equal to the label value Lc assigned to the label Ln, the other EIVE table ETk0 is determined in step S163.
Is rewritten to the label value Lc of the label Ln. When the processing in steps S162 and S163 is completed, the block number n is updated by one in step S164.

The above steps S162 to S164 are repeated until n> N in step S165. If n> N in step S165, step S1
Proceed to 66 and go to the last Equity table ET
k0 and ETk1 are called. Hereinafter, step S1
Returning to S60, the routine of steps S160 to S166 is repeated until k <1 in step S160. As described above, the label value is given to the label Ln for all the blocks Tn having the uniform density. Further, a label value equal to two or more blocks Tn which are close to each other and whose density is continuous is given.

Next, in this embodiment, in the routine after step S167 shown in FIG. 7, the blocks Tn with each label are counted for each label value. The routine of steps S167 to S172 is a routine for clearing all the block numbers SZr for each label value to “0”. Here, r is a variable corresponding to each label value.
Hereinafter, the subsequent routines after step S173 will be described.

First, in step S173, the number n of the block Tn is initialized to "1". Next, in step S174, it is determined whether or not the block Tn is a block having a uniform density. If the block Tn is a block having a uniform density, the process proceeds to step S175. If the block Tn is not a block having a uniform density, the process proceeds to step S177.

In step S175, the label Ln of the block Tn is replaced with a variable r. For example, when the label value of the label Ln is "1", the variable r = 1, and when the label value of the label Ln is "3", the variable r = 3. Step S176 is a step of adding the block Tn for each label value. That is, if the label Ln is "1", "1" is added to the number of blocks SZ1 of r = 1, and if the label Ln is "3", the number of blocks SZ3 of r = 3
Is added to "1". Upon completion of the process in the step S176, the process proceeds to a step S177.

In step S177, the block number n of the block Tn is incremented by one. Next step S178
Then, it is determined whether or not the increased block number n is equal to or less than the total number of blocks “N”. If n ≦ N, the process returns to step S174, and thereafter, steps S174 to S17
8 is repeated. When the routine of steps S174 to S178 is repeated until n> N in step S178, all the labels Ln of the block Tn given the label value Lc are included in any of the block numbers SZr.

Specifically, each label value Lc =
The number of blocks SZn corresponding to 1, 2, 3,...
= 3, SZ2 = 2, SZ3 = 14... This indicates that the number of blocks constituting the catchlight portion HA of the original image OG shown in FIG. 13 is obtained as one of the block numbers SZn.
If n> N in step S178, the routine for detecting blocks close to each other in which the densities are uniform and the densities are continuous, that is, blocks Tn having the same label Ln, ends.

After detecting the block group to which the same label has been attached in this way, step S shown in FIG. 8 and FIG.
In steps 201 to S225, a cumulative relative frequency histogram of the entire original image is created.

First, in steps S201 to S215, a density histogram of the entire original image is created. Steps S201 to S204 are routines for clearing the number of pixels DFj corresponding to the class number j (= 0 to J) of the density histogram hnj to zero.

Next, in step S205, the block number n is set to "1", and the total number of pixels y is set to "0". Thereafter, the routine of steps S206 to S215 is repeated until n> N in step S215, thereby obtaining the number of pixels DFj and the total number of pixels y for each class in the entire original image. The routine of steps S206 to S215 is as follows.

First, in step S206, it is determined whether the density of the block Tn is uniform, that is, the flag FS
It is determined whether n is “1” or “0”. If the flag FSn is "1", the flow proceeds to step S207.
Step S207 is the same as step S175.
The label Ln of the block Tn is replaced with a variable r.
After the processing of step S207 is completed, step S20
Proceed to 8.

In step S208, a coefficient α is set by multiplying a previously selected constant α0 by the reciprocal of the block number SZr. The constant α0 only needs to be a positive number.
It is set arbitrarily as needed. For example, the constant α0 = 1
Then, the coefficient α calculated in step S208 is the reciprocal of the block number SZr. That is, if the number of blocks SZr = 2, the coefficient α becomes 1/2, and SZr = 1
If it is 1, the coefficient α is 1/11.

If the flag FSn is "0" in step S206, the process proceeds to step S209 in which the coefficient α
= 1 is set. Step S208 or step S
When the coefficient α is set in 209, the process proceeds to step S210. In step S210, the class number j of the density histogram is set to “0”.

Next, in steps S211 to S213, (1) the number of pixels DFj of each class of the image composed of the blocks T1 to Tn and (2) the blocks T1 to T
The total number of pixels y of the image consisting of n is obtained.

The above-described routine of steps S206 to S213 is repeated until one block number n is updated in step S214 to determine whether n ≦ N or not in step S215. .

Next, the number of pixels DFj of each class and the total number of pixels y obtained in steps S211 to S213 will be described in detail.

When the block number n = 1 and the class number j = 0, in step S211, the corrected pixel number α1 of the class corresponding to the pixel number DF0 cleared in step S202.
DF0 + α1 obtained by adding h10. H10 is the number of pixels D corresponding to the class number "0" in the image of the block T1.
It is calculated as F0. Here, α1 is set in step S208.
Or the coefficient α of the block T1 set in S209. In this case, since the number of pixels DF0 is set to "0" in step S202, the number of pixels DF0 calculated in step S211 is α1 · h10.

After calculating the number of pixels DF0, step S2
In step S12, one class number j is updated, and the updated class number j is compared with the final class number J in step S213. If j ≦ J in step S213, the process returns to step S211 and repeats the routine of steps S211 to S213. Thus, the pixel numbers DF0 to DFJ of all the classes when n = 1 are sequentially obtained.

Further, the block number n = 1 and the class number j =
When the value is 0, in step S211, the total pixel number y cleared in step S205 is added to the corrected pixel number α of the class number j = 0.
Calculate y + α1 · h10 by adding 1 · h10. Since the total number of pixels y is set to “0” in step S205,
When the class number j = 0, the total number of pixels y calculated in step S211 is α1 · h10.

Next, in step S212, one class number is updated to j = 1. In step S213, 1 ≦
J returns to step S211. Next step S21
In the case of 1, the total number of pixels y calculated when the class number j = 0 is y =
α1 · h10, corrected pixel number α1 when class number j = 1
H11 is added, and the total number of pixels y when the class number j = 1 is y =
Calculate α1 (h10 + h11). Hereinafter, similarly, j =
By repeating the steps S211 to S213 in steps 2 to J, the total number of pixels y of the block T1 is calculated as y =
α1 (h10 + h11 +... h1J) is calculated.

If j> J in step S213, the flow advances to step S214 to update the block number n by one and n = 2
And Then, since n = 2 ≦ N in step S215, the process returns to step S206.

In the next steps S206 to S209, n
= 2, that is, the coefficient α2 of the block T2 is selected. In subsequent steps S211 to S213, the number of pixels DFj = α1 · h1j of each class calculated by n = 1 is set to the block T.
2 is added to the corrected pixel number α2 · h2j of the corresponding class to obtain α1 · h as the pixel number DFj corresponding to the class number j in the image composed of the block T1 and the block T2.
1j + α2 · h2j is calculated. This step S211
Then, the total number of pixels y = α1 (h10 + h11) calculated with n = 1
+... H1J) are sequentially added with the corrected pixel number α2 · h2j. By repeating the routine of steps S211 to S213 until j> J in step S213, the total number of pixels y = α1 (h10 + h11 +... H1J) + α2 (h20 +) in the image composed of block T1 and block T2
h21 +... h2J) are required.

Thereafter, the routine of steps S206 to S215 is repeated for n = 3 to N in the same manner, whereby the number of pixels DFj and the total number of pixels of each class in the entire original image as shown in equations (4) and (5) are obtained. The number of pixels y is calculated.

[0099]

(Equation 4)

[0100]

(Equation 5)

The density histogram of the entire original image can be created based on the number of pixels DFj of each class of the entire original image obtained as described above. As described above, the coefficient α set in step S208 is a coefficient that is inversely proportional to the number of blocks forming the corresponding block group. Therefore, as is clear from the equations (4) and (5), the blocks included in the block group composed of the blocks each having a uniform density and having a continuous density are adjacent to each other, that is, equal in steps S101 to S166. correction number of pixels of a given block of label values, even increasingly number of blocks constituting the corresponding group of blocks, the proportion of the entire original image is not large.

Next, steps S216 to S221 in FIG.
, The cumulative pixel count CDF for each class j of the entire original picture
j (CDF1 to CDFJ) is obtained. First, step S
At step 216, the class number j is initialized to "0", and the process proceeds to step S217. If j ≠ 0 in step S217, that is, if j = 0, the process proceeds to step S218, and if j ≠ 0 in step S215, step S21
Go to 9.

In step S218, the number of pixels DF0 of the class having the class number j = 0 is converted to the cumulative number of pixels CDFO. In step S219, the number of pixels DFj of class number j
Is added to the cumulative pixel number CDFj-1 up to the previous class j-1 to obtain the cumulative pixel number CDF up to the class number j.
Seeking j. In step S220, the class number j is updated by one. The updated class number j is the step S
If j ≦ J in 221, the process returns to step S 217. If j> J in step S221 as a result of increasing the class number j by 1 in step S220, the next step S
Proceed to 222.

Steps S216 to S221 as described above
According to the routine, when the class number j = 1, the cumulative number of pixels CDF1 becomes the cumulative number of pixels CDF0 corresponding to the class number j = 1-1 = 0 and the pixel number DF corresponding to the class number j = 1.
The cumulative pixel number CDF2 = CDF1 + DF2 is calculated in order from the lowest class number j.
, CDF3 = CDF2 + DF3,..., CDFJ = CD
F (J-1) + DFJ is obtained.

In steps S222 to S225,
Each cumulative pixel count CDFj with respect to the total pixel count y of the entire original image
Is obtained. Step S22
In 2, the class number j is initialized to "0". In step S223, the relative frequency RNj (%) is obtained from the equation RNj = CDFj × 100 / y based on the cumulative pixel count CDFj obtained in steps S216 to S221 and the total pixel count y of the entire original image. A step S224 updates the class number j by one. Step S225 is Step S225
The class number j updated in 224 is replaced with the final class number J.
Compared to Steps S223 to j> J
By repeating the routine of S225, the relative frequencies RNj of all the classes are obtained.

By setting the class value DMj (n = 0 to J) of the average density value on the horizontal axis and the relative frequency RNj calculated in steps S222 to S225 on the vertical axis, the cumulative relative frequency as shown in FIG. A histogram can be created. The cumulative relative frequency histogram of FIG. 15 shows that the range between the minimum generation density DMmin and the maximum generation density DMmax ranges from 0% to 1%.
The shape changes up to 00%. Note that the cumulative relative frequency histogram of FIG. 15 is approximated by a curve by making the class width sufficiently small.

Next, steps S301 to S301 shown in FIG.
At 316, a cumulative density value histogram for each color component is created.

[0108] First, in step S301 to S304, each of the blocks T calculated in step S10 3 of FIG. 2
n based on the density histogram hnj for each block T
n, cumulative density values DRnj, DGnj, and DBn for each color component
j. That is, for each class of the density histogram hnj obtained in step S103, the density value of each pixel included therein is extracted, and the density value of each color component is cumulatively added. This process is performed independently for each class . Next, in steps S305 to S314, the cumulative density value of the class number j in the entire original image is calculated. In step S305, the class number j is initialized to "0". In step S306, the accumulated density values DRj, DGj, and DBj for each color component of the entire original image are cleared to "0". Next, in step S307, the block number n
Is “1”.

The next steps S308 to S311 are the same as steps S206 to S209. That is, in step S308, it is determined whether the flag FSn of the block Tn is "1". The flag FSn is "1"
In step S309, the label Ln of the block Tn is replaced with the variable r, and then, in step S31.
Go to 0. In step S310, a coefficient α is set by multiplying the constant α0 by the reciprocal of the block number SZr. If the flag FSn is “0” in step S308, the coefficient α = 1 is set in step S311. After setting the coefficient α in step S310 or S311, the process proceeds to step S312.

In step S312, α · DRn is added to the already calculated cumulative density values DRj, DGj, and DBj, respectively.
j, α · DGnj and α · DBnj. In step S313, the block number n is advanced by one. This one
In step S314, it is determined whether or not the block number n increased by n is n ≦ N. If n ≦ N, the process returns to the step S308, and then n> N in the step S314.
The routine of steps S308 to S314 is repeated until the condition is satisfied.

Thus, the cumulative density value DRj = α1 · DR1j + α2 · DR2j +... + ΑN of the entire original image for each class.
· DRNj, DGj = α1 · DG1j + α2 · DG2j + ...
+ ΑN · DGNj, DBj = α1 · DB1j + α2 · DB2j +
... + ΑN · DBNj is calculated. When the constant α0 is “1”, the coefficients α1, α2... ΑN are 1 or 1 / SZ
r.

When n> N is satisfied in step S314, the class value j is advanced by one in step S315, and it is determined in step S316 whether or not the class value j advanced by 1 satisfies j ≦ J. Step S30
The routine from 6 to S316 is repeated until the class value j> J in step S316. That is, in steps S305 to S316, the cumulative density values of the entire original image for all the classes from j = 0 to J are calculated, whereby the cumulative density value histogram for each color component of the entire original image as shown in FIG. Can be created.

Next, based on the cumulative relative frequency histogram of FIG. 15 and the cumulative density value histogram of each color component of FIG. 16, a highlight point and a shadow point which are reference density points of a gradation conversion curve are obtained.

First, by adopting the cumulative density appearance rates RNH and RNS to the cumulative relative frequency histogram of FIG. 15 created by the routine of steps S201 to S225, the temporary points corresponding to the highlight point HL and the shadow point SD are obtained. The average highlight density value DMH and the average shadow density value DMS are calculated. Cumulative density appearance rate RNH, RN
S corresponds to, for example, a highlight point HL and a shadow point that provide optimal gradation conversion characteristics as empirically obtained from a large number of sample original images prepared in advance. %. The provisional highlight average density value DMH and shadow average density value DMS obtained in this way are applied to the cumulative density value histogram for each color component shown in FIG.

In the cumulative density value histogram shown in FIG. 16, on the highlight side, an area lower than the tentative highlight average density DMH (DMmin≤DM≤DMH), and on the shadow side, the area exceeds the tentative shadow average density value DMS. Area (DMS ≦ D
The classes included in M ≦ DMmax are shown with diagonal lines. In the illustrated example, the provisional highlight average density value DM
H, average density value DMS, class value DM respectively
5, DM (J-2) is set.

For example, for the color component R, the cumulative density value DR1 corresponding to the class values DM1 to DM5 in the entire original image.
Add ~ DR5. Further, the numbers of pixels PR1 to PR5 in the range corresponding to the class values DM1 to DM5 are added. From these two added values, the input-side highlight density value D RH of the color component R is calculated according to Equation 6.
It is calculated by the following equation.

[0117]

(Equation 6)

The same processing is performed on the shadow side, and the input side shadow density value DRS is obtained by the equation (7).

[0119]

(Equation 7)

The cumulative density values DR (J-2), DR (J-1), DRJ and the number of pixels PR (J-2), PR (J-1), PRJ are classified into classes as in the case of the equation (6). It is provided corresponding to the values DM (J-2), DM (J-1), and DMJ. It should be noted that the number of pixels PRj corresponding to each class in the cumulative density value histogram of FIG. 16 is determined by the number of pixels PRnj corresponding to the class value DMj for each block Tn and the above-described step S.
By using the coefficient α set in 310 and S311, PRj = α1 · PR1j + α2 · PR2j +... + ΑN
・ It can be obtained by PRNj.

Although not described in detail here, the input-side highlight density values DGH and DBH and the input-side shadow density value DG are similarly applied to the other color components G and B.
S and DBS can be obtained.

Next, the setup executed using the input-side highlight density values DRH, DGH, and DBH and the input-side shadow density values DRS, DGS, and DBS obtained as described above will be described.

In the case where the original image OG has the catchlight portion HA as shown in FIG.
The cumulative relative frequency histogram HI shown by the solid line in FIG. 15 created by the routine of 1 to S225 is obtained by using the pixel number of the entire original image as it is by the conventional method. Histogram H
The histogram becomes a histogram in which the highlight region is biased toward the high density side as compared with I ′. This is for the following reasons.

That is, the original image OG as shown in FIG.
On the other hand, when the cumulative relative frequency histogram is created by the above-described procedure, all the blocks Tn constituting the catchlight portion HA are given the same label value. Therefore, the number of blocks SZr in the block group increases as the catchlight portion HA becomes wider. As described above, in calculating the number of pixels DFj of each class in the entire original image in step S211,
For the blocks Tn with the same label value detected in 101 to S178, the number of pixels of each class is multiplied by a coefficient α that is inversely proportional to the number of blocks SZr. Since the catchlight portion HA is an extremely bright area, the density histogram of each block Tn constituting the catchlight portion HA is biased toward the low density side. Therefore, when the number of pixels of each class of the density histogram of the block Tn constituting the catchlight portion HA is multiplied by the coefficient α which is inversely proportional to the number of blocks SZr, the number of pixels occupying the low density side in the density histogram in the entire original image is reduced. As a result, the cumulative relative density histogram HI is biased toward a higher density side than the cumulative relative density histogram HI '.

When the cumulative density appearance rate RNH on the highlight side is applied to the cumulative relative frequency histograms HI and HI 'shown in FIG. 15 as described above, a temporary highlight obtained when the cumulative density appearance rate RNH is applied to the cumulative relative frequency histogram HI is obtained. The average density value DMH is larger than the provisional highlight average density value DMH 'when applied to the cumulative relative frequency histogram HI'. Therefore, based on the tentative highlight average density value DMH, the input-side highlight density value DRH of the color component R given by the equation (6) and the input-side highlight density of the color components G and B given by the same equation The values DGH and DBH are input-side highlight density values DRH 'and D for each color component obtained based on the provisional highlight average density value DMH' according to the conventional method.
GH 'and DBH'.

As shown in FIG. 15, the cumulative relative frequency histogram HI shown by the solid line created by the method of the above embodiment.
And the cumulative relative frequency histogram HI ′ indicated by the dashed line formed by the conventional method almost coincides before the relative frequency is 100%. Therefore, the temporary shadow average density value DMS obtained when the shadow-side cumulative density appearance rate RNS is applied to the cumulative relative frequency histogram HI created in the above embodiment, and the cumulative relative frequency histogram HI 'created by the conventional method. A provisional average shadow density value DMS 'obtained when the cumulative density appearance rate RNS is applied.
Is very similar to Therefore, hereinafter, for convenience, the provisional average shadow density values DMS and DMS 'are assumed to be equal. Also, the shadow-side density value DRS of the color component R and the shadow-side density values DGS,
The DBS is also assumed to be equal to the shadow-side density values DRS ', DGS', and DB 'obtained based on the temporary shadow average density value DMS'.

FIG. 17 shows a gradation conversion curve GCR set by using the input-side highlight density value DRH and the shadow-side density value DRS of the color component R obtained by the method of the above embodiment, and the highlight obtained by the conventional method. 7 is a graph showing a tone conversion curve GCR 'set using the side density value DRH' and the shadow side density value DRS '. The horizontal axis is the input density value DI input for each color component, and the vertical axis is the output density value DO. The output side highlight density value DOHL and the output side shadow density value DOSD are values that are fixed in common for all color components. Here, for convenience, all the input-side highlight density values D
RH, DGH, and DBH are the input-side highlight density values DRH ', DG
H 'and DBH', and the ratio of DRH =
It is assumed that DGH = DBH and DRH '= DGH' = DBH '. Also, the input side shadow density values DRS, DGS, and DBS are DRS = D
GS = DBS, and the input side shadow density values DRS ', DG
S 'and DB' are assumed to be DRS '= DGS' = DBS '.
In such a case, the gradation conversion curve GCG of the color component G and the gradation conversion curve GCB of the color component B become equal to the gradation conversion curve GCR of the color component R shown in FIG.
And GCB 'are equal to the tone conversion curve GCR' shown in FIG.

As is apparent from FIG. 17, the highlight point HLR corresponding to the highlight-side density value DRH is set at a position shifted in parallel to the right from the highlight point HLR 'corresponding to the highlight-side density value DRH'. Is done. On the other hand, as described above, the shadow side density value DRS is equal to the shadow side density value DR.
Equal to S '. Accordingly, the shadow point SDR corresponding to the shadow side density value DRS coincides with the shadow point SDR 'corresponding to the shadow side density value DRS'.

In this case, as shown in FIG. 17, the gradation conversion curve GCR passes above the gradation conversion curve GCR ', that is, on the highlight side output set value DOHL side. As described above, here, the gradation conversion curve GCR = GCG = GCB
And the gradation conversion curve GCR '= GCG' = GCB '
Therefore, the gradation conversion curve GCG is equal to the gradation conversion curve GCG.
And the gradation conversion curve GCB passes through the highlight side output setting value DOHL from the gradation conversion curve GCB '. Therefore, even if the same input density value DI is given, the gradation conversion curves GCR, GCG,
The output density value DO converted by the GCB is higher than the output density value DO converted by the tone conversion curves GCR ', GCG', and GCB '.
Approach HL.

As is apparent from the above description, when the original image has a bright portion such as the catchlight portion HA in FIG. 13, the highlight-side density values DRH, DGH, and DBH, the shadow-side density values obtained in the above embodiment are obtained. Density values DRS, DGS, D
When the setup is performed using the BS, compared with the case where the setup is performed using the highlight-side density values DRH ', DGH', and DBH 'and the shadow-side density values DRS', DGS ', and DB' obtained by the conventional method, The highlight area of the output density value DO approaches the highlight-side output set value DOHL, and a duplicate image with a brighter finish can be obtained.

Although not described in detail, according to the above embodiment, when the original image has a dark background, the shadow-side density values DRS, DGS, and DBS are changed to the shadow-side density values DRS.
This is set lower than RS ', DGS' and DBS '. Therefore, in this case, the gradation conversion curve GCR passes through the lower side of the gradation conversion curve GCR ', that is, the shadow side output set value DOSD, and is converted by the gradation conversion curve GCR even if the same input density value DI is given. The output density value DO is equal to the output density value D converted by the gradation conversion curve GCR '.
O approaches the shadow side output set value DOSD. That is, according to the above embodiment, the shadow side density value DR
By setting S, DGS, and DBS, even if the original image is a scene having a dark background, a duplicated image that is not as whitish as the conventional image can be obtained.

The gradation conversion curve does not necessarily have to be a straight line like the gradation conversion curve of the embodiment shown in FIG. That is, the gradation conversion curve may be an appropriate curve.

In the above embodiment, the density histogram for each block Tn obtained in step S103 is a density histogram of an average density value obtained by averaging the density values for each color component for each pixel. Then, a lightness value obtained by a weighted average of the density values of the respective color components may be used. Further, the density histogram may be a density histogram of the density for each color component. In this case, a density histogram may be obtained for all of the color components R, G, and B, or a density histogram may be obtained for only one preselected color component.

If a density histogram is to be obtained for all of the color components R, G, B, steps S201 to S22
The cumulative relative frequency histogram obtained in step 5 is also obtained for each color component, and the cumulative density appearance rates RNH and RNS are obtained for each color component. Then, the cumulative density appearance rate R of each color component
On the basis of NH, RNS and the cumulative density value histograms obtained in steps S301 to S316, the highlight-side density values DRH, DGH, DBH and the shadow-side density values DRS, DGS, DBS
Ask for. On the other hand, when the density histogram is obtained only for one color component selected in advance in step S103, the cumulative density value histogram obtained in steps S301 to S316 may be only the cumulative density value frequency histogram of the corresponding color component.

In the above embodiment, step S11
5, in steps S118, S122, S126, and S133, the difference between the average density values of the two blocks having uniform densities and being close to each other is obtained, and when the difference is smaller than a predetermined value d, the density between the two blocks is calculated. Are determined to be continuous. However, as another embodiment, steps S115, S118, S122, S126, S1
At 33, the variance value of the density of the entire blocks may be obtained, and when the variance value is equal to or less than a predetermined value, it may be determined that the density between the two blocks is continuous.

In the above embodiment, if a block group is composed of blocks that are close to each other and have uniform densities and continuous densities, the density histograms of the blocks included in the block group are included in all the block groups. Is multiplied by a coefficient inversely proportional to the number of blocks included in the block group. However, as another embodiment, a block whose density average value Dn obtained in step S103 is equal to or less than a first predetermined value DnL set on the low density side and / or a block whose density average value Dn is set on the high density side is used. 2 Predetermined value DnH (DnH> Dn
L) Only for the block group composed of the above blocks, the number of pixels of each class in the density histogram of the block included in the block group is multiplied by a coefficient inversely proportional to the number of blocks included in the block group. Is also good.

Further, only when the block group includes a predetermined number of blocks or more, the number of pixels of each class in the density histogram of the block included in the block group is multiplied by a coefficient inversely proportional to the number of blocks included in the block group. May be. In this case, it is preferable that the constant α0 used when obtaining the coefficient α to be set in step S208 is a value larger than “1”.

[0138] Further, as a coefficient which is inversely proportional to the number of blocks may be used the square root of the number of blocks.

Further, the present invention can be used not only for a plate-making scanner but also for a copying machine or a facsimile having gradation reproducibility.

Further, when the image information of the original image is stored in the large-capacity memory in advance, the pre-scan by the scanning reading device is unnecessary, and the image data may be read directly from the memory and used.

Further, according to the present invention, the density value can be represented by luminance, C
It can be performed by mapping to another space such as IEXYZ or CIELAB.

[0142]

According to the first aspect, the reference density point of the gradation conversion curve can be set in a state where the influence of the local low-density area and high-density area of the original image is reduced. It is possible to prevent the image from becoming unnecessarily dark or whitish.

According to the second aspect, it is possible to determine whether or not the density between adjacent blocks is continuous only by calculating the density average value of each block.

According to the third aspect, the reference density points of all the color components can be created only by obtaining one cumulative density value histogram of the entire original image.

According to the fourth aspect, it is only necessary to obtain the cumulative density value histogram of one color component, and the process of obtaining the reference density point can be simplified.

According to the fifth aspect, since the cumulative density value histogram is obtained independently for each color component,
An appropriate reference density point can be obtained for each color component.

[Brief description of the drawings]

FIG. 1 is a flowchart schematically showing a method of setting a reference density point according to the present invention.

FIG. 2 is a flowchart illustrating a method for detecting a block group.

FIG. 3 is a flowchart illustrating a method for detecting a block group.

FIG. 4 is a flowchart illustrating a method for detecting a block group.

FIG. 5 is a flowchart illustrating a method of detecting a block group.

FIG. 6 is a flowchart illustrating a method of detecting a block group.

FIG. 7 is a flowchart illustrating a method for detecting the number of blocks in a block group.

FIG. 8 is a flowchart illustrating a process of obtaining a cumulative relative frequency histogram.

FIG. 9 is a flowchart illustrating a process of obtaining a cumulative relative frequency histogram.

FIG. 10 is a flowchart illustrating a process of obtaining a cumulative density value histogram for each color component.

FIG. 11 is a schematic block diagram of a plate making scanner to which an embodiment of the present invention is applied.

FIG. 12 is a block diagram illustrating an image processing apparatus.

FIG. 13 is an explanatory diagram showing a state in which an original image is divided into a plurality of blocks.

FIG. 14 is an explanatory diagram showing a scanning mask.

FIG. 15 is a diagram showing a cumulative relative frequency histogram.

FIG. 16 is a diagram showing a cumulative density value histogram for each color component.

FIG. 17 is a graph showing a gradation conversion curve.

FIG. 18 is a flowchart illustrating a conventional method of setting a reference density point.

FIG. 19 is a diagram showing a cumulative relative frequency histogram obtained by a conventional method.

FIG. 20 is a diagram showing a cumulative density value histogram for each color component obtained by a conventional method.

[Explanation of symbols]

 400 Highlight / shadow point setting unit Tn block hnj density histogram Dn density average value Sn density variance value DFj pixel count CDFj cumulative pixel count SZr block count HI cumulative relative frequency histogram RNH, RNS cumulative density appearance rate DRH, DGH, DBH input side Highlight density value DRS, DGS, DBS Input side shadow density value HL Highlight point SD Shadow point GCR Tone conversion curve

Claims (5)

(57) [Claims] 1. A method of setting a reference density point through which a gradation conversion curve to be set for performing gradation conversion at the time of duplication based on image data obtained by reading a duplication target original image having gradations. A first step of dividing the original image into a plurality of blocks; and a density for each block indicating a relationship between a density value and the number of pixels giving the density value for each block based on image data of the original image. A second step of obtaining a histogram, a third step of detecting, based on the image data, a block group composed of blocks having uniform densities and mutually adjacent densities, and A fourth step of counting the number of blocks included in the individual block group detected in the step, and each of the block-by-block density histograms of the blocks included in the block group. A fifth step of multiplying the number of pixels for each class by a coefficient inversely proportional to the number of blocks of the block group including the block to obtain a corrected number of pixels; after the fifth step, for each of the classes, the block group A sixth step of obtaining a density histogram of the entire original image by adding the number of pixels of all blocks not included in the block group and the number of corrected pixels of all blocks included in the block group; A seventh step of obtaining a cumulative density value histogram based on the entire density histogram; and an eighth step of setting a reference density point based on the cumulative density value histogram. Setting method. 2. The method of setting a reference density point according to claim 1, wherein the third step is performed when the difference between the density average values of the blocks which are close to each other and whose density is uniform is equal to or less than a predetermined value. A method of setting a reference density point, wherein the density of the blocks is continuous. 3. The reference density point setting method according to claim 1, wherein the density value is an average density value obtained by averaging density values of respective color components for each pixel. How to set. 4. The method of setting a reference density point according to claim 1, wherein the density value is a density value of each pixel of the selected color component. 5. The method of setting a reference density point according to claim 1, wherein the density value is a density value of each color component for each pixel.