JPH0591324A - Method of setting reference density point - Google Patents

Abstract

PURPOSE:To provide the setting method of the reference density point to obtain a gradation conversion curve which reduces an influence of bright and dark background parts. CONSTITUTION:The original picture is divided into plural blocks and a background part, is detected from these blocks in a step S1, and the number of picture elements of the block in the background part is compressed relatively to that of the other blocks and a cumulative relative frequency histogram of the whole of the original picture is obtained in this state in a step S2, and a cumulative density value histogram to which temporary highlight and shadow density values obtained based on the cumulative relative frequency histogram are applied is generate in a step S3, and the reference density point or the gradation conversion curve is obtained based on the cumulative relative frequency histogram and the cumulative density value histogram in a step S4. Thus, a copied picture is prevented from being too dark neither too white even in the case of the existence of bright and dark background parts.

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 which is a reference density point in a gradation converting apparatus used in the field of image processing such as a color scanner for plate making.

[0002]

2. Description of the Related Art As a method for automatically setting highlight density and shadow density in a color separation device such as a plate-making color scanner, Japanese Patent Laid-Open No. 15775/1990 by the present applicant.
Some are disclosed in Japanese Patent No. The method disclosed in this prior art publication obtains the input side highlight density value and the shadow density value of the gradation conversion curve by the procedure shown in FIG.

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

Next, in step S502, the density values for each color component are averaged for each pixel to obtain an average density value, and an average density value frequency histogram is obtained.

Next, in step S503, the cumulative density value is obtained for each class for each color component, and a cumulative density value histogram as shown in FIG. 19 is obtained (however, FIG. 19 shows the color component R).
Only the cumulative density value histogram for the is shown.

Next, in step S504, the relative frequency of the pixel obtained by cumulatively adding the pixels from the lower density class is obtained,
A cumulative relative frequency histogram that changes from 0 to 100% in the range of the minimum and maximum generated density values as shown in FIG. 18 is obtained.

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

Finally, in step S506, the temporary highlight average density value DMH and shadow average density value D
By applying the MS to the cumulative density value histogram for each color component shown in FIG. 19, the input side highlight density value and the input side shadow density value for each color component are obtained. The tone conversion curve should pass based on the input side highlight density value and the input side shadow density value and the output side highlight density value and the output side shadow density value that have been set in advance as described above. A highlight point and a shadow point, that is, a reference density point is set.

[0009]

However, in the method disclosed in Japanese Patent Laid-Open No. 2-157758 mentioned above, when the original image is a scene having a bright background or is a scene photographed by backlight, The highlight density value on the input side was set lower than the desired value, sometimes resulting in a dark finish on the duplicate image. On the contrary, when the original image is a scene with 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 causes. That is, for example, when the original image is a scene having a bright background, the cumulative density histogram shown in FIG. 19 becomes a histogram in which the class of relatively low density has a high frequency due to the influence of this bright background portion. Therefore, the provisional highlight average density value obtained from the cumulative relative frequency histogram of FIG. 18 becomes low, and the input side highlight density value obtained at step S506 also becomes low. When the input side highlight density value becomes low in this way, the gradation conversion curve set based on this 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.

When the original image has a dark background, the cumulative density histogram shown in FIG. 19 is a histogram in which the density of the class having a relatively high density is high, and step S
The input side shadow density value obtained in 506 becomes high. Therefore, the gradation conversion curve is such that the shadow area is shifted to the output side highlight density value side, and the copied image gradation-converted according to this gradation conversion curve has a whitish finish.

It is an object of the present invention to provide a method of setting a reference density point of a gradation conversion curve that can obtain a desired finished copy image without being affected by the background state of the original image.

[0013]

According to a first aspect of the present invention, there is provided a method for setting a reference density point according to claim 1, wherein a gradation is set when copying based on image data obtained by reading an original image to be copied having a gradation. A method of setting a reference density point through which a gradation conversion curve set for conversion is to be set, which comprises: a first step of dividing an original image into a plurality of blocks; The second step of obtaining a density histogram for each block showing the relationship between the density value for each block and the number of pixels giving this density value; and the density is uniform and the density is close to each other based on the image data. The third that selects a block group consisting of consecutive blocks
And a fourth step of detecting, as a background block, a block of a block group including a block whose density is continuous with a predetermined block from the block group selected in the third step, and the number of pixels of the background block Relative to the number of pixels of the block not selected as the background block for each class of the density histogram for each block, and after the fifth step, for each class. A sixth step of obtaining a density histogram of the entire original image by adding the numbers of pixels of all blocks; a seventh step of obtaining a cumulative density value histogram based on the density histogram of the entire original image; An eighth step of setting a reference density point based on the density value histogram. The cumulative density value histogram may include at least a highlight portion and a shadow portion in the entire original image.

According to a second aspect of the present invention, the predetermined density points in the fourth step are the blocks at the four corners of the original image.

The method of setting the reference density point according to claim 3 is the method according to claim 1 or 2, wherein the third step is
When the difference between the density average values of the blocks which are close to each other and have uniform densities is less than or equal to a predetermined value, the densities of these blocks are continuous.

A method of setting a reference density point according to a fourth aspect is the method of setting a reference density point according to any one of the first to third aspects, wherein the number of pixels of each class of the density histogram of the background block is less than one. And multiply the number of pixels in each class of the density histogram of blocks other than the background block by 1
By multiplying by, the number of pixels of the background block is relatively compressed with respect to the number of pixels of blocks other than the background block for each class of the density histogram.

According to a fifth aspect of the present invention, there is provided a method for setting a reference density point according to the fourth aspect, wherein a coefficient by which the number of pixels of each class of the density histogram of the background block is multiplied is zero. ..

The method of setting the reference density points according to claims 6 to 8 is as follows:
The reference density point setting method according to any one of claims 1 to 5, wherein (1) the density value is an average density value obtained by averaging the density values of the respective color components for each pixel, and (2) the density value. Is the density value for each pixel of the selected color component, or
(3) It is characterized in that the density value is the density value of each color component for each pixel.

[0019]

According to the method of setting the reference density point of claim 1, the block is a block which is uniformly bright or dark and has a uniform density and which is directly or indirectly continuous with a predetermined block. Is selected as a background block. In addition, the number of pixels of each background block is relatively compressed with respect to the number of pixels of each block other than the background block, so that the density of the background block in the number of pixels of each class of the density histogram of the entire original image is reduced. The ratio of the number of pixels in each class of the histogram is reduced as compared with the case without the compression. Therefore, when the background of the original image is a bright background, the input side highlight density value is mainly larger than that in the case where the number of pixels of the background block is not relatively compressed. The output side shifts to the highlight density value side. Also, when the background of the original image is a dark background, the gradation value of the tone conversion curve is mainly due to the smaller input side shadow density value compared to when the number of pixels in the background block is not relatively compressed. Shift to the density value side.

According to the reference density point setting method of the second aspect, the background blocks detected in the fourth step are directly or indirectly continuous in density with the blocks at the four corners of the original image.

According to the third aspect of the present invention, in the method of setting the reference density point, the difference between the average density values of these blocks is equal to or less than a predetermined value between the uniformly bright blocks or the uniformly dark blocks having the same density and close to each other. , The density is a continuous block.

According to the reference density point setting method of the fourth aspect, the number of pixels of the block detected as the background block is reduced, so that the number of pixels of this background block is compared with the number of pixels of blocks other than the background block. It is relatively compressed.

According to the reference density point setting method of the fifth aspect, the pixels of the block selected as the background block are completely ignored in obtaining the cumulative density value histogram of the entire original image.

According to the reference density point setting method of the sixth aspect, the influence of the pixels of the background block is reduced according to the ratio of the color components near the reference density.

According to the reference density point setting method of claim 7, when the background block is composed of pixels occupying the majority of the specific color component, the influence of the pixels of the background block on the color component is reduced.

According to the reference density point setting method of the eighth aspect, the influence of the pixels of the background block is reduced for each color component.

[0027]

FIG. 10 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 is
Highlight / shadow point setting unit 4 with functions to be described later
00, and performs processing such as highlight / shadow point setting on the input image signal. Then, the processed image signal is given to the scanning recording apparatus 500. The scanning recording device 500 converts the image signal into a halftone dot signal, and on the basis of this, the halftone dot image is exposed and recorded on the photosensitive film 600.

FIG. 11 shows an image processing apparatus 300 including the highlight / shadow point setting unit 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. The image memory 401 stores the original image OG in a large number of pixels, for example, a vertical 512,
It is decomposed into 262144 pixels of 512 pixels and stored. C configuring the highlight / shadow point setting unit 400
The PU 402 converts the original image OG having a large number of pixels into N blocks T each having a vertical V and a horizontal H, as shown in FIG.
split into n. As described above, the original image having 262144 pixels is divided into N = 1024 blocks Tn having V = 32 and H = 32. In this case, one block Tn is composed of 256 pixels in the vertical direction and 16 pixels in the horizontal direction. Further, the CPU 402 calculates the highlight point / shadow point of the gradation conversion curve based on the original image data read in the image memory 401 by the 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 included in the gradation converting device 301. After that, the uncorrected digital density signal given from the scanning reading device 200 to the gradation converting device 301 becomes a normalized digital density signal according to the gradation converting curve set based on the highlight point / shadow point. To be converted. The normalized digital density signal is subjected to a predetermined color calculation in the color calculation block 302 and then output to the scanning printing apparatus 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 the highlight points and shadow points of the gradation conversion curve as follows. FIG. 1 shows a schematic setting procedure of the highlight point and the shadow point.

As shown in FIG. 1, the highlight / shadow point setting section 400 first detects a background portion BK of the original image OG as shown in FIG. 12, for example, in step S1. In FIG. 12, the background portion BK is a portion other than the main subject portion MA indicated by diagonal lines.

Next, in step S2, the number of pixels of the background portion BK detected in step S1 is set to the background portion B.
A cumulative relative frequency histogram of the entire original image OG is created in a state of being relatively compressed with respect to the number of pixels of blocks other than K (the subject portion MA in FIG. 12).

In step 3, a cumulative density histogram for each color component is created.

Then, 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, reference density points, that is, highlight points, of the gradation conversion curve. And calculate the shadow point.

The steps S1 to S4 will be described below.
Will be described in detail.

In step S1 shown in FIGS.
The background portion BK in the original image OG is detected by the procedure shown in FIG.

First, in step S101 of FIG.
The original image OG is divided into N (H.times.V) blocks Tn having V in the vertical direction and H in the horizontal direction (see FIG. 12). n is block T
It is a block number of n 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 dispersion value Sn are sequentially obtained from n = 1 to N. Where j is the histogram class number and is 0
Is an integer from ~ J. The final class number J is J = Dmax, where Dmax is the maximum density value and ΔD is the density step.
/ ΔD 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 average density values obtained by averaging the density values of the color components of individual pixels, and is created by the following procedure, for example. That is, first, by averaging the density values DR, DG, and DB for the respective color components for each pixel, which are obtained by prescanning the original image OG, by the equation 1 to obtain the average density value DM for each pixel. Ask for.

[0040]

[Equation 1]

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

The average density value Dn is the 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 of this equation 2, I is the number of pixels in the block Tn.

[0043]

[Equation 2]

Next, the density dispersion value Sn is obtained by the following formula 3 as a sample standard deviation using the average density value DMni of each pixel as a sample random variable in this embodiment.

[0045]

[Equation 3]

As described above, the density histogram hn
After obtaining j, the density average value Dn, and the density variance value Sn, in steps S106 to S111 shown in FIG.
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 whose variance value Sn is less than a predetermined value ε is a block with uniform density. Further, a block having a variance value Sn of a predetermined value ε or more is defined as a block having non-uniform density.

Then, for blocks having uniform densities, the flag FSn is set to "1" in step S108.
For blocks with uneven density, step S
At 109, the flag FSn is set to "0". By repeating the routine of steps S107 to S111 for the block numbers n = 1 to N, the flags FSn of all the blocks Tn are set to "1" or "0".

Next, as shown in FIGS. 3 to 4, in steps S112 to S137, with respect to the blocks having uniform densities, the continuity of densities with adjacent blocks is evaluated. The continuity of density is evaluated using a scanning mask as shown in FIG. The scanning mask shown in FIG. 13 includes, in addition to the evaluation block Tn, an already evaluated block Tn-H above the evaluation block Tn, a block Tn-1 evaluated before the evaluation block Tn, and Block Tn-H- evaluated before block Tn-H
It covers one.

In step S112, the process shown in FIG.
Prior to performing the evaluation using the scanning mask of No. 1, the block number n of the blocks T1 to TN, the table number k of the later-described active tables ETk0 and ETk1, and the block T.
All the labels Lc sequentially attached to n are initialized to "1".

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 scan mask of FIG. 13 are blocks having uniform densities. If both the block Tn and the block Tn-H-1 have uniform densities, the difference between the density average value Dn of the block Tn and the density average value Dn-H-1 of the block Tn-H-1 is determined in step S115. The absolute value of 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 read in step S116.
To the same value as the label Ln-H-1 of the block Tn-H-1. When the label Ln has the same value as the label Ln-H-1 in this way, the process proceeds to step S136 described below.

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

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

In this way, when the label Ln of the block Tn to be evaluated is set to the same value as the label Ln-H of the block Tn-H above it, in the next step S120, the previous block of the evaluation block Tn is calculated. It is determined whether the block Tn-1 is a block having a uniform density. If this block Tn-1 is a block having a uniform density, step S12
In step 1, it is determined whether the label Ln-H of the block Tn-H and the label Ln-1 of the block Tn-1 are equal, and if they are not equal, the process proceeds to step S122.

In step S122, the block T
The absolute value of the difference between the density average value Dn-H of nH and the density average value Dn-1 of the block Tn-1 is compared with a predetermined value d. If the absolute value of the difference between the density average value Dn-H and the density average value Dn-1 is smaller than the predetermined value d, in step S123, the active tables ETk0 and ET for recording mutually integrable labels.
Labels Ln-1 and Ln-H are recorded in k1, respectively.

That is, the block Tn and the block Tn-1
If the blocks Tn-H are all blocks having uniform densities, the labels Ln-H and Ln have the same value, and the densities of the blocks Tn-H and Tn-1 are continuous, the labels Ln Since -1 can also be labeled Ln-H and Ln, this is stored in the empty table ETk.
It is recorded at 0 and ETk1. As described above, the labels Ln-1 and Ln-H are added to the active tables ETk0 and ETk1.
After recording, the table number of the active table is advanced by one in step S124, and then step S136.
Proceed to.

In step S120, the block Tn-1
Is not a block with uniform density, step S121
If the label Ln-1 and the label Ln-H already have the same value at this stage, and in step S122, the density average value D
If the absolute value of the difference between nH and the average density value Dn-1 is greater than or equal to the predetermined value d, nothing is recorded in the active tables ETk0 and ETk1 and the process directly proceeds to step S136.

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

In step S125, the block T
It is determined whether n-1 is a block with uniform density.
If the block Tn-1 has a uniform density, in step S126, the absolute value of the difference between the density average value Dn-1 of the block Tn-1 and the density average value Dn of the evaluation block Tn is a predetermined value d. Determine if less than. If the absolute value is smaller than the predetermined value d, the label Ln of the block Tn is changed to the block Tn- in step S127.
The value is the same as the label Ln-1 of 1. After setting the label Ln to the same value as the label Ln-1, the process proceeds to step S136.

In step S125, the block Tn-1
, If it is determined that the block is not a uniform density, or if it is determined in step S126 that the absolute value of the difference between the density average value Dn-1 and the density average value Dn is not less than the predetermined value d,
That is, even though the evaluation block Tn is a block having a uniform density, the density continuity of any of the other blocks Tn-H-1, Tn-H, and TN-1 covered by the scanning mask of FIG. If it is determined that there is not, step S
Proceed to 128.

In this step S128, a new label Lc which has not been used until now is assigned as the label Ln to be attached to the evaluation block Tn. Thus, when the new label Lc is used, the value of the label Lc is advanced by one in step S129. After updating the value of the label Lc in this way, the process also proceeds to step S136.

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

That is, in steps S130 and S131, the blocks Tn-H and Tn-1 are both blocks having uniform densities, and the values of the label Ln-H and the label Ln-1 are not the same in step S132. , 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 between H and the average density value Dn-H is smaller than the predetermined value d, in step S134, the label Ln-
1 and the label Ln-H are recorded as labels that can be integrated with each other in the empty tables ETk0 and ETk1. Then, in step S135, the number in the active tables ETk0 and ETk1 is updated by one and the process proceeds to step S136.

Blocks T in steps S130 and S131
If one of the n-1 and the block Tn-H is not a uniform density block, the label L is added in step S132.
If the values of n-1 and the label Ln-H are already the same, or in step S133, the density average value Dn-1 and the density average value D
If the absolute value of the difference from nH is greater than or equal to 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 scan mask in FIG. 13 is advanced by one, and if the updated block number n is N or less in step S137, the process returns to step S113 and this step is performed. The routine from S113 to step S136 is repeated. By the above routine, all blocks having uniform density are labeled. In step S137, the number n of the evaluation block Tn
When exceeds N, the process proceeds to step S138.

In steps S138 to S144 shown in FIG. 5, the empty tables ETk0, E are set.
Labels recorded in Tk1 are integrated.

First, in step S138, the table number k of the active tables ETk0 and ETk1 is returned to the previous number, and in step S139, the previous active table ET.
It is determined whether the table number k of k0 and ETk1 is 1 or more. If the table number k is less than 1 in step S139, that is, "0", the active table ETk0,
Since ETk1 has not been used, the process proceeds to step S145 in FIG.

If the number k is 1 or more in step S139, the label Ln of the block Tn is compared with one of the active tables ETk0 in steps S140 to S144 (step S141), and the label stored in the active table ETk0 is compared. Is the same as the label Ln, the label of the block recorded in the other active table ETk1 is rewritten to the label Ln (step S142).

When the processes of steps S141 and S142 are completed for the blocks T1 to TN, step S
At 143, the block number n is updated by one. The routine of steps S141 to S143 described above is performed in step S144.
Is repeated until n = N. When n> N in step S144, the process returns to step S138 to return the table number k of the active tables ETk0 and ETk1 to the previous table number, and in step S139, the table number k becomes 1
Equitable ETk0, E until less than 0
The routine of steps S140 to S144 is repeated for Tk1.

Next, in this embodiment, the same labels as the blocks T1, TH, TN-H + 1, TN (see FIG. 12) at the four corners of the original image are attached in the routine of step S145 and subsequent steps shown in FIG. The block is evaluated as a block that constitutes the background. That is, in this embodiment, it is assumed that the four corners of the original image are background portions. In steps S145 to S148, all the flags BGn indicating whether the block Tn is a background block are the block Tn.
Is a routine for clearing to "0". The routines after step S149 will be described below.

First, in step S149, the background block search number m is set to 1. Steps S150-S
In 153, the block with the search number m is the four corner blocks T1, TH, TN-H-1, which serve as the reference for background evaluation.
It is sequentially determined whether or not it is TN. When the search number is m = 1, the block T1 is searched as one of the background evaluation reference blocks T1, TH, TN-H-1 and TN in step S150, and the process proceeds to step S154.

In the next steps S154 to S159, the label Lm, that is, the label L1 of the background evaluation reference block T1 retrieved in step S150 is replaced with all the blocks Tn.
Are sequentially compared with the label Ln (step S155).
If Ln = L1, the flag BGn of the block Tn is set to "1" in step S156. If Ln ≠ L1, the flag BGn of the block Tn is set to "0" in step S157. Step S156 or step S15
When the flag BGn is set to "1" or "0" in step 7, the process proceeds to step S158 and the block number n is updated by one. The routine of steps S155 to S158 is repeated until the block number n = N in step S159. When n> N in step S159, step S1
Proceed to 60.

In step S160, the search number m is updated by 1 to set m = 2. In step S161, the search number m
= 2 is compared with the number of blocks N, and since 2 ≦ N, the process returns to step S150.

Thereafter, the routine of steps S150 to S161 is repeated for the search numbers m = 2 to N. However, when the search number m is m ≠ 1, H, N−H + 1, N, the process directly proceeds from step S153 to step S160 and the search number m is updated by one.

Search number m = H, N-H + 1, or N
In the case of, the process proceeds to step S154 as in the case of m = 1, and the routine of steps S155 to S159 is repeated. That is, when m = H, the flag BGn of the block Tn of Ln = LH is set to "1", while the flag BGn of the block Tn of Ln ≠ LH is set to "0". Also, m
= N-H-1, the flag BGn of the block Tn of Ln = LN-H-1 is set to "1", while the flag BGn of the block Tn of Ln ≠ LN-H-1 is set to "0". Furthermore, m
= N, the flag BGn of the block Tn of Ln = LN
Is set to "1" while the flag BGn of the block Tn of Ln ≠ LN is set to "0".

As described above, all blocks T1 to T
The flags BG1 to BGN of N are set to "1" or "0". However, the background evaluation blocks T1, TH, TN-H-1, T
The flag BGn of the block Tn labeled with the same label as any one of N blocks is set to "1". That is, in the routine of steps S149 to S161, when the flag BGn is set to "1" or "0" for one block Tn, the flag BGn of the block Tn is set to "1". Prioritize.

As described above, steps S101 to S1
The routine of 61 detects the background block Tn from the blocks T1 to TN. After detecting the background block, the cumulative relative frequency histogram of the entire original image is created in steps S201 to S223 shown in FIGS. 7 and 8.

First, in steps S201 to S213, the density histogram hnj of each of the blocks T1 to TN is obtained.
A density histogram of the entire original image is created based on each class j of. In steps S201 to S204, the number of pixels DFj corresponding to the class number j (= 0 to J) of the density histogram hnj is cleared to 0.

Next, in step S205, the block number n is set to "1", the class number j is set to "0", and the total number of pixels y is set to "0". Then step S206-
By repeating the routine of S213 until n> N in step S213, the number of pixels DFj and the total number of pixels y for each class in the entire original image are obtained. Step S20
The routine from 6 to S213 is as follows.

First, in step S206, it is determined whether the flag BGn of the block Tn is "1". When the flag BGn is "1", that is, when the block Tn is evaluated as the background, the coefficient α = α0 is selected in step S207. Coefficient α
0 is 0 ≦ α0 <1. When the flag BGn is "0", that is, when the block Tn is a block evaluated not to be the background, the coefficient α = 1 is selected in step S208.

Next, in steps S209 to S211, (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 Tn.
The total number of pixels y of the image consisting of n is obtained.

The routines of steps S206 to S211 described above are repeated until n> N is satisfied in step S213 as a result of updating the block number n by one in step S212.

Next, the number of pixels DFj of each class and the total number of pixels y obtained in steps S209 to S211 will be described in detail.

When the block number n = 1 and the class number j = 0, in step S209, the correction pixel number α1 of the corresponding class is added 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.
Calculated as F0. Where α 1 is step S206
Is the coefficient α of the block T1 selected in steps S208 to S208.
In this case, the pixel number DF0 is “0” in step S202, and thus the pixel number DF0 calculated in step S209.
Becomes α1 · h10.

After calculating the number of pixels DF0, step S2
At step 10, the class number j is updated by one, and the class number j updated at step S211 is compared with the final class number J. If j ≦ J in step S211, the process returns to step S209 to repeat the routine of steps S209 to S211. As a result, the number of pixels D of all classes when n = 1
F0 to DFJ are sequentially obtained.

Block number n = 1 and class number j =
When it is 0, in step S209, the correction pixel number α of the class number j = 0 is added to the total pixel number y cleared in step S205.
Calculate y + α1 · h10 by adding 1 · h10. Since the total number of pixels y is “0” in step S205, the total number of pixels y calculated in step S209 when the class number j = 0 is α1 · h10.

Next, in step S210, the class number is updated by 1 to set j = 1. In step S211, 1 ≦
By J, the process returns to step S209. Next step S20
In 9, the total number of pixels calculated when the class number j = 0 is y =
α1 · h10 is the number of corrected pixels α1 when class number j = 1
・ Adding h11, total number of pixels when class number j = 1, y =
Calculate α 1 (h10 + h11). Similarly, j =
2 to J, by repeating the routine of steps S209 to S211 above, the total number of pixels in the block T1 is y =
α1 (h10 + h11 + ... h1J) is calculated.

If j> J in step S211, the flow advances to step S212 to update the block number n by 1 to n = 2. Since 2 ≦ N in step S213, the process returns to step S206. Next steps S206 to S20
In 8, n = 2, that is, the coefficient α2 of the block T2 is selected.

In the next steps S209 to S211, n
= 1 is added to the number of pixels in each class DFj = α1 · h1j and the number of corrected pixels in the corresponding class of block T2 α2 · h2j is added to correspond to the class number j in the image composed of blocks T1 and T2. Α1 · h1j + α2 · h2j is obtained as the number of pixels DFj. Further, in this step S209, the total number of pixels y = α1 (h
10 + h11 + ... h1J) is sequentially added with the correction pixel number α2 · h2j. By repeating the routine of steps S209 to S211 until j> J in step S211, the total number of pixels in the image composed of the blocks T1 and T2 y = α1 (h10 + h11 + ... h1J) + α2 (h
20 + h21 + ... h2J) is required.

In the same manner, the routines of steps S206 to S213 are similarly repeated with n = 3 to N, so that the number of pixels DFj and the total number of pixels of each class of the entire original image as shown by the equations (4) and (5) are obtained. The number of pixels y is calculated.

[0090]

[Equation 4]

[0091]

[Equation 5]

By obtaining the number of pixels DFj of each class of the entire original image as described above, the density histogram of the entire original image can be created. As is clear from the equations (4) and (5), the number of pixels in the background block detected in steps S101 to 161 is equal to that in step S2.
Since the coefficient α 0 selected in 07 is 0 ≦ α 0 <1, the number of pixels in blocks other than the background is neglected or ignored.

Next, in steps S214 to S219, the cumulative number of pixels CDFj (C
DF1 to CDFJ). First, step S214
After the class number j is initialized to 0, the process proceeds to step S215. If j ≠ 0 in step S215, that is, if j = 0, the process proceeds to step S216, and if j ≠ 0 in step S215, the process proceeds to step S217.

In step S216, the pixel number DF0 of the class of the class number j = 0 is converted into the cumulative pixel number CDFO. In step S217, the number of pixels DFj of the class number j
By adding the cumulative pixel number CDFj-1 up to the previous class j-1 to the cumulative pixel number CDF up to this class number j
Seeking j. In step S218, the class number j is updated by 1. The updated class number j is step S
If j ≦ J in 219, the process returns to step S215. As a result of advancing the class number j by 1 in step S218, if j> J in step S219, the next step S
Proceed to 220.

Steps S214 to S219 as described above
According to this routine, when the class number j = 1, the cumulative pixel number CDF1 is the cumulative pixel number CDF0 corresponding to the class number j = 1−1 = 0 and the pixel number DF corresponding to the class number j = 1.
It is calculated by adding 1, and thereafter the cumulative number of pixels CDF2, CDF3, ... C in order from the lowest class number j.
DFJ is required.

In steps S220 to S223,
Cumulative pixel count CDFj for the total pixel count y of the entire original image
The relative frequency RNj (%) is calculated. Step S22
At 0, the class number j is initialized to "0". In step S221, the relative frequency RNj (%) is calculated by the expression RNj = CDFj × 100 / y based on the cumulative pixel number CDFj calculated in steps S214 to S219 and the total pixel number y of the entire original image. A step S222 updates the class number j by one. Step S223 is step S2
The class number j updated in 22 is compared with the final class number J. Steps S221 to S until j> J
By repeating the routine of step 223, the relative frequencies RNj of all classes are obtained.

The cumulative relative frequency histogram shown in FIG. 14 is obtained by plotting the average density class value DMj (n = 0 to J) on the horizontal axis and the relative frequency RNj calculated on the above steps S220 to S223 on the vertical axis. Can be created. The cumulative relative frequency histogram of FIG. 14 shows 0% to 1 in the range of the minimum generated density DMmin and the maximum generated density DMmax.
The shape changes up to 00%. The cumulative relative frequency histogram of FIG. 14 is represented by a curve approximation by making the class width sufficiently small.

Next, steps S301 to S3 shown in FIG.
At 15, a cumulative density value histogram for each color component is created.

First, in steps S301 to S304, each block T obtained in step S103 of FIG.
Based on the density histogram hnj for each n, each block T
Cumulative density values DRnj, DGnj, and DBn for each color component for each n
ask for 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 values for each color component are cumulatively added. This process is performed independently for each class. Next, in steps S305 to S313, 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, DRj, DGj, and DBj of the cumulative density values for each color component of the entire original image are cleared. Next, in step S307, the block number n is set to 1.

The following steps S308 to S310 are the same as steps S206 to S208. That is, in step S308, it is determined whether or not the flag BGn of the block Tn is "1", and if the flag BGn is "1", the coefficient α = α in step S309.
Select 0. If the flag BGn is "0", α = 1 is selected as the coefficient in step S310.

In step S311, α · DRn is added to the already calculated cumulative density values DRj, DGj, and DBj, respectively.
j, α · DGnj and α · DBnj are added. In step S312, the block number n is advanced by 1. The block number n increased by 1 in step S313 is n
It is determined whether or not ≦ N. If n ≦ N, the process returns to step S308, and n> in step S313.
The routine of steps S308 to S313 is repeated until it becomes N.

Accordingly, the cumulative density value DRj = α1 · DR1j + α2 · DR2j + ... + αN for each class of the entire original image
・ DRNj, DGj = α1 ・ DG1j + α2 ・ DG2j ＋ ……
+ ΑN · DGNj, DBj = α1 · DB1j + α2 · DB2j +
...... + αN ・ DBNj is calculated. Coefficients α1, α2 ……
αN is 1 or α0.

In steps S306 to S313, the class value j is advanced by 1 in step S314, and the class value j advanced by 1 in step S315 is j≤J.
By determining whether or not, it is repeated until the class value j> J in step S315. That is, in steps S305 to S315, the cumulative density value of the entire original image for all classes from j = 0 to J is calculated, and the cumulative density value histogram for each color component of the entire original image as shown in FIG. Can be created.

The number of pixels Pj corresponding to each class in the cumulative density value histogram for each color component shown in FIG. 15 is Pj = α1.multidot. By using the number of pixels Pj corresponding to the class value DMj for each block Tn. P1j ＋ α2 ・ P2j ＋ …… ＋ αN
-Can be obtained as PNj.

Next, the highlight point and the shadow point of the gradation conversion curve are obtained based on the cumulative relative frequency histogram of FIG. 14 and the cumulative density value histogram of each color component of FIG.

First, for the cumulative relative frequency histogram shown in FIG. 14 created by the routine of steps S201 to S223, an optimum gradation conversion characteristic that can be empirically obtained from a large number of sample original images prepared in advance is set. By adopting the cumulative density appearance rates RNH and RNS corresponding to the given highlight point HL and shadow point SD, the temporary highlight average density value DMH and shadow average density value DMS corresponding to the highlight point HL and shadow point SD are adopted. Respectively. The cumulative density appearance rates RNH and RNS are about 1% and 98%. Temporary highlight average density value DMH and shadow average density value DMS thus obtained
Is applied to the cumulative density value histogram for each color component shown in FIG.

In the cumulative density value histogram of FIG. 15, the area on the highlight side is equal to or lower than the temporary highlight average density DMH (DMmin≤DM≤DMH), and on the shadow side is equal to or higher than the temporary shadow average density value DMS. Area (DMS ≤ D
The classes within M ≤ DMmax) are shown with diagonal lines. In the illustrated example, the temporary highlight average density value DM
H, shadow 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 within the range corresponding to the class values DM1 to DM5 are added. From these two added values, the input side highlight density value DRH of the color component R is calculated by Equation 6
Calculate with the formula.

[0109]

[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).

[0111]

[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 the same as in the case of the equation 6. It is given corresponding to the values DM (J-2), DM (J-1), and DMJ.

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 set in the same manner for the other color components G and B.
S and DBS can be calculated.

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

Now, let us assume that the original image is a scene having a bright background. In this case, the cumulative relative frequency histogram HI shown by the solid line in FIG. 14 created by the routine of steps S201 to S223 in the above-described embodiment is created by the conventional method under the same conditions as the background part and other parts.
Compared with the cumulative relative frequency histogram HI 'indicated by the one-dot chain line in 4, the histogram is biased toward the high density side as a whole. This is because, in the above-described embodiment according to the present invention, in determining the cumulative pixel number CDFj of each class in the entire original image in step S209, steps S101 to S101.
This is because the number of pixels of each class of the block Tn of the background portion detected in 161 is multiplied by a coefficient α0 smaller than 1. That is, by multiplying the number of pixels in each class of the block Tn in the bright background portion by a coefficient α0 smaller than 1, the level of a class having a low density is suppressed in the average density histogram of the entire original image, which is inevitable. Therefore, the cumulative relative frequency histogram is biased toward the high density side as a whole.

In the cumulative relative frequency histogram shown in FIG. 14 as described above, the cumulative density appearance rate RNH on the highlight side is shown.
Is applied, the provisional highlight average density value DMH obtained when applied to the cumulative relative frequency histogram HI is
It becomes larger than the provisional highlight average density value DMH 'when applied to the cumulative relative frequency histogram HI'. Therefore, 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 based on the provisional highlight average density value DMH. The values DGH, DBH are larger than the input side highlight density values DRH ', DGH', DBH 'for each color component obtained based on the temporary highlight average density value DMH' by the conventional method.

Incidentally, as shown in FIG. 14, the cumulative relative frequency histogram HI shown by the solid line created by the method of the above embodiment is shown.
And the cumulative relative frequency histogram HI ′ shown by the one-dot chain line created by the conventional method almost match before the relative frequency of 100%. Therefore, the provisional shadow average density value DMS obtained when the cumulative density appearance ratio RNS on the shadow side is applied to the cumulative relative frequency histogram created in the above embodiment, and the cumulative density appearance in the cumulative relative frequency histogram created by the conventional method. It is very close to the provisional shadow average density value DMS 'obtained when the rate RNS is applied. Therefore, in the following, for the sake of convenience, the temporary shadow average density values DMS and DMS 'are assumed to be equal. In addition, the shadow side density value DRS of the color component R obtained by the equation (7) based on the provisional shadow average density value DMS.
And the shadow side density values DGS, DBS of the color components G, B,
It is assumed that they are equal to the shadow side density values DRS ', DGS', DBS 'obtained based on the temporary shadow average density value DMS'.

FIG. 16 is 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 gradation conversion curve GCR 'set by using a side density value DRH' and a shadow side density value DRS '. The horizontal axis represents the input density value DI input for each color component, and the vertical axis represents the output density value DO. The output-side highlight density value DOHL and the output-side shadow density value DOSD are fixed values common to all color components. Here, for the sake of convenience, all the input side highlight density values D
RH, DGH, and DBH are the input side highlight density values DRH ', DG
It becomes the same as H'and DBH ', and DRH =
It is assumed that DGH = DBH and DRH '= DGH' = DBH '. Also, the input side shadow density values DRS, DGS, DBS are DRS = D
GS = DBS, input side shadow density values DRS ', DG
S'and DBS 'are 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. 16, and the gradation conversion curve GCG '
And GCB 'are equal to the gradation conversion curve GCR' shown in FIG.

As is apparent from FIG. 16, the highlight point HLR corresponding to the highlight side density value DRH is set at a position which is translated to the right from the highlight point HLR 'corresponding to the highlight side density value DRH'. To be done. On the other hand, as described above, the shadow side density value DRS is the shadow side density value DR.
Equal to S '. Therefore, 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. 16, 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.
The output value DOHL on the highlight side passes through, and the gradation conversion curve GCB passes the output value DOHL on the highlight side than 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 gradation conversion curves GCR ', GCG', and GCB ', respectively, and the output setting value DO on the highlight side.
Approach HL.

As is clear from the above description, when the original image is a scene having a bright background, the highlight side density values DRH, DGH, DBH and the shadow side density values DRS, DGS, DBS obtained in the above-mentioned embodiment are obtained. When the setup is performed using, the highlight side density value DR obtained by the conventional method
H ', DGH', DBH ', shadow side density values DRS', DG
Compared with the case of setting up using S'and DBS ',
The output density value DO is such that the highlight region approaches the output setting value DOHL on the highlight side, and a reproduced image with a brighter finish can be obtained.

On the other hand, although not described in detail, according to the above embodiment, when the original image is a scene having a dark background, the shadow side density values DRS, DGS, DBS are the shadow side density values DRS ', It will be set lower than 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 side, and even if the same input density value DI is given, the gradation conversion curve GCR is converted. Output density value DO
In this case, the output density value DO converted by the gradation conversion curve GCR 'is closer to the shadow side output set value DOSD. That is, by setting the density values DRS, DGS, DBS on the shadow side according to the above-described embodiment, even if the original image is a scene having a dark background, it is possible to obtain a duplicate image that is not whitish as in the conventional case.

Note that the gradation conversion curve does not necessarily have to be linear 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 of each block Tn obtained in step S103 is a density histogram of average density obtained by averaging the density values of the color components of each pixel. However, instead of this average density value,
You may use the lightness value calculated by carrying out the weighted average of each color component density value. Further, the density histogram may be a density histogram of the density of each color component. In this case, the color components R,
The density histogram may be calculated for all of G and B, or the density histogram may be calculated for only one preselected color component. To obtain the density histograms for all of the color components R, G, B, step S20
1 to S223, the cumulative relative frequency histogram is also calculated for each color component, and the cumulative density appearance rate RNH is calculated for each color component.
, RNs.

Then, based on the cumulative density appearance rates RNH and RNS of the respective color components and the cumulative density value histograms obtained in steps S301 to S315, the highlight side density values DRH, DGH and DBH and the shadow side density values DRS and DGS are obtained. ，
Ask for DBS. On the other hand, in step S103, when the density histogram is obtained for only one preselected color component, the cumulative density value histogram obtained in steps S301 to S315 may be only the cumulative density value histogram of the corresponding color component.

Also, in the above embodiment, step S11.
In S5, S118, S122, S126, and S133, the difference between the average density values of two blocks having uniform density and close to each other is calculated, and when the difference is smaller than a predetermined value d, the density between both blocks is decreased. Are determined to be continuous. However, the steps S115, S1
In 18, S122, S126, and S133, the dispersion value of the densities of both the blocks is obtained, and when the dispersion value is less than or equal to a predetermined value, it is determined that the densities between the blocks are continuous. Good.

In the above embodiment, step S206.
In step S208, the coefficient α = α 0 (α 0 <1) is set when the block Tn is a background portion block, and the coefficient α = 1 is set when the block Tn is a block other than the background portion. For example, the block Tn is a background portion. When the block is a partial block, the coefficient α = 1, and when the block Tn is a block other than the background part, the coefficient α = α0 '(α0'>
It may be 1). Even in this way, α0 = 1 / α0
If ', then the relative frequency RNj obtained in step S221
Is the same as in the above embodiment.

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

The predetermined blocks are not necessarily blocks at the four corners of the original image, may be blocks at the upper two corners, or may be blocks other than the corner blocks in some cases.

Further, when the image information of the original image is stored in advance in the large-capacity memory, the prescan by the scanning reading device is not necessary, and the image data may be directly read from the memory and used.

[0131]

According to the first aspect of the present invention, when the background of the original image is a light background, the gradation conversion curve as a whole can be shifted to the output side and the highlight density side, so that the reproduced image has a dark finish. Can be prevented. Also,
When the background of the original image is a dark background, the gradation conversion curve can be shifted to the output side and the shadow density side as a whole, so that the finish of the duplicate image does not become whitish.

According to the second aspect, it is possible to obtain an appropriately finished duplicate image when the object is drawn in the center and the surrounding is the background.

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

According to the fourth aspect, the number of pixels in the background block can be relatively compressed with reference to the number of pixels in the blocks other than the background block.

According to the fifth aspect, the reference density point can be set while the influence of the background block is completely removed.

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

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

According to the eighth 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 drawings]

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

FIG. 2 is a flowchart showing a method of detecting a background portion.

FIG. 3 is a flowchart showing a method of detecting a background portion.

FIG. 4 is a flowchart showing a method of detecting a background portion.

FIG. 5 is a flowchart showing a method of detecting a background portion.

FIG. 6 is a flowchart showing a method of detecting a background portion.

FIG. 7 is a flowchart showing a process of obtaining a cumulative relative frequency histogram.

FIG. 8 is a flowchart showing a process of obtaining a cumulative relative frequency histogram for each color component.

FIG. 9 is a flowchart showing a process of obtaining a cumulative density value histogram.

FIG. 10 is a schematic block diagram of a plate-making scanner to which an embodiment of the present invention is applied.

FIG. 11 is a block diagram showing an image processing apparatus.

FIG. 12 is a front view showing a state in which an original image is divided into a plurality of blocks.

FIG. 13 is an explanatory diagram showing a scan mask.

FIG. 14 is a diagram showing a cumulative relative frequency histogram.

FIG. 15 is a diagram showing a cumulative density value frequency histogram.

FIG. 16 is a graph showing a gradation conversion curve.

FIG. 17 is a flowchart showing a conventional method of setting reference density points.

FIG. 18 is a diagram showing a cumulative relative frequency histogram obtained by a conventional method.

FIG. 19 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 section Tn block hnj Density histogram Dn Density average value Sn Density variance value DFj Number of pixels CDFj Cumulative number of pixels 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 Gradation conversion curve

─────────────────────────────────────────────────── ───

[Procedure amendment]

[Submission date] November 12, 1991

[Procedure Amendment 1]

[Document name to be amended] Statement

[Correction target item name] 0004

[Correction method] Change

[Correction content]

Next, in step S502, the average density value is calculated by averaging the density values for each color component for each pixel.
Seek Nehi Sutoguramu.

[Procedure Amendment 2]

[Document name to be amended] Statement

[Correction target item name] 0010

[Correction method] Change

[Correction content]

This is due to the following causes. That is, for example, when the original image is a scene having a bright background, the cumulative density histogram shown in FIG. 19 becomes a histogram in which the class of relatively low density has a high frequency due to the influence of this bright background portion. Therefore, the provisional highlight average density value obtained from the cumulative relative frequency histogram of FIG. 18 becomes low, and the input side highlight density value obtained at step S506 also becomes low. When the input side highlight density value becomes low in this way, the gradation conversion curve set based on this input side highlight density value shifts the highlight area to the output side shadow density value side , resulting in a duplicate image. Is a dark finish.

[Procedure 3]

[Document name to be amended] Statement

[Correction target item name] Figure 8

[Correction method] Change

[Correction content]

8 is a flowchart showing a process of determining the cumulative relative frequency histogram.

[Procedure amendment 4]

[Document name to be amended] Statement

[Correction target item name] Figure 9

[Correction method] Change

[Correction content]

FIG. 9 is a flowchart showing a process of obtaining a cumulative density value histogram for each color component .

[Procedure Amendment 5]

[Document name to be amended] Statement

[Correction target item name] Figure 12

[Correction method] Change

[Correction content]

FIG. 12 is an explanatory diagram showing a state in which an original image is divided into a plurality of blocks.

[Procedure Amendment 6]

[Document name to be amended] Statement

[Correction target item name] Fig. 15

[Correction method] Change

[Correction content]

15 is a diagram showing the cumulative concentration Nehi Sutoguramu.

[Procedure Amendment 7]

[Document name to be corrected] Drawing

[Name of item to be corrected] Figure 7

[Correction method] Change

[Correction content]

[Figure 7]

[Procedure Amendment 8]

[Document name to be corrected] Drawing

[Correction target item name] Figure 8

[Correction method] Change

[Correction content]

[Figure 8]

Claims (8)

[Claims] 1. A method for setting a reference density point through which a gradation conversion curve set for gradation conversion during copying should pass based on image data obtained by reading an original image to be reproduced having gradation. The first step of dividing the original image into a plurality of blocks, and the density for each block showing the relationship between the density value for each block and the number of pixels giving this density value based on the image data of the original image A second step of obtaining a histogram; a third step of selecting a block group consisting of blocks that are close to each other and have uniform densities and continuous densities based on the image data; and a third step A fourth step of detecting, as a background block, a block of a block group including a block whose density is continuous with a predetermined block from the block group selected in step 4, and pixels of the background block Is relatively compressed for each class of the density histogram for each block with respect to the number of pixels of the block not selected as the background block, and after the fifth step, for each class. A sixth step of obtaining a density histogram of the entire original image by adding the pixel numbers of all blocks; a seventh step of obtaining a cumulative density value histogram based on the density histogram of the entire original image; An eighth step of setting a reference density point based on a density value histogram, and a method of setting a reference density point, comprising: 2. The reference density point setting method according to claim 1, wherein the predetermined blocks in the fourth step are blocks at four corners of a rectangular original image. 3. The method of setting a reference density point according to claim 1 or 2, wherein in the third step, the difference between the density average values of the blocks which are close to each other and whose density is uniform is not more than a predetermined value. A method of setting a reference density point, characterized in that the density of these blocks is continuous at a certain time. 4. The method of setting a reference density point according to claim 1, wherein in the fifth step, the number of pixels in each class of the density histogram of the background block is multiplied by a coefficient less than 1. Also, by multiplying the number of pixels of each class of the density histogram of blocks other than the background block by 1, the number of pixels of the background block is relatively compressed for each class with respect to the number of pixels of blocks other than the background block. A method of setting a reference density point, characterized by: 5. The method of setting a reference density point according to claim 4, wherein the coefficient by which the number of pixels of each class of the density histogram of the background block is multiplied is 0. 6. The method for setting a reference density point according to claim 1, wherein the density value is an average density value obtained by averaging the density values of the respective color components for each pixel. How to set the reference concentration point. 7. The reference density according to any one of claims 1 to 5, wherein the density value is a density value for each pixel of the selected color component. How to set points. 8. 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. .