JP2009060388A - Image processor and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To efficiently select data to be utilized for image correction by setting image correction intuitively and predicting the kind of image correction to be performed before image correction processing. <P>SOLUTION: An original image to be subjected to color conversion is inputted from a memory card (S101), and the second amount of feature expressing the feature of the inputted original image is specified (S103, S104). A plurality of types of model images are inputted (S106-S109), and a first amount of feature expressing the feature of each model image is specified (S111, S112). A correction image expressing a color conversion processing result when color conversion processing is performed to an original image to bring the second amount of feature closer to the first amount of feature, is generated for the first amount of feature in each model image (S114). A plurality of correction images thus generated are printed in an index format arranged corresponding to each model image used for generating each correction image (S116-S118). <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an image processing apparatus and an image processing program for performing image correction processing.

2. Description of the Related Art Conventionally, an image processing apparatus that performs image correction according to user preferences is known.
For example, image adjustment is performed based on the adjustment data specified by selecting one adjustment data from a plurality of types of adjustment data prepared in advance or by setting arbitrary adjustment data. There exists a structure to perform (refer patent document 1).
JP 2007-89179 A

  However, in order to set adjustment data so that desired image correction is performed with the above-described configuration, specialized knowledge about images is required, and it is difficult to set intuitively. In addition, it is difficult to predict what image correction is performed by using the adjustment data before the image correction process, and the selection and setting of the adjustment data to be used is not efficient.

  The present invention has been made in view of these problems, and it is possible to intuitively set image correction and predict what image correction will be performed before the image correction processing. An object of the present invention is to provide an image processing apparatus and an image processing program that can efficiently select data used for image correction.

  In order to achieve the above object, an image processing apparatus according to claim 1 of the present invention includes a first image input means capable of inputting a plurality of types of first images, and a first image input means input by the first image input means. First feature amount specifying means for specifying a first feature amount representing the feature of one image for each input first image, and second image input means for inputting a second image that is an image to be corrected And a second feature amount specifying means for specifying a second feature amount representing the feature of the second image input by the second image input means, and a second feature amount for the second image so as to be close to the first feature amount. An image image generation unit that generates a corrected image image representing an image correction process result when the image correction process is performed for each of the plurality of first feature amounts, and a plurality of correction image images generated by the image image generation unit. , Each correction image And an image image output means for outputting a state in which the first feature quantity arranged in correspondence respectively with the first image identified used to generate the image.

  According to such an image processing apparatus, when performing image correction using the first image, the user can efficiently select the first image from which a desired correction result is obtained. In other words, as a method for intuitively setting image correction, a configuration in which the user designates the first image and performs image correction processing so that the feature of the second image approaches the feature of the first image is conceivable. In such a configuration, it is important to select a first image from which a desired correction result can be obtained. However, according to the image processing apparatus of the present invention, since a corrected image for a plurality of first images is output, the user Can efficiently select the first image with reference to the corrected image.

  The image processing apparatus according to claim 2 includes a processed image generating unit that generates a processed image obtained by processing the second image input by the second image input unit, and the image image generating unit includes the processed image generating unit. A corrected image is generated by performing image correction processing on the processed image generated by the above. In this way, it is possible to simplify the process of generating the corrected image compared to the case of generating the corrected image based on the result of actually performing the image correction process on the second image.

Specifically, for example, as described in claim 3, the processed image generation unit may generate an image obtained by reducing the second image input by the second image input unit as a processed image.
Here, the image processing apparatus according to claim 4 includes a selection unit that selects one of the corrected image images output from the image image output unit, and a correction image that is selected by the selection unit as the second feature amount. Image correction means for performing image correction processing on the second image so as to approach the first feature used for generating the image.

  According to such an image processing apparatus, by selecting one of the corrected image images, it is possible to perform the image correction process represented by the corrected image image without inputting the first image again.

  In particular, as described in claim 5, if the printing control means for performing the printing process for printing the second image subjected to the image correction process by the image correction means is provided, one of the corrected image images is selected. By selecting one, it is possible to print the second image that has been subjected to the image correction processing represented by the corrected image.

  On the other hand, in the image processing apparatus according to the sixth aspect, the image image generating means generates a corrected image based on a result of actually performing the image correction process on the second image. The image processing apparatus includes a selection unit that selects one of the corrected image images output from the image image output unit, and a selection unit that selects the second image subjected to the image correction process by the image image generation unit. And a print control means for performing a printing process for printing an image corresponding to the corrected image selected by (1).

  According to such an image processing apparatus, by selecting one of the corrected image images, the first image is again generated using the image correction processing result performed when the corrected image image is generated. Without the input, it is possible to print the second image that has been subjected to the image correction processing represented by the corrected image.

  In the image processing apparatus according to claim 7, the image image output means uses, as the first feature amount, the first image in which the corrected feature image and the first feature amount used for generating the corrected image image are specified. Arrange based on. According to such an image processing device, it is possible to easily compare a plurality of corrected image images.

  On the other hand, in the image processing apparatus according to claim 8, the first image input means inputs an image optically read from a print medium set at a predetermined reading position, and is set at the reading position. When a plurality of images are read from the print medium, each image can be input as an independent first image.

  According to such an image processing apparatus, the user sets a plurality of print media at a reading position at a time or sets a printing medium on which a plurality of images are printed at a reading position. One image can be easily read, and the working time can be shortened.

  Specifically, as described in claim 9, for example, the first image input means inputs an image read in each reading area obtained by dividing the reading position into a plurality of images as an independent first image. A plurality of images can be recognized relatively accurately.

  The image processing apparatus according to claim 10, wherein the first image input means inputs an image optically read from a print medium set at a predetermined reading position, and the reading position is divided into a plurality of parts. The image read in each reading area is input as an independent first image, and the image image output means outputs the corrected image and the first image in which the first feature amount used for generating the corrected image is specified. It is arranged at a predetermined position associated with each reading area.

  According to such an image processing apparatus, as in the eighth and ninth aspects, the user sets a plurality of print media at the reading position at once, or sets the print medium on which a plurality of images are printed at the reading position. By setting it, it becomes possible to easily read a plurality of first images, and the working time can be shortened. In addition, a plurality of images can be recognized relatively accurately. In addition, the user's intention can be reflected in the arrangement of the corrected image and the first image.

  In particular, as described in claim 11, for example, when the average luminance value of the image read in the reading area is not less than a predetermined value, the first image input means has no image in the reading area. By determining, it is possible to prevent a portion where no image exists from being input as the first image.

  In the image processing apparatus according to the twelfth aspect, the image image output means prints out the corrected image and the first image in which the first feature amount used for generating the corrected image is specified. According to such an image processing device, it is possible to accurately output the color tone or the like when the corrected image is printed, as compared with the case where the corrected image is output by being displayed on a display unit such as a display.

  Next, an image processing program according to a thirteenth aspect of the present invention is a first image input unit capable of inputting a plurality of types of first images, and a first feature representing a feature of the first image input by the first image input unit. The first feature amount specifying means for specifying the amount for each input first image, the second image input means for inputting the second image as the image to be corrected, and the second image input means An image when the second feature amount specifying means for specifying the second feature amount representing the feature of the second image and the image correction processing on the second image so that the second feature amount is close to the first feature amount An image image generation unit that generates a correction image image representing the correction processing result for each of the plurality of first feature amounts, and a plurality of correction image images generated by the image image generation unit are used to generate each correction image image. The first special The amount causes the computer to function as an image an image output means for outputting in a state of being arranged in correspondence respectively with the first image specified.

  According to such an image processing program, it is possible to cause a computer to function as the image processing apparatus according to the first aspect, thereby obtaining the above-described effects.

Embodiments to which the present invention is applied will be described below with reference to the drawings.
[1. First Embodiment]
[1-1. overall structure]
FIG. 1 is a perspective view showing an appearance of a multifunction machine 10 as an image processing apparatus according to the first embodiment.

  The multifunction machine 10 has a printer function, a scanner function, a color copy function, and the like, and includes an image reading unit 20 used for reading a document at an upper position in the main body casing 11.

  The image reading unit 20 is a so-called flatbed scanner that optically reads an image from a document set (placed) on a document placement surface (glass table). Here, the upper surface of the document placement surface is covered with a thin plate-like document cover 21, and the document cover 21 is opened upward to set the document on the document placement surface and remove the set document. (In other words, it is possible to put a document in and out). Further, the surface of the document cover 21 opposite to the document placement surface is white, and the document is read when the image is read with the document cover 21 closed (the state shown in FIG. 1). The portion of the placement surface where no document is placed is read in white.

  On the other hand, the multifunction device 10 includes an operation unit 31 in which various operation buttons are arranged at a front position (front side position) of the image reading unit 20 and a display unit (for example, a liquid crystal display) 32 that displays an image such as a message. A panel 30 is provided.

  In addition, the multifunction machine 10 includes an image printing unit 40 that can print a color image on a printing medium such as paper at a position below the image reading unit 20. The sheet on which the image is printed by the image printing unit 40 is discharged from the opening 12 formed on the front surface of the main body casing 11.

  Furthermore, the multifunction machine 10 includes a card slot 50 into which various memory cards (portable storage media) such as an SD card and a CF card can be inserted at a position above the opening 12 on the front surface of the main casing 11. The multifunction device 10 has a function (so-called direct print function) for reading an image (such as an image taken with a digital still camera) directly from a memory card and printing the image without using an information processing device such as a personal computer. is doing.

Next, a control system of the multifunction machine 10 will be described.
FIG. 2 is a block diagram illustrating a schematic configuration of a control system of the multifunction machine 10.
As shown in the figure, the multifunction machine 10 includes the image reading unit 20, the operation panel 30, the image printing unit 40, the card slot 50, the communication unit 60, and the control unit 70 described above. They are connected via a line 80.

  The communication unit 60 performs data transmission / reception processing via the communication cable in a state where the communication cable (LAN cable) is connected. That is, it is for performing data communication with an external device. For example, data communication can be performed with a personal computer in a LAN or a web server on the Internet.

  The control unit 70 is configured mainly with a microcomputer including a CPU 71, a ROM 72, a RAM 73, and the like, and performs overall control of each unit constituting the multifunction machine 10. The ROM 72 stores a program for causing the CPU 71 to execute a color conversion process described later.

[1-2. Overview of color conversion process]
Next, an outline of color conversion processing performed by the multifunction machine 10 will be described.
The MFP 10 according to the present embodiment performs color conversion processing based on an image (hereinafter also referred to as “example image”) serving as a sample for color conversion on an image to be subjected to color conversion. Here, first, the basic flow of such color conversion processing will be described with reference to FIG.

  When a user sets a document (for example, a photograph) on which a model image is printed on the document placement surface of the image reading unit 20 and performs a document reading operation with the operation unit 31 (1), the multifunction machine 10 places the document on the document. A model image is read from a set range on the surface (a range set by the user, such as L size and A4 size) (2). As a result, the model image is read from the document set on the document placement surface.

  Next, when the user inserts a memory card storing an image to be color-converted into the card slot 50 (3), the multi-function device 10 recognizes the inserted memory card and notifies the user of the memory card. The image to be subjected to color conversion is selected from the images stored in (4). In addition, as a process for making a user select an image, a well-known process (For example, the process which displays each image memorize | stored in the memory card on the display part 32, and makes it select by operation in the operation part 31) suitably. It can be adopted.

  When the user selects a color conversion target image (5), the multifunction machine 10 reads the selected image (6). In the following description, the image may be referred to as an “original image”.

Thereafter, the multifunction machine 10 performs a process of correcting the original image read from the memory card using the model image read from the image reading unit 20 as a sample (7).
Here, the procedure for reading the original image from the memory card after reading the model image from the image reading unit 20 is illustrated, but the present invention is not limited to this, and the original image is read from the memory card first, and then A model image may be read from the image reading unit 20.

According to such color conversion processing, the user can perform color conversion of the original image with a simple operation and sensuously by using the model image.
Specifically, for example, if you want to convert the sky blue to the vivid ocean blue for the original image that contains the building and the sky, you can use the model image that captures the vivid ocean. Blue can be converted into vivid sea blue.

  Further, when it is desired to brightly convert the skin color with respect to the original image in which the human face is reflected, the skin color of the original image can be converted into a bright skin color by using a model image showing a bright skin color.

As described above, the user can perform desired color conversion only by causing the image reading unit 20 to read the model image without requiring any specialized knowledge.
However, in order for the user to obtain a desired correction result, it is necessary to search for a model image that can obtain a desired correction result by repeatedly testing a plurality of types of model images.

  Therefore, in the MFP 10 of the present embodiment, a plurality of types of model images can be input for one original image, and each model image and thumbnail images of the correction results for the model image are printed as a list (index printing). Thus, it is possible to efficiently select a model image from which a desired correction result can be obtained.

[1-3. Specific contents of color conversion processing]
Hereinafter, specific contents of the color conversion process performed by the multifunction machine 10 of the present embodiment will be described.
[1-3-1. Color conversion process]
FIG. 4 is a flowchart of the color conversion process executed by the CPU 71.

  When the CPU 71 starts the color conversion process, first, in S 101, the image (original image) stored in the memory card is read into the RAM 73. Although the format of the image to be read is not particularly limited, the present embodiment will be described on the assumption that the RGB format is used.

  Subsequently, in S102, a thumbnail image of the original image read in S101 is generated. That is, a size change process (reduction process) is performed on the original image so as to have a preset thumbnail image size. Since the original image is usually larger than the thumbnail image, it is explained here that the reduction process is performed as the size change process. However, if the original image is smaller than the thumbnail image size, the size is changed. An enlargement process is performed as a process. As the size changing process, for example, a nearest neighbor method, a bilinear method, a bicubic method, and an average pixel method can be used if limited to reduction.

  Subsequently, in S103, each pixel constituting the original image read in S101 (not the thumbnail image generated in S102) is converted into HSV parameters (H value: 0 to 360, S value and V value: 0 to 1). Perform the process.

Note that the conversion from RGB to HSV parameters and the conversion from HSV to RGB can be performed according to the known conversion formulas shown below.
(1) RGB → HSV conversion formula
max (a, b, c) represents the largest value among a, b, and c.
min (a, b, c) represents the smallest value among a, b, and c.
V = max (R / 255, G / 255, B / 255)
When V is not 0
S = [V-min (R, G, B)] ÷ V
When V is 0
S = 0
When [V-min (R, G, B)] is not 0,
r = (V-R / 255) ÷ (V-min (R, G, B)
g = (V-G / 255) / (V-min (R, G, B)
b = (V-B / 255) / (V-min (R, G, B)
When [V-min (R, G, B)] is 0,
r = 0
g = 0
b = 0
When V = R / 255
H = 60 × (bg)
When V = G / 255
H = 60 × (2 + rg)
When V = B / 255
H = 60 × (4 + gr)
However, when H <0
H = H + 360
Are converted from RGB to HSV. Also, from HSV to RGB,
(2) HSV-> RGB conversion formula
(In, fl, m, and n shown below are parameters used in the process of calculating RGB from HSV)
in the integer part of (H / 60)
Let fl be the fractional part of (H / 60).
If in is an even number
fl = 1-fl
m = V × (1-S)
n = V × (1-S × fl)
When in is 0
R = V × 255
G = n × 255
B = m × 255
When in is 1
R = n × 255
G = V × 255
B = m × 255
When in is 2
R = m × 255
G = V × 255
B = n × 255
When in is 3
R = m × 255
G = n × 255
B = V × 255
When in is 4
R = n × 255
G = m × 255
B = V × 255
When in is 5
R = V × 255
G = m × 255
B = n × 255
Is converted as

  Subsequently, in S104, based on the HSV parameter obtained by the conversion process in S103, a second feature quantity specifying process for specifying a second feature quantity that represents a feature quantity of the original image is performed. The specific processing content of the second feature amount specifying processing will be described later (FIG. 7).

  Subsequently, in S105, the original image read in S101 and the second feature amount specified in S104 are stored in the storage maintenance area in the RAM 73. Here, the storage maintenance area is an area in which stored information is held at least until the main color conversion process is completed (an area that is not overwritten by other information). That is, the storage state of the original image and the second feature value is guaranteed until the main color conversion process is completed. The storage means is not limited to the RAM 73. For example, when the multifunction machine 10 includes a hard disk, it may be stored in the hard disk.

  Subsequently, in S106, the entire document placement surface of the image reading unit 20 is read as one whole image. Although the format of the read image is not particularly limited, the present embodiment will be described on the assumption of the RGB format.

  Subsequently, in S107, a model image is extracted from the entire image read in S106. In this embodiment, an image read in each reading area obtained by dividing the document placement surface into a plurality of parts is recognized as an independent model image. More specifically, for example, as shown in FIG. 5B, the document placement surface can be divided into four reading areas by dividing the original placement surface into two vertically and horizontally. In this case, FIG. If a plurality of photos (three in the example shown in the figure) are set as shown in FIG. 5, the images of the photos are recognized as independent images as shown in FIG. Therefore, at this stage, the reading area (Image 1 in FIG. 5C) where no photograph is set is also recognized as one image (white image). Note that the number of divisions of the reading area may be set in advance or may be set by the user.

  Subsequently, in S108, it is determined whether or not there is a sample image extracted in S107 having an average luminance value meanV of the entire image equal to or higher than a preset threshold value ThreV (for example, 0.97). judge. Here, the threshold value ThreV is set as a reference value for determining whether or not a document is set on the document placement surface of the image reading unit 20, and the average luminance value meanV is equal to or greater than the threshold value ThreV ( If it is determined that the image is white only), there is a high possibility that the document does not exist.

  If it is determined in S108 that there is a model image whose average luminance value meanV is equal to or greater than the threshold value ThreV, the process proceeds to S109, and after the model image is excluded, the process proceeds to S110. That is, in the example of FIG. 5 described above, an image (white image) read by Image1 is excluded from the four reading regions.

On the other hand, if it is determined in S108 that there is no model image having the average luminance value meanV equal to or greater than the threshold value ThreV, the process proceeds to S110 as it is.
In S110, one of a plurality of types of model images extracted as a result of the processing in S106 to S109 (that has not been subjected to the processing in S110 to S114) is selected as a processing target, and the thumbnail image of the model image is selected. Is generated. That is, the size change process (reduction process) similar to S102 is performed on the model image. Since the model image is usually larger than the thumbnail image, it is explained here that the reduction process is performed as the size change process. However, if the model image is smaller than the thumbnail image size, the size is changed. An enlargement process is performed as a process.

  Subsequently, in S111, a process of converting each pixel constituting the model image (not the thumbnail image generated in S110) selected as the processing target in S110 into an HSV parameter is performed.

  Subsequently, in S112, based on the HSV parameter obtained by the conversion process in S111, a first feature quantity specifying process for specifying a first feature quantity that is a feature quantity representing the feature of the model image is performed. The specific processing content of the first feature quantity specifying process will be described later (FIG. 8).

  Subsequently, in S113, the first feature amount specified in S112 is associated with the identification information of the model image to be processed and stored in the storage maintenance area in the RAM 73, as in S105. Here, if the first feature value for another model image is already stored in the storage maintenance area, the first feature value is stored in a newly added form while maintaining the storage state of the first feature value. Therefore, by repeating the processing of S110 to S114, a plurality of first feature values for a plurality of model images are stored in the RAM 73. The identification information of the model image here may be information that can identify the model image to be processed from a plurality of types of model images extracted as a result of the processing of S106 to S109. A numerical value indicating the order can be used as identification information.

  Subsequently, in S114, the thumbnail image of the original image generated in S102 is corrected based on the first feature amount specified in S112 (first feature amount of the model image to be processed) and the second feature amount specified in S104. To do. A specific correction method will be described later.

  Subsequently, in S115, it is determined whether or not the processing of S110 to S114 has been performed for all the model images. Note that whether or not all the model images have been processed can be determined based on whether or not the processing has been repeated by the number of model images extracted as a result of the processing of S106 to S109.

If it is determined in S115 that processing has not been performed for all the model images (an unprocessed model image exists), the process returns to S110.
On the other hand, if it is determined in S115 that the processing has been performed for all the model images, the process proceeds to S116, and the arrangement (layout) of thumbnail images when performing index printing based on the first feature amount specified for each model image. Position). Specific processing contents will be described later.

  Subsequently, in S117, in accordance with the layout position determined in S116, the thumbnail image of each model image generated in S110 and the thumbnail image of the original image after correction corrected in S114 using the model image as a model (hereinafter referred to as “corrected image image”). Is also placed in the index buffer. Specifically, as shown in FIG. 6, correction image images representing color conversion processing results based on the model image are arranged on the right side of the model image to form one set (one set), and the image is displayed on the left side of each model image. A number (sequential number by a natural number in this example) is arranged. The index buffer is a storage area prepared in the RAM 73 for temporarily storing an image for index printing.

  In step S118, the image printing unit 40 is caused to print an index printing image (FIG. 6) arranged in the index buffer. Thereby, the user can grasp at a glance what color conversion processing result is obtained by which model image. The user can print an image obtained by correcting the original image with the model image corresponding to the image number by performing an operation of designating the image number from which the desired color conversion processing result is obtained by the operation unit 31. .

That is, the CPU 71 inputs an image number based on the operation performed on the operation unit 31 in S119.
Subsequently, in S120, the first feature amount specified from the model image having the image number input in S119 (the first feature amount stored in association with the identification information of the model image) is stored from the storage maintenance area in the RAM 73. read out.

  Subsequently, in S121, based on the first feature value read in S120 and the second feature value stored in the storage maintenance area, the original image (not the thumbnail image) stored in the storage maintenance area is corrected. To do. A specific correction method will be described later.

Subsequently, in S122, after the original image corrected in S121 is printed on the image printing unit 40, the main color conversion process is terminated.
[1-3-2. Second feature value specifying process]
Next, the second feature amount specifying process performed in S104 in the color conversion process (FIG. 4) will be described with reference to the flowchart of FIG. In the second feature amount specifying process, the H value takes a value of −30 to 330. If the H value is not within this range, the H value is appropriately converted (for example, “ H value + 360 × n ”or“ H value−360 × n ”, where n is an integer), and adjust within this range.

When the CPU 71 starts the second feature amount specifying process, first, in S201, the CPU 71 divides the original image into a plurality of regions. In this embodiment, the image is divided based on six commonly used hues. Specifically, based on the H value of each pixel,
-R region: -30 or more and less than 30-Y region: 30 or more and less than 90-G region: 90 or more and less than 150-C region: 150 or more and less than 210-B region: 210 or more and less than 270-M region: Divide into 270 or more and less than 330. That is, a process of classifying the constituent pixels of the original image into six classification items according to the classification standard corresponding to the hue value. The correspondence relationship between these areas and the H value is merely an example, and can be changed as appropriate.

  Subsequently, in S202, for each region divided in S201, the ratio of each region in the original image and the representative value (HSV value) representing the characteristics of the constituent pixels belonging to each region are calculated as the second feature amount. To do.

Here, the representative value (HSV value) of each region is defined as follows.
・ Representative value of R region: iHr, iSr, iVr
・ Representative value of G region: iHg, iSg, iVg
・ Representative value of B area: iHb, iSb, iVb
-Typical value of C region: iHc, iSc, iVc
-Typical values in the M region: iHm, iSm, iVm
・ Representative value of Y area: iHy, iSy, iSy
In this embodiment, the average value of each HSV value of the constituent pixels belonging to each region is specified as a representative value. The representative value is not limited to the average value, and for example, an intermediate value can be used.

Further, the ratio of each area in the original image is defined as follows.
-Ratio of R area in original image: iRateR
-Ratio of the G area in the original image: iRateG
-Ratio of B area in original image: iRateB
-Ratio of the C area in the original image: iRateC
-Ratio of M area in original image: iRateM
The ratio of the Y area in the original image: iRateY
For example, for the R region:
iRateR = (number of pixels in the R region in the original image) / (total number of pixels in the original image)
It can be. In addition, you may define by another type | formula.

[1-3-3. First feature amount specifying process]
Next, the first feature amount specifying process performed in S112 in the color conversion process (FIG. 4) described above will be described with reference to the flowchart of FIG. In the first feature amount specifying process, the same processing as that performed on the original image in the second feature amount specifying process (FIG. 7) described above is performed on the model image.

  That is, when starting the first feature amount specifying process, the CPU 71 first divides the model image to be processed into six regions in S301. Since this processing content is the same as the processing of S201 in the second feature amount specifying processing, a detailed description thereof will be omitted.

Subsequently, in S302, the first feature value is calculated by performing the same process as the process of S202 in the second feature value specifying process on the model image. Here, the representative value (HSV value) of each region is defined as follows.
-Typical values of R region: sHr, sSr, sVr
-Typical values in the G region: sHg, sSg, sVg
-Typical values of the B region: sHb, sSb, sVb
-Typical value of C region: sHc, sSc, sVc
-Typical values in the M region: sHm, sSm, sVm
-Typical values of the Y region: sHy, sSy, sSy
Further, the ratio of each area in the model image is defined as follows.
The ratio of the R region in the model image: sRateR
-Ratio of the G area in the model image: sRateG
-Ratio of area B in the model image: sRateB
-Ratio of the C area in the model image: sRateC
-Ratio of M area in the model image: sRateM
The ratio of the Y area in the model image: sRateY
[1-3-4. Original image correction processing]
Next, a specific method of the correction process of the original image (the thumbnail image of the original image in S114, which will be read in the same manner below) performed in S114 and S121 in the color conversion process (FIG. 4) described above will be described. This process is performed by converting the H value, S value, and V value of each pixel of the original image.

First, the conversion process for the H value will be described.
The representative value of the H value of the second feature value is plotted on the X axis and the representative value of the H value of the first feature value is plotted on the Y axis, and the representative value of the H value for each region is plotted. Then, a hue correction table shown in FIG. 9 is created by linear interpolation between the plotted points, for example. Here, the H value after correction by the hue correction table (H value of the Y-axis) is H ′. When H ′ <0, H ′ = H ′ + 360, and when H ′> 360, H ′> 360. '= H'-360.

Then, the H value is corrected by applying the hue correction table to each pixel of the original image. Specifically, H ′ after correction can be defined by the following equation.
H ′ = (y2−y1) ÷ (x2−x1) × H
-(y2-y1) ÷ (x2-x1) x x2 + y2
... (Formula 1)
Here, x1, x2, y1, and y2 are defined as follows.

When H <iHr,
(X1, y1) = (iHm-360, sHm-360)
(X2, y2) = (iHr, sHr)
When iHr ≦ H <iHy,
(X1, y1) = (iHr, sHr)
(X2, y2) = (iHy, sHy)
When iHy ≦ H <iHg,
(X1, y1) = (iHy, sHy)
(X2, y2) = (iHg, sHg)
When iHg ≦ H <iHc,
(X1, y1) = (iHg, sHg)
(X2, y2) = (iHc, sHc)
When iHc ≦ H <iHb,
(X1, y1) = (iHc, sHc)
(X2, y2) = (iHb, sHb)
When iHb ≦ H <iHm,
(X1, y1) = (iHb, sHb)
(X2, y2) = (iHm, sHm)
When iHm ≦ H,
(X1, y1) = (iHm, sHm)
(X2, y2) = (iHr + 360, sHr + 360)
Next, conversion in the S value and the V value will be described.

The S value and the V value are converted for each area divided by the H value. For example, for the R region:
When S ≦ iSr,
S ′ = S × (sSr ÷ iSr) (Formula 2)
When S> iSr,
S ′ = 1 + (S−1) × {(1-sSr) ÷ (1-iSr)} (Formula 3)
When V ≦ iVr,
V ′ = V × (sVr ÷ iVr) (Formula 4)
When V> iVr,
V ′ = 1 + (V−1) × {(1−sVr) ÷ (1−iVr)} (Formula 5)
It can be calculated by the following formula. Further, other areas can be similarly calculated. In the following, the conversion table defined by the S value conversion formula may be referred to as a saturation correction table, and the conversion table defined by the V value conversion formula may be referred to as a brightness correction table. .

  Thereafter, the converted HSV value is converted into a format suitable for the image printing unit 40 (for example, RGB value). The conversion from the HSV value to the RGB value can be performed according to the above-described known conversion formula.

  In this way, the hue of the original image is converted to the hue of the model image by performing correction processing on the original image so that the second feature amount approaches the first feature amount for each region divided based on the H value. can do.

[1-3-5. Layout position determination process]
Next, the layout position determination process performed in S116 in the above-described color conversion process (FIG. 4) will be described by taking two methods (A) and (B) as examples.

Here, as a specific example, it is assumed that three model images A, B, and C are input, and the ratio of each area in the original image and each model image A, B, and C is the following value: To do.
<Original image>
The ratio of the R region to the original image: iRateR = 40%
-Ratio of the G area in the original image: iRateG = 15%
-Ratio of B area to original image: iRateB = 20%
-Ratio of the C area to the original image: iRateC = 10%
-Ratio of the M area in the original image: iRateM = 8%
-Ratio of the Y area in the original image: iRateY = 7%
<Example image A>
The ratio of the R region to the sample image A: sRateR_A = 25%
-Ratio of the G area in the model image A: sRateG_A = 15%
-Ratio of the B area to the model image A: sRateB_A = 30%
-Ratio of the C area to the model image A: sRateC_A = 10%
The ratio of the M area to the sample image A: sRateM_A = 8%
The ratio of the Y area to the model image A: sRateY_A = 12%
<Example image B>
The ratio of the R area to the model image B: sRateR_B = 30%
The ratio of the G area to the model image B: sRateG_B = 12%
-Ratio of the B area to the model image B: sRateB_B = 8%
The ratio of the C area to the model image B: sRateC_B = 15%
The ratio of the M area to the model image B: sRateM_B = 10%
The ratio of the Y area to the model image B: sRateY_B = 25%
<Example image C>
The ratio of the R region to the model image C: sRateR_C = 8%
The ratio of the G area to the model image C: sRateG_C = 10%
-Ratio of the B area to the model image C: sRateB_C = 12%
The ratio of the C area to the model image C: sRateC_C = 25%
The ratio of the M area to the model image C: sRateM_C = 30%
The ratio of the Y area to the model image C: sRateY_C = 15%
(A) Method of comparing and sorting the existence ratios of the respective colors (A-1) First, the ratio values of the respective areas in the original image are sorted in descending order, and numbers are assigned in descending order of ratio. In this example, they are numbered as follows:

[1] iRateR = 40%
[2] iRateB = 20%
[3] iRateG = 15%
[4] iRateC = 10%
[5] iRateM = 8%
[6] iRateY = 7%
(A-2) Next, the ratio value of each area in each model image A, B, C is numbered according to the order specified in (A-1).
<Example image A>
[1] sRateR_A = 25%
[2] sRateB_A = 30%
[3] sRateG_A = 15%
[4] sRateC_A = 10%
[5] sRateM_A = 8%
[6] sRateY_A = 12%
<Example image B>
[1] sRateR_B = 30%
[2] sRateB_B = 8%
[3] sRateG_B = 12%
[4] sRateC_B = 15%
[5] sRateM_B = 10%
[6] sRateY_B = 25%
<Example image C>
[1] sRateR_C = 8%
[2] sRateB_C = 12%
[3] sRateG_C = 10%
[4] sRateC_C = 25%
[5] sRateM_C = 30%
[6] sRateY_C = 15%
(A-3) Next, the ratio value (iRate) of each area in the original image and the ratio value (sRate) of each area in each model image A, B, C are compared, and the model images A, B, Sort C in order from the closest to the original image. Specifically, the ratio values of the smallest numbered areas in each of the model images A, B, and C are compared and sorted in order from the ratio of the original image to the ratio value. If the ratio values are the same, the ratio values of the next younger areas are compared. In this example, the R region is in the order close to the ratio iRateR (40%) in the original image. [1] Model image B: sRateR_B (30%)
[2] Model image A: sRateR_A (25%)
[3] Model image C: sRateR_C (8%)
Sort in the order.

(B) Method of Sorting the Model Image and the Original Image in the Same Order from the Nearest RGB Value (B-1) First, the representative value of each area in the original image and each model image is converted from HSV to RGB.

(B-2) Next, the ratio value of each area in the original image is sorted in descending order.
(B-3) Next, in the region having the largest ratio value in (B-2), the model images A, B, and C are sorted in order from the one closest to the RGB value of the original image.

Although two methods (A) and (B) are illustrated here, it goes without saying that the layout position can be determined by various methods other than this.
[1-4. effect]
As described above, the multifunction machine 10 according to the present embodiment inputs an original image to be subjected to color conversion from the memory card (S101), and specifies the second feature amount representing the feature of the input original image (S103). , S104). Also, a plurality of types of model images are input from the image reading unit 20 (S106 to S109), and first feature amounts representing the characteristics of each model image are specified (S111, S112). Further, a corrected image image representing a color conversion process result when the color conversion process is performed on the original image so that the second feature value is close to the first feature value is generated for the first feature value of each model image (S114). ). The plurality of corrected image images generated in this way are printed in an index format arranged in association with the model image used for generating each corrected image image (S116 to S118).

  According to such a multifunction machine 10, the user can prepare a plurality of types of model images and print a list of correction image images for the model images, so that the result of the color conversion process can be confirmed in advance. A model image that provides a desired correction result can be efficiently selected. Therefore, color conversion processing that accurately reflects the user's intention can be performed.

  The multifunction device 10 generates a thumbnail image of the input original image (S102), and performs a color conversion process on the generated thumbnail image to generate a corrected image image (S114). For this reason, compared with the case where a correction image image is generated based on the result of performing direct color conversion processing on the original image, the process of generating the correction image image can be simplified.

  Further, when one of the corrected image images is selected by the user (S119), the multifunction machine 10 performs color conversion on the original image so as to be close to the first feature value used for generating the selected corrected image image. Processing is performed (S120, S121), and the original image after the color conversion processing is printed (S122). Therefore, the user selects one of the corrected image images, and prints the original image that has been subjected to the color conversion processing represented by the corrected image image without causing the model image or the original image to be read again. be able to.

  In the multifunction machine 10, the model image and the corrected image are arranged and printed in order from the model image whose features are similar to those of the original image (S116 to S118). Image images can be easily compared visually.

  On the other hand, in the multi-function device 10, an image read in each reading area obtained by dividing the document placement surface into a plurality of parts is input as an independent model image, so that the user can easily read a plurality of model images. And the working time can be shortened.

  In addition, if the average luminance value of the image read in the reading area is greater than or equal to the threshold value ThreV, it is determined that no image exists in the reading area. Inputting as an image can be prevented.

[1-5. Correspondence with Claims]
In the MFP 10 of the first embodiment, the CPU 71 that executes the process of S101 in the color conversion process (FIG. 4) corresponds to the second image input unit, and the CPU 71 that executes the process of S102 generates the processed image. The CPU 71 that corresponds to the means and executes the processes of S103 and S104 corresponds to the second feature amount specifying means. The CPU 71 that executes the processes of S106 to S109 corresponds to a first image input unit, and the CPU 71 that executes the processes of S111 and S112 corresponds to a first feature amount specifying unit. Further, the CPU 71 that executes the process of S114 corresponds to an image generation unit, and the CPU 71 that executes the processes of S116 to S118 corresponds to an image output unit. The CPU 71 that executes the process of S119 corresponds to a selection unit, the CPU 71 that executes the processes of S120 and S121 corresponds to an image correction unit, and the CPU 71 that executes the process of S122 corresponds to a print control unit. .

[2. Second Embodiment]
Next, the multifunction machine 10 of the second embodiment will be described.
The MFP 10 of the second embodiment is different from the MFP 10 of the first embodiment in that the color conversion process shown in the flowchart of FIG. 10 is performed instead of the color conversion process (FIG. 4) of the first embodiment described above. Different. Description of other common contents will be omitted.

The color conversion process of FIG. 10 is different from the color conversion process of FIG. 4 in the following points (1) and (2).
(1) In the color conversion process of FIG. 4, a corrected image is generated by performing a color conversion process on the thumbnail image of the original image, whereas in the color conversion process of FIG. 10, color conversion is performed directly on the original image. Processing is actually performed to generate a corrected image, and a corrected image is generated from the corrected image. That is, a correction image for each model image is generated by actually performing color conversion processing based on each model image on the original image itself. For this reason, when an image number is selected from the index printed image, the already generated corrected image is printed as it is without performing image correction again.

  (2) In the color conversion process of FIG. 4, the layout position is determined based on the first feature amount of the model image, whereas in the color conversion process of FIG. 10, which reading area on the document placement surface of the image reading unit 20 The layout position is determined according to whether the model image has been read.

  Specifically, in the color conversion process of FIG. 10, the processes of S411 and S412 are performed instead of the processes of S102, S105, S113, and S121 in the color conversion process of FIG. Moreover, it replaces with the process of S114, S116, and S120, and performs the process of S413, S415, and S419. On the other hand, the processing contents of S101, S103, S104, S106 to S112, S115, S117 to S119, and S122 are the same as the processing contents of S401 to S410, S414, S416 to S418, and S420. Therefore, the processing related to this difference will be mainly described, and description of common portions will be omitted.

  In step S411, the original image read in step S401 is corrected based on the first feature amount specified in step S410 (the first feature amount of the model image to be processed) and the second feature amount specified in step S403. The specific correction method is as described above.

  Subsequently, in S412, the corrected original image (corrected image) corrected in S411 is stored in a storage maintenance area in the RAM 73 in association with identification information of the model image to be processed. Here, when a corrected image for another model image is already stored in the storage maintenance area, it is stored in a newly added form while maintaining the storage state of the corrected image. Therefore, by repeating the processing of S408 to S413, a plurality of corrected images for a plurality of model images are stored in the RAM 73. The storage means is not limited to the RAM 73. For example, when the multifunction machine 10 includes a hard disk, it may be stored in the hard disk.

  Subsequently, in S413, a thumbnail image (corrected image image) of the corrected original image (corrected image) corrected in S411 is generated. That is, a size change process (reduction process) is performed on the corrected image so as to have a preset thumbnail image size. Note that since the correction image (original image) is usually larger than the thumbnail image, the reduction process is described as the size change process. However, if the correction image is smaller than the thumbnail image size, The enlargement process is performed as the change process. As the size changing process, for example, a nearest neighbor method, a bilinear method, a bicubic method, and an average pixel method can be used if limited to reduction.

  In step S415, the arrangement (layout position) of thumbnail images for index printing is determined based on the reading position of each model image. Specifically, each reading area obtained by dividing the document placement surface into a plurality of reading areas and the arrangement of thumbnail images may be associated in advance. For example, in the example illustrated in FIG. 5, the reading areas are Image1, Image2, Image3, Image4. The thumbnail images are arranged in the order of.

  In S419, a corrected image corrected based on the model image having the image number input in S418 (a corrected image stored in association with the identification information of the model image) is read from the storage maintenance area in the RAM 73, and in S420. After printing on the image printing unit 40, the color conversion process ends.

  As described above, in the MFP 10 of the second embodiment, the corrected image is generated based on the result of actually performing the color conversion process on the original image (S411, S413). A corrected image that accurately represents the processing result can be generated. When one of the corrected image images is selected by the user (S418), the corrected image can be printed without performing image correction processing again (S419, S420).

  In the MFP 10, the arrangement of thumbnail images (layout position) for index printing is determined based on the reading position of each model image. Therefore, the arrangement of thumbnail images is determined by the user's intention. can do.

  In the multifunction machine 10 of the second embodiment, the CPU 71 that executes the process of S401 in the color conversion process (FIG. 10) corresponds to the second image input unit, and the CPU 71 that executes the processes of S402 and S403 is the first. This corresponds to two feature amount specifying means. The CPU 71 that executes the processes of S404 to S407 corresponds to a first image input unit, and the CPU 71 that executes the processes of S409 and S410 corresponds to a first feature amount specifying unit. The CPU 71 that executes the process of S411 corresponds to an image image generation unit, and the CPU 71 that executes the processes of S415 to S417 corresponds to an image image output unit. Further, the CPU 71 that executes the process of S418 corresponds to a selection unit, and the CPU 71 that executes the process of S420 corresponds to a print control unit.

[3. Other forms]
As mentioned above, although embodiment of this invention was described, it cannot be overemphasized that this invention can take a various form.

  (1) In the above embodiment, an example has been described in which the multifunction peripheral 10 inputs an image read in each reading area obtained by dividing the document placement surface into a plurality of independent sample images. However, the present invention is not limited to this. . For example, the entire document placement surface may be read as one whole image, and the contents of the whole image may be analyzed to extract the image. In this way, as shown in FIG. 11, a plurality of images arranged at unspecified positions on the document placement surface can be extracted as independent images. Here, examples of a method for extracting a plurality of images from the entire image include the following methods (a) and (b).

  (A) A method of recognizing, as one image, a place where pixel values determined to be not white based on a threshold value set based on a reading color (white color) of a portion where no document exists exist as a certain image.

  (B) An image in a specific range set on the document placement surface is recognized as one image, and a pixel value determined to be non-white based on a threshold value similar to (a) in the specific range. A method for determining that an image is present when a certain amount or more exists.

Further, one model image is not necessarily required for each document, and each image may be extracted as an independent model image from a document on which a plurality of images are printed.
(2) In the above-described embodiment, an example in which a plurality of model images are read at a time has been described. However, the present invention is not limited to this, and the model images may be read one by one by repeatedly performing a model image reading operation. It may be. In this way, it is possible to reliably prevent one image from being misrecognized as a plurality of images, or conversely, misrecognizing a plurality of images as one image.

  (3) In the above embodiment, the example in which the multifunction machine 10 reads the model image by the image reading unit 20 has been described, but the present invention is not limited to this. For example, an image read from a memory card or an image received from the outside via the communication unit 60 may be used as a model image.

  (4) In the above-described embodiment, an example in which an image (original image) to be subjected to color conversion is input from a memory card has been described. However, the present invention is not limited to this. For example, an image read by the image reading unit 20 or an image received from the outside via the communication unit 60 may be the target of color conversion.

(5) In the above embodiment, in the color conversion process, before performing the process for specifying the first feature value and the second feature value, the process of converting the constituent pixels of the model image and the original image into HSV parameters. However, the present invention is not limited to this. For example, instead of the HSV parameter, it may be converted into another parameter such as an L ^* c ^* h ^* parameter or an RGB parameter.

  (6) In the above embodiment, in the color conversion process, the first feature value is specified after specifying the second feature value. However, the present invention is not limited to this, and the first feature value is specified after specifying the first feature value. Two feature quantities may be specified.

  (7) In the above embodiment, the algorithm for specifying the first feature quantity and the algorithm for specifying the second feature quantity have been described as being the same. However, the present invention is not limited to this. Also good.

  (8) In the above-described embodiment, the corrected image is printed. However, the present invention is not limited to this. For example, instead of printing, communication with the display unit 32 of the multifunction machine 10 or the multifunction machine 10 is possible. You may make it display on the display etc. of the personal computer in a various state. However, when the corrected image is printed as in the above-described embodiment, the hue or the like when the corrected image is actually printed can be determined more accurately than when the corrected image is displayed on the display unit 32 or the like. It is effective in terms.

  (9) In the above-described embodiment, the information stored in the storage maintenance area in the RAM 73 has been described as an example of the configuration in which the information is stored until the color conversion process is completed. However, the present invention is not limited to this. For example, you may make it memorize | store semipermanently until erase operation by a user is performed.

  (10) In the above embodiment, the natural number is exemplified as the image number. However, the present invention is not limited to this, and it is only necessary that one image can be specified from a plurality of images such as characters and symbols. It is also possible to specify a desired image based on the arrangement of images (for example, what number from the top) without printing the image number.

  (11) In the above embodiment, the color conversion process is exemplified as the image correction process. However, the present invention is not limited to this, and an image correction process other than that exemplified in the above embodiment may be performed.

  (12) In the above embodiment, image correction is performed for all six regions divided according to hue. However, the present invention is not limited to this. For example, for a region with a small proportion of the image, the image is corrected. The correction may not be performed (or the correction amount is decreased). Specifically, before performing the image correction process, a representative value resetting process is performed in which the values of the first feature value and the second feature value used in the image correction process are reset according to conditions.

Here, an example of the representative value resetting process will be described with reference to the flowchart of FIG.
When the CPU 71 starts the representative value resetting process, first, in S501, the CPU 71 determines whether one of the six areas divided for each hue in the original image is to be subjected to the color conversion process. Here, whether or not the area is to be subjected to color conversion processing is determined depending on whether or not the area satisfies a conversion target condition described later. If it is determined that the color conversion is to be performed (S501: YES), the process proceeds to S503. On the other hand, if it is determined not to be subject to color conversion (S501: NO), the process proceeds to S502, the representative values of the first feature value and the second feature value related to the region are reset, and the process proceeds to S503. To do. A method for resetting the representative value will be described later.

  In S <b> 503, it is determined whether or not the determination process for determining whether or not all six areas are to be subjected to color conversion has been performed. If it is determined that there remains an area for which determination processing for determining whether or not to perform color conversion remains (S503: NO), the processing returns to S501 and is repeated. On the other hand, if it is determined that the determination process has been performed for all regions (S503: YES), the representative value resetting process is terminated.

Here, the conversion target condition of S501 will be described.
(A) Method Using Threshold Thre In S501, the ratio value (the ratio occupied in the model image or the original image) for the target area in the first feature value and the second feature value is equal to or greater than the threshold value Thre. Then, it is determined that the conversion target condition is satisfied (the color conversion target). If the ratio value for the target region in at least one of the first feature amount and the second feature amount is less than the threshold value Thre, the representative of the first feature amount and the second feature amount related to the region is S502. The value is changed to the same value, and correction processing is performed using the changed representative value. Specifically, the representative value is reset as follows.

When sRateR <Thre or iRateR <Thre,
sHr = 0, sSr = 0.5, sVr = 0.5,
iHr = 0, iSr = 0.5, iVr = 0.5
When sRateG <Thre or iRateG <Thre,
sHg = 120, sSg = 0.5, sVg = 0.5,
iHg = 120, iSg = 0.5, iVg = 0.5
When sRateB <Thre or iRateB <Thre,
sHb = 240, sSb = 0.5, sVb = 0.5,
iHb = 240, iSb = 0.5, iVb = 0.5
When sRateC <Thre or iRateC <Thre,
sHc = 180, sSc = 0.5, sVc = 0.5,
iHc = 180, iSc = 0.5, iVc = 0.5
When sRateM <Thre or iRateM <Thre,
sHm = 300, sSm = 0.5, sVm = 0.5,
iHm = 300, iSm = 0.5, iVm = 0.5
When sRateY <Thre or iRateY <Thre,
sHy = 60, sSy = 0.5, sVy = 0.5,
iHy = 60, iSy = 0.5, iVy = 0.5
In the present embodiment, for the S value and the V value, 0.5, which is an intermediate value of possible values (0 to 1), is adopted, and for the H value, the intermediate value of each region is adopted. These are merely examples, and are not limited to these numerical values.

By changing the representative value in this way, in the correction process, the values of the S value and the V value are not converted as is apparent from the conversion expressions (Expression 2) to (Expression 5) described above. That is, for example, with respect to the R region, when S ≦ iSr, as described above (Formula 2),
S ′ = S × (sSr ÷ iSr)
In this equation, sSr = 0.5 and iSr = 0.5, so the above-described equation is
S ′ = S × (0.5 ÷ 0.5) = S (Expression 6)
It becomes. Similarly, when S> iSr, S ′ = S. Similarly, the V value and other areas are not converted.

  On the other hand, for the H value, the point plotted in FIG. 9 is changed to the representative value, so that the conversion amount in that region can be reduced. That is, even when the above-described conversion formula (Formula 1) is used, the conversion amount is reduced by changing the representative value.

  Next, a method for determining the threshold value Thre will be described. This value can be determined based on sensory evaluation, for example. In sensory evaluation, it was confirmed that if the area occupied about 6% or more, the area was easily perceived. Therefore, 6% can be adopted as the threshold value Thre. However, the threshold value Thre is not limited to 6%.

  For example, the threshold value Thre may be determined so as to extract a region having a relatively large area with respect to other regions. Specifically, if the number of regions to be divided is 6, the reciprocal 1/6 is set as the threshold value Thre.

  Here, the number of divided areas is six. The remaining six vertices excluding white and black, which are achromatic colors, from the RGB space (number of vertices 8) which is one of the color gamuts expressing colors. It is. In order for a person to identify a color, it is sufficient to classify the color gamut into the number of vertices of 6, and if the number is less than 6, the user is more likely to feel that the original image is not converted like a model image. On the other hand, if the data is divided more finely than 6, the conversion accuracy is improved, but the possibility that the person cannot be identified increases. In addition, since the amount of calculation increases as the number of divisions increases, the time until a print result is obtained increases and the possibility of increasing user dissatisfaction increases. Therefore, the number of divided regions is preferably six.

In the present embodiment, the same threshold value Thre is used in all regions. However, the present invention is not limited to this, and the threshold value Thre may be changed for each region.
(B) Method Using Information on Maximum Region In the method (A) above, a threshold value Thre is set, and the representative value is changed based on the threshold value Thre, that is, the color conversion process is stopped, and the conversion amount is changed. Reduction control was performed. Here, a method of using information on the maximum area in the image in order to reflect only a specific color of the model image in the original image will be described.

  In this case, in S501, it is determined that the conversion target condition is satisfied (to be color conversion target) when the region has the largest ratio value in both the first feature amount and the second feature amount. And about the area | region which does not satisfy | fill the conditions for conversion, in S502, the typical value of the 1st feature-value and 2nd feature-value which concern on the area | region is reset as follows. Here, the largest ratio value among the ratio values of the first feature value is sMaxRate. Further, the largest ratio value among the ratio values of the second feature amount is assumed to be iMaxRate.

When sRateR ≠ iMaxRate or iRateR ≠ sMaxRate,
sHr = 0, sSr = 0.5, sVr = 0.5,
iHr = 0, iSr = 0.5, iVr = 0.5
When sRateG ≠ iMaxRate or iRateG ≠ sMaxRate,
sHg = 120, sSg = 0.5, sVg = 0.5,
iHg = 120, iSg = 0.5, iVg = 0.5
When sRateB ≠ iMaxRate or iRateB ≠ sMaxRate,
sHb = 240, sSb = 0.5, sVb = 0.5,
iHb = 240, iSb = 0.5, iVb = 0.5
When sRateC ≠ iMaxRate or iRateC ≠ sMaxRate,
sHc = 120, sSc = 0.5, sVc = 0.5,
iHc = 120, iSc = 0.5, iVc = 0.5
When sRateM ≠ iMaxRate or iRateM ≠ sMaxRate,
sHm = 300, sSm = 0.5, sVm = 0.5,
iHm = 300, iSm = 0.5, iVm = 0.5
When sRateY ≠ iMaxRate or iRateY ≠ sMaxRate,
sHy = 60, sSy = 0.5, sVy = 0.5,
iHy = 60, iSy = 0.5, iVy = 0.5
Since the representative value is set in this way, only the region having the largest ratio value in both the first feature value and the second feature value is to be converted, and therefore the S value and V of the region that has not been converted. No conversion is performed on the value, and the conversion amount can be reduced for the H value.

  Specifically, for example, when only the B area is to be converted, a hue correction table as shown in FIG. 13 is created. In this hue correction table, the H value representative value (iHc = 180, sHc = 180) in the C region adjacent to the B region in the color space and the H value representative value (iHb, sHb) in the B region are linear. In addition, the representative value of the H value (iHm = 300, sHm = 300) in the M region adjacent to the B region in the color space and the representative value of the H value in the B region (iHb, sHb) are connected by a straight line. It will be.

Therefore, the C region where the H value is 180 <H ≦ 210 and the M region where the H value is 270 <H ≦ 300 are also converted. This conversion amount becomes larger as the value is closer to the B region.
In this way, the conversion target region can be selected, and even if the region is not the conversion target, the H value adjacent in the color space is partially converted. It is possible to prevent a pseudo contour (tone jump) from being generated between a region that is not a conversion target.

  By performing such a representative value resetting process, it is possible to stop a part of the correction process or reduce the conversion amount for each divided area based on the size of the area. The user can reflect only a part of the hue of the model image in the hue of the original image.

  (13) In the above (12), the H value of the region not to be converted can reduce the conversion amount, but cannot reduce the conversion amount to zero. This is because, as shown in FIG. 9, linear interpolation is performed between the representative values of the regions that are not to be converted, and therefore, it is affected by the representative values of other regions.

  Therefore, a hue correction table as shown in FIG. 14 can be employed. FIG. 14 is a hue correction table in the case where only the B region is to be converted. In this figure, the number of areas to be converted is one, but the present invention can be similarly applied to cases where a plurality of areas are to be corrected.

  In FIG. 14, since the H values other than the B region are H ′ = H, color conversion is not performed. The H ′ value in the B region can be obtained by the following equation, where the minimum value in the B region is Hmin and the maximum value in the B region is Hmax.

When H <iH,
H ′ = Hmin + (sHb−Hmin) × (H−Hmin) ÷ (iHb−Hmin)
When H> iH,
H ′ = sHb + (Hmax−sHb) × (H−iHb) ÷ (Hmax−iHb)
By using this equation, only the region to be converted can be converted.

In this way, since only the H value to be converted can be converted, the effect of color conversion can be increased.
(14) In the above embodiment, since a correction curve (conversion formula) is independently used for each region with respect to the S value and the V value, a pseudo contour (tone jump) may be generated. That is, as shown in FIG. 15, each region has a table indicating the relationship between S and S ′, and no consideration is given to the nature of the table in the adjacent region.

On the other hand, as shown in FIG. 16, gradation skipping can be prevented by smoothing the correction curve in each color region.
Here, specific processing will be described below. The color conversion process for a part of the C area and a part of the B area will be described with reference to FIGS. 17 and 18, but the contents of the process are basically the same for the other areas.

  The corrected S value (Sb ″) includes the H value (H) of the conversion target area, the intermediate value (Hbmid) of the H value of the conversion target area, and the hue coordinate position of the H value of the pixel to be converted. The hue coordinate position of the intermediate value of the H value of the area to be converted is compared, the hue coordinate position of the H value of the pixel to be converted is close, and the intermediate value of the H value of the area to be converted The H value representative value (Hcmid) of the adjacent region far from the hue coordinate position is converted by a conversion formula corresponding to the conversion formula (Formula 2) (that is, using the saturation correction table of the B region). The S value (Sb ′) of the conversion target area (calculated) and adjacent converted by the conversion expression corresponding to the conversion expression (Expression 3) (that is, calculated using the saturation correction table of the C area) Using the S value (Sc ′) of the region, it can be obtained by the following equation.

Sb ″ = {(H−Hcmid) × Sb ′ + (Hbmid−H) × Sc ′}
÷ {(Hbmid−Hcmid)} (Expression 7)
The Hbmid and Hcmid are the “representative values” that have been reset.

  Further, the corrected V value (Vb ″) in this example includes the H value (H) of the conversion target region, the representative value (Hbmid) of the H value of the conversion target region, and the representative value of the H value of the adjacent region (Hbmid). Hcmid), the V value (Vb ′) of the conversion target area converted by the conversion expression corresponding to the conversion expression (Expression 4) (that is, calculated using the brightness correction table of the B area), and the conversion expression ( Using the S value (Vc ′) of the adjacent region converted by the conversion equation corresponding to Equation 5 (that is, calculated using the brightness correction table of the C region), the following equation can be used. .

Vb ″ = {(H−Hcmid) × Vb ′ + (Hbmid−H) × Vc ′}
÷ {(Hbmid−Hcmid)} (Equation 8)
The above-described processing is performed on part of the B area (H value range: 210 <H ≦ 240) and part of the C area (H value range 180 <H ≦ 210) shown in FIG. As a result, the output saturation value (S ″) and lightness value (V ″) are obtained by weighting calculation according to the input hue value (H), thereby smoothing the correction effect between the hues. Can do.

  (15) In the above embodiment, the multifunction machine 10 is illustrated as the image processing apparatus, but the present invention is not limited to this, and may be an information processing apparatus such as a personal computer.

1 is a perspective view illustrating an appearance of a multifunction machine according to a first embodiment. FIG. 2 is a block diagram illustrating a schematic configuration of a control system of the multifunction machine. FIG. 6 is an explanatory diagram illustrating an outline of a user operation and a process of a multifunction machine in a color conversion process. It is a flowchart of the color conversion process of 1st Embodiment. It is explanatory drawing of the display screen of a correction | amendment image image. It is explanatory drawing of the image for index printing. It is a flowchart of the 2nd feature-value specific process. It is a flowchart of a 1st feature-value specific process. It is explanatory drawing of a hue correction table. It is a flowchart of the color conversion process of 2nd Embodiment. FIG. 5 is an explanatory diagram showing a state in which a plurality of images are arranged at unspecified positions on a document placement surface. It is a flowchart of a representative value reset process. It is explanatory drawing of the hue correction table produced when only B area | region is made into conversion object. It is the figure which showed the hue correction table in a modification. It is the figure which showed the saturation correction table. It is the figure which showed the change of the curve of saturation correction | amendment. It is the figure which showed the saturation correction table in B area | region and C area | region. It is the figure which showed the corrected S value in B area | region and C area | region. It is the figure which showed that a part of B area | region and C area | region became conversion object.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... MFP, 11 ... Main body casing, 12 ... Opening, 20 ... Image reading part, 21 ... Document cover, 30 ... Operation panel, 31 ... Operation part, 32 ... Display part, 40 ... Image printing part, 50 ... Card slot , 60 ... communication section, 70 ... control section, 71 ... CPU, 72 ... ROM, 73 ... RAM, 80 ... signal line

Claims (13)

First image input means capable of inputting a plurality of types of first images;
First feature amount specifying means for specifying, for each input first image, a first feature amount representing a feature of the first image input by the first image input means;
Second image input means for inputting a second image which is an image to be subjected to image correction;
Second feature amount specifying means for specifying a second feature amount representing a feature of the second image input by the second image input means;
For each of the plurality of first feature amounts, a corrected image image representing an image correction processing result when image correction processing is performed on the second image so that the second feature amount is close to the first feature amount is generated. Image generating means for performing,
Image image output for outputting a plurality of corrected image images generated by the image image generating means in a state of being arranged in association with the first image in which the first feature amount used for generating each corrected image image is specified. Means,
An image processing apparatus comprising: A processed image generating means for generating a processed image obtained by processing the second image input by the second image input means;
The image processing apparatus according to claim 1, wherein the image image generation unit generates the corrected image image by performing the image correction process on the processed image generated by the processed image generation unit. The image processing apparatus according to claim 2, wherein the processed image generation unit generates an image obtained by reducing the second image input by the second image input unit as the processed image. Selection means for selecting one of the corrected image images output by the image image output means;
Image correction means for performing image correction processing on the second image so that the second feature quantity is close to the first feature quantity used for generation of the corrected image image selected by the selection means;
The image processing apparatus according to claim 2, further comprising: The image processing apparatus according to claim 4, further comprising: a print control unit that performs a printing process for printing the second image that has been subjected to the image correction process by the image correction unit. The image image generation means generates the corrected image image based on a result of actually performing the image correction process on the second image,
Selection means for selecting one of the corrected image images output by the image image output means;
A print control unit that performs a printing process for printing a second image that has been subjected to the image correction process by the image image generation unit and that corresponds to the corrected image image selected by the selection unit;
The image processing apparatus according to claim 1, further comprising: The said image image output means arrange | positions the 1st image by which the 1st feature-value used for the production | generation of the said correction | amendment image image and its correction image image is arrange | positioned based on the 1st feature-value. The image processing apparatus according to any one of claims 1 to 6. The first image input means inputs an image optically read from a print medium set at a predetermined reading position, and when a plurality of images are read from the print medium set at the reading position The image processing apparatus according to any one of claims 1 to 7, wherein each image can be input as an independent first image. The image processing apparatus according to claim 8, wherein the first image input unit inputs an image read in each reading area obtained by dividing the reading position into a plurality of parts as an independent first image. The first image input means inputs an image that is optically read from a printing medium set at a predetermined reading position, and the images read in each reading area obtained by dividing the reading position into a plurality of parts are independent of each other. Input as the first image,
The image image output means arranges the corrected image and the first image in which the first feature used for generating the corrected image is specified at a predetermined position associated with each reading area. The image processing apparatus according to any one of claims 1 to 6, wherein the image processing apparatus is characterized. The first image input means determines that there is no image in the reading area when the average luminance value of the image read in the reading area is equal to or greater than a predetermined value. The image processing apparatus according to claim 10. The said image image output means prints out the 1st image by which the 1st feature-value used for generation | occurrence | production of the said correction | amendment image image and its correction | amendment image image was printed out, The Claims 1-11 characterized by the above-mentioned. The image processing apparatus according to any one of the above. First image input means capable of inputting a plurality of types of first images;
First feature amount specifying means for specifying, for each input first image, a first feature amount representing a feature of the first image input by the first image input means;
Second image input means for inputting a second image which is an image to be subjected to image correction;
Second feature amount specifying means for specifying a second feature amount representing a feature of the second image input by the second image input means;
For each of the plurality of first feature amounts, a corrected image image representing an image correction processing result when image correction processing is performed on the second image so that the second feature amount is close to the first feature amount is generated. Image generating means for performing,
Image image output for outputting a plurality of corrected image images generated by the image image generating means in a state of being arranged in association with the first image in which the first feature amount used for generating each corrected image image is specified. An image processing program for causing a computer to function as means.