JP4831019B2 - Image processing apparatus, image processing method, and image processing printing program - Google Patents

Description

  The present invention relates to an image processing apparatus capable of correcting an image with an easy operation.

  With the popularization of digital cameras and the like, it has become popular to print images taken by individuals using a home printing device. In addition, printing (copying) an image stored on a print medium with a home printing apparatus is also widespread. In such a case, if the acquired image does not have a desired color or tone, it is desired to correct the image before printing.

  Here, for example, Patent Document 1 below discloses a printing apparatus capable of image adjustment. In this printing apparatus, it is necessary to input adjustment data in order to adjust the image quality of the acquired image.

JP 2007-89179 A

  In the printing apparatus as described above, it is necessary to input specific data in order to adjust the image, so knowledge about the image is required, and sensory color adjustment cannot be performed. Further, in the above document, since it is necessary to select or set adjustment data, it is not a simple operation. Further, when considering selecting a model image for color correction, it is necessary to reduce the capacity for storing the model image as much as possible as well as a simple operation is desired.

  In order to solve the above problems, an object of the present invention is to provide an image processing apparatus, an image processing method, and an image processing printing program capable of performing desired image correction with a simple operation.

In order to achieve the above object, an invention according to claim 1 is an image processing apparatus comprising storage means, display means, image specifying means, image acquisition means, feature amount extraction means, and image correction means, wherein the storage means includes a plurality of storage means. For each type of first image, the first feature value extracted from the first image, which is a first representative value relating to the hue of the first image, and for each region divided based on color information said first feature quantity including a first representative value which is specified, the third image first image created by reducing the, in association with the display means, stored in said storage means The plurality of third images thus displayed are displayed on a display unit, and the image specifying unit specifies one third image from the plurality of third images in response to an input from a user, and the image acquisition unit includes: The second image is acquired and the feature amount is extracted Stage, on the basis of the second image is a second representative values for hue of the second image, extracting a second feature quantity including a second representative value which is specified for each said region, said The image correction conversion means specifies the same region with respect to the second representative value based on the first feature value and the second feature value corresponding to one third image specified by the specifying means. The second image is corrected to the first representative value, and the second image is corrected by interpolating values other than the second representative value .

  The invention according to claim 2 is the image processing apparatus according to claim 1, further comprising correction data generation means for generating correction data based on the first feature quantity and the second feature quantity, wherein the image correction means includes: The second image is image-corrected based on the correction data.

  According to a third aspect of the present invention, in the image processing apparatus according to the first or second aspect, the feature amount extraction unit extracts a feature amount related to a hue from the second image, and the second feature based on the feature amount related to the hue. The image is divided into a plurality of regions, and feature amounts related to other than hue are extracted for each of the divided regions, and the second feature amount is a feature amount related to the hue and feature amount other than the hue, and the first feature amount Has the same type of feature quantity as the second feature quantity, and the feature quantity related to the hue includes the ratio of the area in the entire first image that is the basis of the feature quantity for each hue. The hue specifying unit further specifies a hue of a region having the largest area among the plurality of regions into which the second image is divided as a specific hue, and the region of the specific hue is Occupied in the second image The ratio is specified as a specific hue ratio, and the display unit preferentially displays the third image corresponding to the first feature amount having the ratio of the specific hue area close to the specific hue ratio. And

  According to a fourth aspect of the present invention, in the image processing apparatus according to the first or second aspect, the feature amount extraction unit extracts a feature amount related to a hue from the second image, and the second feature based on the feature amount related to the hue. The image is divided into a plurality of regions, and feature amounts related to other than hue are extracted for each of the divided regions, and the second feature amount is a feature amount related to the hue and feature amount other than the hue, and the first feature amount Has the same type of feature quantity as the second feature quantity, and the feature quantity related to the hue includes the ratio of the area in the entire first image that is the basis of the feature quantity for each hue. The first feature values extracted from each of the plurality of first images are grouped on the basis of the hue of the region having the largest ratio, and further include a hue specifying unit, and the hue specifying unit includes: The second image was split The hue of the area having the largest area among the number of areas is specified as the specific hue, the ratio of the specific hue area in the second image is specified as the specific hue ratio, and the display means The third image corresponding to the first feature amount that exists in the group corresponding to the hue and has the ratio of the specific hue area close to the specific hue ratio is preferentially displayed.

  The invention according to claim 5 is the image processing apparatus according to claim 3 or 4, wherein each of the plurality of first feature amounts has a plurality of components, and a part or all of the plurality of components approximate to each other. When there are a plurality of feature amounts, for the plurality of third images corresponding to the plurality of first feature amounts, at least one third image is left and the other third image is sent to the display unit by the display means. It is excluded from the display object of.

  According to a sixth aspect of the present invention, in the image processing apparatus according to the fifth aspect, the other third image to be excluded is determined based on a time when the first feature amount is generated.

  According to a seventh aspect of the present invention, in the image processing device according to any one of the third to sixth aspects, the correction data creating unit includes a representative value of the feature value related to the hue for each region into which the first image is divided, and The feature value related to other than the hue is extracted, and the representative value of the feature value related to the hue and the feature value related to other than the hue are extracted for each area obtained by dividing the second image, and the feature related to the hue of the first image is extracted. A hue correction data is created based on a representative value of the quantity and a representative value of the feature quantity related to the hue of the second image, and the image correction means converts the feature quantity related to the hue of each pixel of the second image to the hue correction The correction is performed based on the data, and the feature amount related to other than the hue for each pixel of the second image is corrected based on the representative value for each region.

  The invention according to claim 8 is the image processing apparatus according to any one of claims 1 to 7, further comprising print control means, wherein the print control means is for printing the image-corrected second image information. It is characterized by performing processing.

  According to a ninth aspect of the present invention, in the image processing apparatus according to the third to eighth aspects, the feature amount related to the hue is calculated based on an H value in the HSV space.

According to a tenth aspect of the present invention, in the image processing method, for each of a plurality of types of first images, the first feature value extracted from the first image, the first representative value relating to the hue of the first image The first feature value including the first representative value specified for each region divided based on the color information is associated with the third image created by reducing the first image. display step of displaying a third image of a plurality of types stored in the storage means stored Te, in response to an input from the user, identifies one of the third image from a plurality of the third image An identification step, an image acquisition step of acquiring a second image, a second representative value relating to a hue of the second image based on the second image, and the second representative value specified for each region feature extracting a second feature quantity including Extraction step, based on the first feature amount and the second feature amount corresponding to one third image specified in the specifying step, the for the second representative value is specified for the same said region and the correcting the first representative value, the value other than the second representative value, by interpolation, the image correction step of image correction the second image, and wherein the Tona Turkey.

According to an eleventh aspect of the present invention, in the image processing program, the first feature amount extracted from the first image for each of the plurality of types of first images, the first feature relating to the hue of the first image. A first feature value including the first representative value identified for each region divided based on color information, a third image created by reducing the first image, display step of displaying a plurality of kinds of the third image stored in the storage means in association with one another, in response to an input from the user, one of the third image from a plurality of the third image A step of specifying the second image, an image acquiring step of acquiring the second image, a second representative value related to the hue of the second image based on the second image, and the second representative value specified for each region the first including a representative value Feature amount extraction step of extracting a feature quantity, the based on the first feature amount and the second feature amount corresponding to one third image specified in the specifying step, the for the second representative value, the same is corrected to the first representative value which is specified for the region, the value other than the second representative value, by interpolating, to thereby perform the image correction step, the image correcting said second image Features.

  According to the first and second aspects of the invention, the feature amount extracted from the first image and the third image created by reducing the image are stored in association with each other, the third image is displayed, and the desired image is displayed. Since the user selects the third image and performs the image correction process based on the feature amount associated with the selected third image, the second image can be used as a model for image correction with a sensuous and simple operation. One image can be selected. In addition, since the reduced third image is stored, a necessary storage area can be reduced. Various parameters can be used to specify the hue.

  According to the third aspect of the present invention, the third image having the closest hue ratio of the largest region in the first image to be corrected can be preferentially displayed, that is, the user selects as a model image. Since the images can be displayed in order from the most likely images, the selection efficiency can be increased.

  According to the invention of claim 4, the first feature amounts are further grouped, and the display order of the third image is the order in which the ratio of the hue of the largest region in the image to be corrected is close. For this reason, since the user is preferentially displayed from images that are highly likely to be selected as model images, the selection efficiency can be increased. Furthermore, since the process for determining the display order is performed only on the first feature amount in the specific group, the calculation amount can be reduced.

  According to the fifth aspect of the invention, the plurality of first feature quantities each have a plurality of components, and when there are a plurality of first feature quantities that all of the plurality of components approximate, Since only a predetermined third image among the plurality of third images corresponding to the first feature amount is to be displayed, the user can efficiently determine a model image.

  According to the sixth aspect of the present invention, since the third image corresponding to the first feature amount generated later is the display target among the plurality of third images, the user can efficiently display the third image. A model image can be determined.

  According to the seventh aspect of the invention, since different image correction processing is performed separately for the feature amount related to hue and the feature amount related to other than the hue, desired image correction processing can be easily performed.

  According to the eighth aspect of the present invention, since the print control unit is further included, it is possible to print the second image whose image has been corrected.

  According to the ninth aspect of the invention, it is possible to calculate the feature value related to the hue based on the H value in the HSV space.

  The inventions according to claims 10 and 11 can also achieve the effects described above.

[First embodiment of the present invention]
First, an example of the appearance of the image processing apparatus according to the present invention is shown in FIG. An example of the internal configuration of the image processing apparatus 1 is shown in FIG.

  The image processing apparatus 1 is an example of a multi-function printer or the like, and has a scanner 2 for reading image information from a photograph or the like on the top thereof (in FIG. 1, the scanner is in a covered state). is there.). Image information read by the scanner is used as sample information for correcting the color and the like of image information read from the memory slot shown below. Details of the image correction process will be described later.

  The image processing apparatus 1 has a memory slot 3 that functions as an IC card reader for reading image information recorded in an external memory that is a recording medium such as an SD card (trademark) or a CF card (trademark). Here, as the external memory (storage medium), a known medium and format can be appropriately selected. Further, it may be configured to have a plurality of memory slots so as to be applied to a plurality of types of media. In addition, a communication function (for example, a LAN card, not shown) that can be connected to a network such as a LAN to which a PC storing image information is connected is added to the signal line to which the CPU, ROM, RAM, etc. shown in FIG. 2 are connected. Thus, instead of the external memory, desired image information may be read from a network to which the PC is connected. In that case, the image processing apparatus 1 has a communication function.

  The image processing apparatus 1 has an operation panel 4 for performing various operations. Various buttons and the like are arranged on the operation panel 4.

  The image processing apparatus 1 includes a printer 10 illustrated in FIG. The image information subjected to the image correction is printed by the printer 10. The printed paper is discharged from the discharge port 5, for example.

  The image processing apparatus 1 has display means 6. Various character information and images can be displayed. Further, a transparent touch panel may be provided on the front surface of the display unit 6 and a part or all of the functions of the operation panel 4 may be substituted. Moreover, you may comprise so that a touch panel and the operation panel 4 can be used together.

  The image processing apparatus 1 is entirely controlled by a processor (CPU 9). In addition, there are a RAM 7 for temporarily storing information and a ROM 8 for storing predetermined information. Moreover, you may have mass storage means, such as HDD, as needed.

  Each element mentioned above is mutually connected by the signal wire | line.

  Further, various functions (for example, FAX) can be added to the image processing apparatus 1. Further, an interface for connecting to a personal computer or the like and an interface for connecting to another printing apparatus may be provided.

  In addition, the said external appearance is only an example, and it cannot be overemphasized that this invention is not limited to the said external appearance. Moreover, in the above-mentioned external appearance, an apparatus in which all functions are integrated is shown, but some functions may be realized by an external apparatus. Furthermore, the internal configuration is merely an example, and it goes without saying that the present invention is not limited to the internal configuration.

  Next, an outline of user operations and apparatus main body processing will be described as an example of basic processing using the image processing apparatus 1 according to the present invention. FIG. 3 shows an outline of the processing. First, the user sets a memory (storage medium) in which an image to be corrected is stored in a memory slot of the image processing apparatus 1. As described above, as this memory, a known storage medium such as a memory card can be appropriately employed.

  When the memory is set in the memory slot 3 (1), the image processing apparatus recognizes the set memory and causes the user to select an image to be corrected from the images stored in the memory. (2). In addition, the process for selecting an image can employ | adopt suitably a well-known technique (For example, switching and displaying an image sequentially, etc.). Further, the display means 6 may be used.

  When the image to be corrected is determined (3), the image processing apparatus 1 reads the image from the memory and writes it in, for example, the RAM (4). Hereinafter, the image may be referred to as an “original image”.

  Next, the image processing apparatus 1 reads the display image from the internal memory (for example, RAM) and displays it on the display means 6. When a plurality of display images are stored in the internal memory, the images may be sequentially displayed one by one on the display means 6 or may be displayed at a time for a predetermined number of images. Details of the display image will be described later.

  The user selects a desired display image displayed on the display means 6 (6). The display image is selected using the operation panel 4 or the touch panel.

  Next, the image processing apparatus 1 performs image correction processing of the original image using the feature amount corresponding to the selected display image (7). Details of the image correction processing will be described later.

  In the process of FIG. 3 described above, the display image is selected after reading the image from the memory. However, the display image may be determined first and then read from the memory. Further, the image subjected to the image correction process may be printed by the printer 10.

  Next, basic processing in the image processing apparatus 1 will be described. FIG. 4 is a flowchart showing an outline of basic processing in the present embodiment.

First, in S11, an original image is read. The data is read into the RAM 7 of the image processing apparatus 1. Here, the format of the read image is not limited.
In S12, a display image is displayed, the user is allowed to select a desired display image, and a display image that serves as a model for image correction with respect to the original image is specified.

  In S13, the first feature amount associated with the determined display image is specified. Details of the first feature amount will be described later.

  In S14, each pixel of the read original image is converted into an HSV parameter. The conversion process to the HSV parameter will be described later.

  In S15, a second feature value is extracted as a feature value of the original image based on the HSV parameter. Specific processing for extracting the second feature amount will be described later.

  In S16, the original image is corrected based on the first feature value and the second feature value. Specific processing for correction will be described later.

  In S17, the corrected original image is printed. At this time, the HSV parameters are converted into other parameters (for example, RGB parameters) as necessary. The conversion process to RGB parameters will be described later.

  In the above-described processing, the second feature amount is extracted after specifying the first feature amount. However, the first feature amount may be specified after extracting the second feature amount. Further, L * c * h * parameters may be extracted instead of HSV parameters. Further, the present invention can be realized even with known parameters such as RGB parameters. In the following, a case where HSV parameters are extracted will be described.

  The display image specified in S12 and the first feature amount specified in S13 will be described. FIG. 5 shows an example of information stored in the internal memory (for example, RAM 7) of the image processing apparatus 1. As shown in FIG. 5, the internal memory stores a plurality of pairs of a plurality of first feature amounts (feature amount A, feature amount B,...) And a display image. Here, the first feature amount is a feature amount extracted from a predetermined image, and the corresponding display image is generated from the predetermined image. The display image is an image obtained by converting the predetermined image into a specified size. Since the predetermined image is not stored in the internal memory, only the display image is stored. Therefore, when a reduced image generated by reducing the predetermined image is used as the display image, the storage capacity of the internal memory is used. Can be reduced. Each first feature amount may be of the same type as a second feature amount described later. The display image may be one that reduces the size of the predetermined image, or one that reduces the resolution of the predetermined image.

  In addition, the predetermined image that is the basis of each first feature amount and display image may be, for example, an image read by the scanner 2.

  Next, an example of processing for converting into HSV parameters in S14 of FIG. 4 will be described. When the original image is acquired as an RGB parameter, it can be converted into an HSV parameter by the following conversion formula. In addition, the conversion formula shown below is an example, and it cannot be overemphasized that you may convert with another conversion formula.

[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

  Next, the process for extracting the second feature amount in S15 of FIG. 4 will be described with reference to FIG. FIG. 6 is a flowchart of the second feature amount extraction process. In the following processing, the H value takes a value of −30 to less than 330. When the H value is not within the above range, by appropriately converting the H value (for example, “H value + 360 × n” or “H value−360 × n”, n is an integer), the value in the above range is taken. adjust.

In S21, the original image is divided into a plurality of regions. Here, 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. In other words, the constituent pixels of the model image are classified into six classification items according to the classification criteria corresponding to the hue value. Here, the correspondence relationship between the region and the H value is an example and can be changed as appropriate.

In S22, “representative value (HSV value) of each region” and “ratio of each region in the original image” are calculated for each region divided in S21. 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

  Here, the representative value can be an average value of each HSV value in each region. Moreover, an intermediate value can be used.

Next, the proportion 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

  Here, for example, for the R region, the ratio can be defined by sRateR = (number of pixels in the R region in the original image) / (total number of pixels in the original image). Also good.

  Next, details of the process of converting the original image based on the first feature value and the second feature value, which are executed in S16 of FIG. 4, will be described. This process is performed by converting the H value, S value, and V value of each pixel of the original image.

The following information is included as the first feature amount specified in S13. In the following, the predetermined image that is the origin of the display image specified in S12 may be referred to as a model image.
-Representative values of the R region in the model image: sHr, sSr, sVr
-Representative values of the G region in the model image: sHg, sSg, sVg
・ Representative values of region B in the model image: sHb, sSb, sVb
-Representative values of the C region in the model image: sHc, sSc, sVc
-Typical values of the M region in the model image: sHm, sSm, sVm
-Representative values of the Y area in the model image: sHy, sSy, sSy
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

  First, the conversion process for the H value will be described. The representative value of the H value of the original image is taken on the X axis, the representative value of the H value of the identified first feature amount is taken as the Y axis, and the representative value of the H value for each region is plotted. Then, a hue correction table shown in FIG. 7 is created by linear interpolation between the plotted points, for example. Here, when H ′> 360, H ′ = H′-360. In the hue correction table shown in FIG. 7, the representative value of the H value of the original image is taken as “H” on the X axis, and the representative value of the H value of the model area is taken as “H ′” on the Y axis. .

  Then, the H value is corrected by applying the hue correction table to each pixel of the original image. More specifically, the corrected H ′ can be defined by the following equation.

H ′ = (y2−y1) ÷ (x2−x1) × H− (y2−y1) ÷ (x2−x1) × 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 Y 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. .

  Next, a conversion formula for converting into a format (for example, RGB value) suitable for the printer 10 in S17 of FIG. 4 will be described. In addition, the conversion formula shown below is an example, and it cannot be overemphasized that you may convert with another conversion formula.

[HSV⇒RGB]
(In, fl, m, and n shown below are parameters used in the process of calculating RGB from HSV)
Let in be the integer part of (H ÷ 60) and fl be the decimal 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

  As described above, according to the first embodiment of the present invention, a display image corresponding to the first feature amount is presented, the user selects the image, and the first feature amount corresponding to the selected display image is used. Since correction is performed, an image serving as a model for image processing can be selected with a simple operation. Further, the storage capacity can be reduced by using the reduced image as the display image. Further, when performing the image correction process a plurality of times using the same feature amount, the operation of repeatedly scanning the model image can be omitted.

[Second Embodiment of Image Correction Processing]
In the second embodiment of the image correction process described below, for each divided area, a part of the image correction process can be stopped and the amount of change can be controlled based on the size of the area. . Thereby, the user can reflect only a part of the hue of the model image in the hue of the original image.

  FIG. 8 shows a flowchart of basic processing of this embodiment. The basic processing flow of the present embodiment is the same as that in FIG. 4, but a representative value resetting process (S36) is added after the second feature amount extraction process. Hereinafter, the representative value resetting process will be described with reference to the drawings.

  FIG. 9 is a flowchart of the representative value resetting process in the present embodiment. In S41, it is determined whether or not the divided area is a conversion target. In this process, it is determined whether or not the area satisfies a predetermined condition.

  When it is determined that it is a conversion target (S41: YES), the process proceeds to S43. When it is determined that it is not a conversion target (S41: NO), the process proceeds to S42. In S42, the representative value is reset, and then the process proceeds to S53. A method of resetting the representative value will be described later.

  In S43, it is determined whether or not all the regions (six regions in the case of 6 divisions) are determined to be converted. If an unidentified area remains (S43: NO), the process returns to S41 and the process is repeated. When the determination for all areas is completed (S43: YES), the representative value resetting process is terminated.

[Method using threshold Thre]
As the predetermined condition described above, the magnitude relationship between the size of the divided area and the threshold value Thre can be used. When the ratio of the original image or the model image is smaller than the threshold Thre, the first feature value of the original image and the second feature value of the model image related to the region are changed to the same value, and the change is made. The above-described conversion processing for each pixel is performed using the representative value. Hereinafter, this process will be described. The representative value can be 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 above-described example, as the S value and the V value, 0.5, which is an intermediate value between possible values (0 to 1), is employed. Moreover, the intermediate value of each area | region was employ | adopted about H value. However, the representative values described above are examples, and the present invention is not limited to the above numerical values.

  By using this value and using the conversion expressions (Expression 2) to (Expression 5) described above for conversion for each pixel, the values are not changed in the S value and the V value. That is, for example, with respect to the R region, when S ≦ iSr, as described above (Formula 2),

    S ′ = S × (sSr ÷ iSr)

In this formula, sSr = 0.5 and iSr = 0.5, so the above formula is

S ′ = S × (0.5 ÷ 0.5) = S (Expression 6)
It becomes. Similarly, when S> iSr, S ′ = S. Similarly, the V value and other regions are not converted.

  Further, in the H value, the point plotted in FIG. 7 is changed to the representative value, so that the conversion amount in that region can be reduced. That is, even when the above-described changing 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. 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 Thre. However, the present invention is not limited to the threshold Thre of 6%.

  Further, the threshold Thre may be determined so as to extract a region having a relatively large area relative to other regions. For example, if the number of areas to be divided is 6, 1/6, which is the reciprocal thereof, can be set as the threshold value Thre.

  The number of divided areas “6” given as an example of the present invention is the number of vertices excluding white and black, which are achromatic colors, from the RGB space (number of vertices 8) that is one of the color gamuts expressing colors. It is the same as the number “6”. In order for a person to identify a color, six is sufficient as the number of color regions, and if it is less than six, the user may feel that the image has not been converted as in the model image. If the data is divided more finely than 6, the conversion accuracy becomes higher. However, there is a high possibility that the person cannot be identified, and the amount of calculation increases as the number of divisions increases. In the case of using a printer, the time until obtaining an image to be corrected as a printing result is delayed, and there is a high possibility that user dissatisfaction will increase.

  In the example described above, the threshold value is the same in all the regions, but the threshold value Thre may be changed for each region.

[Method using information of maximum area]
In the above-described method, the threshold Thre is set, and the representative value is changed based on the threshold Thre, that is, the image correction processing is stopped and the conversion amount is reduced. Here, an image correction process 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 below.

  In this case, in S41 in FIG. 9, whether or not the area is the largest in both the original image and the model image can be set as a predetermined condition. In this case, the representative value is reset by the following formula. Here, the ratio of the divided area of the model image that has the largest ratio to the model image is iMaxRate. Further, the ratio of the divided area of the original image that has the largest ratio to the original image is sMaxRate.

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 = 180, sSc = 0.5, sVc = 0.5,
iHc = 180, 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

  As a result, since only the region having the largest area in both the original image and the model image is to be converted, the conversion is not performed for the S value and the V value that are not the conversion target, and the H value is converted. The amount can be reduced.

  Here, when only the region B is to be converted, a hue correction table as shown in FIG. 10 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.

As described above, in the second embodiment of the image correction process, a conversion target region can be selected, and even in a region that is not a conversion target, H values adjacent in the color space are partially converted. Therefore, it is possible to prevent a pseudo contour (tone jump) from being generated between the conversion target area and the non-conversion area.
[Third Embodiment of Image Correction Processing]
In the second embodiment of the image correction process, although the conversion amount can be reduced, the conversion amount of the H value of the region not to be converted cannot be set to zero. This is because, as shown in FIG. 10, linear interpolation is performed with the representative values of the areas that are not to be converted, and therefore, it is affected by the representative values of other areas.

  Therefore, a hue correction table as shown in FIG. 11 can be employed. FIG. 11 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 a plurality of areas may be targeted for correction.

  In FIG. 11, since the H values other than the B region are H ′ = H, image correction 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.

  As described above, in the third embodiment of the image correction process, since only the H value to be converted can be converted, the effect of image correction can be increased.

[Fourth Embodiment of Image Correction Processing]
In the second embodiment of the image correction process, 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. 12, each region has a table indicating the relationship between S and S ′, and no consideration is given to the nature of the table in adjacent regions.

  In the present embodiment, as shown in FIG. 13, gradation jumps can be prevented by smoothing the correction curve in each color region.

Specific processing of this embodiment will be described below. The image correction process for a part of the area B will be described with reference to FIGS. 14 and 15, but the contents of the process are basically the same for the other areas.
The corrected S value (Sb ″) in this embodiment is
-H value (H) of the conversion target area,
-H value representative value (Hbmid) of the area to be converted,
A representative value (Hcmid) of the H value of the adjacent region (in this case, the C region) closer to the hue coordinate position of the H value of the pixel to be converted,
-S value (corresponding to Sb ', Hbmid) of the conversion target area converted by the conversion formula corresponding to the above formula (2) (that is, calculated using the saturation correction table of the B area),
-S value of the adjacent area (which corresponds to Sc ', Hcid) converted by the conversion formula corresponding to the above formula (3) (that is, calculated using the saturation correction table of the C area),
Can be obtained by the following equation.

Sb ″ = {(H−Hcmid) × Sb ′ + (Hbmid−H) × Sc ′} ÷
{(Hbmid−Hcmid)} (Expression 7)

Further, the corrected V value (Vb ″) in the present embodiment is
-H value (H) of the conversion target area,
-H value representative value (Hbmid) of the area to be converted,
A representative value (Hcmid) of the H value of the adjacent region (in this case, the C region) closer to the hue coordinate position of the H value of the pixel to be converted,
-V value (corresponding to Vb ', Hbmid) of the conversion target area converted by the conversion formula corresponding to the above formula (4) (that is, calculated using the brightness correction table of the B area),
-Using the S value (corresponding to Vc ', Hcid) of the adjacent area converted by the conversion formula corresponding to the above formula (5) (that is, calculated using the brightness correction table of the C area) And can be calculated by the following formula.

Vb ″ = {(H−Hcmid) × Vb ′ + (Hbmid−H) × Vc ′} ÷
{(Hbmid−Hcmid)} (Equation 8)

The above-described processing is performed on part of the B region (H value range: 210 <H ≦ 240) and part of the C region (H value range 180 <H ≦ 210) shown in FIG. Thus, the saturation value (S ″) and the lightness value (V ″) of the output are calculated by weighting calculation according to the hue value (H) of the input.
Thus, the correction effect between the hues can be smoothed.

[First Embodiment of Determination of Display Order of Display Images]
With respect to the information shown in FIG. 5, variations (options) for image correction can be increased by storing various types of display images and first feature amounts. However, on the other hand, in order to present many display images as options, if the desired display image is not presented at an early stage, the user takes time to determine the desired display image.

  Therefore, processing for efficiently presenting stored display images will be described below. FIG. 17 is a flowchart showing the basic processing of this embodiment. Since the individual processes in this flowchart are basically the same as those in FIG. 4, only the presentation order determination process in S54 will be specifically described.

  Since it is considered that there is a tendency to select an image having the characteristics of the original image as a model image, a display order in which a display image with a similar area of the color area having the largest proportion of the base image is preferentially presented first is presented. To decide.

  As an example, assume that there are three display images (A, B, and C), and the ratios of the respective color regions in the model image that is the basis of each image are as follows.

The ratio of the R region in the sample image A: sRateR_A = 25%
The ratio of the G area in the sample image A: sRateG_A = 15%
-Ratio of area B in sample image A: sRateB_A = 30%
The ratio of the C area in the sample image A: sRateC_A = 10%
The ratio of the M area in the sample image A: sRateM_A = 8%
The ratio of the Y area in the sample image A: sRateY_A = 12%

-Ratio of the R region in the sample image B: sRateR_B = 30%
-Ratio of the G area in the model image B: sRateG_B = 12%
The ratio of the B area in the sample image B: sRateB_B = 8%
-Ratio of the C area in the sample image B: sRateC_B = 15%
The ratio of the M area in the model image B: sRateM_B = 10%
The ratio of the Y area in the sample image B: sRateY_B = 25%

The ratio of the R region in the sample image C: sRateR_C = 8%
The ratio of the G area in the sample image C: sRateG_C = 10%
The ratio of the B area in the sample image C: sRateB_C = 12%
The ratio of the C area in the sample image C: sRateC_C = 25%
-Ratio of the M area in the sample image C: sRateM_C = 30%
The proportion of the Y area in the sample image C: sRateY_C = 15%

Further, it is assumed that the ratio of each color area in the original image is as follows.
-Ratio of the R region in the original image: iRateR = 40%
-Ratio of the G area in the original image: iRateG = 15%
-Ratio of B area in original image: iRateB = 20%
-Ratio of the C area in the original image: iRateC = 10%
-Ratio of the M area in the original image: iRateM = 8%
The ratio of the Y area in the original image: iRateY = 7%

The color areas in the original image are sorted in descending order of area as follows.
R>B>G>C>M> Y
That is, the R region has the largest area.

Therefore, when the model images A, B, and C are arranged in the size of the ratio of the R region, the following is obtained.
[1] Model image B (R = 30%)
[2] Model image A (R = 25%)
[3] Model image C (R = 8%)
By presenting in this order, the selection efficiency can be improved for the user.

  In the above example, if there are a plurality of R regions having the same ratio, the next sorting is performed by the size of the area of the color region having the second largest area (B region in the above example) in the original image. What should I do? Similarly, the third, fourth, etc. can be processed.

[Second Embodiment of Determination of Display Order of Display Images]
In the first embodiment of the presentation order determination described above, all display images stored in the internal memory are the processing targets for the presentation order determination. For this reason, as the number of display images increases, it takes time to sort the feature amounts for determining the presentation order, and it may take longer to present the display image to the user. Therefore, the process can be shortened by attaching a label to the display image in advance, and setting only the display image with the specific label as the processing target for the presentation order determination.

Specifically, a label corresponding to the color area having the largest area is attached to each model image. In the example images 1, 2, and 3 in the above example,
Model image A: B label Model image B: R label Model image C: M label is attached. In addition, when there is another color area having the same area as that of the color area having the largest area, a plurality of labels may be attached to one model image. In this case, it is conceivable that the same set value is 3 to 5%, but the setting value is not limited to this.

Here, if the set value is 5%,
Model image A: R label, B label Model image B: R label, Y label Model image C: C label, M label will be attached.

Here, in the case of the original image in the above example, since the R area has the largest area, only the model image with the R label is subjected to sorting. In this case, the model images A and B are to be sorted, and the presentation order is
[1] Model image B
[2] Model image A
It becomes.
The method for determining the presentation order within the group is the same as in the first embodiment. In addition, as a labeling method, a label corresponding to a color region having an area of a predetermined value or more may be attached. That is, for example, a label corresponding to a color area of 30% or more may be attached.

[Third Embodiment of Determination of Display Order of Display Images]
In the first and second embodiments of the presentation order determination process described above, all of the display images to be approximated are to be presented. As described above, the user uses the scanner 2 or the like to input an image serving as a display image. Then, there is a possibility of inputting a display image whose characteristics are approximate. At this time, it is not preferable in terms of presentation efficiency to present a plurality of display images whose characteristics are approximate.

  Thus, a display image whose feature quantity approximates can be processed so as not to present one display image. At this time, the criterion for determining whether or not the feature amounts are approximate can be that the difference in the proportions of the color regions is within a predetermined range, and the difference between the representative values is within the predetermined range. Moreover, you may exclude from the display object the display image corresponding to the feature-value produced | generated previously in time among several display images with which feature-value approximates. In addition, a display image corresponding to a feature amount closer to the feature amount of the original image may be displayed. In addition, a display image corresponding to a feature amount generated later in time may be excluded from display targets. That is, the display image to be excluded from the display target may be determined based on the next period when the feature amount is generated.

  For example, it is assumed that the model images D and E exist and the ratio of each area in each image is as follows.

The ratio of the R region in the sample image D: sRateR_D = 25%
-Ratio of the G area in the model image D: sRateG_D = 15%
-Ratio of the B area in the sample image D: sRateB_D = 30%
The ratio of the C area in the sample image D: sRateC_D = 10%
-Ratio of the M area in the model image D: sRateM_D = 8%
The proportion of the Y area in the sample image D: sRateY_D = 12%
・ Representative value of R area in model image D:
(SHr, sSr, sVr) = (25, 0.3, 0.5)
・ Representative value of the G region in the model image D:
(SHg, sSg, sVg) = (120, 0.2, 0.7)
・ Representative value of area B in model image D:
(SHb, sSb, sVb) = (220, 0.4, 0.6)
・ Representative value of area C in model image D:
(SHc, sSc, sVc) = (190, 0.3, 0.1)
-Representative values of the M region in the model image D:
(SHm, sSm, sVm) = (315, 0.9, 0.9)
-Typical value of the Y area in the model image D:
(SHy, sSy, sSy) = (70, 0.4, 0.3)

The ratio of the R region in the sample image E: sRateR_E = 26%
-Ratio of the G area in the model image E: sRateG_E = 16%
-Ratio of the B area in the sample image E: sRateB_E = 28%
-Ratio of the C area in the model image E: sRateC_E = 10%
The ratio of the M area in the model image E: sRateM_E = 8%
The proportion of the Y area in the sample image E: sRateY_E = 12%
・ Representative value of R area in model image E:
(SHr, sSr, sVr) = (25, 0.3, 0.55)
・ Representative value of G area in model image E:
(SHg, sSg, sVg) = (125, 0.2, 0.7)
・ Representative value of area B in model image E:
(SHb, sSb, sVb) = (220, 0.45, 0.6)
・ Representative value of region C in model image E:
(SHc, sSc, sVc) = (190, 0.3, 0.15)
・ Representative value of M area in model image E:
(SHm, sSm, sVm) = (320, 0.9, 0.9)
-Representative value of the Y area in the model image E:
(SHy, sSy, sSy) = (75, 0.4, 0.3)

  In this case, it is determined that the model images D and E are approximate, and one of them is excluded from the presentation target. In other words, in this embodiment, the total of 24 types of components, ie, the ratio of each color region (six types) and the representative values (18 types) for H, S, and V in each color region are all approximated. If there are, the model images are close to each other. In addition, when there are a plurality of first feature quantities that approximate some of the plurality of components, it may be determined that the model images corresponding to the plurality of first feature quantities are approximated. . In addition, when it is determined that three or more model images are approximate, at least one model image can be displayed. That is, it is possible to display two model images out of three approximate model images.

  According to the above-described embodiment in which the display image presentation order is considered, an image that is highly likely to be selected by the user is preferentially presented first, so that the selection efficiency is improved.

  Further, according to the image correction processing in the present invention, for example, when it is desired to convert the sky blue to the bright sea blue with respect to the original image in which the building and the sky are reflected as the original image, You can convert the blue color of the original image into vivid sea blue by scanning the photo with the scanner.

  Also, if you want to brighten the skin color in an image showing a human face, you can convert the skin color of the original image to a light skin color by reading a picture of the palm that has moved to a bright skin color with a scanner. .

  Since the user does not need to input parameters or the like, he / she does not need any specialized knowledge and can perform desired image correction only by reading an image serving as a model for image correction. Furthermore, since it is possible to automatically select a region for performing image correction, it is possible to stop or reduce conversion of a region that is difficult to perceive and convert only a region that is easily perceived.

  In addition, this invention is not limited to the said embodiment, Of course, various improvement and deformation | transformation are possible within the range which does not deviate from the summary of this invention. For example, instead of reading a model image from a scanner, the image can be read as a model image from a storage medium. Further, instead of reading the original image from the storage medium, the original image can be read from the scanner. For example, a multifunction machine equipped with a scanner and a printer is described as an embodiment as an image processing apparatus. However, the present invention is not limited to this. For example, an information processing apparatus such as a personal computer performs image correction, and the information processing apparatus. The printer may be configured to perform printing processing.

  Further, the above-described flowchart is merely an example, and the process may be realized by another flowchart as long as it can obtain a result equivalent to the above-described process.

  The present invention can also be realized as an image processing method for executing the above-described processing. Further, the present invention can be realized as a program for causing the computer to execute the image processing method and a recording medium on which the program is recorded.

  Furthermore, not only can the corrected original image be printed, but it can also be stored in a storage medium or transmitted to another device.

It is the figure which showed the external appearance of the image processing apparatus. It is the figure which showed the internal structure of the image processing apparatus. It is the figure which showed the user's operation | movement and the flow of a process of an image processing apparatus. It is a flowchart of the basic processing in 1st Embodiment of this invention. It is the figure which showed an example of the information memorize | stored in an internal memory. It is a flowchart of the 2nd feature-value extraction process. It is the figure which showed the hue correction table. It is a flowchart of the basic process in another embodiment of this invention. It is a flowchart of a representative value reset process. It is the figure which showed the hue correction table. It is the figure which showed the hue correction table. 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. It is a flowchart of the basic process in another embodiment of this invention.

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Scanner 3 Memory slot 4 Operation panel 5 Ejection port 6 Display means 10 Printer

Claims (11)

In an image processing apparatus comprising storage means, display means, image specifying means, image acquisition means, feature amount extraction means, and image correction means,
The storage means is a first feature value extracted from the first image for each of a plurality of types of first images , and is a first representative value relating to a hue of the first image, and is based on color information. said first feature quantity including a first representative value which is specified for each divided by area, and a third image created by reducing the first image, the association is stored,
The display means displays a plurality of the third images stored in the storage means on a display unit,
The image specifying means specifies one third image from the plurality of third images in response to an input from a user,
The image acquisition means acquires a second image,
The feature quantity extraction means is a second representative value that is a second representative value related to the hue of the second image based on the second image and includes the second representative value specified for each region. Extract and
The image correction conversion means, based on the first feature value and the second feature value corresponding to one third image specified by the specifying means, for the second representative value, for the same region Correcting to the first representative value specified, and correcting the second image by interpolation for values other than the second representative value ;
An image processing apparatus. Correction data creating means for creating correction data based on the first feature quantity and the second feature quantity;
The image processing apparatus according to claim 1, wherein the image correction unit corrects the second image based on the correction data. The feature amount extraction means includes:
Extracting feature values related to hue from the second image;
Dividing the second image into a plurality of regions based on a feature amount relating to the hue;
Extract features other than hue for each divided area,
The second feature amount is a feature amount related to the hue and a feature amount related to other than the hue,
The first feature amount has the same type of feature amount as the second feature amount,
The feature amount related to the hue includes, for each hue, the ratio of the area in the entire first image that is the basis of the feature amount,
A hue specifying means;
The hue specifying unit specifies a hue of a region having the largest area as a specific hue among a plurality of regions obtained by dividing the second image, and determines a ratio of the specific hue region in the second image. Specified as a specific hue ratio,
The display means preferentially displays a third image corresponding to the first feature amount having a ratio of the specific hue area close to the specific hue ratio.
The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. The feature amount extraction means includes:
Extracting feature values related to hue from the second image;
Dividing the second image into a plurality of regions based on a feature amount relating to the hue;
Extract features other than hue for each divided area,
The second feature amount is a feature amount related to the hue and a feature amount related to other than the hue,
The first feature amount has the same type of feature amount as the second feature amount,
The feature amount related to the hue includes, for each hue, the ratio of the area in the entire first image that is the basis of the feature amount,
The first feature amount extracted from each of the plurality of first images is grouped based on the hue of the region having the largest ratio,
A hue specifying means;
The hue specifying unit specifies a hue of a region having the largest area as a specific hue among a plurality of regions obtained by dividing the second image, and determines a ratio of the specific hue region in the second image. Specified as a specific hue ratio,
The display means preferentially displays a third image corresponding to the first feature amount that exists in a group corresponding to the specific hue and has a ratio of the specific hue area close to the specific hue ratio. To
The image processing apparatus according to claim 1 or 2, characterized in that: Each of the plurality of first feature amounts has a plurality of components,
In the case where there are a plurality of first feature quantities that approximate some or all of the plurality of components, with respect to the plurality of third images corresponding to the plurality of first feature quantities, at least one third image is left, Excluding other third images from being displayed on the display unit by the display means;
The image processing apparatus according to claim 3, wherein the image processing apparatus is an image processing apparatus. Determining another third image to be excluded based on the time when the first feature value is generated;
The image processing apparatus according to claim 5. The correction data creating means includes
For each region obtained by dividing the first image, extract a representative value of the feature value related to the hue and a feature value related to other than the hue,
For each region obtained by dividing the second image, extract a representative value of the feature value related to the hue and a feature value related to other than the hue,
Creating hue correction data based on the representative value of the feature quantity related to the hue of the first image and the representative value of the feature quantity related to the hue of the second image;
The image correcting means includes
Correcting a feature amount related to a hue for each pixel of the second image based on the hue correction data;
Correcting a feature amount related to other than the hue for each pixel of the second image based on a representative value for each region;
The image processing apparatus according to claim 3, wherein the image processing apparatus is an image processing apparatus. A printing control unit;
The image processing apparatus according to claim 1, wherein the print control unit performs a process for printing the image-corrected second image information. The feature amount related to the hue is calculated based on the H value in the HSV space.
The image processing apparatus according to claim 3, wherein the image processing apparatus is an image processing apparatus. For each of the plurality of types of first images, the first feature value extracted from the first image, which is a first representative value related to the hue of the first image, and is divided based on color information A plurality of types stored in a storage unit in which the first feature value including the first representative value specified in the above and a third image created by reducing the first image are stored in association with each other display step of displaying the third image on the display unit of,
A specifying step of specifying one third image from the plurality of third images in response to an input from the user;
An image acquisition step of acquiring a second image;
A feature quantity extraction step for extracting a second feature quantity that is a second representative value related to the hue of the second image and includes the second representative value specified for each region based on the second image ;
Based on the first feature value and the second feature value corresponding to one third image specified in the specifying step, the second representative value is specified for the same region. An image correction step of correcting the second image by correcting the representative image and interpolating values other than the second representative value ;
Consist of ,
An image processing method characterized by and this. On the computer,
For each of the plurality of types of first images, the first feature value extracted from the first image, which is a first representative value related to the hue of the first image, and is divided based on color information A plurality of types stored in a storage unit in which the first feature value including the first representative value specified in the above and a third image created by reducing the first image are stored in association with each other display step of displaying the third image on the display unit of,
A specifying step of specifying one third image from the plurality of third images in response to an input from the user;
An image acquisition step of acquiring a second image;
A feature quantity extraction step for extracting a second feature quantity that is a second representative value related to the hue of the second image and includes the second representative value specified for each region based on the second image ;
Based on the first feature value and the second feature value corresponding to one third image specified in the specifying step, the second representative value is specified for the same region. An image correction step of correcting the second image by correcting the representative image and interpolating values other than the second representative value ;
Ru allowed to run,
Images processing program.

Images