JPH11136514A - Picture processor, picture processing method and medium recording picture processing control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a picture processor which can automatically select an optimum feature amount in accordance with a picture processing and executes the picture processing and to provided a picture processing method and a medium recording a picture processing control program. SOLUTION: A computer being the center of the picture processing obtains a luminance distribution histogram based on an added result sampled by different evaluation reference in a step S310. It obtains different feature amounts from the different luminance distribution histograms in steps S320 and S330 and converts picture data based on the respective feature amounts in a step S350. Thus, the optimum picture processing can be realized.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, an image processing method, and a medium in which an image processing control program for automatically executing optimum image processing on real image data such as digital photographic images. About.

[0002]

2. Description of the Related Art Various types of image processing are performed on image data of actual photographs such as digital photographic images. For example, image processing such as increasing contrast, correcting color tone, or correcting brightness. Normally, these image processings can be executed by a microcomputer, and an operator checks an image on a monitor and selects necessary image processing, or determines parameters of the image processing. That is, the operator determines the characteristics of the image and selects or executes various operations.

[0003]

In recent years, various image processing techniques have been proposed and have actually been effective. However, when it comes to how much processing is done with which technique, humans still have to be involved.
This is because it is not possible to determine where the digital image data to be subjected to image processing is important.

[0004] For example, in consideration of image processing for correcting brightness, it is assumed that automatic processing is performed in which, if the average of the entire screen is dark, the image is corrected to be bright, and if the average is bright, the image is corrected to be dark. Here, it is assumed that there is image data of a real image of a person image taken at night. It is assumed that although the background is almost completely dark, the person itself has been well photographed. If the image data of the actual photograph is automatically corrected, an attempt is made to correct the image data brightly because the background is completely dark, resulting in a daytime image.

In this case, if a human is involved, attention is paid only to the part of the human figure. Then, if the person image is dark, the correction is made slightly brighter, and conversely, if the image is too bright due to the effect of flash or the like, the correction is made darker.

In view of such a problem, the present applicant has proposed an invention in Japanese Patent Application No. xx to judge an important part in an image. In the present invention, it is considered that an original subject (object) should exist in a sharp portion of an image, and a pixel having a large degree of change is determined as an object by focusing on the degree of change of the image at each pixel. In addition, a feature amount is obtained for the object based on a predetermined evaluation criterion, and image processing is performed using the feature amount.

However, in some cases, it is preferable to perform image processing using the feature amount of the object, and in other cases, it is preferable to perform image processing using the average feature amount of the entire actual photographed image. For example, when considering a real image of a certain person image, the person is not always the brightest (highlight). Therefore, when there is a highlight portion in the background, if the contrast is corrected and focused on only the person, the highlight portion of the background may be lost in white. In this case, a better result can be obtained by focusing on the entire real image including the background. In performing image processing in this way, the need to select an optimal feature amount still remains.

The present invention has been made in view of the above problems, and has an image processing apparatus, an image processing method, and an image processing method capable of automatically selecting an optimal feature amount according to an image processing method and performing image processing. It is intended to provide a medium in which an image processing control program is recorded.

[0009]

In order to achieve the above object, according to the first aspect of the present invention, image data of a photographed image composed of pixels in a dot matrix form is inputted, and a predetermined value is obtained while totalizing the image data of each pixel. The feature amount is obtained by the evaluation criteria of
An image processing apparatus that performs image processing based on the feature amount, an evaluation unit that obtains the feature amount according to a plurality of evaluation criteria, and the image data can be converted by a plurality of methods. An image processing unit that selectively uses the feature amount obtained by the evaluation unit is provided.

In the invention according to claim 1 configured as described above, image data of a real photographed image composed of dot-matrix pixels is input, and the evaluation means aggregates the image data of each pixel and generates a plurality of evaluation criteria. The feature amount is obtained according to. Then, when converting the same image data by a plurality of methods, the image processing means converts the same image data by properly using the feature amounts obtained by the evaluation means according to each method.

That is, in some cases, it is preferable to convert image data using a feature amount focused on an object, such as brightness correction, or on the other hand, using a feature amount focused on the entire image, such as contrast expansion correction. In some cases, it is preferable to convert the image data, and the image data is converted by appropriately selecting a plurality of these feature amounts.

Note that this feature amount may be any value that can be used to determine the characteristics of the image when converting the image data, and it is not necessary to specifically obtain the type of the image. For example, it also includes an index such as a histogram of luminance when an image is determined to be bright or dark, and it is not necessary to obtain a determination result such as a bright image or a dark image. Of course, other than light and dark, it may be an index for determining whether an image is sharp or an index for determining vividness.

[0013] Further, the actual image data refers to image data intended to image a real object as it is. This is because the image processing is trying to correct an image having a defect in comparison with such a real object. Therefore, the object is not limited to natural objects and may be artificial, and more specifically, image data captured by a scanner or image data captured by a digital camera. included.

Further, various modes can be applied to the operation modes of the evaluation unit and the image processing unit, and there is no particular limitation. For example, on the premise that image processing of a plurality of methods is performed, a plurality of feature amounts are obtained and held in advance by the evaluation means in accordance with a plurality of evaluation criteria, and the image processing means appropriately performs the processing as necessary. The image data may be converted by selecting a feature amount. As another example, every time the image processing unit performs one image processing, the evaluation unit may be configured to total the image data of each pixel according to a predetermined evaluation criterion in order to obtain an appropriate feature amount. Is included.

The evaluation criteria that the evaluation means obtains the feature amount depends on the target image processing method. In some cases, it is better to perform image processing by focusing on objects as described above. Not always. As a preferred example of the former case, the invention according to claim 2 is the image processing apparatus according to claim 1,
The evaluation means has an evaluation criterion for extracting an object in the photographed image, summing up image data of pixels of the object, and obtaining a feature amount, and the image processing means has the image processing method in one image processing method. When using the feature amount of the central part of the data, the feature amount obtained from the pixel of the object is used.

In the invention according to claim 2 configured as described above, when the image processing means converts the image data based on the characteristic amount of the central part of the image data, the evaluation means includes Are extracted, and the image data of the pixels of the object are totaled according to a predetermined evaluation criterion to obtain a feature amount.

Here, the central part of the image data means the following. For example, when it is determined whether a certain photographed image is bright or dark, it is easily understood that it is appropriate to make the determination based on the intermediate density of the image. This intermediate density can also be called the median when considering the luminance distribution, that is, the center in the luminance distribution, and in this sense, it is called the central part of the image data. Then, if an object exists in the actual photographed image, it can be said that there is a high necessity for correcting the brightness in accordance with the brightness of the object.

According to a third aspect of the present invention, in the image processing apparatus according to the second aspect of the present invention, as the basic example of the object extracting method, the evaluation unit includes a degree of change in image data between each pixel and an adjacent pixel. , A pixel having the same degree of change is extracted as an object.

In the invention according to claim 3 configured as described above, the evaluation means extracts, as an object, a pixel whose image data has a large degree of change between adjacent pixels. When the pixels are arranged at regular intervals like pixels in a dot matrix, the difference in image data between adjacent pixels is proportional to the first derivative, so that such a difference can be used to determine the degree of change in the image. in this case,
The difference may be considered as the magnitude of the vector, and the vector may be synthesized in consideration of the adjacent direction.

According to another aspect of the object extracting method, the invention according to claim 4 is the image processing apparatus according to claim 2, wherein the evaluation means is arranged such that the chromaticity of each pixel is within a predetermined range. The configuration is such that pixels are extracted as objects.

In the invention according to claim 4 configured as described above, the evaluation means obtains the chromaticity of each pixel. Chromaticity represents the absolute percentage of the color stimulus value and is independent of brightness. Therefore, the objects in the image can be divided according to the range in which the chromaticity can be obtained. For example, it may be a range where skin color can be taken, or a range where green of trees can be taken. Since such a thing can be said for the chromaticity, the evaluation unit extracts a pixel whose chromaticity is within a predetermined range as an object.

As a preferred example of a case where image processing is performed without focusing on an object, the invention according to claim 5 is the image processing apparatus according to any one of claims 1 to 4, wherein the evaluation means In addition to having an evaluation criterion for equally sampling each pixel of the image data and summing them up to obtain a feature amount, the image processing means uses an average feature amount of a real image in one image processing method. The configuration is such that the feature amounts obtained by uniformly sampling are used.

In the invention according to claim 5 configured as described above, when the image processing means converts the image data based on the average feature amount, the evaluation means converts each pixel of the image data. Sampling is performed evenly according to a predetermined evaluation criterion to obtain a feature amount. Of course, it may be possible to aggregate all the pixels constituting the real image,
It can be said that it is not a good idea because the processing amount increases.

As another example of the case where the image processing is performed without focusing on the object, the invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5, wherein Has an evaluation criterion for evenly sampling and totaling each pixel of the image data to obtain a feature amount, and the image processing means determines an end of the feature amount distribution of the real image in one image processing method. At the time of use, the feature amount obtained by uniformly sampling the above is used.

In the invention according to claim 6 configured as described above, it is assumed that the image processing means uses an end portion of the feature amount distribution obtained by the evaluation means. For example, when the contrast is enlarged, image processing is performed so as to obtain a luminance distribution and widen the end of the luminance distribution. In such a case, when the luminance distribution of the object is used, other highlight parts are used. May appear white. Therefore, in such a case, the evaluation unit obtains a feature amount by uniformly sampling and summing each pixel of the image data according to a predetermined evaluation criterion.

The method of performing a plurality of image processings based on feature amounts obtained by a plurality of different evaluation criteria is not necessarily limited to a physical device.
According to a seventh aspect of the present invention, image data of a photographed image composed of pixels in a dot matrix is input, a feature amount is obtained based on a predetermined evaluation criterion while the image data of each pixel is totaled, and based on the feature amount. An image processing method for performing image processing,
When the feature amount is obtained in accordance with a plurality of evaluation criteria and the image data is converted by a plurality of methods, the feature amount obtained by the evaluation means is selectively used according to each method.

That is, there is no difference in that the present invention is not necessarily limited to a substantial device and is also effective as a method.

As described above, an image processing apparatus that performs a plurality of image processes based on feature amounts obtained based on a plurality of different evaluation criteria may exist alone, or may be installed in a certain device. The concept of the invention includes various aspects, for example, it may be used.
Further, it can be appropriately changed, for example, by hardware or software.

When the software for controlling the image processing apparatus is realized as an embodiment of the idea of the present invention, the software naturally exists on a recording medium on which such software is recorded.
I have to say that it is used.

As an example of the invention, the invention according to claim 8 is to input image data of a real photographed image composed of pixels in a dot matrix form, and obtain a feature amount based on a predetermined evaluation criterion while totalizing the image data of each pixel. A medium on which an image processing control program for performing image processing based on the feature amount is recorded, wherein the feature amount is obtained according to a plurality of evaluation criteria, and the image data is converted by a plurality of methods. Thus, the feature amount obtained by the evaluation means is selectively used.

Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any recording medium to be developed in the future. Also, the duplication stages of the primary duplicated product, the secondary duplicated product, and the like are equivalent without any question. In addition, the present invention is still used even when a supply method is performed using a communication line, and the same applies to a case where the information is written on a semiconductor chip.

Further, even when a part is implemented by software and a part is implemented by hardware, there is no difference in the concept of the invention, and it is necessary to store a part on a recording medium. It may be in a form that is appropriately read in accordance with it.

[0033]

As described above, according to the present invention, when a feature value is obtained in accordance with a plurality of evaluation criteria and image processing is performed by a plurality of methods, the feature value used according to each method is selectively used. It is possible to provide an image processing apparatus capable of performing image processing based on a simple evaluation criterion.

According to the second aspect of the present invention, it is suitable for performing image processing based on the characteristic amount of the central portion of the image data, such as brightness correction.

Further, according to the third aspect of the present invention,
Since only the difference between the image data between the adjacent pixels is obtained, the calculation is easy and the processing amount for object determination can be reduced.

Further, according to the invention of claim 4,
Although the object is determined based on the chromaticity, the object can be extracted without depending on the brightness of the object.

Further, according to the invention of claim 5,
This is suitable for performing image processing based on an average feature amount of a captured image, such as saturation correction.

Further, according to the invention of claim 6,
This is suitable when performing image processing using the end of the feature amount distribution in a real image such as contrast expansion correction.

Further, according to the invention of claim 7,
Similarly, it is possible to provide an image processing method capable of performing image processing based on an optimum evaluation criterion. According to the invention according to claim 8, it is possible to provide a medium in which an image processing control program is recorded. Can be.

[0040]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing an image processing system to which an image processing apparatus according to an embodiment of the present invention is applied, and FIG. 2 is a schematic block diagram showing an example of a specific hardware configuration.

In FIG. 1, an image input device 10 outputs real image data representing a photograph or the like as pixels in a dot matrix to an image processing device 20 and outputs the data to the image processing device 2.
0 performs a plurality of image processes, obtains a plurality of feature amounts based on an evaluation criterion most suitable for each image process, and executes each image process while selectively using the feature amounts. Image processing device 20
Outputs the image-processed image data to the image output device 30, and the image output device outputs the image-processed image by dot matrix pixels.

The image processing apparatus 20 obtains a plurality of feature amounts by previously compiling image data according to a plurality of evaluation criteria. In this sense, the image processing device 20 includes an evaluation means. Since the image processing is appropriately selected and performed on the basis of the feature amount, it can be said that the image processing device includes an image processing unit.

A specific example of the image input device 10 corresponds to the scanner 11, the digital still camera 12, or the video camera 14 in FIG. 2, and a specific example of the image processing device 20 is a computer 21, a hard disk 22, a keyboard 23, a CD-ROM. A computer system including a drive 24, a floppy disk drive 25, a modem 26, and the like corresponds to the computer system. Specific examples of the image output device 30 correspond to a printer 31, a display 32, and the like. In the case of the present embodiment, real image data such as a photograph is preferable as the image data because the feature amount of the image is obtained and image processing is performed to correct a defect or the like of the image. The modem 26 is connected to a public communication line, is connected to an external network via the public communication line, and can download and install software and data.

In this embodiment, the image input device 10
The scanner 11 and the digital still camera 12 output RGB (green, blue, red) gradation data as image data, and the printer 3 as the image output device 30.
1 requires CMY (cyan, magenta, yellow) or CMYK binary data obtained by adding black to the input as gradation data, and the display 32 has RGB data.
Is required as an input.

On the other hand, an operating system 21a is running in the computer 21, and a printer driver 21b and a display driver 21c corresponding to the printer 31 and the display 32 are incorporated. The image processing application 21d is controlled in its execution by the operating system 21a, and the printer driver 21b and the display driver 2
A predetermined image processing is executed in cooperation with 1c. Therefore, the specific role of the computer 21 as the image processing apparatus 20 is to input RGB gradation data, create RGB gradation data subjected to optimal image processing, and display the RGB data through the display driver 21c. , And converted into CMY (or CMYK) binary data via the printer driver 21b and printed by the printer 31.

As described above, in this embodiment, a computer system is incorporated between image input / output devices to perform image processing. However, such a computer system is not necessarily required, and image data is not necessarily required. Can be applied to a system that performs various types of image processing. For example, as shown in FIG. 3, a system may be used in which an image processing device for performing image processing is incorporated in the digital still camera 12a, and the converted image data is used to display on the display 32a or print on the printer 31a. Further, as shown in FIG. 4, in a printer 31b that inputs and prints image data without passing through a computer system, the image data input through a scanner 11b, a digital still camera 12b, a modem 26b, or the like is used. It is also possible to configure so as to perform image processing.

The above-described acquisition of the image feature amount and the associated image processing are performed by the image processing program corresponding to the flowchart shown in FIG. The flowchart shown in the figure corresponds to the former part of the image processing program, and executes a process of totalizing image data based on a plurality of evaluation criteria.

Here, two evaluation criteria used in this embodiment will be described. What is common is that not all pixels are targeted, but pixels are sampled according to a predetermined criterion, and the luminance of the sampled pixels is totaled. The difference is that one samples the pixels uniformly while the other samples the edge pixels. As will be described later, the result of summation of the luminances can be obtained by changing the so-called sampling method in this way to obtain a plurality of feature amounts according to different evaluation criteria. Equally sampling pixels is equivalent to summing up the brightness of the pixels of the entire image, and also finding the distribution of the brightness of the image data as a whole image. Thus, a feature amount that can be used as a reference for evaluation such as narrowness is obtained.

On the other hand, since the edge pixels are sharp portions of the image, the luminance of the pixels related to the original subject in the image is totaled. Even if the background is dark, the subject has sufficient brightness. Then, a feature amount serving as a reference for evaluation that the brightness of the image is sufficient can be obtained. In the present embodiment, these feature amounts are automatically selected according to the image processing method.

Referring to the flowchart shown in FIG.
In this totaling process, as shown in FIG. 6, the target pixel is moved in the vertical direction by sub-scanning while moving the target pixel horizontally in the image data composed of the pixels in the dot matrix, and whether each pixel is a sampling target is determined. It is counted by judging.

If the image data is composed of dot matrix pixels, the above-described RG
It is represented by gradation data representing the luminance of B, and the difference of the same data between adjacent pixels becomes large in the edge portion of the image. This difference is a luminance gradient, which is called an edge degree. In step S110, the edge degree at each pixel is determined.

When considering the XY orthogonal coordinates as shown in FIG. 7, the vector of the degree of change of the image is the X-axis direction component and the Y-axis component.
The calculation can be performed by obtaining the axial components. In a digital image including pixels in a dot matrix, pixels are adjacent to each other in the vertical and horizontal directions as shown in FIG. 8, and the brightness is represented by f (x, y). In this case, f (x, y) is R, which is each luminance of RGB.
(X, y), G (x, y), B (x, y),
Alternatively, the luminance may be the whole luminance Y (x, y). Note that R (x, y), G (x,
The relationship between y), B (x, y) and the overall luminance Y (x, y) cannot be converted unless strictly referring to a color conversion table or the like. The relationship may be used.

In FIG. 8, the difference value fx in the X direction and the difference value fy in the Y direction are

[0055]

(Equation 1)

Is represented as follows. Therefore, the magnitude | g (x, y) |

[0057]

(Equation 2)

Is represented as follows. Of course, the edge degree is represented by | g (x, y) |. Note that, as shown in FIG. 9, the pixels are originally arranged in a grid in the vertical and horizontal directions, and there are eight adjacent pixels when focusing on the central pixel. Therefore, similarly, the difference between the image data and each adjacent pixel may be represented by a vector, and the sum of the vectors may be determined as the degree of change of the image.

Since the edge degree is obtained for each pixel as described above, a pixel having a larger edge degree than a certain threshold value may be determined as an edge pixel. In addition,
Considering empirical facts, a subject with a high focus is often located at the center of the composition. Therefore, by adopting a structure in which a large number of pixels are extracted from the central portion, a more favorable effect can be obtained when used for determination of image processing.

For this reason, as shown in FIG. 10, the threshold values Th1, Th2, and Th3 to be compared for each portion in the image may be different. Of course, in this example,

[0061]

(Equation 3)

The threshold value is lower in the portion closer to the center, and it is determined that the focus is concentrated even if the edge degree is relatively low.

In step S120, it is determined whether or not the degree of change is large by comparing the edge degree with the same threshold value.
As a result of the comparison, if the degree of edge is larger, the pixel is determined to be an edge pixel, and in step S130, the image data of the pixel is sampled and stored in the work area. The work area may be the RAM in the computer 21 or the hard disk 22.

In the present embodiment, an object is extracted based on the degree of edge.
The object extraction method is not limited to this. For example,
It is also possible to obtain the chromaticity of each pixel and extract the pixels whose chromaticity is within a predetermined range as an object.

That is, the processing of steps S110 and S120 is replaced with the processing of steps S115 and S125, respectively.
At 15, the chromaticity of each pixel is calculated. Now, the RGB gradation data of the target pixel in the RGB color system is (R,
G, B)

[0066]

(Equation 4)

In other words, between the chromaticity coordinates x and y in the XYZ color system,

[0068]

(Equation 5)

The following relationship is established. put it here,
Since the chromaticity represents the absolute ratio of the stimulus value of the color without being affected by the brightness, it can be said that it is possible to determine what kind of object the pixel is based on the chromaticity. For example, taking skin color as an example,

[0070]

(Equation 6)

Since the chromaticity of each pixel is determined when the chromaticity of the pixel is within this range, it can be said that the pixel is not mistaken even if it is considered to be a pixel indicating human skin.

Therefore, in step S125, it is determined whether or not the xy chromaticity converted based on the RGB gradation data of each pixel is within the range of the flesh color. The image data of the pixel is sampled and stored in the work area in the same manner.

On the other hand, in step S140, it is determined whether or not the target pixel is a target pixel for uniform sampling in parallel with the above-described edge degree determination. Even if sampling is performed uniformly, it is necessary to perform sampling to such an extent that it is within a certain error range. According to the statistical error, the error with respect to the number of samples N is approximately 1 / (N ** (1/2))
Can be expressed as Here, ** indicates a power. Therefore, 1
In order to perform processing with an error of about%, N = 10000.

Here, the bit map screen shown in FIG. 6 has the number of pixels of (width) × (height), and the sampling period ratio is

[0075]

(Equation 7)

It is assumed that Here, min (widt
h, height) is the smaller of width and height, and A is a constant. Also, the sampling period ratio here indicates how many pixels are sampled, and the pixels marked with a circle in FIG. 11 show the case where the sampling period ratio = 2. In other words, one pixel is sampled every two pixels in the vertical direction and the horizontal direction, and sampling is performed every other pixel. A =
The number of sampling pixels in one line when the number is set to 200 is as shown in FIG.

As can be seen from the figure, except for the case where the sampling period ratio = 1 where sampling is not performed, when the width is 200 pixels or more, the number of samples is at least 100 pixels or more. Therefore, when the number of pixels is 200 or more in the vertical and horizontal directions, (100 pixels) × (100 pixels) = (10000 pixels)
Is ensured, and the error can be reduced to 1% or less.

Here, min (width, hei)
(ght) is based on the following reason.
For example, assuming that width >> height as in the bitmap image shown in FIG. 13A, when the sampling period ratio is determined by the longer width, as shown in FIG. 13B. In addition, it may happen that only two lines, the upper and lower lines, are extracted in the vertical direction. However, min (wi
(dth, height), if the sampling period ratio is determined based on the smaller one, it is possible to perform the thinning including the intermediate part even in the smaller vertical direction as shown in FIG. Become. That is, it is possible to perform sampling while securing a predetermined number of extractions.

In step S140, while employing such a uniform sampling method, it is determined whether or not the target pixel is to be sampled. If so, the image data is sampled in step S150.

Sampling the image data in steps S130 and S150 means that the luminance is totalized based on the image data. As described above, in this embodiment, the computer 21 handles RGB
And does not directly have a luminance value. Although it is possible to perform color conversion to the Luv color space in order to obtain the luminance, this is not advisable due to problems such as the amount of calculation. For this reason, the following conversion formula for directly obtaining luminance from RGB used in the case of a television or the like is used.

[0081]

(Equation 8)

The luminance is tabulated as a histogram. Of course, the counting area of step S130 and step S150
Are separate areas.

In order to perform the above processing for each pixel of the image data, the pixel to be processed is moved in step S160, and the processing is repeated until it is determined in step S170 that all the pixels have been completed.

After that, according to the image processing method, the total amount Dist_ave obtained by the uniform sampling and the total result Dist_edg obtained by the edge pixel sampling are selectively used to acquire the characteristic amount, and the optimum image processing is executed based on the characteristic amount. . FIG. 14 shows a flowchart for executing image processing for enlargement of contrast and correction of brightness as an example.

In the basic method for increasing the contrast in the present embodiment, a luminance distribution is obtained based on image data, and this luminance distribution uses only a part of the original gradation width (255 gradations). If so, the distribution is expanded.

As described above, when a real image of a certain person image is considered, a person is not always a highlight portion. Therefore, if the luminance distribution of the same person is enlarged, another highlight portion will not be highlighted. It could end up white. Therefore, in step S310, a histogram of the luminance distribution is created as the aggregation result Dist_ave by the uniform sampling, and in step S320, the width to be enlarged is determined. In determining the enlargement width, consider obtaining both ends of the luminance distribution. As shown in FIG. 15, the luminance distribution of the photographic image appears substantially in a mountain shape. Of course, the position and shape are various. The width of the luminance distribution is determined depending on where the both ends are determined. However, the point where the number of distributions becomes “0” by extending the base cannot be set as the both ends. This is because the number of distributions may change around “0” in the tail part, and the number of distributions changes while approaching “0” without limit statistically.

For this reason, a portion which is located inside the distribution range by a certain distribution ratio from the side where the luminance is the largest and the side where the luminance is the smallest is set as both ends of the distribution. In the present embodiment, as shown in the figure, the distribution ratio is set to 0.5%. Of course, this ratio can be changed as appropriate. In this manner, by cutting the upper and lower ends by a certain distribution ratio, white points and black points caused by noise or the like can be ignored. That is, if such processing is not performed, even if there is even one point, a white point or a black point will be at both ends of the luminance distribution. "0"
And the uppermost end is the gradation “255”,
Such a problem is eliminated by setting a portion which is 0.5% of the number of pixels inside from the upper end portion as an end portion.

In the actual processing, 0.5% of the number of sampled pixels is calculated, and the number of distributions is accumulated while going inward from the luminance value at the upper end and the luminance value at the lower end in the reproducible luminance distribution. The luminance value having a value of 0.5% is obtained as a feature amount in this case. Hereinafter, this upper end is called ymax, and the lower end is called ymin.

The reproducible luminance range is from “0” to “25”.
When “5” is set, the luminance Y to be converted is obtained from the luminance y before the conversion and the maximum value ymax and the minimum value ymin of the luminance distribution range based on the following equation.

[0090]

(Equation 9)

However,

[0092]

(Equation 10)

If Y <0 in the above conversion formula, Y = 0.
If Y> 255, Y = 255. Here, “a” is a slope, and “b” is an offset. According to this conversion formula, as shown in FIG. 16, a luminance distribution having a certain narrow width can be expanded to a reproducible range. However, when the luminance distribution is expanded by making the most of the reproducible range, a highlight portion may be lost in white, or a high shadow portion may be lost in black. In order to prevent this, in this embodiment,
Reproducible range is limited. That is, the luminance value “5” is left as a range that does not expand to the upper and lower ends of the reproducible range. As a result, the parameters of the conversion equation are as follows.

[0094]

[Equation 11]

In this case, y <ymin and y>
No conversion is performed in the range of ymax.

However, if the enlargement ratio (corresponding to a) is applied as it is, a very large enlargement ratio may be obtained. For example, in a twilight state such as in the evening, the width of the contrast from the brightest part to the dark part is naturally narrow, but as a result of trying to greatly increase the contrast of this image, it is converted like a daytime image. I could end up. Since such conversion is not desired, a limit is set for the enlargement ratio, and a is set to 1.5 (to
2) Restrict it so that it does not exceed the above. Thereby, twilight comes to be expressed as twilight. In this case, processing is performed so that the center position of the luminance distribution does not change as much as possible.

By the way, it is irrational to execute the above conversion formula (Y = ay + b) every time the luminance is converted.
That is, the possible range of the luminance y is “0” to “25”.
Since it can be only 5 ", the converted luminance Y can be obtained in advance for all possible values of the luminance y. Therefore, it is stored as a table as shown in FIG.

Forming such a conversion table corresponds to the enlargement width determination processing in step S320, and it is possible to change the image data. However, since it is extremely effective not only to enhance the contrast by expanding the range of brightness but also to adjust the brightness together, the brightness of the image is determined in step S330.
Generate parameters for correction.

For example, in the case where the peaks of the luminance distribution are entirely on the dark side as shown by the solid line in FIG. 18, it is preferable to move the peaks to the overall bright side as shown by the broken line. Conversely, when the peaks of the luminance distribution are generally shifted toward the bright side as shown by the solid line in FIG. 19, the peaks may be moved to the dark side as a whole as indicated by the wavy line.

By the way, it can be easily understood that the characteristic amount for determining whether the object is bright or dark is preferably obtained from the aggregation result Dist_edg of the edge pixel sampling. Therefore, a histogram of the luminance distribution as a total result Dist_edg of the edge pixel sampling is created in step S310.

As a result of conducting various experiments, in this embodiment, the median ymed in the luminance distribution as Dist_edg is obtained as a feature value, and the median ymed is obtained.
When d is less than “85”, the image is determined to be a dark image, and is brightened by γ correction corresponding to the following γ value.

[0102]

(Equation 12)

Alternatively,

[0104]

(Equation 13)

It is assumed that

In this case, even if γ <0.7, γ =
0.7. Unless such a limit is set, a night image looks like daytime. If the image is made too bright, the image becomes whitish as a whole and the image tends to have a low contrast. Therefore, a process such as emphasizing with saturation is preferable.

On the other hand, when the median ymed is larger than “128”, the image is determined to be a bright image and darkened by γ correction corresponding to the following γ value.

[0108]

[Equation 14]

Alternatively,

[0110]

(Equation 15)

It is assumed that In this case, even if γ> 1.3, a limit is set to γ = 1.3 so as not to be too dark.

Note that this γ correction may be performed on the luminance distribution before conversion or on the luminance distribution after conversion. FIG. 20 shows a correspondence relationship in the case where γ correction is performed. If γ <1, the curve expands upward, and if γ> 1, the curve expands downward. Needless to say, the result of the γ correction may be reflected in the table shown in FIG. 17, and the correction is performed on the table data.

Finally, in step S340, it is determined whether contrast correction and brightness correction are necessary. In this determination, the enlargement ratio (a) and the γ value are compared with appropriate thresholds, and if the enlargement ratio is larger or the γ value exceeds a predetermined range, it is determined that the necessity exists. If it is determined that there is a need, the image data is converted.

When it is determined that image processing is necessary,
The conversion based on the equation (13) is performed.
This can also be applied to the correspondence relationship with the component values of GB, and the component values (R, G, B) after conversion with respect to the component values (R0, G0, B0) before conversion are

[0115]

(Equation 16)

Can be obtained as Here, the RGB component values (R0, G0, B0), (R, G, B) corresponding to the luminances y, Y of the gradation "0" to the gradation "255".
Are in the same range, and it can be said that the conversion table of the luminances y and Y described above may be used as it is.

Therefore, in step S350, the image data (R0, G0, B0) of all the pixels are (22) to (2).
The processing of obtaining the converted image data (R, G, B) with reference to the conversion table corresponding to the expression 4) is repeated.

By the way, in this case, a feature amount consisting of both ends and a median of the brightness distribution is obtained by properly using the result of summation of brightness, and contrast correction and lightness correction are performed using the feature amount. The specific example of the processing is not limited to this, and various feature amounts are used.

FIG. 21 is a flowchart showing a case in which image processing for enhancing saturation is executed.

First, if the pixel data has saturation as its component element, it is possible to obtain the distribution using the saturation value. However, since the pixel data has only RGB component values, it is inherently necessary. Can not obtain a saturation value without conversion to a color space in which the saturation value is a direct component value. For example, in a Luv space as a standard color system, the L axis represents luminance (brightness), and the U axis and the V axis represent hue. Here, in the U-axis and the V-axis, since the distance from the intersection of both axes represents the saturation, substantially (U **
2 + V ** 2) ** (1/2) is the saturation.

The color conversion between these different color spaces requires the use of an interpolation operation while referring to a color conversion table in which the correspondence relationship is stored, and the amount of operation processing becomes enormous. In view of such a situation, in the present embodiment, the alternative value X of the saturation is obtained as follows by directly using standard RGB gradation data as image data.

[0122]

[Equation 17]

Originally, the saturation becomes "0" when R = G = B, and becomes the maximum value when mixing a single color of RGB or a predetermined ratio of any two colors. Although it is possible to directly represent the saturation directly from this property, even with a simple equation (25), if the color is a single color of red and yellow which is a mixed color of green and blue, the saturation becomes the maximum value. It is "0" when the components are uniform. In addition, green and blue single colors also reach about half of the maximum value. Of course,

[0124]

(Equation 18)

The formula can be substituted.

In step S410, the distribution of the histogram for the saturation alternative value X is obtained while employing the uniform sampling technique. In other words, in terms of saturation enhancement, the saturation of an object is not always large, and if the saturation of the object is increased when the saturation of the object is small, the original color cannot be reproduced. It could be. Therefore, since the feature amount used for the saturation enhancement can be said to be average in the image, the method of uniform sampling is adopted in step S410. In the equation (25), the saturation is from the minimum value “0” to
The distribution is within the range of the maximum value “511”, and the distribution is roughly as shown in FIG. In the next step S420,
A saturation index for this image is determined based on the aggregated saturation distribution.

In deriving the saturation index, in the present embodiment, a range occupied by the upper 16% as the number of distributions in the range of the number of sampled pixels is obtained. Then, assuming that the lowest saturation “A” within this range represents the saturation of this image, the feature amount of the saturation enhancement index S is determined based on the following equation.

That is,

[0129]

[Equation 19]

It is assumed that FIG. 23 shows the relationship between the saturation “A” and the saturation enhancement index S. As shown in the drawing, the saturation index S is large when the saturation “A” is small and is small when the saturation “A” is large in the range of the maximum value “50” to the minimum value “0”. It will change gradually.

In enhancing the saturation based on the saturation enhancement index S, if the image data has the saturation parameter as described above, the parameter may be converted. When the space is adopted, the space is temporarily converted to the Luv space which is a standard color system, and the Lu space is used.
It can be said that the displacement must be made in the radial direction in the v space. However, once the RGB image data is
After converting to v-space image data and emphasizing saturation,
Work such as returning to B is required, and the amount of calculation must be increased. Therefore, the saturation is enhanced using the RGB gradation data as it is.

When each component is a component value of a hue component having a substantially equal relationship as in the RGB color space, R = G
If = B, it is gray and achromatic. Therefore, R
Assuming that the minimum component of each component of GB simply reduces the saturation without affecting the hue of each pixel, the minimum value of each component is subtracted from all component values. It can be said that the saturation can be enhanced by enlarging the difference value.

First, a saturation enhancement parameter Sratio advantageous for calculation is calculated from the saturation enhancement index S described above.

[0134]

(Equation 20)

Is obtained. In this case, the saturation enhancement index S
When = 0, the saturation enhancement parameter Sratio = 1, and saturation enhancement is not performed. Next, assuming that the component value of blue (B) in each component (R, G, B) of the RGB gradation data is the minimum value, the conversion is performed as follows using the saturation enhancement parameter Sratio.

[0136]

(Equation 21)

As a result, it is not necessary to perform two round trips of color conversion between the RGB color space and the Luv space, so that the calculation time can be reduced. In this embodiment, a method of simply subtracting the minimum value component from the other component values for the achromatic component is employed, but another conversion formula is employed for subtracting the achromatic component. It does not matter. However, in the case where only the minimum value is subtracted as in the equations (33) to (35), there is an effect that the amount of calculation becomes easy because no multiplication / division is involved.

Even when the equations (29) to (31) are adopted, good conversion is possible, but in this case, when the saturation is enhanced, the luminance tends to be improved and the whole image tends to be brighter. Therefore, the conversion is performed on the difference value obtained by subtracting the luminance equivalent value from each component value.

First, in order to obtain the luminance, the above-described Lu is used.
Since the amount of calculation is large if the color conversion is performed in the v space, the RGB used in the case of television etc.
Equation (12) for directly obtaining the luminance from the following equation is used.

On the other hand, saturation enhancement

[0141]

(Equation 22)

It is assumed that: The addition and subtraction values △ R, △ G, and △ B are obtained from the difference value with the luminance Y as follows. That is,

[0143]

(Equation 23)

As a result,

[0145]

(Equation 24)

The conversion is possible. The preservation of the brightness is apparent from the following equation.

[0147]

(Equation 25)

When the input is gray (R = G = B), the luminance Y = R = G = B.
G = △ B = 0, and there is no achromatic color.
By using the equations (42) to (44), the luminance is saved,
Emphasizing saturation does not make the whole picture brighter.

After the saturation enhancement index Sratio is obtained as described above, it is compared with a predetermined threshold value in step S430 to determine whether the image needs saturation enhancement. Then, if necessary, in step S440, equations (42) to
Image data is converted for all pixels based on equation (44).

Accordingly, while the equal sampling method is selected and the image data is totaled (step S410), the former part of the program and the former part of the program up to the acquisition of the characteristic amount of the saturation enhancement index S (step 420) are obtained. The hardware configuration to be executed constitutes the evaluation means, and the latter part of the program for converting the image data (step S440) and the latter part of the program constitute the image processing means.

In each of the above-described contrast correction, lightness correction, and saturation enhancement, it is determined whether to perform image processing. However, it is not always necessary to determine whether to perform image processing. That is, the degree of emphasis is set for each, and image processing may be performed with the degree of emphasis set in this way.

Next, the operation of the present embodiment having the above configuration will be described.

Assume that a photographic image is read by the scanner 11 and printed by the printer 31. Then, first,
Operating system 21a on computer 21
Is running, the image processing application 21
d is started, and the scanner 11 starts reading a photograph. When the read image data is captured by the image processing application 21d via the operating system 21a, the processing target pixel is set to the initial position. Subsequently, in step S110, equations (1) to
The edge degree is determined based on equation (3), and step S12 is performed.
At 0, the threshold is compared with the same edge degree. If the edge degree is higher, it is determined that the pixel to be processed is an edge pixel, and the image data of the pixel is stored in the work area in step S130. Step S1
In 40, it is determined whether or not the pixel to be processed is a target of uniform sampling.
If 0, the image data of the pixel is stored in another work area. The above process is repeated while moving the pixel to be processed in step S160 until it is determined in step S170 that all pixels have been executed.

In this embodiment, the image data itself is stored in the work area. However, it is not always necessary to store the image data itself in the work area from the viewpoint of memory capacity and processing time. That is, since a histogram of the luminance distribution and the saturation alternative value distribution is finally created for the sampling target pixel, the information of the histogram may be accumulated in advance in steps S120 and S150.

After the summation processing is completed for all the pixels, step S31 is executed to execute contrast correction and lightness correction.
Total result Dist_av by equal sampling at 0
e and the aggregation result Dist by edge pixel sampling
A histogram of the luminance distribution as _edg is obtained, and in step S320, based on the former histogram (16)
Using the equation (17), parameters for the contrast enlargement processing are determined. Then, in step S330, parameters for lightness correction are determined using equations (18) to (21) based on the latter histogram. Thereafter, in step S340, these parameters are compared with a predetermined threshold value. If it is determined that image processing is to be performed, luminance conversion is performed based on the parameters in step S350. In this case, a luminance conversion table shown in FIG. 17 is first created in order to reduce the amount of calculation, and (22) to (2)
4) Convert the image data based on the equation.

Thereafter, the image data subjected to the image processing is displayed on the display 32 via the display driver 21c. If the image data is good, the image data is printed by the printer 31 via the printer driver 21b. That is, the printer driver 21b inputs the corrected RGB gradation data,
After a predetermined resolution conversion, the rasterization corresponding to the print head area of the printer 31 is performed, and the rasterized data is color-converted from RGB to CMYK.
MYK gradation data is converted into binary data and output to the printer 31.

According to the above processing, the image data of the photograph read through the scanner 11 is automatically subjected to optimal contrast correction and brightness correction, displayed on the display 32, and printed by the printer 31. . That is,
A plurality of feature amounts are acquired by employing a plurality of evaluation criteria, and optimal image processing can be realized by selectively using the feature amounts according to an image processing method such as contrast correction or brightness correction.

On the other hand, not only in such contrast correction and lightness correction, but also in the case of saturation enhancement, the saturation is sampled with an appropriate evaluation criterion in accordance with the saturation enhancement processing to acquire a feature amount. Since the image processing is performed based on the feature amount, optimal image processing can be realized.

As described above, the computer 21, which is the center of the image processing, obtains the luminance distribution histogram based on the totaled results sampled with different evaluation criteria in step S310, and separates the luminance distribution histogram in steps S320 and S330. Since individual feature values are obtained from the histogram and the image data is converted based on each feature value in step S350, optimal image processing can be realized.

[Brief description of the drawings]

FIG. 1 is a block diagram of an image processing system to which an image processing device according to an embodiment of the present invention is applied.

FIG. 2 is a block diagram of specific hardware of the image processing apparatus.

FIG. 3 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.

FIG. 4 is a schematic block diagram illustrating another application example of the image processing apparatus of the present invention.

FIG. 5 is a flowchart showing a sampling processing part in the image processing apparatus of the present invention.

FIG. 6 is a diagram illustrating the size of image data and a state in which a processing target pixel is moved.

FIG. 7 is an explanatory diagram in a case where the degree of change of an image is represented by each component value of rectangular coordinates.

FIG. 8 is an explanatory diagram in a case where the degree of change of an image is obtained by a difference value between adjacent pixels in a vertical axis direction and a horizontal axis direction.

FIG. 9 is an explanatory diagram in a case where the degree of change of an image is obtained between all adjacent pixels.

FIG. 10 is a diagram showing a region where a threshold value is changed.

FIG. 11 is a diagram showing a sampling cycle.

FIG. 12 is a diagram showing the number of sampling pixels.

FIG. 13 is a diagram illustrating a relationship between a conversion source image and pixels to be sampled.

FIG. 14 is a flowchart showing a feature value acquisition processing part and an image processing part.

FIG. 15 is a diagram showing edge processing of a luminance distribution and an edge obtained by the edge processing.

FIG. 16 is a diagram showing an enlarged luminance distribution and a reproducible luminance range.

FIG. 17 is a diagram showing a conversion table when expanding a luminance distribution.

FIG. 18 is a diagram illustrating a concept of increasing brightness by γ correction.

FIG. 19 is a diagram showing a concept of darkening by γ correction.

FIG. 20 is a diagram illustrating a correspondence relationship of luminance changed by γ correction.

FIG. 21 is a flowchart in the case where saturation is emphasized.

FIG. 22 is a schematic diagram of a tallying state of a saturation distribution.

FIG. 23 is a diagram illustrating a relationship between a saturation A and a saturation enhancement index S.

[Explanation of symbols]

 Reference Signs List 10 image input device 20 image processing device 21 computer 21a operating system 21b printer driver 21c display driver 21d image processing application 22 hard disk 23 keyboard 24 CD-ROM drive 25 floppy disk drive 26 modem 30 ... Image output device

Claims (8)

[Claims] An image data of a photographed image composed of pixels in a dot matrix form is input, a feature amount is obtained based on a predetermined evaluation criterion while totalizing the image data of each pixel, and image processing is performed based on the feature amount. An image processing apparatus, comprising: an evaluation unit that obtains the feature amount according to a plurality of evaluation criteria; and a feature amount that is capable of converting the image data by a plurality of methods and that is obtained by the evaluation unit according to each method. An image processing device comprising: an image processing unit that selectively uses the image processing. 2. The image processing apparatus according to claim 1, wherein the evaluation unit has an evaluation criterion for extracting an object in the real image and summing up image data of pixels of the object to obtain a feature amount. An image processing apparatus, wherein the image processing means uses a feature amount obtained from a pixel of the object when using a feature amount of a central portion of the image data in one image processing method. . 3. The image processing apparatus according to claim 2, wherein the evaluation unit extracts a pixel having a large change degree as an object based on a change degree of image data between each pixel and an adjacent pixel. Characteristic image processing device. 4. The image processing apparatus according to claim 2, wherein said evaluation means extracts pixels whose chromaticity of each pixel is within a predetermined range as an object. 5. An image processing apparatus according to claim 1, wherein said evaluation means samples each pixel of said image data uniformly and sums up to obtain a feature amount. And the image processing means uses the feature amount obtained by uniformly sampling when using the average feature amount of the actual photographed image in one image processing method. Processing equipment. 6. An image processing apparatus according to claim 1, wherein said evaluation means samples each pixel of said image data uniformly and sums up to obtain a feature amount. And wherein the image processing means uses the feature amount obtained by uniformly sampling when using the end of the feature amount distribution of the real image in one image processing method. Image processing device. 7. An image data of a photographed image composed of pixels in a dot matrix form is input, a feature amount is obtained based on a predetermined evaluation criterion while summing up image data of each pixel, and image processing is performed based on the feature amount. An image processing method, comprising: obtaining the feature amount according to a plurality of evaluation criteria; and converting the image data by a plurality of methods, selectively using the feature amount obtained by the evaluation means according to each method. Characteristic image processing method. 8. An image data of a real image composed of pixels in a dot matrix form is input, a feature amount is obtained based on a predetermined evaluation criterion while summing up image data of each pixel, and image processing is performed based on the feature amount. A medium in which an image processing control program is recorded, wherein the feature amount is obtained according to a plurality of evaluation criteria, and the image data is converted by a plurality of methods. A medium in which an image processing control program characterized by using different amounts is recorded.