JPH10340332A - Image processor, image processing method and medium recording image processing control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically select and execute an optimum image processing by deciding image processing contents based on the image data of a picture element judged as an object and performing the image processing based on the decided contents. SOLUTION: An image input device 10 outputs real projection image data for indicating a photograph or the like as the picture elements in a dot matrix shape to the image processor 20 and the image processor 20 decides the contents and degree of the image processing through a prescribed processing and then, executes the image processing. The image processor 20 outputs image-processed image data to an image output device 30 and the image output device 30 outputs the image-processed images by the picture elements in the dot matrix shape. In this case, for the image data outputted by the image processor 20, the picture element of the large degree of the change of the images in the respective picture element is judged as the object, the contents and degree of the image processing are judged based on the conditions of the image data of such an object and the image processing is performed.

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 photographed image data such as digital photographic images. .

[0002]

2. Description of the Related Art Various types of image processing are performed on digital image data. For example, it is an image processing for expanding the contrast, correcting the color tone, or correcting the 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.

[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 real image data 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. When the real image data is automatically corrected, the background is completely dark, and the image is to be corrected to be bright, 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.

As described above, in the conventional image processing, it is impossible to determine an important part (this is called an object) in the actual photographed image data, so that there is a problem that a human must be involved. there were.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is possible to detect an important portion in photographed image data such as a digital photographic image and automatically select and execute an optimum image processing. It is an object of the present invention to provide a medium storing an image processing apparatus, an image processing method, and an image processing control program.

[0008]

According to a first aspect of the present invention, there is provided an image processing apparatus for inputting real image data consisting of pixels in a dot matrix and performing predetermined image processing. Object determination means for determining a pixel having a large degree of change as an object based on the degree of change of an image at each pixel; and determining image processing content based on image data of the pixel determined to be an object, and determining the same. The configuration includes a processing unit that performs image processing based on the content.

Assuming a photographic image of a general human figure,
It is common to take a picture focusing on that person. Therefore, a sharp image is obtained by focusing on the person portion. When the image is sharp, its outline is clear, and the degree of change of the image is large. For this reason,
Even if it is assumed that a pixel having a large degree of change in the image is an original object that has been focused, it is highly likely that it is not an error.

In the first aspect of the present invention, the actual image data is composed of pixels in a dot matrix, and image processing is performed on a pixel basis. A pixel having a large degree of change is determined as an object based on the degree of change of the image in the pixel. Then, the processing means determines the image processing content based on the image data of the pixel determined as the object as described above, and performs the image processing based on the determined content.

Various methods can be adopted as appropriate for the object judging means to judge the degree of change of the image. As an example, the invention according to claim 2 is the image processing apparatus according to claim 1, wherein the object determination unit determines a degree of change of an image based on a difference in image data between adjacent pixels. There is.

In the invention according to claim 2 configured as described above, when judging the degree of change of the image,
The object determining means makes a determination based on a difference in image data between adjacent pixels. When the pixels are arranged at regular intervals like pixels in a dot matrix, the difference in data between adjacent pixels is proportional to the primary differential value, and thus such 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 a third aspect of the present invention, in the image processing apparatus according to any one of the first and second aspects, the object determining means determines whether or not the degree of change of the image is large. Is changed depending on the part of the image.

Assuming the composition of a photograph, a human figure is often set in the center. In this case, it can be said that it is preferable that the pixel to be determined as an object in order to determine the content of the image processing be selected from the central portion. By the way, it can be said that whether or not the degree of change of the image is large is a difference from a value to be compared, and there is no reason why such a value of the comparison must be always constant.

Therefore, in the invention according to claim 3 configured as described above, in determining whether or not the degree of change of the image is large, the object determining means changes the criterion according to the part of the image, and Is compared with the degree of change of the image in each pixel.

Various policies can be adopted for changing the standard. As an example, a certain tendency may be determined, or as another example, a policy of changing the tendency itself may be read from an image. As an example of the former, the invention according to claim 4 is the image processing apparatus according to claim 3, wherein the object determination means is configured such that the reference is lower at the center of the image than at the periphery.

In the invention according to claim 4 having the above-described configuration, by setting the reference at the center of the image to be lower than that at the peripheral portion, the image can be changed even if the image has the same degree of change. Is more easily determined as an object at the center. Therefore, if there is a human image at the center, more pixels of this human image are determined as pixels of the object.

According to a fifth aspect of the present invention, in the image processing apparatus according to the third aspect of the present invention, the object determining means determines the criterion for each part of an image. The configuration is determined based on the distribution of the degree of change in the image.

In the invention according to claim 5 configured as described above, the object determining means obtains the distribution of the degree of change of the image for each part of the image, and determines the reference after obtaining this distribution. Then, after that, it is determined whether the pixel is a pixel of the object by comparing with the same reference.

When the reference is determined on the basis of the distribution, a portion where many pixels having a large degree of change are gathered may be determined to have a high possibility of an object, and the reference may be lowered. In this case, a reference setting pattern corresponding to the distribution pattern is prepared, and the reference setting pattern is selected based on the detected distribution pattern.

On the other hand, the processing means only needs to determine image processing contents based on the image data of the pixel determined to be an object and perform image processing based on the determined contents. There is no particular limitation. For example, image processing for obtaining a luminance distribution of a pixel determined as an object and correcting the luminance so that the luminance distribution is expanded at a predetermined rate if the luminance distribution range is narrow can be executed. If the luminance distribution of the object is dark as a whole, a correction for increasing the brightness may be executed. Further, the color distribution of the pixel determined as the object is obtained, and it is determined whether the gray balance is shifted.
If it is deviated, the gray balance can be corrected using a tone curve or the like.

The method of determining a pixel having a large degree of change in an image as a pixel of an object is not necessarily limited to a substantial device, and as an example, the invention according to claim 6 is based on a method of determining pixels from a dot matrix. An image processing method for inputting real image data and performing predetermined image processing, wherein a pixel having a large degree of change is determined to be an object based on a degree of change of an image at each pixel, and is determined to be an object. The image processing content is determined based on the image data of the pixels, and the image processing is performed based on the determined content.

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, the image processing apparatus that determines an object and performs image processing as described above may exist alone or may be used while being incorporated in a certain device. Includes various aspects. 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 thereof, the invention according to claim 7 is a medium in which an image processing control program for performing a predetermined image processing by inputting real image data composed of dot matrix pixels by a computer is provided. Based on the degree of change of the image at each pixel, determine the pixel having the same degree of change as an object, determine the image processing content based on the image data of the pixel determined as the object, and based on the determined content. It is configured to perform image processing.

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 the 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 realized by software and a part is realized 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.

[0029]

As described above, according to the present invention, it is possible to automate the judgment of an object, which could not be judged without human involvement, by judging it as a pixel having a large degree of change in the image. Accordingly, it is possible to provide an image processing apparatus capable of appropriately changing image processing contents and executing optimum image processing.

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

Further, according to the third aspect of the present invention,
Since the evaluation of the degree of change of the image is changed depending on the part of the image, a more flexible judgment can be made in consideration of the composition and the like.

Further, according to the invention of claim 4,
This makes it possible to make a determination with emphasis on the central part of the composition of the photograph, and to efficiently process a large amount of image data.

Further, according to the invention of claim 5,
The object can be determined in consideration of the distribution of the degree of change of the image for each image, and flexible measures can be taken.

Further, according to the invention of claim 6,
It is possible to provide an image processing method capable of executing the optimum image processing by automatically changing the content of the image processing by automating the determination of the object. According to the invention according to claim 7, the same processing is performed by a computer. A medium recording an image processing control program to be executed can be provided.

[0035]

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 actual image data representing a photograph or the like as pixels in a dot matrix form to an image processing device 20, and the image input device 10
Performs the image processing after determining the content and degree of the image processing through a predetermined process. The 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 as pixels in a dot matrix. Here, the image data output by the image processing apparatus 20 determines the degree of change of the image at each pixel, determines that the pixel having the same degree of change is an object, and matches the state of the image data of such an object. The image processing is performed by determining the content and degree of the image processing. Therefore, the image processing device 20 determines the degree of change of the image at each pixel in this way, and determines that the pixel having the large degree of change is an object, and determines the state of the image data of such an object. Processing means for determining the content and degree of image processing and performing image processing.

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, since an object is found as image processing and appropriate image processing is performed, actual image data such as a photograph is suitable as image data. 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 provided.
Is incorporated. The image processing application 21d is controlled by the operating system 21a to execute processing, and executes predetermined image processing in cooperation with the printer driver 21b and the display driver 21c as necessary. 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 the printer driver 21
The data is converted into binary data of CMY (or CMYK) via b 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. Any system can be used as long as it performs various types of image processing. For example, as shown in FIG.
An image processing apparatus that determines an object and performs image processing on the object may be incorporated in 2a, and the converted image data may be used to display on the display 32a or print on the printer 31a. As shown in FIG. 4, in a printer 31b that inputs and prints image data without passing through a computer system, the printer 31b automatically receives image data input through a scanner 11b, a digital still camera 12b, a modem 26b, or the like. It is also possible to adopt a configuration in which an object is determined and image processing is performed.

The above-described object determination and the associated image processing are performed in the computer 21 by an image processing program corresponding to the flowchart shown in FIG. In the flowchart shown in the figure, it is determined whether or not the object is an object.

Based on the empirical fact that the image of the object is sharper than other parts, the present invention determines that the pixels of the image that are sharper are the pixels of the object. When the image data is composed of pixels in a dot matrix, each pixel is represented by the above-described gradation data representing the RGB luminance, and the same data between adjacent pixels is used at the edge portion of the image. Becomes larger. 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. 6, the vector of the degree of change of the image can be calculated by obtaining the X-axis direction component and the Y-axis direction component, respectively.
In a digital image composed of pixels in a dot matrix, pixels are adjacent to each other in the vertical and horizontal directions as shown in FIG. 7, and the brightness thereof is represented by f (x, y). In this case, f (x, y) is R (x, y), G (x, y), B (x, y) which is each luminance of RGB, or the whole luminance Y (x, y). May be
Note that R (x, y) and 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. 7, the difference value fx in the X direction and the difference value fy in the Y direction are: fx = f (x + 1, y) -f (x, y) (1) fy = f (x, y + 1) -f (x, y) (2) Therefore, the magnitude | g (x, y) | of a vector having these as components is | g (x, y) | = (fx ** 2 + fy ** 2) ** (1/2) (3) It is represented as Of course, the degree of edge is | g (x,
y) | Note that the pixels are originally arranged vertically and horizontally in a grid as shown in FIG. 8, and there are eight adjacent pixels when focusing on the central pixel. Therefore, similarly, the difference between the image data of each of the adjacent pixels is represented by a vector,
The sum of the vectors may be determined as the degree of change in the image.

Since the edge degree is obtained for each pixel as described above, basically, a pixel having a larger edge degree than a certain threshold value may be determined as a pixel of an object. However, considering empirical facts, objects are often located in the center of the composition. This fact confirms that a favorable effect can be obtained by adopting a structure in which a large number of pixels are extracted from the central portion so as to be used for determining image processing to be executed.

For this reason, as shown in FIG. 9, the threshold values Th1, Th2, Th3 to be compared for each part in the image are made different. Needless to say, in this example, there is a relationship of Th1 <Th2 <Th3 (4), and the threshold value is lower in the portion closer to the center,
Even if the edge degree is relatively low, it is determined that the object is an object.

As shown in the figure, in order to change the threshold value, the region is equally divided into three in the horizontal and vertical directions from the center of the image. Then, in step S120, a threshold value for comparison is determined based on the area in which the pixel whose edge degree has been determined is located. In step S130, the edge degree is compared with the threshold value to determine the degree of change. Is determined to be large. As a result of the comparison, if the edge degree is larger, it is determined that the pixel is a pixel of the object, and the image data of the pixel is stored in the work area in step S140. Work area is computer 21
Or a hard disk 22.

In order to perform the above processing for each pixel of the image data, the pixel to be processed is moved in step S150, and the processing is repeated until it is determined in step S160 that all the pixels have been completed.

In the above-described embodiment, when the threshold value is changed, the area is always divided with reference to the central part of the image. However, the manner of dividing the area is changed based on the distribution of the edge degree. You may do it. FIG.
FIG. 11 shows a flowchart for appropriately changing the division of the area in this way, and FIG. 11 shows the area divided by this.

In this case, the following processing is executed for each pixel while moving the pixel to be processed. After the edge degree is determined in step S210, the total is calculated in the horizontal axis direction in step S220.
At 30, the data is totaled in the vertical axis direction. The target pixel is moved in step S240, and the process loops until it is determined in step S250 that all pixels have been completed.

After the summation in the horizontal axis direction and the vertical axis direction is completed, the maximum distribution position on the horizontal axis is determined in step 260, and the maximum distribution position on the vertical axis is determined in step S270. As shown in FIG. 11, a portion having a high edge degree on the horizontal axis and the vertical axis is considered as the center of the image, and the region is divided as follows.

In the horizontal direction and the vertical direction, the distance from the center to the end is bisected, the threshold value Th1 is set for the inside area, and the remaining distance is divided into two equal parts, and the threshold is set for the inside area. In addition to the value Th2, the threshold value Th3 is set for the outer region. Step S280
In this manner, the comparison criterion is determined by dividing the region in this way. In step S290, sampling based on the edge degree is performed by the same processing as in steps S110 to S160 described above based on the correspondence between this region and the threshold. Is determined.

In this example, the center is obtained in each of the horizontal axis direction and the vertical axis direction, and then the area is divided into two equal parts. However, the area dividing method is changed based on the distribution of the edge degree. It is only necessary to be able to perform the division, and a specific division method can be appropriately changed.

For example, in the above-described example, the totaling in the horizontal axis direction and the vertical axis direction is performed in pixel units. However, as shown in FIG. 12, the image is divided into relatively large cells, and the totaling is performed in units of this cell. Then, the maximum distribution position may be determined, and the area division may be performed.

When the pixels of the object can be extracted as described above, the optimum image processing is determined and executed based on the image data of these pixels. FIG.
As an example, a flowchart for executing image processing for contrast enhancement and brightness correction is shown.

The basic method for increasing the contrast in the present embodiment is to obtain a luminance distribution based on the image data of the object, and to determine the luminance distribution based on the original gradation width (2).
If only a part of (55 gradations) is used, the distribution is expanded.

Accordingly, in step S310, a histogram of the luminance distribution is created, and in step S320, the width to be enlarged is determined. In determining the enlargement width, consider obtaining both ends of the luminance distribution. Fig. 1 shows the brightness distribution of a photographic image.
As shown in FIG. Of course, its position,
The shapes vary. 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, the portions extending inward by a certain distribution ratio from the side with the highest luminance and the side with the lowest luminance in the distribution range are defined 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 pixels extracted as an object is calculated, and the number of distributions is accumulated in order from the upper and lower luminance values in the reproducible luminance distribution in order. And 0.5%
Is obtained. Hereafter, this upper end is ym
ax and the lower end side is called ymin.

The range of reproducible luminance 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.

Y = ay + b (5) where a = 255 / (ymax−ymin) (6) b = −a · ymin or 255−a · ymax (7) In the above conversion formula, Y <0 Then, Y = 0 and Y> 2
If 55, Y = 255. Here, “a” is a slope, and “b” is an offset. According to this conversion formula, as shown in FIG. 15, a luminance distribution having a certain narrow width can be expanded to a reproducible range. However,
When the luminance distribution is expanded by maximizing 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 the present embodiment, the 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.

A = 245 / (ymax−ymin) (8) b = 5−a · ymin or 250−a · ymax (9) In this case, in the range of y <ymin and y> ymax Causes no conversion.

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 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 as a whole are closer to the dark side as shown by the solid line in FIG. 17, it is better to move the peaks to the brighter side as a whole as shown by the wavy line. Conversely, when the peaks of the luminance distribution are generally shifted to the bright side as shown by the solid line in FIG. 18, it is preferable to move the peaks to the dark side as a whole as indicated by the wavy line.

As a result of conducting various experiments, in this embodiment, the median ymed in the luminance distribution is obtained, and when the median ymed is less than “85”, the image is determined to be a dark image and corresponds to the following γ value. Brighten with gamma correction.

Γ = ymed / 85 (10) or γ = (ymed / 85) ** (1/2) (11)

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.

Γ = ymed / 128 (12) or γ = (ymed / 128) ** (1/2) (13) In this case, even if γ> 1.3, γ = 1.3.
A limit is set 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. 19 shows a correspondence relationship when the γ correction is performed. If γ <1, the curve expands upward, and if γ> 1, the curve expands downward. Of course, the result of the γ correction may be reflected in the table shown in FIG. 16, 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. That is, the necessity and the degree of the image processing are determined in steps S310 to S340, and the image processing determined to be necessary in step S350 is executed. This constitutes processing means.

If it is determined that image processing is necessary,
Conversion based on equation (5) is performed, and the conversion equation of the same equation is RG
This can also be applied to the correspondence relationship with the component value of B, and the component value (R, G, B) after conversion is calculated as R = a · R0 + b with respect to the component value (R0, G0, B0) before conversion. (14) G = a · G0 + b (15) B = a · B0 + b (16) Here, corresponding to the luminance y, Y of the gradation "0" to the gradation "255", the RGB component values (R0, G0, B0) and (R, G, B) are also in the same range. 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 pixels are (14) to (1).
The process of obtaining the converted image data (R, G, B) by referring to the conversion table corresponding to the expression (6) is repeated.

By the way, in the case of this processing means, the judgment is made only for the contrast correction and the lightness correction, but the specific example of the image processing is not limited to this.

FIG. 20 is a flowchart showing a case where image processing for enhancing saturation is executed.

First, if the pixel data of a pixel determined to be an object has saturation as its component element, the distribution can be obtained using the saturation value.
Since it has only GB component values, saturation values cannot be obtained unless conversion to a color space in which saturation values are directly component values is performed. 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.

In color conversion between such different color spaces, it is necessary to use an interpolation operation together with referring to a color conversion table in which correspondences are 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.

X = | G + B | −2 × R (17) The saturation is originally “0” when R = G = B,
The maximum value is obtained at the time of mixing at a predetermined ratio of a single color of RGB or any two colors. Although it is possible to directly represent the saturation directly from this property, the saturation of the maximum value can be obtained by a simple equation (17) if the color is a single color of red and yellow which is a mixed color of green and blue. 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, X '= | R + B | -2 × G (18) X ″ = | G + R | -2 × B (19)

In step S410, the distribution of the histogram for such an alternative value X of the saturation is obtained. (1
In the equation (7), the saturation ranges from the minimum value “0” to the maximum value “51”.
1 ", 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 the present embodiment, within the range of the number of pixels determined to be an object, “16
% ". Then, assuming that the lowest saturation “A” within this range represents the saturation of this image, the saturation enhancement index S is determined based on the following equation.

That is, if A <92, S = −A × (10/92) +50 (20) If 92 ≦ A <184, S = −A × (10/46) +60 (21) 184 ≦ A <230 If S = −A × (10/23) +100 (22) If 230 ≦ A, then S = 0 (23). FIG. 22 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 a 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 to the calculation is obtained from the saturation enhancement index S as Sratio = (S + 100) / 100 (24). In this case, when the saturation enhancement index S = 0, the saturation enhancement parameter Sratio = 1 and the saturation enhancement is not performed. Next, each component (R, G, B) of the RGB gradation data
If the component value of blue (B) at is the minimum value,
Using the saturation enhancement parameter Sratio, conversion is performed as follows.

R ′ = B + (RB) × Sratio (25) G ′ = B + (GB) × Sratio (26) B ′ = B (27) As a result, the RGB color space and Luv Since it is not necessary to perform two rounds of color conversion to make one round trip to and from the space, 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 (25) to (27), there is an effect that the amount of calculation becomes easy because no multiplication / division is involved.

Even when the equations (25) to (27) 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 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.
The following conversion formula for directly obtaining the luminance from is used.

The luminance Y is given by: Y = 0.30R + 0.59G + 0.11B (28) On the other hand, the saturation enhancement is given by: R ′ = R + ΔR (29) G ′ = G + ΔG (30) B ′ = B + △ B (31) The addition and subtraction values △ R, △ G, and 求 め る B are obtained by the following equation based on the difference value with the luminance. That is, ΔR = (R−Y) × Sratio (32) ΔG = (G−Y) × Sratio (33) ΔB = (BY) × Sratio (34) '= R + (R−Y) × Sratio (35) G ′ = G + (G−Y) × Sratio (36) B ′ = B + (B−Y) × Sratio (37) The preservation of the brightness is apparent from the following equation.

Y ′ = Y + △ Y (38) ΔY = 0.30 △ R + 0.59 △ G + 0.11 △ B = Sratio ｛(0.30R + 0.59G + 0.11B) −Y｝ = 0 (39) When the input is gray (R = G = B), the luminance Y
= R = G = B, the adjustment value △ R = △ G = △ B = 0
And there is no achromatic color. Equation (35)
By using the expression (37), the luminance is preserved, and even if the saturation is emphasized, the entire image does not become bright.

After the saturation enhancement index Sratio has been obtained as described above, it is compared with a predetermined threshold value in step S430 to determine whether the image requires saturation enhancement. Then, if necessary, in step S440, equations (35) to
The image data is converted for all pixels based on equation (37).

Therefore, in steps S410 to S430, the necessity and the degree of the saturation emphasis processing are determined, and if it is determined in step S430 that the necessity is high, the saturation emphasis processing is executed. The processing means is constituted by the hardware configuration to be executed and the software.

[0092] The content and degree of image processing may be determined based on the pixels of the object, and the object may be subjected to edge enhancement processing. FIG. 23 shows a flowchart of the edge enhancement processing. Since the pixel of the object has been selected, in step S510, the edge degree of the pixel of the object is averaged by dividing the accumulated edge degree by the number of pixels. That is, the sharpness SL of the object image
Is, if the number of pixels is E (I) pix,

[0093]

(Equation 1)

The calculation can be performed as follows. In this case, it can be determined that an image having a smaller SL value has a lower degree of sharpness (visually blurred), and an image having a larger SL value has a higher degree of sharpness (clearly visible).

On the other hand, since the sharpness of the image is intuitive, the sharpness SL is obtained in the same manner for the image data of the optimum sharpness obtained experimentally, and the value is used as the ideal sharpness SLopt. In addition to the setting, in step S520, the degree of edge enhancement E
enhance, Asking. Here, the coefficient ks changes based on the size of the image. When the image data is composed of height dots and width dots in the vertical and horizontal directions, respectively, as shown in FIG. 24, ks = min (height, width) / A ... (42) Here, min (he
height, width) is the height dot and the width
The smaller one of the th dots is indicated, and A is a constant “768”. Of course, these are obtained from the experimental results, and it goes without saying that they can be appropriately changed. However, good results are basically obtained by increasing the degree of emphasis for a larger image.

After the edge enhancement degree Eenhance is obtained in this way, it is determined in step S530 whether or not edge enhancement is necessary by comparing it with a predetermined threshold. If it is determined that edge enhancement is required, in step S540. An edge enhancement process is performed on all pixels.

In the edge emphasizing process, the luminance Y 'after emphasis is calculated with respect to the luminance Y of each pixel before emphasis as follows: Y' = Y + Enhance. (Y-Yunsharp) (43) Here, Yunsharp is obtained by performing unsharp mask processing on the image data of each pixel. Here, the unsharp mask processing will be described. FIG. 25 shows an unsharp mask 41 of 5 × 5 pixels as an example. This unsharp mask 41
The value of “100” at the center is used as the weight of the pixel Y (x, y) to be processed in the matrix image data, and the peripheral pixels are weighted according to the numerical values in the cells of the same mask and integrated. Used. When using this unsharp mask 41,

[0098]

(Equation 2)

The integration is performed based on the following arithmetic expression. (44)
In the formula, “396” is the total value of the weighting coefficients, and in the case of unsharp masks of different sizes, it is the total value of each cell. Mij is a weight coefficient described in the unsharp mask cell, and Y
(X, y) is image data of each pixel. Note that ij is indicated by horizontal and vertical coordinate values with respect to the unsharp mask 41.

The meaning of the edge emphasis operation calculated based on the equation (43) is as follows. Yunsharp
Since (x, y) is obtained by adding the peripheral pixel to the target pixel with a lower weight, so-called "unsharp" image data is obtained.
What has been blunted in this way has the same meaning as that obtained by applying a so-called low-pass filter. Therefore,
“Y (x, y) −Yunsharp (x, y)” has the same meaning as a high-pass filter obtained by subtracting a low frequency component from all original components. Then, by multiplying the high-frequency component passed through the high-pass filter by the edge enhancement Eenhance and adding it to “Y (x, y)”, the high-frequency component is increased in proportion to the edge enhancement Eenhance. The result is that the edges are enhanced. In consideration of the situation where edge enhancement is required, since the image is a so-called edge portion of the image, the calculation may be performed only when there is a large difference in image data between adjacent pixels. In this way, it is not necessary to perform the unsharp mask calculation on the image data portion which is not the most edge portion, and the processing is drastically reduced.

Note that the actual calculation is as follows: if the luminance Y ′ after emphasis and the luminance Y before emphasis are replaced by delta = Y−Y ′ (45), R′G′B ′ after conversion becomes '= R + delta G' = G + delta B '= B + delta ... (46)

Accordingly, in this edge enhancement processing, the necessity and the degree of the edge enhancement processing are determined in steps S510 to S530, and if it is determined in step S530 that the image enhancement is necessary, the image processing is executed. The processing means is constituted by a hardware configuration and software for executing these.

It is determined whether to perform image processing for each of the above-described contrast correction, lightness correction, saturation enhancement, and edge enhancement. 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. Of course, even in this case, it can be said that the content and the degree of the image processing to be executed are determined and executed.

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.
If it is 0, a threshold determined according to the position of the processing target pixel with respect to the entire image is determined, and the threshold is compared with the edge degree in step S130. If the degree of edge is higher, it is determined that the processing target pixel is a pixel of the object, and the image data of the pixel is stored in the work area in step S140. The above process is repeated while moving the pixel to be processed in step S150 until it is determined in step S160 that all pixels have been executed.

When the execution has been completed for all the pixels, the work area stores image data for the pixels determined to be objects. Therefore, even if the situation of the photographic image read from the image data in the work area is determined, it can be said that the quality of the image is not erroneously recognized due to the influence of the background or the like. In the present embodiment, the image data itself is stored in the work area, but 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, a histogram of the luminance distribution and the saturation alternative value distribution is created for a pixel determined as such an object.
The information of the histogram may be accumulated at 0.

When the contrast correction and the brightness correction are automatically executed, a histogram of the luminance distribution is obtained in steps S140 and S310, and in step S320, based on the equations (8) and (9). In addition to determining parameters for the enlargement process, step S3
At 30, a parameter for brightness correction is determined based on the equations (10) to (13). Then, step S340
Then, these parameters are compared with a predetermined threshold value, and 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. 16 is first created in order to reduce the amount of calculation, and image data is converted based on equations (14) to (16).

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 receives the RGB tone data of which the edge is emphasized, performs rasterization corresponding to the print head area of the printer 31 through a predetermined resolution conversion, and performs color conversion of the rasterized data from RGB to CMYK. After that, the CMYK 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,
It is determined whether contrast correction or brightness correction is necessary based on the object portion of the photographic image, and if necessary, image processing can be performed to an optimum degree.

On the other hand, not only in such contrast correction and brightness correction, but also in the case of saturation enhancement or edge enhancement, a pixel having a large degree of edge, which is a degree of change of an image, is determined as a pixel of an object, and a pixel of the object is determined. The content and the degree of the image processing to be executed are determined based on the image data, and the necessary image processing is executed.

Since the extraction of the pixels of the object is necessary to determine the parameters, the determination is not made by determining the edge degree for all the pixels, but by determining the edge degree for the sampled pixels. Alternatively, it may be determined whether or not the object is a pixel.

As described above, the computer 21, which is the center of the image processing, obtains the edge degree which is the degree of change of the image from the difference value of the data between adjacent pixels in step S110, and in steps S120 and S130, the edge degree is calculated. Only the large image is selected and determined as the pixel of the object, and in steps S310 to S330, the optimal parameters for the contrast correction and the brightness correction are determined from the image data of the pixel of the object. A guideline for image processing is determined based on the image data, and optimal image processing can be automatically executed.

[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 illustrating a first part of a main process in the image processing apparatus of the present invention.

FIG. 6 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. 7 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 the vertical axis direction and the horizontal axis direction.

FIG. 8 is an explanatory diagram in a case where the degree of change of an image is obtained between all adjacent pixels.

FIG. 9 is a diagram showing a region where a threshold value is changed.

FIG. 10 is a flowchart for automatically dividing a region.

FIG. 11 is a diagram showing an area setting situation.

FIG. 12 is a diagram showing an area setting situation according to a modification.

FIG. 13 is a flowchart showing a latter part of the main processing.

FIG. 14 is a diagram illustrating edge processing of a luminance distribution and an edge obtained by the edge processing.

FIG. 15 is a diagram showing an enlarged luminance distribution and a reproducible luminance range.

FIG. 16 is a diagram showing a conversion table when expanding a luminance distribution.

FIG. 17 is a diagram showing a concept of increasing brightness by γ correction.

FIG. 18 is a diagram showing a concept of darkening by γ correction.

FIG. 19 is a diagram showing a correspondence relationship of luminance changed by γ correction.

FIG. 20 is a flowchart in a case where saturation is emphasized in a latter part of the main processing.

FIG. 21 is a schematic diagram of a tallying state of a saturation distribution.

FIG. 22 is a diagram illustrating a relationship between a saturation A and a saturation enhancement index S.

FIG. 23 is a flowchart in the case where edge enhancement is performed at a later stage of the main processing.

FIG. 24 is a diagram illustrating the size of image data and a state in which a processing target pixel is moved.

FIG. 25 is a diagram showing an unsharp mask of 5 × 5 pixels.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 ... Image input apparatus 20 ... Image processing apparatus 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 41 unsharp mask

Claims (7)

[Claims] An image processing apparatus for inputting real image data composed of pixels in a dot matrix and performing predetermined image processing, wherein a pixel having a large degree of change is determined based on a degree of change of an image at each pixel. An image processing apparatus comprising: an object determination unit configured to determine an object; and a processing unit configured to determine image processing content based on image data of a pixel determined to be an object, and to perform image processing based on the determined content. apparatus. 2. An image processing apparatus according to claim 1, wherein said object judging means judges a degree of change of an image based on a difference in image data between adjacent pixels. apparatus. 3. The image processing apparatus according to claim 1, wherein the object determining means changes a criterion for determining whether a degree of change of the image is large depending on a part of the image. An image processing apparatus characterized by the above-mentioned. 4. An image processing apparatus according to claim 3, wherein said object determination means sets said reference at a center of an image lower than at a periphery thereof. 5. An image processing apparatus according to claim 3, wherein said object determining means determines said reference based on a distribution of a degree of change of said image for each part of the image. apparatus. 6. An image processing method for performing predetermined image processing by inputting real image data consisting of pixels in a dot matrix form, wherein a pixel having a large degree of change is determined based on the degree of change of an image at each pixel. An image processing method comprising: determining an image processing content based on image data of a pixel determined to be an object; and performing image processing based on the determined content. 7. A medium in which an image processing control program for performing a predetermined image processing by inputting real image data composed of dot matrix pixels by a computer is recorded based on a degree of change of an image in each pixel. A pixel having the same degree of change as an object, determining image processing content based on image data of the pixel determined as the object, and performing image processing based on the determined content. Medium on which control programs are recorded.