JP2002165089A - Program, method and device for interpolating image data - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image data interpolating program for preventing an error in deciding an interpolating method based on the discrimination of property of an image to be interpolated and performing an adequate interpolating corresponding to printing quality and to provide an image data interpolating method and an image data interpolating device. SOLUTION: A first interpolating is superimposed on a second interpolating based on a prescribed evaluating function. Since the evaluating function relies on image property, an interpolating rate corresponding to image property is decided and also the rate of a more proper interpolating is increased so as to permit the advantage of each interpolating to be conspicuous. Besides, a defect is not made to be prominent in the both processings. As a result, the error in deciding the interpolating method based on the discrimination of property of the image to be interpolated is prevented. Moreover the superimposition rate of the more proper interpolating is increased in accordance with print quality to give influence on the effect of the interpolating so that the adequate interpolating is performed in accordance with print quality.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an image data interpolation program, an image data interpolation method, and an image data interpolation device.

[0002]

2. Description of the Related Art When handling images on a computer or the like,
An image is represented by pixels in a dot matrix, and each pixel is represented by a gradation value. For example, photographs and computer graphics are often displayed on a computer screen with 640 dots in the horizontal direction and 480 dots in the vertical direction. On the other hand, the performance of color printers has been remarkably improved, and the dot density is extremely high, such as 720 dpi (dot / inch). Then 640x
When an image of 480 dots is printed in correspondence with each dot, the size becomes extremely small. In this case, since the gradation value is different and the meaning of the resolution itself is different, it is necessary to convert between dots to print data by interpolating between dots. That is, if the image is printed small in a one-to-one correspondence, a process of increasing the pixels of the image data (this is called high resolution or enlargement) is performed.
In the opposite case, a process of reducing the number of pixels of the image data (this is called lower resolution or reduction) is performed.

Conventionally, as a method of interpolating a dot in such a case, a nearest neighbor interpolation method (hereinafter, referred to as a nearest neighbor method) or a cubic convolution interpolation method (a cubic convolution interpolation method). : Hereinafter referred to as a cubic method) and a pattern matching method. Each of these interpolation techniques has its own characteristics. For example, in the cubic method, although a large amount of calculation is performed, image interpolation can be performed without impairing the gradation of the original image. Therefore, it is generally suitable for use in image interpolation of a natural image composed of multi-tone pixels.
In the pattern matching method, the gradation of the original image tends to be lost, but the outline of the image can be sharpened. Therefore, it is generally suitable for use in image interpolation of logos, illustrations, and the like having a small number of gradations of an image.

[0004]

Conventionally, in order to print on a printing apparatus based on image data, a pattern matching method is conventionally used when an image to be interpolated is considered to be a logo or an illustration. I use the cubic method when it seems to be. However, it is generally not easy to automatically and accurately distinguish the nature of the image, that is, the image of the logo or illustration from the natural image, and if the determination is incorrect, an inappropriate interpolation process may be executed. .
Further, the distinction between a logo or an illustration and a natural image is not absolute, and there may be an object in a natural image which is like a logo and whose contour is to be sharpened.

Further, even if processing according to the nature of an image is performed, interpolation processing for reproducing subtle gradations for a natural image is performed even though low-quality printing is performed during actual printing. It is useless. SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is possible to prevent an error in determining an interpolation method based on a property determination of an image to be interpolated and to perform an image data interpolation capable of performing an accurate interpolation process according to print quality. It is an object to provide a program, an image data interpolation method, and an image data interpolation device.

[0006]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides an image data interpolation program for performing pixel interpolation on image data in which a computer expresses an image in multiple gradations using dot matrix pixels. An image data acquisition function for acquiring the image data; a first interpolation processing function for performing interpolation without reducing the degree of pixel change with respect to the image data; A second interpolation processing function for performing interpolation without impairing the tonality, and determining the properties of the image based on reference pixels around the pixel to be interpolated, and performing the first and second interpolation processing based on the same properties. A first superimposition ratio determining function for determining a superimposition ratio, and an image in which the image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function are superimposed at the determined superimposition ratio. And over data superimposed function, a configuration for executing an image data output function of outputting the same superimposed data as the data subjected to interpolation in the computer.

In the invention configured as described above, when performing pixel interpolation on image data in which a computer expresses an image in multiple gradations using dot matrix pixels, image data is acquired by an image data acquisition function. I do. For the obtained image data, it is possible to execute an interpolation process by a first interpolation processing function and a second interpolation processing function, and the first interpolation processing function performs a pixel change on the image data. Interpolate without reducing the degree
The second interpolation processing function performs the interpolation without deteriorating the gradation of the image.

The first superimposition ratio determining function determines the characteristics of the image based on reference pixels around the pixel to be interpolated, and determines the superimposition ratios of the first and second interpolation processes based on the same characteristics. The image data superimposition function superimposes the image data obtained by the first interpolation processing function and the interpolation image data obtained by the second interpolation processing function at the superimposition ratio determined by the first superimposition ratio determination function. Then, the data superimposed by the image data output function is output as data after the interpolation processing. That is, according to the present invention, it is possible to perform two types of interpolation processing, that is, a first interpolation processing function and a second interpolation processing function, and both processed image data are superimposed at a ratio reflecting the property of the image. . Therefore, it is assumed that the interpolation target image is one of a logo, a natural image, and the like, and only the interpolation processing that is not suitable for performing the interpolation of the target image is performed. There is no.

However, the above-mentioned first superimposition ratio is not limited to a ratio when the first and second interpolation processes are mixed, but can naturally take a value of "0" or "1" indicating only one of them. It is. As described above, according to the present invention, the two types of interpolation processing are superimposed based on the evaluation function depending on the data of the reference pixels, so that it is possible to prevent an error in the interpolation method determination based on the property determination of the interpolation target image. Is possible. Here, in the first interpolation processing function, it is sufficient that the interpolation processing can be executed without reducing the degree of change of the pixel, and the edge portion is maintained or emphasized without blurring the edge. Is applicable. Further, in the second interpolation processing function, it is sufficient that the interpolation processing can be executed without deteriorating the gradation of the image, and the subtle gradation value that changes slightly between pixels is not uniformed. Interpolation processing that can reproduce a change in the tone value corresponds to this. In addition, by referring to a plurality of pixels around the pixel to be interpolated in the first superimposition ratio determination function, it is possible to grasp the change tendency of the pixel in the referred pixel, and the image to be interpolated is “natural image”, The nature of the image such as "illustration" is determined. Therefore, it can be said that the data of the reference pixel reflects the property of the image to be interpolated, and the superimposition ratio of the first interpolation process and the second interpolation process that is more appropriate is increased according to the property of the image to be interpolated. be able to.

As described above, there are various interpolation processing methods. Even when the two types of interpolation processing are executed in this way, it is not necessary to limit the superimposition to only two types of interpolation processing. As an example of the configuration for this, in another aspect of the present invention, the first interpolation function and the second interpolation function can select an interpolation process to be executed from a plurality of types of interpolation processes. That is, there are a plurality of types of interpolation that do not reduce the degree of change in pixels performed by the first interpolation processing function, and interpolation that does not impair the gradation of the image performed by the second interpolation processing function. There are several types. Therefore, by selecting a suitable one to be used for the interpolation target image or selecting a usable one from these, interpolation processing more suitable for the type of the interpolation target image can be performed. As described above, according to the present invention, options for the interpolation processing are expanded, and more appropriate interpolation processing can be executed.

As a specific example of such a configuration, in another aspect of the present invention, the first interpolation processing function performs interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel. Pattern matching interpolation and interpolation by the nearest method can be executed. That is,
In pattern matching interpolation, a predetermined pattern is often detected on the basis of the degree of change in pixels. Therefore, interpolation is performed on the detected pattern so that the degree of change is maintained or emphasized. By determining the rules, it is possible to execute image interpolation without reducing the degree of change.

In the near-list method, the value of the original pixel closest to the interpolation pixel is used as the value of the interpolation pixel, so that the degree of change is maintained. Therefore, both are systems of the first interpolation processing function, and either of them can be selected according to the case. More specifically, in the above-described pattern matching interpolation, the interpolation process itself cannot be executed when a predetermined pattern does not exist in the reference pixel. As described above, according to the present invention, it is possible to easily perform interpolation without reducing the degree of pixel change.

Further, as a specific example of the second interpolation processing function, in another aspect of the present invention, the second interpolation processing function is configured to be capable of executing interpolation by the cubic method. That is, in the interpolation by the cubic method,
Generally, 16 pixels around an interpolation pixel are used as reference pixels, and interpolation pixel data is generated while reflecting the gradation value of the reference pixel with the degree of influence depending on the distance between the reference pixel and the interpolation pixel. Therefore, according to the cubic method, it is possible to reflect a subtle gradation value change of the reference pixel to the interpolation pixel, and the cubic method is a system of the second interpolation processing function. By superimposing the cubic method, it is possible to cope with image interpolation of a multi-tone natural image or the like. As described above, according to the present invention, it is possible to easily perform interpolation without deteriorating the gradation of an image.

In superimposing the two types of interpolation processing on the image to be interpolated, the first superimposition ratio determining function only needs to be able to determine the superimposition ratio of the two, and there are various methods of determining the superimposition ratio. As an example of such a configuration, in another aspect of the present invention, the first superimposition ratio determining function may be configured to determine the superimposition ratio by an evaluation function depending on the data of the reference pixel. That is, the evaluation function is a function depending on the data of the reference pixel, and the reference pixel reflects the property of the image to be interpolated as described above. Therefore, by using the evaluation function depending on the data of the reference pixel, the superimposition ratio of the first and second interpolation processes can be easily determined. As described above, according to the present invention, the superimposition ratio can be easily calculated.

As another example of a specific method for determining the superimposition ratio, in another aspect of the present invention, the first superimposition ratio determination function determines the superimposition ratio based on the gradation value of the reference pixel. Is also good. That is, the gradation value of the reference pixel reflects the property of the interpolation target image, and the superimposition ratio can be determined according to the property of the interpolation target image by making the function dependent on the gradation value. As described above, according to the present invention, the superimposition ratio can be easily determined in accordance with the properties of the interpolation target image.

As another example of the configuration in this case, in another aspect of the present invention, the first superposition ratio determination function determines the superposition ratio based on the number of appearances of different gradation values in the reference pixel. It may be. That is, since an image such as a logo or an illustration usually has a small number of colors, it is considered that several pixels in the reference pixels have the same gradation value, and the number of pixels having different gradation values is small. On the other hand, since a natural image usually has a large number of colors, the number of pixels having different gradation values in the reference pixels is large.
As described above, the number of appearances of different gradation values in the reference pixel reflects the property of the image to be interpolated, and the superimposition ratio according to the property of the image can be determined. As a specific example, the number of appearances of different tone values in the reference pixel is checked, and the smaller the number of appearances is, the higher the superimposition ratio of the first interpolation process is. Thus, according to the present invention,
The superimposition ratio can be easily determined according to the natural image or the non-natural image.

Further, as an example of such a configuration, in another aspect of the present invention, the first superimposition ratio determining function is provided when the number of appearances of different gradation values in the reference pixel is smaller than a predetermined threshold value. A configuration may be adopted in which a superimposition ratio that uses only the first interpolation process is provided. That is, as described above, since the number of appearances of the gradation value of the multi-tone image tends to be large and the number of appearances of the gradation value of the low-tone image tends to be small, when the number of appearances of the reference pixel is smaller than the predetermined threshold value, The image is regarded as an image such as an illustration or a logo, and a superimposition ratio for performing only the first interpolation processing is given. As a result, it is possible to prevent the above-mentioned cubic method or the like from being superimposed on an image such as an illustration or a logo, to increase the calculation time unnecessarily, and to prevent the outline from being blurred. As described above, according to the present invention, the superimposition ratio can be easily determined according to the natural image or the non-natural image.

Further, as another example of the configuration, in another aspect of the present invention, the first superimposition ratio determining function increases the superimposition ratio of the first interpolation processing as the gradation value width of the reference pixel increases. It is good also as a structure which performs. Here, the gradation value width is a difference between the maximum value and the minimum value of the gradation value, and when the reference pixel forms a so-called edge, the gradation value width increases. Therefore, by increasing the superimposition ratio of the first interpolation processing as the gradation value width increases, image interpolation is performed so that the edge is not reduced as the reference pixel has a portion that seems to be an edge. Specifically, it can be realized by giving the superimposition ratio of the first interpolation process with the value of the evaluation function, and making the evaluation function a monotonically increasing function with respect to the gradation value width. As described above, according to the present invention, it is possible to perform image interpolation such that the edge is not reduced as the reference pixel has a portion that seems to be an edge.

Further, as another specific example of the gradation value, in another aspect of the present invention, the gradation value of the reference pixel may be a luminance value of the reference pixel. That is, when an image is represented by multi-gradation using pixels in a dot matrix, usually, one pixel has gradation value data of each color, and the luminance value of the pixel is determined from the gradation value of each color. By using the value, the characteristics of one pixel can be accurately grasped and reflected in the calculation of the superimposition ratio. As described above, according to the present invention, the characteristics of one pixel can be accurately reflected on the evaluation function.

Incidentally, such an image interpolation program is also applied to a print control program for executing a print control process while executing an interpolation process on a computer in order to input such image data and print it on a printing apparatus. It is possible. For this reason, in another aspect of the present invention, a print quality obtaining function for obtaining print quality when printing is performed by a printing apparatus using the image data, and the first print quality based on the obtained print quality In addition, the configuration may be such that a second superimposition ratio determining function of determining a superimposition ratio of the second interpolation process and a print control processing function of executing a print control process based on the superimposed data are executed.

In the invention configured as described above, the printing control process is performed while the computer executes the interpolation process so that the image data represented by the dot matrix pixels is input and printed by the printing apparatus. Is executed, the print quality obtaining function obtains the print quality at the time of printing by the printing apparatus using the image data. The second superimposition ratio determination function determines the superimposition ratios of the first and second interpolation processes based on the acquired print quality, and the image data superimposition function is determined by the second superimposition ratio determination function. The image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function are superimposed at the superimposition ratio. Then, the print control processing function executes the print control processing based on the superimposed data.
That is, according to the present invention, two-system interpolation processing of a first interpolation processing function and a second interpolation processing function is possible,
Both processed image data are superimposed and printed at a ratio based on the print quality. Therefore, a print result can be obtained in a state where effective interpolation processing has been performed for each print quality. In addition, it is assumed that the nature of the interpolation target image is one of a logo, a natural image, and the like, and only interpolation processing that is not suitable for performing the interpolation of the target image is performed. No erroneous print results are obtained.

Of course, in this case as well, the second superimposition ratio can take a value of "0" or "1" indicating only one of the ratios when the first and second interpolation processes are mixed. It is. As described above, according to the present invention, since the two types of interpolation processing are superimposed based on the evaluation function depending on the data of the reference pixels, it is possible to prevent an error in determining the interpolation method based on the property determination of the image to be interpolated. It is. The second superimposition ratio determining function determines the superimposition ratio based on the print quality. That is, if the print quality is low, even if the interpolation processing is executed so as not to impair the subtle gradation, the effect does not appear in the print result, and there is a causal relationship between the print quality and the effect of the interpolation processing. Therefore, by determining the superimposition ratio based on the print quality, it is possible to increase the appropriate superimposition ratio of the first interpolation process and the second interpolation process according to the effect that the interpolation process has on the print result. it can. As a specific example, it is conceivable to use an evaluation function that depends on print quality, and by using the evaluation function, the superimposition ratio can be easily determined. As described above, according to the present invention, the superimposition ratio can be easily calculated using the evaluation function.

In another aspect of the present invention, as an example of a configuration for increasing the ratio of the one in which the effect of the interpolation process is likely to be exhibited based on the print quality, the second superimposition ratio determining function includes The superimposition ratio of the second interpolation process is increased as the print quality becomes higher. That is, the second interpolation process interpolates pixels without deteriorating the gradation, and the subtle gradation change reproduced in the interpolation process is also reproduced in the print result. There is no point unless the quality is high. Therefore, by increasing the superimposition ratio of the second interpolation process as the print quality is higher, it is possible to superimpose the interpolation process according to the print quality.

As described above, according to the present invention, it is possible to easily perform the superposition of the interpolation processing according to the print quality. Further, various modes are conceivable for the determination of the second superimposition ratio. As an example of the configuration, in another embodiment of the present invention, the second superimposition ratio determination function is provided in such a manner that the acquired print quality is high. In such a case, only the first interpolation processing is not executed. That is, the first interpolation processing does not reduce the degree of pixel change, but tends to impair the gradation. If only the first interpolation processing is performed during high-quality printing, the gradation of the original image is reduced. May be printed with interpolation that does not reproduce at all. Therefore, by preventing the superimposition ratio from being such that only the first interpolation process is performed during high-quality printing, there is no decisive error in selecting an interpolation method in relation to the printing result. As described above, according to the present invention, there is no decisive error in selecting an interpolation method in relation to the print result.

Here, the print quality is determined by various parameters, and the print quality acquisition function can acquire the print quality by acquiring the various parameters. For example, when the quality of the printing paper is obtained, the printing quality is obtained. There are various types of printing paper, such as glossy paper and plain paper, and even if printing is performed under the same conditions other than printing paper based on the same image data, the printing result will be very different if the printing paper is different. Because it comes. Therefore, if the quality of the printing paper is acquired by the print quality acquisition function and the superimposition ratio is determined based on the quality of the printing paper by the second superimposition ratio determination function, the superimposition of the interpolation processing according to the print quality can be performed. It can be carried out.

As described above, according to the present invention, it is possible to easily perform the superposition of the interpolation processing according to the print quality. Another example of print quality is print speed. That is, a recent printing apparatus may be provided with a high-speed printing mode in which the printing speed is prioritized even if the printing quality is reduced to some extent, and a low-speed printing mode in which the printing quality is prioritized even if the printing speed is reduced. Therefore, if such a difference in print speed is acquired, a difference in print quality will be acquired.
If the superimposition ratio is determined based on the printing speed by the second superimposition ratio determination function, it is possible to superimpose the interpolation processing according to the print quality. As described above, according to the present invention, it is possible to easily superimpose the interpolation processing according to the printing speed.

Another example of print quality is print resolution. That is, as described above, the printing accuracy of the printing apparatus is extremely high, and during normal printing, the dots between the original image data are interpolated. At this time, even if the original image data has the same data amount and the printed area is the same, the printing result becomes very different due to the difference in the printing resolution (dpi). Therefore, if the print resolution is acquired, the print quality will be acquired.If the print quality acquisition function acquires the print resolution, and the second superimposition ratio determination function determines the superimposition ratio based on the print resolution, It is possible to superimpose the interpolation processing according to the print quality. As described above, according to the present invention, it is possible to easily superimpose the interpolation processing according to the print resolution.

Another example of print quality is the type of ink. That is, the ink used in the printing apparatus includes a pigment and a dye, and the pigment has a property that it is less likely to bleed when both are compared. Therefore, when printing is performed on printing paper, the so-called edge tends to be more conspicuous in the pigment, and the printing quality is different if the type of ink is different. Therefore, if the type of ink is acquired by the print quality acquisition function and the first interpolation processing ratio is smaller in the case of using a pigment in the second superimposition ratio determination function, the print quality is improved. Can be superimposed in accordance with the interpolation processing. As described above, according to the present invention, it is possible to easily superimpose the interpolation processing according to the type of ink.

It is needless to say that the present invention can be realized not only as a program itself but also as a recording medium. Such a recording medium may be a magnetic recording medium or a magneto-optical recording medium, and any recording medium to be developed in the future can be considered in the same manner. Further, the duplication stages of the primary duplicated product, the secondary duplicated product and the like are equivalent without any question. Although different from the above-described medium, if the supply method is performed using a communication line, the communication line is used as a transmission medium and the present invention is used. Further, even when a part is realized by software and a part is realized by hardware, the concept of the present invention is not completely different, and a part is stored on a recording medium and appropriately It may be in a form that can be read.

As described above, an interpolation processing program capable of executing two systems of interpolation processing, and superimposing the two based on an evaluation function depending on reference image data, performs processing in accordance with such control. It goes without saying that the invention exists at the root of the procedure, and it can be easily understood that the invention is applicable as a method. For this reason, the invention according to claims 12 to 22 has basically the same operation. In other words, there is no difference that the method is not necessarily limited to a tangible medium or the like and is effective as a method.

Further, it can be easily understood that such a program is realized in a substantial computer, and in that sense, the present invention can be applied to an apparatus including such a computer. For this reason, Claims 23-
In the invention according to claim 33, the operation is basically the same. Of course, such an apparatus may be implemented alone, or may be implemented together with another method while being incorporated in a certain device. And can be changed as appropriate.

[0032]

As described above, claim 1 and claim 1 have been described.
2. According to the twenty-third aspect of the present invention, the two types of interpolation processing are superimposed on the basis of the evaluation function depending on the data of the reference pixels. Is possible. Further, according to the second, thirteenth, and twenty-fourth aspects of the present invention, it is possible to easily perform interpolation without reducing the degree of change in pixels.

Further, claim 3, claim 14, and claim 2
According to the fifth aspect, the superimposition ratio can be easily calculated. Furthermore, according to the invention of claims 4, 15 and 26, the superimposition ratio can be easily determined according to the natural image or the non-natural image. Furthermore, according to the invention of claims 5, 16 and 27, the superimposition ratio can be easily determined according to the natural image or the non-natural image.

Furthermore, claim 6, claim 17, and claim 2
According to the eighth aspect, image interpolation can be performed such that the edge is not reduced as the reference pixel has a portion that seems to be an edge. Furthermore, claim 7, claim 18, and claim 29
According to the invention, the characteristics of one pixel can be accurately reflected in the evaluation function. Furthermore, claim 8, claim 19,
According to the thirty-first aspect of the present invention, the two types of interpolation processing are superimposed on the basis of the evaluation function depending on the data of the reference pixels. It is.

Further, claim 9, claim 20, claim 3
According to the first aspect, the superimposition ratio can be easily calculated using the evaluation function. Furthermore, according to the tenth, twenty-first, and thirty-second aspects of the invention, it is possible to easily superimpose the interpolation processing according to the print quality. Furthermore, according to the invention of claims 11, 22, and 33, the selection of the interpolation method is not erroneously determined in relation to the print result.

[0036]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a main configuration of an image data interpolation device according to the present invention. Assuming digital processing, an image is represented by pixels in a dot matrix, and image data is composed of a group of data representing each pixel. In a system that performs processing in units of pixels, the image is enlarged or reduced in units of pixels. The present image data interpolating apparatus performs such an enlarging process on a pixel-by-pixel basis, and the image data obtaining means C11 obtains such image data, and outputs the first The processing unit C13 performs an interpolation process for increasing the number of constituent pixels in the image data. Here, the first interpolation processing means C12 can execute the pattern matching method and the near-list method as the interpolation processing, and the second interpolation processing means C13 can execute the cubic method as the interpolation processing. .

The first superimposition ratio determining means C14 determines the properties of the image based on reference pixels around the pixel to be interpolated in the image data acquired by the image data acquiring means C11.
The superimposition ratio of the interpolated pixels by the first interpolation processing means C12 and the second interpolation processing means C13 is determined based on the same property. When the first superimposition ratio determining unit C14 determines the superimposition ratio, the image data superimposing unit C15 superimposes the interpolated pixel data by the first interpolation processing unit C12 and the second interpolation processing unit C13 at the superimposition ratio. Then, the image data output means C16 is
15 outputs the superimposed pixel data.

In the present embodiment, the computer system 10 is employed as an example of hardware for realizing such an image data interpolation device. FIG. 2 is a block diagram showing the computer system 10. The computer system 10 includes a scanner 11a, a digital still camera 11b, and a video camera 11c as image input devices.
2 are connected. Each input device is capable of generating image data representing an image with pixels in a dot matrix and outputting the image data to the computer main body 12. Here, the image data is 2 in each of the three primary colors of RGB.
By displaying 56 gradations, about 16.7 million colors can be expressed.

The computer main body 12 has a floppy (R) disk drive 13a as an external auxiliary storage device.
And hard disk 13b and CD-ROM drive 13c
The main hard disk 13b stores system-related main programs, and can read necessary programs and the like from the floppy (R) disk 13a1, the CD-ROM 13c1, and the like. A modem 14a is used as a communication device for connecting the computer main body 12 to an external network or the like.
Is connected to an external network via the public communication line, and software and data can be downloaded and introduced. In this example, the modem 14a
, The outside is accessed via a telephone line, but a configuration in which a network is accessed via a LAN adapter is also possible. In addition, a keyboard 15a and a mouse 15b are connected for operating the computer body 12.

Further, a display 17a and a color printer 17b are provided as image output devices.
The display 17a has a display area of 800 pixels in the horizontal direction and 600 pixels in the vertical direction, and each pixel can display the above-described 16.7 million colors. Of course, this resolution is only an example, 640 × 48
0 pixels or 1024 x 768 pixels
It can be changed as appropriate.

The color printer 17b is an ink jet printer, and is capable of printing an image with dots on printing paper as a recording medium using four color inks of CMYK. The image density can be printed at a high density of 360 × 360 DPI or 720 × 720 DPI, but the gradation table is expressed in two gradations such as whether or not to apply color ink. On the other hand, a predetermined program is executed in the computer main body 12 in order to display or output an image output device while inputting an image using such an image input device.
Among them, an operating system (OS) 12a is operating as a basic program, and the operating system 12a has a display driver (DSP DR) for performing display on the display 17a.
V) 12b and a printer driver (PRT DRV) 12c for causing the color printer 17b to perform print output are incorporated. These drivers 12b and 12c depend on the models of the display 17a and the color printer 17b, and can be additionally changed to the operating system 12a according to each model. In addition, additional functions beyond standard processing can be realized depending on the model. That is, it is possible to realize various additional processes within an allowable range while maintaining a common processing system on the standard system of the operating system 12a.

Of course, the premise of executing such a program is that the CPU 12e
, A RAM 12f, a ROM 12g, an I / O 12h, and the like.
While using M12f as a temporary work area or setting storage area, or as a program area,
The basic program written in 12g is appropriately executed, and I
/ O12h, and controls external devices and internal devices connected thereto.

Here, the application 12d is executed on the operating system 12a as a basic program. The processing contents of the application 12d are various, monitor operations of the keyboard 15a and the mouse 15b as operation devices, and when operated, appropriately control various external devices to execute corresponding arithmetic processing and the like. Further, the processing result is displayed on the display 17a or output to the color printer 17b.

In the computer system 10, image data is acquired by a scanner 11a or the like, which is an image input device, and predetermined image processing is executed by an application 12d. It is possible to In this case, focusing on the correspondence between pixels, if the pixel density of the color printer 17b and the pixel density of the scanner 11a match, the size of the scanned original image matches the size of the printed image.
If there is a difference between the two, the size of the image will be different.
In many cases, the pixel density of the scanner 11a is similar to the pixel density of the color printer 17b. It is often higher than the pixel density. In particular, the display 17a
The display density is higher in each stage as compared with the display density, and if the display on the display 17a is made to correspond to each pixel and printed, an extremely small image may result.

Therefore, the operating system 12
Resolution conversion is performed in order to eliminate the difference in the pixel density of each actual device while determining the reference pixel density in a. For example, if the resolution of the display 17a is 72D
When it is assumed to be a PI, the operating system 1
If 2D is based on 360 DPI, the display driver 12b performs resolution conversion between the two. In a similar situation, the resolution of the color printer 17b is 72
If it is 0 DPI, the printer driver 12c performs resolution conversion.

Since the resolution conversion corresponds to a process for increasing the number of constituent pixels in the image data, it corresponds to an interpolation process.
c implements interpolation as one of its functions. Here, the display driver 12b and the printer driver 12c can execute the first interpolation processing means C12 and the second interpolation processing means C13 described above, and furthermore, the first superimposition ratio determination means C14, the image data superimposition means C15, The data output unit C16 is executed to superimpose a ratio according to the nature of the image.

The display driver 12b
The printer driver 12c is stored in the hard disk 13b, and is read and operated by the computer main body 12 at the time of startup. At the time of introduction, CD-ROM1
3c1 or a medium such as a floppy (R) disk 13a1. Therefore, these media constitute a medium on which the image data interpolation program is recorded. In the present embodiment, the image data interpolation device is realized as the computer system 10,
Such a computer system is not necessarily required, and any system that requires interpolation processing for similar image data may be used. For example, as shown in FIG. 3, a digital still camera 11b1 incorporates an image data interpolating device for performing an interpolation process, and displays the image data on the display 17a1 using the interpolated image data.
7b1 may be used. As shown in FIG. 4, in a color printer 17b2 that inputs and prints image data without going through a computer system, image data input via a scanner 11a2, a digital still camera 11b2, a modem 14a2, or the like is automatically processed. It is also possible to configure so as to perform resolution conversion and print processing. In addition, the present invention can be naturally applied to various devices that handle image data, such as a color facsimile device 18a as shown in FIG. 5 and a color copying device 18b as shown in FIG.

FIG. 7 shows a software flow relating to the resolution conversion executed by the printer driver 12c described above. In FIG. 7, in step S102, original image data is obtained. When an image is read from the scanner 11a by the application 12d and subjected to predetermined image processing and then subjected to print processing, print data of a predetermined resolution is transmitted to the printer driver 1 via the operating system 12a.
This stage corresponds to the acquisition of 2c. This processing is an image data acquisition step or an image data acquisition function when viewed as software, and various steps to be executed by a computer including the image data acquisition step are directly performed by the operating system 12a itself or hardware. Can be understood as not including. On the other hand, if it is considered that it is organically integrated with hardware such as a CPU, it corresponds to the image data acquisition unit C11.

In step S104, in the read image data, the pixel to be interpolated is set as a target pixel, and the pixel data of a peripheral 5 × 5 pixel area around the target pixel is set as a reference pixel, and the luminance value of the reference pixel is set. Create a histogram of In step S106, the number of times different luminance values appear in the obtained histogram is obtained, and it is determined whether the number of appearances is smaller than 15. Because the number of colors in the reference pixel increases as the number of different luminance values increases, the one in which the number of appearances of the luminance value is smaller than 15 is regarded as a non-natural image. When it is determined, the superimposition ratio (rate) of the first interpolation processing is set to 1 in step S110.

In step S106, the number of appearances of the luminance value is 1
If it is determined that it is not smaller than 5, step S10
At 8, the rate is determined by the evaluation function F. Although the details will be described later, the evaluation function F is determined by the luminance value width of the reference pixel, that is, the maximum luminance value Ymax and the minimum luminance value Y in the reference pixel.
It is a function of the difference from min. Therefore, step S10
A series of processes shown in steps S4, S106, S108, and S110 correspond to a first superposition ratio determination step or a first superposition ratio determination function. This constitutes one superposition ratio determining means C14.

Step S108 or step S11
When the rate is determined at 0, it is determined at step S112 whether or not the rate is "0". When the rate is "0", the first interpolation processing is not performed. If the rate is not determined to be "0" in step S112, a first interpolation process is executed in step S114. Therefore, this processing corresponds to the first interpolation processing step or the first interpolation processing function,
If these are considered to be organically integrated with hardware such as a CPU, they constitute the first interpolation processing means C12.

Further, in step S116, it is determined whether or not the rate is "1". When the rate is "1", the second interpolation processing is not performed. If rate is not determined to be "1" in step S116, a second interpolation process is executed in step S118. Therefore, this processing corresponds to the second interpolation processing step or the second interpolation processing function.
Assuming that the second interpolation processing means C13 is organically integrated with hardware such as U, the second interpolation processing means C13 is configured. After the rate is determined in this manner and the first interpolation processing or the second interpolation processing is executed, step S1 is executed.
In step 20, based on the rate, pixel data is superimposed by the following equation to generate an interpolated pixel. Superimposed data = (first interpolation processing) × rate + (second interpolation processing) × (1-rate) (1) Therefore, this processing corresponds to the image data superimposing step or the image data superimposing function, and these are If it is considered that the image data is organically integrated with hardware such as a CPU, the image data superimposing means C15 is configured. When the generation of the interpolation data for the target pixel is completed in this way, it is determined in step S122 whether or not the superimposition processing of the input original image data for all the target pixels has been completed. Is repeated until the processing is completed. When the superimposition process for all the target pixels is completed, the image data interpolated in step S124 is output. Printer driver 12c
In the case of, print data cannot be obtained only by resolution conversion, but color conversion or halftone processing is required. Therefore, outputting image data here means transferring data to the next stage, and this processing corresponds to the above-described image data output step or image data output function, and these are organically integrated with hardware such as a CPU. Assuming that the image data is combined, the image data output means C16 is configured.

Next, the first interpolation processing flow in step S114 of the above flow will be described. FIG. 8 shows the first interpolation processing flow. In FIG. 8, in step S202, pattern matching is performed.
From the pixel data of the 5 × 5 pixel area acquired in step S104, the surrounding 3
The pixel data of the area of × 3 pixels is extracted. In step S204, the pixel data is converted into a binary pattern based on whether or not the luminance value of the extracted pixel is greater than a predetermined threshold value described later.

In step S206, the above step S20
In step 4, it is determined whether or not the binary pattern matches a predetermined edge pattern prepared in advance. Here, the edge pattern is a binary pattern when the 3 × 3 pixel area has an edge of “30 °”, “45 °”, or the like. It is determined whether or not the 3 × 3 pixel area is an edge by determining whether or not the value pattern matches.

If it is determined in step S206 that the two patterns match, in step S208, an interpolated pixel is generated according to a rule predetermined according to the edge pattern. In the flow, the interpolation processing at the time of pattern matching is described as a pattern matching method.
If it is not determined in step S206 that the two patterns match, in step S210, an interpolated pixel is generated by the near-list method. After the interpolation process for the target pixel is performed in this manner, the data is stored in the RAM 12f in step S212, and the flow returns to the flow shown in FIG.

Next, a more specific process for the above flow will be described. In the present embodiment,
It is determined whether the original image acquired in step S102 is a computer graphic (non-natural image) or a photograph (natural image), and the result of the determination is reflected in the superimposition ratio. Of course, in order to prevent an error in determining the interpolation method based on the property determination of the image to be interpolated, it is possible to adopt a configuration in which the above rate is not fixed to “1” or “0”. Is smaller than 15, it is assumed that the image is a non-natural image, and the emphasis is placed on executing the second interpolation process on the non-natural image so as not to blur the outline of the image.

Specifically, this determination uses a histogram of luminance values shown in FIG. 9. In step 104, a luminance value is obtained for each reference pixel in a 5 × 5 pixel area, and a range in which the luminance can be obtained. , A histogram of the number of pixels is totaled. Then, the number of occurrences of the luminance value, that is, the number of luminance values whose distribution number is not “0” is counted, and the determination in step S106 is performed. Of course, it is natural that there are a plurality of colors having the same luminance out of 16.7 million colors in one image, and if attention is paid only to comparison with non-natural images, whether there are many colors or luminances It is possible to compare whether the image is small or not, and it is also possible to determine whether or not the image is a non-natural image by counting the number of appearances of the color in addition to the luminance value.

If the pixel data of the reference pixel has luminance as a component element, the distribution can be obtained using the luminance value. However, even in the case of image data whose luminance value is not a direct component value, a component value representing luminance is provided indirectly. Therefore, a luminance value can be obtained by converting from a color space in which the luminance value is not a direct component value to a color space in which the luminance value is a direct component value.

The color conversion between the different color spaces is not uniquely determined by the conversion formula. Relative correspondences are obtained for the color spaces having the respective component values as coordinates, and this correspondence is stored. It is necessary to sequentially perform conversion by referring to the color conversion table obtained. Then, strictly speaking, it is necessary to have a color conversion table of 16.7 million elements. As a result of considering efficient use of storage resources, instead of preparing correspondences for all coordinate values, usually prepare correspondences for appropriate discrete grid points and use interpolation together I am trying to do it. However, since such an interpolation operation can be performed through several multiplications and additions, the amount of operation processing becomes enormous.

That is, if a full-size color conversion table is used, the amount of processing is reduced, but the table size becomes an unrealistic problem. If the table size is made realistic, the amount of arithmetic processing becomes unrealistic. Often becomes the target. In view of such a situation, the present embodiment employs a simplified luminance value calculation method. That is, the interpolation target image data of the present embodiment employs an RGB color space, and each component value indicates the brightness of a color. Therefore, when each component value is viewed alone, it is linear in luminance. There is a property that corresponds to. Therefore, it can be said that the data reflects the luminance value of each pixel even if Y = (R + G + B) / 3 without considering the addition ratio of each color. In this embodiment, the value degree value is calculated by the equation. .

Of course, the calculation of the luminance value is not limited to such a method, and the following conversion formula for obtaining the luminance from the three primary colors of RGB, as used in a television or the like, may be adopted. good. Y = 0.30R + 0.59G + 0.11B In this way, a luminance value can be obtained only by three multiplications and two additions. If the number of distributions whose distribution number is not “0” is more than 15 in such a luminance value histogram, the superimposition ratio rate is determined by the evaluation function F determined in advance in step S108.
The solid line in FIG. 10 shows a specific example of such an evaluation function F (y), in which “y” has a value in the range of “0 ≦ y ≦ 255” and “0 ≦ F (y)”. ≦ 1 ”. In the range of “0 ≦ y ≦ 64”, “F (y) =
0 ”, and“ F ”in the range of“ 192 ≦ y ≦ 255 ”.
(Y) = 1 ", and F (y) increases linearly from" 0 "to" 1 "in the range of" 64≤y≤192 ". Here, “y” is the luminance value width “Ymax” in FIG.
−Ymin ”is substituted. Accordingly, the rate increases as the luminance value width increases, that is, as the relationship between the reference pixels is more like an edge.

In addition, as the edge-like appearance, r
The function for increasing ate need not be limited to the example shown in FIG. 10, but may be any function that monotonically increases with the luminance value width when the luminance value width is used as a variable. Further, it is not always necessary to set the maximum value of the evaluation function to “1” and the minimum value to “0”, and to set the maximum value of the evaluation function to “0.7” as shown by the dotted line in FIG. It is also possible to prevent execution of only the interpolation processing, or to employ an evaluation function that smoothly changes over the luminance value width “0” to “255”.

In the case of the histogram shown in FIG. 9, since there are 19 luminance values whose distribution number is not "0", step S1 is executed.
After the determination of step 06, the rate is determined by the evaluation function shown in FIG.
In step S118, a first interpolation process and a second interpolation process are executed. FIG. 11A shows an example of a 3 × 3 pixel luminance pattern extracted in step S202 in FIG. In step S204, a binary pattern shown in FIG. 6B is created from the luminance pattern shown in FIG.
, The luminance value Yij is calculated based on the above-described luminance value calculation formula.

Then, the maximum value Ymax of these luminance values is obtained.
And a minimum value between the minimum value Ymin and the intermediate value is set as the threshold value Yt. After the threshold value is calculated, each luminance value Yij is compared with the threshold value Yt, and the luminance value Yij is converted into a binary pixel pattern by dividing the luminance value into a higher luminance value and a lower luminance value. Needless to say, the method of selecting a threshold value and the method of creating a binary pattern need not be limited to the above-described embodiment. When the intermediate value 128 of the luminance value is used as the threshold value, or when the minimum value of the luminance value is “45” or less, Various modes can be adopted, such as not using such an extreme luminance value and treating the minimum value as “45”.

When the binary pattern shown in FIG. 11B is created, it is determined in step S206 whether or not the binary pattern matches an edge pattern prepared in advance. Here, various patterns can be adopted as the edge pattern prepared in advance. Specifically, the pattern shown in FIG. 11B matches an edge pattern prepared in advance. Here, the coincidence with the edge pattern in FIG. 3B means that the luminance value of the pixel located on the upper side in the j direction of FIG. 3 and the luminance value of the pixel located on the lower side in the j direction in FIG. This indicates that the value difference tends to be large and that there is not much difference in the brightness value in the left-right direction on the paper. Therefore, such a pattern is an edge parallel to the left-right direction of the drawing. Therefore, pixel interpolation processing is performed on the edge pattern according to a predetermined rule.

The predetermined rules include various rules depending on the edge pattern.
The weighted average is calculated by weighting the inverse ratio of the distance between the pixel and the pixel to be interpolated with respect to the gradation value of the predetermined pixel in the pixel area. Here, various variations can be considered in terms of what kind of pointer is used to select a pixel for which weighted averaging is performed when generating an interpolation pixel, and how to reflect the angle, direction, and the like of an edge. In order to make a predetermined variation correspond to each edge pattern, pixel interpolation processing is performed on the edge pattern according to a predetermined rule.

The edge pattern is an area of 3 × 3 pixels, but the reference pixels of 5 × 5 pixels are used to determine the angle and direction of the edge. That is, the edge pattern needs to have various binary patterns as a database in advance, but if a predetermined pattern is prepared in advance in a 5 × 5 pixel area, the number of the patterns becomes enormous. Therefore, in the present embodiment, a binary pattern is generated for a 3 × 3 pixel area where the number of edge patterns prepared in advance is a realistic number, and whether the generated pattern matches the edge pattern is determined. After determining whether or not the region is a 5 × 5 pixel region, the characteristics of the edge are further reflected.

Hereinafter, the pixel interpolation processing rule when it is determined that the pixel pattern matches the binary pattern shown in FIG. 11B will be described. FIG. 12A shows an area of 5 × 5 pixels as the reference pixel data, and the pixels are distinguished from each other by adding alphabets A to Y to the respective pixels. In the above notation, the above 3 used for the pattern matching
The area of × 3 pixels is “G, H, I, L, M, N, Q, R,
The pixel of interest is the pixel M. FIG. 12A shows a state after the binary pattern has already been formed in the 3 × 3 pixel area.

FIG. 12B shows the pixel after the interpolation processing. The target pixel M is interpolated into pixels a to i. The basic formula of the calculation at the time of generating such a pixel is shown in the following formula.
Weighted averaging is performed using the inverse ratio of the distance from the interpolation pixel to the original pixel as a weight. Here, the grayscale value of the generated interpolation pixel is xbar, and Pn is the original pixel (G, H,
I, L, M, N, Q, R, S), and rn is the distance from the generated interpolation pixel to each of the original pixels referred to.

[0070]

(Equation 1) The characteristic point is that the gradation value of the interpolation pixel thus generated is generated from the original pixel. That is, binarization is performed on pixels having multiple gradation values, and then pattern matching is attempted. When the pattern matching is performed, the gradation value of the interpolated pixel is generated from the gradation values of the surrounding pixels using a calculation formula according to the matched pattern. As described above, while making the pattern matching judgment, the generated pixels are generated from the surrounding original pixels based on the rules prepared based on the matched pattern, so that it can be obtained only by interpolation processing based on simple pattern matching. No natural interpolated pixels can be generated. In the above calculation, the luminance was calculated from each of the RGB values. However, the gradation value of the generated interpolation pixel actually has each of the RGB values.
Each value is calculated by performing predetermined weighting using the RGB values of the original pixel. Therefore, in a case where calculation is convenient, the weighting may be derived in a state where the luminance is used, and the calculation may be performed by applying the same weighting to the RGB values.

In the pattern of 3 × 3 pixels shown in this example, there is little difference in luminance value in the horizontal direction (i direction) as described above.
That is, since the edge is in the horizontal direction, only the three pixels arranged horizontally are assigned to the formula Pn. In principle, the tone values of the original pixels L, M, and N are assigned. You. Further, in the present embodiment, interpolation is performed so as to reflect the change in the luminance value in the direction perpendicular to the edge in the horizontal direction, and the luminance value changes in the direction perpendicular to the edge in the horizontal direction. In this case, the gradation values of the original pixels Q, R, and S are substituted for Pn, and the state of the edge change is reflected on the gradation values of the interpolation pixels g, h, and i. That is, if the value of the pixel of interest M is larger (smaller) than the value of the pixel R on the edge side, the pixel of interest R will not be emphasized even if it is filled. Therefore, the pixel of interest is used as it is.

For this reason, the luminance values of the pixel M and the pixel R are compared before performing the interpolation calculation, and if the luminance value of the pixel R is larger than the luminance value of the pixel M, The interpolation values g, h, and i are generated by substituting the gradation values into the above equation.
As a result, compared with the interpolation pixels d, e, and f, the interpolation pixels g,
The edges are further emphasized in the interpolated image data in which the luminance values of h and i become larger. Specific determination conditions in this case are ((M = edge) &(R> M)) or ((M ≠ edge)
& (R <M)) where a = d = (Lx2 + Mx2 + N) / 5 b = e = (L + Mx2 + N) / 4 c = f = (L + Mx2 + Nx2) / 5 g = (Qx4 + Rx6 + Sx3) / 13 h = (Q + Rx2 + S) / 4 i = (Qx3 + Rx6 + Sx4) / 13 On the other hand, when the luminance value of the pixel R is smaller than the luminance value of the pixel M, the enhancement processing is not performed and only the gradation values of the original pixels L, M, and N are used.

The specific judgment conditions in this case are: not (((M = edge) &(R> M)) or ((M ≠
Edge) & (R <M))) and the calculation formula at that time is as follows: a = d = g = (Lx2 + Mx2 + N) / 5 b = e = h = (L + Mx2 + N) / 4 c = f = i = (L + Mx2 + Nx2) / 5. In calculating the gradation value of the interpolation pixel by the above equation, when the original pixel M is substituted for Pn, if the inverse ratio of the distance is used as the weight, the value becomes too large. It is to be.

In the present embodiment, the interpolation process is performed on the horizontal edge in consideration of the change in the luminance value in the vertical direction on the basis of the above rule. FIG. 13 shows an example thereof. Step S206 above
When the pattern shown in FIG. 7A is found, the interpolation pixel shown in FIG. 7B is generated with reference to a predetermined pixel outside the 3 × 3 pixel area. According to the rule in the same pattern, the original pixels L, M, and R are used in calculating the gradation values of the interpolation pixels a, d, e, g, h, and i.

The calculation of the gradation values of the interpolation pixels b, c, and f is 5 × 5.
Which pixel is the original pixel is changed based on the luminance value of a predetermined pixel included in the pixel area. That is,
If the pattern is a right-angled edge, interpolated pixels b, c, and f are generated using the original pixels L, M, and R, and if not a right-angled edge, interpolation is performed using the original pixels G, H, N, and S. The pixels b, c, and f are generated. First, the condition for determining that this pattern is a right angle is (((M = K = edge) & (F = not edge)) or
((M = W = edge) & (X = not edge))) or
(((M = K = not an edge) & (F = edge)) or
((M = W = not an edge) & (X = edge))) and the calculation formula at that time is as follows: c = (L + Mx2 + R) / 4 b = (Lx4 + Mx6 + Rx3) / 13 f = (Lx3 + Mx6 + Rx4) / 13 a = (Lx2 + Mx2 + R) / 5 d = (Lx3 + Mx3 + Rx2) / 8 e = (L + Mx2 + R) / 4 g = (L + M + R) / 3 h = (Lx2 + Mx3 + Rx3) / 8 i = (L + Mx2 + Rx2) / 5. Next, the condition determination as the first pattern other than the right angle but close to the right angle is (((M = F = edge) & (A = not edge))
((M = X = edge) & (Y = not edge))) or
(((M = F = not an edge) & (A = edge)) or
((M = X = not an edge) & (Y = edge))) and the calculation formula at that time is c = (G + Hx2 + Nx2 + S) / 6 b = (Lx4 + Mx6 + Rx3) / 13 f = (Lx3 + Mx6 + Rx4) / 13, and a, d, e, g, h, and i have the same calculation formula.
Then, the calculation formula when neither of the above two conditions is satisfied is shown as follows: c = (G + Hx2 + Nx2 + S) / 6 b = (Gx15 + Hx30 + Nx20 + Sx12) / 77 f = (Gx12 + Hx20 + Nx30 + Sx15) / 77. In this case, a, d, e, g, h, and i have the same calculation formula.

Further, when the pattern shown in FIG. 13C is found, an interpolated pixel shown in FIG. 13D is generated. That is, the pattern shown in FIG. 3C seems to be part of a straight line (45-degree edge) connecting the diagonal lines of the 3 × 3 pixel area. To generate interpolation pixels a, e, and i. Also, 5 × 5
The edge angle and line thickness of the original pixel are determined based on the luminance value of a predetermined pixel included in the pixel area, and used for generating the interpolated pixels b, c, f, d, g, and h according to the determination. The original pixel to be changed between "G, M, S" and "H, N"
"G, M, S" and "L, R".

The processing branches into three depending on whether the detected 45 degree edge is a part of the 45 degree edge or a part of the 30 degree edge. FIG. 36 shows this determination condition.
Note that the description enclosed by a broken line in the figure is the content intended for each judgment. The calculation formula when process 1 is selected is b = (Gx5 + Mx10 + Sx4) / 19 c = (G + Mx2 + S) / 4 f = (Gx4 + Mx10 + Sx5) / 19 The calculation formula at the time of selection is b = (Gx5 + Mx10 + Sx4) / 19 c = (H + N) / 2 f = (Gx4 + Mx10 + Sx5) / 19 The calculation formula is b = (Hx3 + Nx2) / 5 c = (H + N) / 2 f = (Hx2 + Nx3) / 5. The calculation formulas for the other pixels are as follows: a = (Gx3 + Mx6 + Sx2) / 11 d = (Gx5 + Mx10 + Sx4) / 19 e = (G + Mx2 + S) / 4 g = (G + Mx2 + R) / 4 h = (Gx4 + Mx10 + Sx5) / 19 i = (Gx2 + Mx6 + Sx3) / 11

When the pattern shown in FIG. 13E is found, an interpolated pixel shown in FIG. 13F is generated. In other words, it seems that the straight line has a smaller angle (30-degree edge) than the straight line shown in FIG. 3C, and the interpolation pixels a, b, c, and c are obtained by using the original pixels G, M, and N constituting these straight lines.
Generate e and f. Further, the edge angle of the original pixel, the protruding pixel, and the like are determined based on the luminance value of the predetermined pixel included in the 5 × 5 pixel area, and the interpolation pixels d, g,
The original pixels used for generating h and i are changed between "G, M, N" and "L, R, S".

More specifically, the processing branches to four for the detected 30-degree edge. FIG. 37 shows this determination condition. Note that the description enclosed by a broken line in the figure is the content intended for each judgment. The calculation formula when process 1 is selected is: d = (G + Mx2 + N) / 4 g = (G + Mx2 + S) / 4 h = (Gx3 + Mx8 + Nx5) / 16 i = (G + Mx3 + Nx3) / 7, and the calculation formula when process 2 is selected is d = (G + Mx2 + N) / 4 g = (Lx2 + Rx2 + S) / 5 h = (Lx4 + Rx6 + Sx3 ) / 13 i = (Lx3 + Rx6 + Sx4) / 13, and the calculation formula when processing 3 is selected is d = (G + Mx2 + N) / 4 g = (Lx2 + Rx2 + S) / 5 h = (Lx4 + Rx6 + Sx3) / 13 i = (G + Mx3 + Nx3) / 7, and the calculation formula when processing 4 is selected is d = (Lx15 + Rx10 + Sx6) / 31 g = ( Lx2 + Rx2 + S) / 5 h = (Lx4 + Rx6 + Sx3) / 13 i = (Lx3 + Rx6 + Sx4) / 13 The calculation formulas for pixels other than these are: Nx3) / 13 f = (Gx2 + Mx5 + Nx5) / 12.

The above-described method of referring to the edge pattern and 5 × 5 pixel area, the method of selecting the original pixel to be used, and the like are merely examples, and various modes can be adopted. As described above, various magnifications such as 2 × and 4 × can be adopted in addition to the mode of generating pixels 3 × vertically and horizontally as described above. First, an example of double interpolation will be described. FIGS. 38 to 42 show the case where interpolation pixels a to d are generated from the original pixel M, and FIG. 38 shows the case where a horizontal edge is found by pattern matching. Also in this case, if the value of the target pixel M is larger (smaller) than the value of the pixel R on the edge side, even if the value is filled with the pixel R, no emphasis is given, and the target pixel is used as it is. A more specific judgment condition is ((M = edge) &(R> M)) or ((M = not edge) & (R <M)). If this condition is satisfied, the emphasis process is performed. In this case, the calculation formula is a = b = M c = d = R. In other cases, no emphasis processing is performed, so that a = b = c = d = M.

Next, FIG. 39 shows a case where a corner edge is found by pattern matching. The condition for judging whether or not the edge is a right-angled edge is as follows: (((M = K = edge) & (F = not an edge)) or
((M = W = edge) & (X = not edge))) or
(((M = K = not an edge) & (F = edge)) or
((M = W = not an edge) & (X = edge))), and if this holds, in order to emphasize that it is a right angle, calculate with the formula b = (L + R) / 2. If not, calculate with b = (Gx2 + Hx3 + Nx3 + Sx2) / 10. The other pixel calculation formula is a = (Lx3 + Rx2) / 5 c = (L + R) / 2 d = (Lx2 + Rx3) / 5.

FIG. 40 shows a case where a 45-degree edge is found by pattern matching, and it is determined whether the edge is a part of a 45-degree edge or a 30-degree edge.
The process branches to two processes according to the condition judgment shown in FIG. Here, the calculation formula in the case of processing 1 is b = (G + S) / 2, and the calculation formula in the case of processing 2 is b = (H + N) / 2. The other pixel calculation formula is a = (Gx2 + S) / 3 c = (G + S) / 2 d = (G + Sx2) / 3.

FIG. 42 shows a case where a 30-degree edge is found by pattern matching, and branches to three processes according to the condition judgment shown in FIG. Here, the calculation formula in the case of processing 1 is c = (G + N) / 2 d = (G + Nx2) / 3, and the calculation formula in the case of processing 2 is c = (Lx3 + Rx3 + Sx2) / 8 d = (Lx2 + Rx3 + Sx3) / 8 Becomes The other pixel calculation formula is as follows: a = (Gx3 + Nx2) / 5 b = (Gx2 + Nx3) / 5.

If it is not determined that the edge pattern prepared in advance in the 3 × 3 pixel area matches the binary pattern, such a pattern matching method is not used as the first interpolation processing, and the near-list method is used. Is executed. In the near list method, as shown in FIG. 14, four surrounding grid points Pij, Pi + 1j, Pij + 1, Pi + 1
The distance between j + 1 and the point Puv to be interpolated is obtained, and the data of the closest lattice point is transferred as it is. When this is represented by a general formula, Puv = Pij, where i = [u + 0.5] and j = [v + 0.5]. [] Indicates that a Gaussian symbol takes an integer part.

FIG. 15 shows that the number of pixels is set to 3
This shows a situation where interpolation is performed twice at a time. Assuming that there are four corner pixels (□ に は ●●) before the interpolation, the data of the closest pixel among these pixels is directly transferred to the pixels generated by the interpolation. That is, in this example, the pixels adjacent to the four corner pixels are copied. When such processing is performed, the original image in which black pixels are arranged obliquely with white pixels as a background as shown in FIG. Direction. The near-list method has a feature that an edge of an image is held as it is. Therefore, when enlarged, jaggies are noticeable but edges are retained as edges.

After the interpolation pixel is generated by the first interpolation processing as described above, if the rate is not "1", the second interpolation processing is further executed. In the present embodiment, the so-called cubic interpolation processing is executed as the second interpolation processing. Such processing will be described below. As shown in FIG. 18, the cubic method uses data of a total of 16 grid points including not only four grid points surrounding a point Puv to be interpolated but also grid points around one point.

When a total of 16 grid points surrounding the interpolation point Puv have respective values, the interpolation point Puv is determined under the influence of these. For example, if an attempt is made to interpolate using a linear expression, weighting addition may be performed in inverse proportion to the distance from two grid points sandwiching the interpolation point. Focusing on the X-axis direction, the distance from the interpolation point Puv to the above-mentioned 16 grid points is x1, the distance to the left outer grid point is x1, the distance to the left inner grid point is x2, and the right inner The degree of influence corresponding to such a distance is represented by a function f (x) while expressing the distance x3 to the lattice point and the distance x4 to the right outer lattice point. In addition, focusing on the Y-axis direction, the interpolation point Pu
The distance from v to the 16 grid points is a distance y1 to the upper outer grid point and a distance y to the upper inner grid point.
Similarly, the degree of influence can be expressed by a function f (y) while expressing the distance y3 to the lower inner grid point and the distance y4 to the lower outer grid point.

The 16 grid points contribute to the interpolation point Puv with the degree of influence according to the distance as described above. The general formula to be accumulated is as follows.

[0089]

(Equation 2) If the degree of influence according to the distance is represented by a third-order convolution function, f (t) = {sin (πt)} / πt. Note that the above-described distances x1 to x4, y1 to y4
Is calculated as follows using the absolute value of the coordinate value (u, v) of the lattice point Puv. x1 = 1+ (u- | u |) y1 = 1+ (v- | v |) x2 = (u- | u |) y2 = (v- | v |) x3 = 1- (u- | u | ) y3 = 1- (v- | v |) x4 = 2- (u- | u |) y4 = 2- (v- | v |)

[0090]

(Equation 3) Becomes The degree of influence f (t) according to the distance, which is called a third-order convolution function, is approximated by the following cubic expression.

[0091]

(Equation 4) The cubic method has a feature that the gradual change gradually proceeds from one lattice point to the other lattice point, and the degree of the change becomes a so-called cubic function.

FIGS. 19 and 20 show a specific example when interpolation is performed by the cubic method. For ease of understanding, a model in which there is no change in data in the vertical direction and an edge occurs in the horizontal direction will be described.
The number of pixels to be interpolated is three. First, specific numerical values in FIG. 20 will be described. The gradation value of the pixel before the interpolation is shown as “Original” in the left column, and the gradation value “6” is displayed.
4 pixels (P0, P1, P2, P3) are arranged at four points, one pixel (P4) having a gradation value of "128" is sandwiched, and a gradation value of "1"
Five 92 pixels (P5, P6, P7, P8, P9) are arranged. In this case, the edge is a portion of the pixel having the gradation value “128”.

Here, three pixels (Pn1 and Pn1) are located between each pixel.
n2, Pn3), the distance between the interpolated pixels is “0.25”, and the above-described x1 to x4
Is the value in the middle column of the table for each interpolation point. f (x1) to f (x4) are also uniquely calculated corresponding to x1 to x4. For example, x1, x2, x3, and x4 are "1.25" and "0.25", respectively. , "0.75",
When “1.75” is obtained, f (t) corresponding thereto is approximately “−0.14”, “0.89”, “0.3”
0 "and" -0.05 ". X1, x2, x
3, x4 are “1.50”, “0.50”,
When “0.50” and “1.50” are obtained, f (t) corresponding thereto is “−0.125” and “0.62”.
5 "," 0.625 ", and" -0.125 ". In addition, x1, x2, x3, and x4 are each "1.7.
5 "," 0.75 "," 0.25 ", and" 1.25 ", the f (t) corresponding thereto is approximately"-
0.05 "," 0.30 "," 0.89 "," -0.1
4 ". The result of calculating the tone value of the interpolation point using the above result is shown in the right column of the table, and is also shown graphically in FIG. The meaning of this graph will be described later in detail.

Assuming that there is no change in the data in the vertical direction, the calculation is simplified, and only the data (P1, P2, P3, P4) of the four grid points arranged in the horizontal direction are referred to.
Influence degree f according to distance from interpolation point to each grid point
Using (t), it can be calculated as follows. P = P1 ・ f (x1) + P21f (x2) + P3 ・ f (x3) + P4 ・ f (x4) Therefore, when calculating for the interpolation point P21, P21 = 64 * f (1.25) +64 * f (0.25) + 64 * f (0.75) + 128 * f (1.75) = 64 * (-0.14063) + 64 * (0.890625) + 64 * (0.296875) +128 * (-0.04688) = 61

According to the cubic method, since it can be expressed as a cubic function, the quality of the interpolation result can be influenced by adjusting the shape of the curve. As an example of the adjustment, 0 <t <0.5 f (t) =-(8/7) t ** 3- (4/7) t ** 2 + 1 0.5 <t <1 f (t) = (1 -t) (10/7) 1 <t <1.5 f (t) = (8/7) (t-1) ** 3+ (4/7) (t-1) ** 2- (t-1 ) 1.5 <t <2 f (t) = (3/7) (t−2) is called a hybrid bicubic method or an M (modified) cubic method.

FIG. 21 shows a specific example when the interpolation is performed by the M cubic method, and shows a result of the interpolation performed on a model having the same assumption as in the case of the cubic method. FIG. 19 also shows the result of the interpolation processing by the M cubic method. In this example, the cubic function curve becomes slightly steep, and the entire image becomes sharp. M
Regardless of whether the cubic method or the ordinary cubic method is used, in the second interpolation processing, if the interpolation pixel is generated by the cubic method, the variables of the superimposition data calculation formula, that is, the first interpolation processing data, rat
e, since all of the second interpolation processing data has been calculated, the superimposition data of the interpolation pixel is calculated by substituting the values into the same equation.

The manner in which the superimposition process is performed on image data as shown in FIG. Here, in the 5 × 5 area around the pixel of interest in the description, the number of appearances of the luminance value Y is larger than 15, and
Is not “0” or “1”. Therefore, after the rate is calculated in step S108, step S11
At 4, the first interpolation processing is executed. In step S202, a 3 × 3 pixel area (solid line area) around the target pixel shown in FIG. 22A is extracted, and in step S204, a binary pattern shown in FIG. 22B is created from the area data.

This binary pattern coincides with a predetermined edge pattern prepared in advance. In step S208, processing by the pattern matching method is performed, and an interpolation pixel shown in FIG. 22C is generated. At this time, the 3 × 3 pixels shown in FIG. 22A are diagonal edges and have a luminance gradient toward the lower left. Accordingly, in the interpolated pixel shown in FIG. 22C, the edge is emphasized with a luminance gradient toward the lower left while retaining the edge in the diagonal direction while reflecting the property of the 3 × 3 pixel.

On the other hand, in step S118, FIG.
Then, the interpolation pixel shown in FIG. 22D is generated using the pixel data shown in FIG. Here, as described above, 16 pixel data around the interpolation pixel generation are used, and for example,
In order to generate the upper right pixel shown as a black point in FIG.
Use pixels. Due to the nature of the cubic method, the edges of the interpolated pixels are ambiguous as compared with the pattern matching method, while reflecting the subtle changes between the pixels in the solid line and dotted line regions. That is, there is an edge in the diagonal direction of the interpolation pixel, but the luminance gradient in the lower left direction is gentle and the edge is ambiguous.

After the interpolation pixels are generated in the first interpolation processing and the second interpolation processing as described above, each interpolation pixel obtained by the first interpolation processing is multiplied by rate, and each interpolation pixel obtained by the second interpolation processing is obtained. Is multiplied by (1-rate) and FIG.
A superimposed pixel is generated as shown in FIG. As shown in FIG. 11E, when a superimposed interpolation pixel for the target pixel is generated, all the target pixels having the new coordinate values are subjected to the interpolation processing through the determination in step S122, and step S12 is performed.
At 4, the interpolated image data is transferred to the next stage of processing. However, the data amount of the interpolated image data may be extremely large depending on the interpolation magnification, and the memory area available for the printer driver 12c may not be so large in the first place. In such a case, the data may be output separately for each fixed data amount.

Finally, FIG. 34 shows a summary of the present embodiment. The digitalized image obtained by scanning the photograph by the scanner 11a becomes the original image data, which will be processed in the subsequent process. The means for acquiring the original image data is the image data acquiring means C11. In step S104, the original image data is read, and a histogram of luminance values is created in an area of 5 × 5 pixels around the target pixel. In step S106, the number of times that different luminance values appear in the obtained histogram is obtained. Although the superimposition ratio (rate) of the processing is set to 1, if the number is large, the rate is determined by the evaluation function F by judging that the nature of the image is a mixed image of a natural image or a non-natural image. This evaluation function F is the maximum luminance value Ym in the above area.
This is a function of the difference between ax and the minimum luminance value Ymin, and is an evaluation of the likelihood of an edge. The means for obtaining the superimposition ratio from the properties of the image in this way is the first superimposition ratio determination means C1
4.

In step S114, an interpolation process that tends to easily preserve edges is performed in a predetermined area centered on the target pixel by the pattern matching method or the near-list method. This is the first interpolation processing means C12. On the other hand, in step S118, the interpolation processing is performed by the cubic method in which the edges are ambiguous as compared with the pattern matching method while reflecting the subtle change between the pixels, and this is the second interpolation processing means C13.

In step S120, the pixel data separately interpolated are weighted and added by the calculation formula (1) using the superposition ratio (rate) determined as described above, and the interpolation is performed. Generate pixels. The sum of the superimposition ratios is naturally “1”. Of course,
This means is the image data superimposing means C15. Thereafter, when the pixel of interest is sequentially moved and the processing is performed for all the pixels, an interpolation image is created. Therefore, step S
At 124, the completed interpolation image data is output. This process is performed by the image data output unit C16. For example, when the image data is output to the printer 17b, a printed matter is obtained.

Next, another embodiment of the present invention will be described.
In this embodiment, an image data interpolation program is used for a printing system. Note that components and the like common to the above-described embodiments will be referred to as they are. The processing result of the application 12d is output as print data to the above-described color printer 17b via the printer driver 12c, and the color printer 17b prints the corresponding image by adding dots on printing paper using color ink. I do.

FIGS. 23 to 25 show a schematic configuration of a color ink jet printer 21 as an example of such a color printer. The color inkjet printer 21 includes a print head 21a including three print head units, a print head controller 21b that controls the print head 21a, a print head digit moving motor 21c that moves the print head 21a in the digit direction, A paper feed motor 21d for feeding the print paper in the row direction; a dot printing mechanism including a print controller 21e, which is an interface between the print head controller 21b, the print head girder moving motor 21c, and the paper feed motor 21d, and an external device; An image can be printed while scanning the print head 21a on a recording medium, which is a print sheet, according to the print data.

FIG. 24 shows a specific configuration of the print head 21a, and FIG. 25 shows an operation at the time of ink ejection. The print head 21a has a color ink tank 21a.
A fine pipe 21a3 extending from No. 1 to the nozzle 21a2 is formed, and an ink chamber 21a4 is formed at the end of the pipe 21a3. The wall surface of the ink chamber 21a4 is formed of a flexible material, and a piezo element 21a5, which is an electrostrictive element, is provided on the wall surface. The piezo element 21a5 has a crystal structure that is distorted by applying a voltage and performs high-speed electrical-mechanical energy conversion. The crystal structure distorting operation pushes the wall surface of the ink chamber 21a4, and the ink chamber 21a4 is pressed. Decrease the volume of Then, a predetermined amount of color ink particles is vigorously ejected from the nozzle 21a2 communicating with the ink chamber 21a4. This pump structure will be called a micropump mechanism.

It should be noted that two independent rows of nozzles 21a2 are formed in one print head unit, and color ink is supplied independently to the nozzles 21a2 in each row. Therefore, each of the three print head units is provided with two rows of nozzles, and it is possible to use the six color inks by making the best use. FIG.
In the example shown in FIG. 2, two rows in the left print head unit are used for black ink, only one row in the middle print head unit is used for cyan ink, and left and right print head units in the right row are used. Two rows are used for magenta ink and yellow ink, respectively.

On the other hand, the vertical spacing of the nozzles 21a2 formed in the print head 21a does not match the printing resolution, and the nozzles 21a2 are generally formed with a larger spacing than the printing resolution. Despite this, higher resolution printing is possible because the paper feed motor 21d is controlled in the paper feed direction. For example,
The resolution is improved by feeding the paper in eight steps between the nozzles 21a2 and printing by operating the print head 21a in the digit feed direction at each step. Of course, in the digit feed direction, if the color ink is ejected at an arbitrary interval, it can be said that it is the resolution, so it can be said that it depends on the timing control. Note that, in a strict sense, the dot diameter of the color ink can also be a factor of the resolution, but is ignored here for the sake of easy understanding.

In the present embodiment, the printing is executed based on the image data acquired by the image input device of the computer system 10 on the premise of the hardware system described above. At this time, when there is a difference between the resolution of the original image data and the resolution of the color printer 17b, the interpolation processing is executed. Here, when the application 12d executes the printing process, the color printer 17
The change in resolution and gradient when print data is output for b will be described.

FIG. 26 shows the flow of image data.
Application 12d is operating system 1
A print request is issued to 2a, and at that time, the output size, print paper, print speed, ink type, and RGB 256-gradation image data are transferred. Then, the operating system 12a transfers the output size, printing paper, printing speed, ink type, and image data to the printer driver 12c. Here, operating system 1
2a outputs the operation result of the keyboard 15a and the mouse 15b to the printer driver 12c while displaying on the display 17a via the display driver 12b,
The printer driver 12c performs image interpolation processing so that the output size becomes the designated output size, and generates print data. Normally, this print data has two gradations of CMYK, and is output from the hardware port to the color printer 17b via the operating system 12a.

As described above, in this embodiment, the print control program is executed by the computer system 10 to output print data to the color printer 17b.
The target printing device is not limited to the above-described inkjet type color printer 21. For example,
The color printer 21 is of an ink jet type employing a micro pump mechanism, but it is also possible to employ a printer other than the micro pump mechanism. As shown in FIG. 27, a heater 21a8 is provided on the wall of a pipe 21a7 near the nozzle 21a6, and the heater 21a8
A bubble jet (R) type pump mechanism has been put to practical use in which bubbles are generated by heating the ink in such a manner as to discharge color ink at the pressure.

FIG. 28 shows a schematic structure of a main part of a so-called electrophotographic color printer 22 as another mechanism. A charging device 22b and an exposure device 22c are provided on the peripheral edge of the rotating drum 22a as a photosensitive member in accordance with the rotation direction.
, A developing device 22d, and a transfer device 22e. After the charging device 22b uniformly charges the peripheral surface of the rotating drum 22a, the exposure device 22c removes the charge of the image portion, and the developing device 22d charges. The toner is adhered to a portion that is not covered, and the toner is transferred onto paper as a recording medium by the transfer device 22e. Thereafter, the toner is melted by passing between the heater 22f and the roller 22g, and is fixed on the paper. Since these are combined to perform printing with one color toner, a total of four colors are separately provided.

That is, the specific configuration of the printing apparatus is not particularly limited, and various changes can be made not only in the application range of such an individual printing method but also in the application mode. Next, processing for executing optimal image processing according to the output resolution by using the above-described printing system will be described. FIG. 29 is a block diagram showing a schematic configuration of the printing system. Print quality acquisition means C
21 acquires the print quality to be printed in the executed print job, and the first interpolation processing means C22 and the second interpolation processing means C23 perform interpolation processing for increasing the number of constituent pixels in the image data. Here, the first interpolation processing means C22 can execute the pattern matching method and the near-list method as the interpolation processing, and the second interpolation processing means C23 can execute the cubic method as the interpolation processing. .

The second superimposition ratio determining means C24 performs the first interpolation processing means C22 and the second interpolation processing means C2 on the basis of the print quality acquired by the print quality acquisition means C21.
3 to determine the superimposition ratio of the interpolated pixels. When the second superimposition ratio determining unit C24 determines the superimposition ratio, the image data superimposing unit C25 superimposes the interpolated pixel data by the first interpolation processing unit C22 and the second interpolation processing unit C23 at the superimposition ratio. Then, the print control processing means C2
Reference numeral 6 generates print data and executes printing based on the pixel data superimposed by the image data superimposing means C25.

FIG. 30 shows the printer driver 12c described above.
2 shows a software flow relating to a printing process executed by the. In FIG. 30, in step S302, original image data is obtained. For example, when an image is read from the scanner 11a by the application 12d and subjected to predetermined image processing and then subjected to print processing, image data of a predetermined resolution is transferred to the printer driver 12c via the operating system 12a. Stages apply.

In step S303, print settings are input. When the printing process is executed by the application 12d, assuming that the operating system 12a provides a GUI environment, a window for a printing operation is displayed as shown in FIG. Various parameters and the like can be adopted here. In the present embodiment, there are “number of copies (for printing)”, “start page”, and “end page”. As the operation instruction buttons, a "printer setting" button is prepared in addition to the "OK" button and the "cancel" button.

When "printer setting" is instructed,
A window is displayed as shown in FIG. This window display is prepared for performing various settings according to the function of each printer, and as described later, the superimposition ratio is changed according to the settings in this window. In this example, “(print) resolution” is “360 dpi” and “72 dpi”.
0 dpi "can be selected. Also, the size of “A4” or “B5” as “paper”, the quality of “plain paper” or “glossy paper”, and the “print speed” of “fast” or “slow”
Alternatively, a "pigment" or a "dye" can be selected as the "ink". Of course, this print setting is an example, and there is no need to configure the user to select all of them, and various modes such as performing ink type and the like by performing bidirectional communication with the color printer 17b can be adopted. is there.

In the present embodiment, the above-mentioned setting contents are stored in a setting file under the control of the operating system 12a. When the setting is changed by the printing operation by the operator, the changed setting is read as the print setting. When the processing in step S303 is viewed as software, the print quality obtaining step or the print quality obtaining function is performed. And hardware are not directly included. On the other hand, if it is considered that it is organically integrated with hardware such as a CPU, it corresponds to the print quality acquisition unit C21.

In step S304, in the read image data, the pixel to be interpolated is set as the target pixel, and the pixel data of the surrounding 5 × 5 pixel area around the target pixel is set as the reference pixel, and the luminance value of the reference pixel is set. Create a histogram of In step S306, the number of times different luminance values appear in the obtained histogram is obtained, and it is determined whether the number of appearances is smaller than 15. Since the number of colors in the reference pixel increases as the number of different luminance values increases, the image in which the number of occurrences of the luminance value is smaller than 15 is regarded as a non-natural image. When it is determined, the superimposition ratio (rate) of the first interpolation processing is set to 1 in step S310.

In step S306, the number of appearances of the luminance value is 1
If it is determined that it is not smaller than 5, step S30
In step 7, the evaluation function F is determined based on the print setting contents input in step S303. In the determination of the evaluation function F in step S307, the evaluation function F is determined so that the superimposition ratio of the second interpolation process is increased when the print quality is high. Although details will be described later, in the present embodiment, two types of evaluation functions are prepared for a case where the print quality is high and a case where the print quality is low. It is smaller than the value. The evaluation function F is determined by the luminance value width of the reference pixel, that is, the maximum luminance value Ymax and the minimum luminance value Ymin in the reference pixel.
Is a function of the difference between

In step S308, step S30 is performed.
The difference between the maximum luminance value Ymax and the minimum luminance value Ymin based on the histogram is substituted for the evaluation function F determined in step 7, and the rate is determined. Therefore, step S
A series of processing shown in 304, S306, S307, S308, and S310 corresponds to a second superposition ratio determination step or a second superposition ratio determination function, and these are considered to be organically integrated with hardware such as a CPU. And the second superimposition ratio determining means C24.

Step S308 or step S31
When the rate is determined at 0, it is determined at step S312 whether or not the rate is "0". When the rate is "0", the first interpolation processing is not performed. If it is not determined in step S312 that the rate is "0", a first interpolation process is executed in step S314. Therefore, this processing corresponds to the first interpolation processing step or the first interpolation processing function,
If these are considered to be organically integrated with hardware such as a CPU, they constitute the first interpolation processing means C22.

Further, in step S316, it is determined whether or not the rate is "1". When the rate is "1", the second interpolation processing is not performed. If the rate is not determined to be "1" in step S316, a second interpolation process is executed in step S318. Therefore, this processing corresponds to the second interpolation processing step or the second interpolation processing function.
Assuming that the second interpolation processing unit C23 is organically integrated with hardware such as U, the second interpolation processing unit C23 is configured. After the rate is determined in this manner and the first interpolation processing or the second interpolation processing is executed, step S3 is performed.
In step 20, based on the rate, the pixel data is superimposed by the above-described equation (1) to generate an interpolation pixel. Superimposed data = (first interpolation processing) × rate + (second interpolation processing) × (1-rate) (1) Therefore, this processing corresponds to the image data superimposing step or the image data superimposing function, and these are If it is considered that the image data is organically integrated with hardware such as a CPU, the image data superimposing means C25 is configured. When the generation of the interpolation data for the target pixel is completed in this way, it is determined in step S322 whether or not the superimposition processing of the input original image data for all the target pixels has been completed, and the processing for all the target pixels is determined. Is repeated until the processing is completed.

When the superimposition processing for all the target pixels is completed, printing is performed based on the interpolated image data in step S324. In the case of the printer driver 12c, print data cannot be obtained only by resolution conversion, but requires color conversion or halftone processing. Therefore,
Here, the print data is output to the color printer 17b after executing the conversion processing and the like, and this processing corresponds to the print control processing step or the print control processing function. If it is considered that they are organically integrated, the print control processing means C26 is constituted.

Next, more specific processing for the above flow will be described. In the present embodiment,
It is determined whether the original image acquired in step S302 is a computer graphic (non-natural image) or a photograph (natural image), and the determination result is reflected not only in print quality but also in the superimposition ratio. . Of course, a configuration in which the above rate is not fixed to “1” or “0” is also possible in order to prevent an error in determining the interpolation method. It is emphasized that the image is an image and the second interpolation process is performed on the non-natural image so that the outline of the image is not blurred.

Specifically, this determination uses a histogram of luminance values shown in FIG. 14. In step 104, a luminance value is obtained for each reference pixel in a 5 × 5 pixel area, and a range in which the luminance can be obtained is obtained. , The histogram of the number of pixels is totaled. Then, the number of appearances of the luminance value, that is, the number of luminance values whose distribution number is not “0” is counted, and the determination in step S306 is performed. As described above, when the number of distributions whose distribution number is not “0” is more than 15 in the luminance value histogram, the superimposition ratio of the first interpolation processing is determined by the evaluation function. In the present embodiment, two types of evaluation functions are prepared in advance, one for high print quality and the other for low print quality, and one of the setting contents set in the window shown in FIG. If there is, it is determined in step S306 to use the high print quality evaluation function. FIG. 33 shows an example of the evaluation function F (y). FIG. 33 shows an evaluation function for low printing quality, and FIG. 33 shows an evaluation function for high printing quality.

In the evaluation function F1 (y), “y” is “0 ≦ y ≦
255 ”and fluctuates in a range of“ 0 ≦ F (y) ≦ 1 ”. In the range of “0 ≦ y ≦ 64”, “F
(Y) = 0 ”,“ F (y) = 1 ”in the range of“ 192 ≦ y ≦ 255 ”, and F (y) in the range of“ 64 ≦ y ≦ 192 ”from“ 0 ”to“ 0 ”. 1 "in a linear fashion. Further, the evaluation function F2 (y) is such that “y” is “0 ≦ y ≦ 2”.
55, and fluctuates within a range of “0 ≦ F (y) ≦ 0.7”. In the range of “0 ≦ y ≦ 64”, “F (y) = 0”, in the range of “192 ≦ y ≦ 255”, “F (y) = 0.7”, and in the range of “64 ≦ y”. ≦ 19
In the range of “2”, F (y) linearly increases from “0” to “0.7”.

Here, the luminance value width “Ymax−Ymin” in FIG. 14 is substituted for “y”. Therefore,
As the luminance value width is larger, that is, as the relationship between the reference pixels is more like an edge, the rate is larger. Furthermore, the value of the evaluation function F1 is always equal to or greater than the value of the evaluation function F2 even for the same luminance value width. That is, in the case of high-quality printing even with the same luminance value width, the superimposition ratio of the second interpolation processing for performing the interpolation processing without deteriorating the gradation of the image increases.

“High print resolution”, “High print paper quality”,
If one or a combination of "slow printing" is selected, that is, if printing is performed with high print quality and interpolation that does not impair the gradation of the original image is performed, subtle gradation changes may occur in the print result. Reproduced as a visible effect. On the other hand, when printing is performed with low print quality, even if printing is performed using print data that can reproduce subtle gradation changes, the effect is not reproduced as a user-visible effect in the print result. Therefore, it can be said that the second interpolation process is worthwhile at the time of high print quality. In this embodiment, the superimposition ratio of the second interpolation process is increased by using the evaluation function F2.

In the present embodiment, the type of ink also affects the selection of the evaluation function. Here, the type of ink can be selected from "pigment" and "dye", and it cannot be unconditionally determined that any one is of high quality, but "pigment" tends to be less blurred than "dye" There is a tendency that the edge of the “pigment” is more conspicuous in the printing result when both are compared. Therefore, it can be said that the type of the ink reflects the print quality, and in the present embodiment, if it is intended to obtain the same printing result with both inks, the "dye"
In this case, the ratio of the second interpolation processing should be made higher than that of. Therefore, in the present embodiment, when the ink is “pigment”, the above evaluation function F2 is used.

Of course, the method of reflecting the evaluation function and the acquired print settings in the present embodiment is merely an example, and various modes can be adopted. For example, the evaluation functions need not be two types as described above, and a plurality of types can be prepared. When there are four types of print setting items that affect print quality as in the above-described embodiment, four evaluation functions are prepared, and an evaluation function having a smaller maximum value is selected each time an item set to high quality increases by one. It is possible to do it.

As described above, there is no need to prepare a plurality of evaluation functions in advance, and one evaluation function F1 described above is prepared and the evaluation function is increased by “0.7 times” during high quality printing.
It is also possible to configure the rate to be determined using the result, or to configure the evaluation function to be “0.9 times” every time one item is set to high quality. Furthermore, the shape of the function need not be limited to the example shown in FIG. 33 as long as it is a function that monotonically increases with the luminance value width when the luminance value width is a variable, and the luminance value width is “0”.
It is also possible to employ an evaluation function that smoothly changes over a range from “255” to “255”.

When the evaluation function to be used is determined in step S307 as described above, in step S308, the superimposition rate is determined based on the determined evaluation function. In the case of the histogram as shown in FIG. 14, since there are 19 luminance values for which the number of distributions is not "0", step S30 is performed.
After the determination in step 6, the processing in step S307 is executed. Also, as shown in FIG. 32, the resolution is "720 dpi",
When "Glossy paper" is selected for paper, "Fast" for printing speed, and "Pigment" for ink, print settings to select the evaluation function for high quality printing with resolution and paper and ink Are performed, the evaluation function F2 is selected in step S307. Then, in step S308, the rate is determined by the evaluation function F2, and in step S31
4 and step S318, a first interpolation process and a second interpolation process are executed, respectively.

After generating the interpolation pixel by the first interpolation processing, if the rate is not "1", the second interpolation processing is further executed. Regardless of which of the M cubic method and the normal cubic method is used, if the interpolation pixel is generated by the cubic method in the second interpolation processing, the variable of the superimposition data calculation formula (1), that is, the first All of the interpolation processing data, rate, and the second interpolation processing data have been calculated. Substitution data of the interpolated pixel is calculated by substituting the value into the equation (1).

Here, as shown in FIG. 32, the print quality in this state is high, and a subtle gradation change can be reflected on the print result. At this time, since the evaluation function is F2, the superposition ratio of the cubic method is high. On the other hand, in the window shown in FIG. 32, the print quality is low when “360 dpi” is selected for resolution, “plain paper” for paper, “fast” for print speed, and “dye” for ink. The effect of the second interpolation process is inconspicuous. In this state, as described above, the evaluation function F1
Are used, and the superimposition ratio of the second interpolation processing is lower than the setting state shown in FIG. At this time, the superposition ratio of the pattern matching method for emphasizing the edge or the near-list method is higher than that of the cubic method for reproducing the gradation value, and a sharp print result can be obtained despite low print quality.

Finally, an overview of the present invention is shown in FIG. Various settings can be made according to the function of each printer in the window of “printer settings”, and the settings are stored in a setting file. If the setting has already been made, the setting file is read with reference to the setting file. If the setting content is changed along with the printing operation, the changed setting content is read. In step S303, the print settings are input in this manner, which corresponds to the print quality acquisition unit C21.

On the other hand, in step S304, the original image data is read, and a histogram of luminance values is created in an area of 5 × 5 pixels around the target pixel. Step S
In step 306, the number of times that different luminance values appear in the obtained histogram is obtained. If the number is less than a predetermined threshold value, it is determined that the nature of the image is a simple non-natural image, and the first interpolation processing is performed. Although the superimposition ratio (rate) is set to 1, the image function is determined to be a mixed image of a natural image and a non-natural image if the ratio is too large, and the evaluation function is determined by the evaluation function F to determine the rate. The evaluation function F is determined according to the print quality, and includes an evaluation function F1 for low image quality and an evaluation function F2 for high image quality. The evaluation function F is a function of the difference between the maximum luminance value Ymax and the minimum luminance value Ymin in the area, and is an evaluation of the likelihood of an edge. If the likelihood of an edge is high, the ratio of interpolation processing with a relatively low computational load in which the edge is likely to be emphasized by the evaluation function F1 is increased; Enhance. The means for obtaining the superimposition ratio from the print quality in this way is the second superimposition ratio determination means C24.

In step S314, an interpolation process which tends to preserve edges in a predetermined area centering on the pixel of interest by the pattern matching method or the near-list method is performed. This is the first interpolation processing means C22. On the other hand, in step S318, since the interpolation processing is performed by the cubic method in which the edges are ambiguous compared to the pattern matching method while reflecting the subtle change between pixels, this is the second interpolation processing means C23.

In step S320, the pixel data separately interpolated are weighted and added by the calculation formula (1) using the superimposition ratio (rate) determined as described above, and interpolation is performed. Generate pixels. The sum of the superimposition ratios is naturally “1”. Of course,
This means is the image data superimposing means C25. Thereafter, if the pixel of interest is sequentially moved and the processing is performed for all the pixels, an interpolated image is created. In step S324, the printer 17b prints using the completed interpolated image data. This process is performed by the print control processing means C2.
6.

As described above, in the present invention, the first interpolation processing and the second interpolation processing are superimposed based on a predetermined evaluation function. Therefore, the edges of the superimposed pixels are ambiguous as compared with the case using only the pattern matching method, but the edges are sharper as compared with the case using only the cubic method. Further, the subtle gradation change is reduced as compared with the case using only the cubic method, but the gradation change is richer as compared with the case using only the pattern matching method. Further, since the evaluation function is a function of the brightness value width, it is possible to determine the interpolation processing ratio according to the properties of the image. Further, the superimposition ratio of the interpolation processing more suitable for the print quality that directly affects the effect of the interpolation processing is increased. As a result, the advantages of the individual interpolation processes become more noticeable. Also, there is no case where the drawbacks of both are highlighted. Therefore, it is possible to perform an accurate interpolation process according to print quality while preventing an error in determining an interpolation method based on the property determination of an image to be interpolated.

[Brief description of the drawings]

FIG. 1 is a block diagram of an image data interpolation device according to an embodiment of the present invention.

FIG. 2 is a block diagram of specific hardware of the image data interpolation device.

FIG. 3 is a schematic diagram showing another application example of the image data interpolation device of the present invention.

FIG. 4 is a schematic diagram showing another application example of the image data interpolation device of the present invention.

FIG. 5 is a schematic diagram showing another application example of the image data interpolation device of the present invention.

FIG. 6 is a schematic diagram showing another application example of the image data interpolation device of the present invention.

FIG. 7 is a flowchart of a process related to resolution conversion executed by a printer driver.

FIG. 8 is a flowchart of a first interpolation process.

FIG. 9 is a diagram illustrating a histogram of luminance values.

FIG. 10 is a diagram showing a specific example of an evaluation function F (y).

FIG. 11 is a diagram illustrating an example of a luminance pattern of 3 × 3 pixels.

FIG. 12 is a diagram showing an area of 5 × 5 pixels as reference pixels.

FIG. 13 is a diagram showing a specific example of an edge pattern.

FIG. 14 is a conceptual diagram of a near-list method.

FIG. 15 is a diagram showing a situation in which data of each grid point is transferred by the near list method.

FIG. 16 is a schematic diagram showing a situation before interpolation in a near-list method.

FIG. 17 is a schematic diagram showing a situation after interpolation in the near-list method.

FIG. 18 is a conceptual diagram of the cubic method.

FIG. 19 is a diagram showing a data change situation when the cubic method is specifically applied.

FIG. 20 is a diagram showing a specific application example of the cubic method.

FIG. 21 is a diagram showing a specific application example of the M cubic method.

FIG. 22 is a diagram illustrating a specific manner in which superimposition processing is performed on image data.

FIG. 23 is a schematic block diagram of an ink jet type color printer.

FIG. 24 is a schematic explanatory view of a print head unit in the color printer.

FIG. 25 is a schematic explanatory view showing a situation in which color ink is ejected by the print head unit.

FIG. 26 is a flowchart showing the flow of image data in the printing system.

FIG. 27 is a schematic explanatory view showing a state in which color ink is ejected by a print head of a bubble jet (R) system.

FIG. 28 is a schematic explanatory view of an electrophotographic printer.

FIG. 29 is a block diagram illustrating a schematic configuration of a printing system.

FIG. 30 is a flowchart of a process related to a printing process executed by the printer driver.

FIG. 31 is a diagram illustrating an operation window of a printing process.

FIG. 32 illustrates an operation window for setting a printer.

FIG. 33 is a diagram illustrating a specific example of an evaluation function F (y).

FIG. 34 is a diagram showing a schematic configuration of the present invention.

FIG. 35 is a diagram showing a schematic configuration of the present invention.

FIG. 36 is a diagram showing a condition determination for a 45-degree edge.

FIG. 37 is a diagram showing a condition determination for a 30-degree edge.

FIG. 38 is a diagram showing the correspondence between original pixels and interpolated pixels when a horizontal edge is found in the case of double interpolation.

FIG. 39 is a diagram showing the correspondence between original pixels and interpolated pixels when a right-angled edge is found in the case of double interpolation.

FIG. 40 is a diagram illustrating a correspondence between an original pixel and an interpolated pixel when a 45-degree edge is found in the case of double interpolation;

FIG. 41 is a diagram showing a condition determination for a 45-degree edge.

FIG. 42 is a diagram illustrating a correspondence between an original pixel and an interpolated pixel when a 30-degree edge is found in the case of double interpolation.

FIG. 43 is a diagram showing a condition judgment for a 30-degree edge.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 ... Computer system 11a ... Scanner 11b ... Digital still camera 11c ... Video camera 12 ... Computer main body 12a ... Operating system 12b ... Display driver 12c ... Printer driver 12d ... Application 13a ... Floppy (R) disk drive 13a1 ... Floppy (R) disk 13b Hard disk 13c CD-ROM drive 13c1 CD-ROM 14a Modem 15a Keyboard 15b Mouse 17a Display 17b Color printer 18a Color facsimile device 18b Color copy device 21 Color inkjet printer 22 Color printer

Continued on front page F term (reference) 2C262 AA24 AA26 AB13 BB03 BC13 DA09 DA11 DA18 EA04 EA06 5B057 AA11 CA08 CA12 CA16 CB08 CB12 CB16 CC01 CD06 CG07 DA08 DB02 DB09 DC23 5C076 AA21 AA22 AA27 AA31 BA05 BA06 BB25

Claims (33)

[Claims] 1. An image data interpolation program for performing pixel interpolation on image data in which a computer expresses an image in multi-tones using dot matrix pixels, comprising: an image data acquisition function for acquiring the image data; A first interpolation processing function for performing interpolation on the image data without reducing the degree of pixel change, and a second interpolation processing function for performing interpolation on the image data without impairing the gradation of the image A first superimposition ratio determining function of determining the characteristics of the image based on reference pixels around the pixel to be interpolated, and determining the superimposition ratio of the first and second interpolation processes based on the same characteristics; An image data superimposition function for superimposing the image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function at the set superimposition ratio; and interpolating the superimposed data. An image data interpolation program for causing a computer to execute an image data output function of outputting as subsequent data. 2. The image data interpolation program according to claim 1, wherein the first interpolation processing function performs interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel. An image data interpolation program capable of executing interpolation and interpolation by nearest neighbor interpolation. 3. The image data interpolation program according to claim 1, wherein the first superimposition ratio determination function determines a superimposition ratio by an evaluation function that depends on data of the reference pixel. An image data interpolation program, characterized in that: 4. The image data interpolation program according to claim 1, wherein the first superimposition ratio determining function superimposes based on the number of appearances of different gradation values in the reference pixel. An image data interpolation program for determining a ratio. 5. The image data interpolation program according to claim 4, wherein the first superimposition ratio determining function is configured to execute the first superimposition ratio when the number of appearances of different gradation values in the reference pixel is smaller than a predetermined threshold value. An image data interpolation program for providing a superimposition ratio that uses only a first interpolation process. 6. The image data interpolation program according to claim 4, wherein the first overlapping ratio determining function is configured to perform the first interpolation processing as the gradation value width of the reference pixel increases. Image data interpolation program characterized by increasing the superimposition ratio of 7. The image data interpolation program according to claim 4, wherein the gradation value of the reference pixel is a luminance value of the reference pixel. . 8. An image data interpolation program according to claim 1, wherein a print quality is obtained when printing is performed by a printing apparatus using said image data. A function, a second superimposition ratio determining function for determining a superimposition ratio of the first and second interpolation processes based on the obtained print quality, and a print control for executing a print control process based on the superimposed data. An image data interpolation program for causing a computer to execute a processing function. 9. The image data interpolation program according to claim 8, wherein the second superimposition ratio determination function determines the superimposition ratio by an evaluation function that depends on print quality. 10. The image data interpolation program according to claim 8, wherein the second superimposition ratio determination function is configured to superimpose the second interpolation process as the acquired print quality becomes higher. An image data interpolation program characterized by increasing the ratio. 11. The image data interpolation program according to any one of claims 8 to 10, wherein the second superimposition ratio determination function is configured to perform only the first interpolation processing when the acquired print quality is high. The image data interpolation program is characterized in that the image data interpolation program is not executed. 12. An image data interpolation method for performing pixel interpolation on image data in which an image is expressed by multi-gradation using pixels in a dot matrix, comprising: an image data obtaining step of obtaining the image data; A first interpolation processing step of performing interpolation without reducing the degree of change of pixels by performing the interpolation, and a second interpolation processing step of performing interpolation on the image data without impairing the gradation of the image. Determining a property of the image based on reference pixels around the pixel, and determining a superimposition ratio of the first and second interpolation processes based on the same property, a first superimposition ratio determining step; An image data superimposing step of superimposing the image data obtained by the first interpolation processing step and the interpolation image data obtained by the second interpolation processing step, and outputting the superimposed data as data after the interpolation processing Image data interpolating step. 13. The image data interpolation method according to claim 12, wherein the first interpolation processing step performs pattern matching that performs interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel. An image data interpolation method capable of performing interpolation and interpolation by nearest neighbor interpolation. 14. The image data interpolation method according to claim 12, wherein the first superimposition ratio determining step determines a superimposition ratio by an evaluation function depending on the data of the reference pixel. A method for interpolating image data, comprising: 15. The image data interpolation method according to any one of claims 12 to 14, wherein the first superimposition ratio determining step superimposes based on the number of appearances of different gradation values in the reference pixel. An image data interpolation method characterized by determining a ratio. 16. The image data interpolation method according to claim 15, wherein the first superimposition ratio determining step is performed when the number of appearances of different gradation values in the reference pixel is smaller than a predetermined threshold value. An image data interpolation method characterized by giving a superimposition ratio such that only the first interpolation processing is used. 17. The image data interpolation method according to claim 15, wherein in the first superimposition ratio determining step, the first interpolation processing is performed as the gradation value width of the reference pixel increases. Image data interpolation method characterized by increasing the superimposition ratio of the image data. 18. The image data interpolation method according to claim 15, wherein the gradation value of the reference pixel is a luminance value of the reference pixel. . 19. The image data interpolation method according to claim 12, wherein a print quality is obtained when printing is performed by a printing apparatus using the image data. A step of determining a superimposition ratio of the first and second interpolation processes based on the acquired print quality; and a process of performing a print control process based on the superimposed data. An image data interpolation method characterized by causing a computer to execute the processing steps. 20. The image data interpolation method according to claim 19, wherein the second superimposition ratio determining step determines the superimposition ratio by an evaluation function dependent on print quality. 21. The image data interpolation method according to claim 19, wherein the step of determining the second superimposition ratio includes the step of superimposing the second interpolation process as the acquired print quality becomes higher. An image data interpolation method characterized by increasing the ratio. 22. The image data interpolation method according to any one of claims 19 to 21, wherein the second superimposition ratio determining step includes only the first interpolation processing when the acquired print quality is high. The image data interpolation method is characterized in that the image data is not executed. 23. An image data interpolating apparatus that performs pixel interpolation on image data in which an image is represented by multi-gradation using pixels in a dot matrix, comprising: an image data acquiring unit that acquires the image data; First interpolation processing means for performing interpolation without reducing the degree of change of pixels, and second interpolation processing means for performing interpolation on the image data without impairing the gradation of the image. First overlapping ratio determining means for determining the characteristics of the image based on the reference pixels around the pixels and determining the overlapping ratios of the first and second interpolation processes based on the same characteristics; An image data superimposing means for superimposing the image data by the first interpolation processing means and the interpolated image data by the second interpolation processing means; and outputting the superimposed data as data after the interpolation processing. An image data interpolating apparatus comprising: 24. The image data interpolation device according to claim 23, wherein said first interpolation processing means performs interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel. An image data interpolation apparatus capable of executing interpolation and interpolation by nearest neighbor interpolation. 25. The image data interpolation device according to claim 23, wherein the first superimposition ratio determining means determines a superimposition ratio by an evaluation function depending on the data of the reference pixel. An image data interpolation device characterized by the above-mentioned. 26. The image data interpolation device according to claim 23, wherein the first superimposition ratio determining unit superimposes the image data based on the number of appearances of different gradation values in the reference pixel. An image data interpolation device for determining a ratio. 27. The image data interpolation device according to claim 26, wherein said first superimposition ratio determining means is configured to determine that the number of appearances of different tone values in the reference pixel is smaller than a predetermined threshold value. An image data interpolating device, which provides a superimposition ratio that uses only a first interpolation process. 28. The image data interpolation device according to claim 26, wherein the first superimposition ratio determination means performs the first interpolation processing as the gradation value width of the reference pixel increases. An image data interpolation apparatus characterized by increasing the superimposition ratio of the image data. 29. The image data interpolation apparatus according to claim 26, wherein the gradation value of the reference pixel is a luminance value of the reference pixel. . 30. The image data interpolation apparatus according to claim 23, wherein a print quality is obtained when printing is performed by a printing apparatus using the image data. Means, a second superposition ratio determining means for determining a superposition ratio of the first and second interpolation processes based on the obtained print quality, and a print control for executing a print control process based on the superimposed data. An image data interpolating apparatus, which causes a computer to execute processing means. 31. The image data interpolation device according to claim 30, wherein said second superimposition ratio determining means determines the superimposition ratio by an evaluation function dependent on print quality. 32. The image data interpolation device according to claim 30, wherein the second superimposition ratio determination unit performs superimposition of the second interpolation process as the acquired print quality becomes higher. An image data interpolation device characterized by increasing the ratio. 33. The image data interpolation device according to claim 30, wherein said second superimposition ratio determination means performs only said first interpolation processing when said acquired print quality is high. Is not executed.