JP2004254334A - Image data interpolation program, image data interpolation method and image data interpolation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve problems in a conventional image data interpolation device that has difficulty of automatically and properly discriminating the property of an image such as logo and illustration images and a natural image, has performed improper interpolation processing under wrong discrimination, and has caused useless interpolation processing used to reproduce delicate gradation for the natural image when printing with low quality is actually performed. <P>SOLUTION: First interpolation processing and second interpolation processing are overlappingly applied to an image on the basis of a prescribed evaluation function. Since the evaluation function depends on the property of image quality, an interpolation processing ratio in response to the property of the image can be decided so as to increase a ratio of more proper interpolation processing thereby making the advantages of respective interpolation processing processes more conspicuous and not standing out disadvantages of the both. As a result, an error in deciding an interpolation method can be prevented on the basis of the discrimination of the property of an interpolation object image. Furthermore, increasing a superimposing ratio of more proper interpolation processing in response to the print quality affecting the effect of the interpolation processing can perform proper interpolation processing in response to the print quality. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

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

When an image is handled by a computer or the like, the image is represented by dot matrix pixels, 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). In this case, when an image of 640 × 480 dots is printed in correspondence with each dot, the size becomes extremely small. In this case, since the tone value is different and the meaning of the resolution itself is different, it is necessary to interpolate between dots and convert the data into printing data. 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, and in the opposite case, the pixels of the image data are reduced. Processing (this is called lower resolution or reduction) is performed.

  Conventionally, as a method of interpolating dots 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: hereinafter, Techniques such as a cubic method) and a pattern matching method are known. 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, although the tone of the original image tends to be lost, 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 image gradations.

  Conventionally, in order to print on a printing device based on image data, the pattern matching method is conventionally used when the interpolation target image is considered to be a logo or illustration, and the cubic method is used when it is considered to be a natural image are doing. However, it is generally not easy to automatically and accurately discriminate the nature of an image, that is, an image of a logo or an illustration from a natural image, and an inappropriate interpolation process may be performed when the discrimination is mistaken. . Further, the distinction between a logo or 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.

Furthermore, even if the processing according to the properties of the image is performed, it is wasteful to perform the interpolation processing for reproducing the delicate gradation for the natural image despite the low quality printing when actually printing. It is.
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 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.

  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, and acquires the image data. An image data acquisition function, a first interpolation processing function for performing interpolation on the image data without reducing the degree of pixel change, and performing an interpolation on the image data without impairing the gradation of the image A second interpolation processing function, and a first superimposition ratio that determines a property of an image based on reference pixels around a pixel to be interpolated, and determines a superimposition ratio of the first and second interpolation processes based on the same property. A determination function, and an image data superimposition function of superimposing the image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function at the determined superimposition ratio. There the image data output function of outputting the data as the data after the interpolation processing as configured to be executed by a computer.

  In the invention configured as described above, in 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. Interpolation processing can be performed on the acquired image data by a first interpolation processing function and a second interpolation processing function, and the first interpolation processing function The interpolation is performed without reducing the degree, and the second interpolation processing function performs the interpolation without impairing the gradation of the image.

  Further, the first superimposition ratio determination function determines the properties of the image based on reference pixels around the pixel to be interpolated, determines the superimposition ratios of the first and second interpolation processes based on the same properties, and The superimposition function superimposes the image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function at the superimposition ratio determined by the first superimposition ratio determination function. Then, the image data output function outputs the superimposed data as data after the interpolation processing. That is, according to the present invention, two types of interpolation processing of a first interpolation processing function and a second interpolation processing function are possible, 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 any 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-described first superimposition ratio is not limited to the ratio in the case where the first and second interpolation processes are mixed, but can naturally take a value of “0” or “1” indicating only one of them.
As described above, according to the present invention, two types of interpolation processing are superimposed on the basis of an evaluation function that depends on data of reference pixels, so that it is possible to prevent an error in determining an interpolation method based on property determination of an image to be interpolated. 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 performed without deteriorating the gradation of the image, and the subtle gradation values that change slightly between pixels are not uniformed. Interpolation processing that can reproduce a change in the tone value corresponds to this. Also, by referring to a plurality of pixels around the pixel to be interpolated in the first superimposition ratio determination function, it becomes possible to grasp the change tendency of the pixel in the referred pixel, and the image to be interpolated is “natural image”, The characteristics of the image such as "illustration" are found. 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 processing and the second interpolation processing that is more suitable is increased according to the property of the image to be interpolated. be able to.

As described above, there are various types of interpolation processing methods. Even if 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 may be capable of selecting 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 an available one from among 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 pattern matching interpolation in which interpolation is performed according to a predetermined rule when a predetermined pattern exists in a reference pixel. And interpolation by the near-list method can be made executable. In other words, in pattern matching interpolation, a predetermined pattern is often detected based on the degree of change in pixels. Therefore, interpolation is performed on the detected pattern while maintaining the degree of change or emphasizing the detected pattern. By determining the interpolation rule in advance, image interpolation can be performed 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. In this case, the execution of the near-list method may be selected.
As described above, according to the present invention, interpolation can be easily performed without reducing the degree of change in pixels.

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 a cubic method. That is, in the interpolation by the cubic method, generally, 16 pixels around the interpolation pixel are used as reference pixels, and the interpolation pixel data is reflected 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. Generate 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 such a 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 execute interpolation without deteriorating the gradation of an image.

In superimposing two systems of interpolation processing on the interpolation target image, the first superimposition ratio determination function only needs to be able to determine the superimposition ratio of the two, and there are various determination methods. 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 that depends on the data of the reference pixel, and the reference pixel reflects the property of the interpolation target image as described above. Therefore, by using the evaluation function depending on the data of the reference pixel, it is possible to easily determine the superimposition ratio of the first and second interpolation processes.
As described above, according to the present invention, the superimposition ratio can be easily calculated.

As an example of a specific method of determining the superimposition ratio, in another aspect of the present invention, the first superimposition ratio determination function may be configured to determine the superimposition ratio based on the tone value of the reference pixel. That is, the gradation value of the reference pixel reflects the property of the image to be interpolated, and by making the function dependent on the gradation value, the superimposition ratio can be determined according to the property of the image to be interpolated.
As described above, according to the present invention, the superimposition ratio can be easily determined according to the properties of the interpolation target image.

Further, as an example of the configuration in this case, in another aspect of the present invention, the first superimposition ratio determination function may determine the superimposition ratio based on the number of appearances of different gradation values in the reference pixel. . 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 among 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, the higher the superimposition ratio of the first interpolation process.
As described above, according to the present invention, it is possible to easily determine the superimposition ratio 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 determination function is configured to perform the first superimposition ratio determination 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 is used such that only the interpolation processing is used. That is, as described above, 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. 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 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, it is possible to easily determine the superimposition ratio according to the natural image or the non-natural image.

Further, as another configuration example, in another aspect of the present invention, the first superimposition ratio determination function is configured to increase the superimposition ratio of the first interpolation process as the gradation value width of the reference pixel increases. Is also good. 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, the image interpolation is performed so that the edge is not reduced as much as the reference pixel has an edge-like portion. Specifically, it can be realized by giving the superimposition ratio of the first interpolation processing 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 so 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 embodiment 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 multiple gradations using dot matrix pixels, each pixel usually has gradation value data of each color, and the luminance value of a 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 superposition ratio calculation.
Thus, according to the present invention, the characteristics of one pixel can be accurately reflected in the evaluation function.

By the way, such an image interpolation program is also applicable 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. .
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 Alternatively, a configuration may be adopted in which 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, image data representing an image represented by pixels in a dot matrix form is input, and a printing control process is executed while executing an interpolation process by a computer so that the printing apparatus prints the image data. In this case, 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 determining function determines the superimposition ratios of the first and second interpolation processes based on the acquired print quality, and the image data superimposing function is determined by the second superimposition ratio determining 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 types of interpolation processing of the first interpolation processing function and the second interpolation processing function are possible, and the processed image data of both are superimposed at a ratio based on the print quality and printed. You. 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 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, as well as a ratio when the first and second interpolation processes are mixed.
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.
Further, the second superimposition ratio determination 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 of the interpolation process on the print result. it can. As a specific example, it is conceivable to use an evaluation function that depends on print quality, and it is possible to easily determine the superimposition ratio by using the evaluation function.
As described above, according to the present invention, the superimposition ratio can be easily calculated using the evaluation function.

  As described above, according to another aspect of the present invention, as an example of a configuration for increasing the ratio of the components in which the effect of the interpolation process is likely to be exhibited based on the print quality, the second superimposition ratio determination function includes the print quality Is higher, the superimposition ratio of the second interpolation process is increased. That is, the second interpolation process interpolates the 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 superimpose the interpolation processing according to the print quality.
Further, various modes are conceivable for such a second superimposition ratio determination, but as another 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, and performing only the first interpolation processing during high-quality printing does not reduce the gradation of the original image. 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 executed 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 will come. Therefore, if the quality of the printing paper is acquired by the print quality acquiring function and the superimposition ratio is determined based on the quality of the printing paper by the second superimposition ratio determining function, the superimposition of the interpolation processing according to the printing quality can be performed. It can be carried out.

As described above, according to the present invention, it is possible to easily superimpose 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 high printing quality is prioritized even if the printing speed is slowed down. Therefore, if such a difference in print speed is acquired, a difference in print quality is acquired.Therefore, the print speed is acquired by the above-described print quality acquisition function, and is superimposed based on the print speed by the second superimposition ratio determination function. If the ratio is determined, 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.

Further, another example of print quality includes print resolution. That is, as described above, the printing accuracy of the printing apparatus is extremely high, and during normal printing, interpolation is performed between dots of the original image data. 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, when the print resolution is acquired, the print quality is acquired.If the print resolution is acquired by the print quality acquisition function, and the superimposition ratio is determined based on the print resolution 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 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 has a property that the pigment 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 according to 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 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 implemented by software and a part is implemented 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 that can perform two types of interpolation processing and superimposes both based on an evaluation function that depends on reference image data is based on the fact that the processing proceeds according to such control. It is natural that the invention exists in 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 but is effective as a method.

  In addition, it can be easily understood that such a program is realized by a substantial computer, and in that sense, the present invention can be applied to an apparatus including such a computer. For this reason, in the inventions according to claims 23 to 33, basically the same operation is obtained. 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.

As described above, according to the first, twelfth, and twenty-third aspects 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, so that the nature of the image to be interpolated is determined. It is possible to prevent an error in the determination of the interpolation method based on.
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 of pixels.

Furthermore, according to the inventions of claims 3, 14, and 25, the superimposition ratio can be easily calculated.
Further, 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.
Further, according to the fifth, sixteenth, and twenty-seventh aspects of the present invention, the superimposition ratio can be easily determined according to the natural image or the non-natural image.

Further, according to the inventions of claims 6, 17 and 28, it is possible to perform image interpolation so that the edge is not reduced as the reference pixel has a portion which seems to be an edge.
Furthermore, according to the invention of claims 7, 18 and 29, the characteristics of one pixel can be accurately reflected on the evaluation function.
Furthermore, according to the invention of claims 8, 19, and 30, the two-system interpolation processing is superimposed on the basis of the evaluation function depending on the data of the reference pixel, so that the interpolation based on the property judgment of the interpolation target image is performed. It is possible to prevent an error in determining the method.

Furthermore, according to the ninth, twentieth, and thirty-first aspects of the present invention, the superposition 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.
Further, according to the inventions of claims 11, 22, and 33, there is no decisive error in selecting an interpolation method in relation to the print result.

Hereinafter, an embodiment of the present invention will be described 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 dot matrix pixels, and image data is composed of a group of data representing each pixel. In a system that performs processing on a pixel-by-pixel basis, image scaling is performed on a pixel-by-pixel basis. The present image data interpolating device performs such enlargement processing in pixel units, and the image data obtaining means C11 obtains such image data, and performs the first interpolation processing means C12 and the second interpolation processing. 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 property 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, and performs the first interpolation processing based on the same property. The superimposition ratio of the interpolated pixels by means C12 and second interpolation processing means C13 is determined. 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 outputs the pixel data superimposed by the image data superimposing means C15.

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 shows the computer system 10 in a block diagram.
The computer system 10 includes a scanner 11a, a digital still camera 11b, and a video camera 11c as image input devices, and is connected to a computer main body 12. Each input device is capable of generating image data representing an image by dot matrix pixels and outputting the image data to the computer main unit 12. Here, the image data is displayed in 256 gradations in three primary colors of RGB. , About 16.7 million colors can be expressed.

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

  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 can display the above-mentioned 16.7 million colors for each pixel. Of course, this resolution is merely an example, and can be changed as appropriate, such as 640 × 480 pixels or 1024 × 768 pixels.

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 has a two-gradation expression 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. Of these, an operating system (OS) 12a is operating as a basic program, and the operating system 12a performs a print output to a display driver (DSP DRV) 12b for displaying on a display 17a and a color printer 17b. A printer driver (PRT DRV) 12c to be installed is incorporated. These drivers 12b and 12c depend on the model 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, as a premise of executing such a program, the computer main body 12 includes a CPU 12e, a RAM 12f, a ROM 12g, an I / O 12h, and the like. While being used as a storage area or a program area, the basic program written in the ROM 12g is appropriately executed to control external devices and internal devices connected via the I / O 12h.

  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 the operation of the keyboard 15a and the mouse 15b as operation devices, and when operated, appropriately control various external devices to execute corresponding arithmetic processing, 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 as an image input device, and after performing predetermined image processing by an application 12d, the image data can be displayed on a display 17a or a color printer 17b as an image output device. It is possible. 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. , The size of the image will be different. In the case of the scanner 11a, there are many which approximate the pixel density of the color printer 17b. However, the pixel density of the color printer 17b whose pixel density is improved for higher image quality is higher than that of a general image input device. It is often higher than the pixel density. In particular, when compared with the display density of the display 17a, the density is higher in each stage, and if the display on the display 17a is made to correspond to each pixel and printed, an extremely small image may result.

  For this reason, resolution conversion is performed in order to eliminate the difference in the actual pixel density of each device while determining the reference pixel density in the operating system 12a. For example, assuming that the resolution of the display 17a is 72 DPI and the operating system 12a is based on 360 DPI, the display driver 12b performs resolution conversion between the two. In the same situation, if the resolution of the color printer 17b is 720 DPI, the printer driver 12c performs the resolution conversion.

  Since the resolution conversion corresponds to a process of increasing the number of constituent pixels in image data, it corresponds to an interpolation process, and the display driver 12b and the printer driver 12c perform the interpolation process as one of the 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 the ratio according to the property of the image.

The display driver 12b and the printer driver 12c are stored in the hard disk 13b, and are read and operated by the computer main body 12 at the time of startup. At the time of introduction, it is recorded on a medium such as a CD-ROM 13c1 or a floppy (R) disk 13a1 and installed. 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, but 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 interpolation processing, and uses the interpolated image data to display on a display 17a1 or print on a color printer 17b1. Is also good. 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 a resolution process after performing the resolution conversion.
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. If the application 12d reads an image from the scanner 11a, performs predetermined image processing, and then performs print processing, print data of a predetermined resolution is acquired by the printer driver 12c via the operating system 12a. . 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 performed by directly operating the operating system 12a itself or hardware. It 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. create. In step S106, the number of times that different luminance values appear in the obtained histogram is obtained, and it is determined whether the number of occurrences is smaller than 15. Since the number of colors in the reference pixel increases as the number of different luminance values increases, it is assumed here that the number of occurrences of the luminance value is less than 15 is a non-natural image. When it is determined, the superimposition ratio (rate) of the first interpolation processing is set to 1 in step S110.

  When it is determined in step S106 that the number of appearances of the luminance value is not smaller than 15, the rate is determined by the evaluation function F in step S108. Although the details will be described later, the evaluation function F is a function of the luminance value width of the reference pixel, that is, the difference between the maximum luminance value Ymax and the minimum luminance value Ymin in the reference pixel. Therefore, a series of processes shown in steps S104, S106, S108, and S110 correspond to a first superposition ratio determination step or a first superposition ratio determination function, and these are combined with hardware such as a CPU in an organically integrated manner. Considering this, it constitutes the first superposition ratio determining means C14.

  When the rate is determined in step S108 or step S110, it is determined whether or not the rate is "0" in step S112. When the rate is "0", the first interpolation processing is not performed. ing. If the rate is not determined to be "0" in step S112, a first interpolation process is executed in step S114. Accordingly, this processing corresponds to the first interpolation processing step or the first interpolation processing function, and if it is considered that these are organically integrated with hardware such as a CPU, the first interpolation processing means C12 is used. Will be configured.

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. Considering that these are organically integrated with hardware such as a CPU, the second interpolation processing means C13 is used. Will be configured. After determining the rate in this way and executing the first interpolation processing or the second interpolation processing, in step S120, the pixel data is superimposed on the basis of the rate by the following equation to generate an interpolated pixel.
Superimposition 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 when these are considered to be organically integrated with hardware such as a CPU, the image data superimposing means C15 is constituted. 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 for all the target pixels of the input original image data is completed, and the process for all the target pixels is performed. 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. 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, 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, which is integrated with hardware such as a CPU and the like. If it is considered that the image data output means C16 is combined, the image data output means C16 is constituted.

  Next, a 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, in order to perform pattern matching, the pixel data of the region of 5 × 5 pixels acquired in step S104 is further added to the target pixel. The pixel data of the area of 3 × 3 pixels around the pixel 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, it is determined whether or not the binary pattern in step S204 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 such as “30 °” or “45 °”. 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 way, the data is stored in the RAM 12f in step S212, and the flow returns to the flow shown in FIG.

  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 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, the rate may not be fixed to “1” or “0”. Is smaller than 15, the image is regarded as 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 contour of the image.

  Specifically, this determination uses the luminance value histogram shown in FIG. 9. In step 104, the luminance value is obtained for each reference pixel in the 5 × 5 pixel area, and the number of pixels in the range where luminance can be obtained is determined. Tally the histogram of 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. It goes without saying 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 a non-natural image, whether there are many colors or luminances is large. 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 colors 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, the image data has a component value representing luminance indirectly. Therefore, a luminance value can be obtained by performing a conversion 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 different color spaces is not uniquely determined by the conversion formula, but the mutual relationship is calculated for the color spaces having the respective component values as coordinates, and the color conversion storing the correspondence is stored. It is necessary to convert sequentially by referring to the table. 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 processing amount is reduced, but the table size becomes an unrealistic problem. If the table size is set to a realistic size, the calculation processing amount becomes impractical. Often. 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 the RGB color space, and each component value indicates the brightness of the color. Therefore, when each component value is viewed alone, it is linear in luminance. There is a property that corresponds to. Therefore, Y = (R + G + B) / 3 without considering the addition ratio of each color.
Can be said to be data reflecting the luminance value of each pixel, and in this embodiment, the value degree value is calculated by the expression.

Obviously, the calculation of the luminance value is not limited to such a method, and a conversion formula of the following expression for obtaining luminance from three primary colors of RGB may be employed as used in a television or the like.
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 ”. Further, in the range of “0 ≦ y ≦ 64”, “F (y) = 0”, in the range of “192 ≦ y ≦ 255”, “F (y) = 1”, and in the range of “64 ≦ y ≦ 192”. , The F (y) increases linearly from “0” to “1”. Here, the luminance value width “Ymax−Ymin” in FIG. 9 is substituted for “y”. Therefore, 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, the function for increasing the rate for an edge-like element is not limited to the example shown in FIG. 10, but may be any function that increases monotonically 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", the rate is determined by the evaluation function shown in FIG. In S118, a first interpolation process and a second interpolation process are respectively performed. FIG. 11A shows an example of a luminance pattern of 3 × 3 pixels extracted in step S202 of FIG. In step S204, a binary pattern shown in FIG. 4B is created from the luminance pattern shown in FIG. 4A. First, a luminance value Yij is calculated for each pixel Pij based on the above-described luminance value calculation formula. I do.

  Then, an intermediate value between the maximum value Ymax and the minimum value Ymin of these luminance values is obtained, 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 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 in FIG. 4 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 page. Therefore, such a pattern is an edge parallel to the left-right direction on the paper. Therefore, pixel interpolation processing is performed on the edge pattern according to a predetermined rule.

  There are various rules in the predetermined rule depending on the edge pattern. In general, the distance between the pixel and the pixel to be interpolated with respect to the gradation value of the predetermined pixel in the 3 × 3 pixel area is described. To calculate a weighted average using the inverse ratio of. Here, various variations can be considered in terms of what kind of pointer is used to select a pixel on which weighted averaging is performed when an interpolation pixel is generated, and how the angle, direction, and the like of an edge are reflected. 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 also used to determine the angle and direction of the edge. In other words, it is necessary for the edge patterns 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 above-mentioned reference pixel data, and the pixels are distinguished from each other by adding alphabets A to Y to each of the pixels. In this notation, the 3 × 3 pixel area used for the pattern matching is “G, H, I, L, M, N, Q, R, S” pixels, and 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 expression for the calculation at the time of pixel generation is shown in the following expression, and weighted averaging is performed using the inverse ratio of the distance from the interpolation pixel to the original pixel as a weight. Here, the gradation value of the generated interpolation pixel is xbar, and Pn is the gradation value of the referenced 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.

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 interpolation pixel is generated from the gradation values of the surrounding pixels using a calculation formula according to the matched pattern. As described above, since the generated pixels are generated from the surrounding original pixels based on the rules prepared based on the matched pattern while performing the pattern matching determination, it can be obtained only by interpolation processing based on simple pattern matching. There can be no natural interpolated pixels. In the above calculation, the luminance is calculated from each of the RGB values. However, the gradation value of the generated interpolated pixel actually has each of the RGB values. It is calculated by performing predetermined weighting using the RGB values. 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.

  As described above, since the pattern of 3 × 3 pixels shown in this example has little difference in luminance value in the horizontal direction (i-direction), that is, it is a horizontal edge, the 3 × 3 pixel pattern shown in FIG. Only the pixel values are substituted, and the gradation values of the original pixels L, M, and N are substituted in principle. Further, in the present embodiment, interpolation is performed so as to reflect the luminance value change in the direction perpendicular to the horizontal edge, and the luminance value changes in the direction perpendicular to the horizontal edge. 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. In other words, if the value of the target pixel M is larger (smaller) than the value of the pixel R on the edge side, the pixel R will not be emphasized even if it is filled. Therefore, the target pixel is used as it is.

Therefore, before performing the interpolation calculation, the luminance values of the pixel M and the pixel R are compared, and if the luminance value of the pixel R is larger than the luminance value of the pixel M, the gradation values of the original pixels Q, R, and S Is substituted into the above equation to generate interpolation pixels g, h, and i. As a result, the luminance values of the interpolated pixels g, h, and i become larger than those of the interpolated pixels d, e, and f, and the edge is further emphasized in the interpolated image data.
The specific criteria in this case are:
((M = edge) &(R> M)) or ((M ≠ edge) & (R <M))
And the calculation formula at that time is as follows:
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
It becomes. 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 criteria 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
It becomes. 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 according to the above rule. FIG. 13 shows an example. When the pattern shown in FIG. 7A is found in step S206, 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.

In the calculation of the gradation values of the interpolation pixels b, c, and f, which pixel is to be the original pixel is changed based on the luminance value of a predetermined pixel included in the area of 5 × 5 pixels. 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. If not, the original pixels G, H, N, and S are used. To generate interpolation pixels b, c, and f.
First, the conditions for determining that this pattern is a right angle are:
(((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
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
It becomes. Next, other than the right angle, the condition determination as the first pattern close to the right angle is as follows.
(((M = F = edge) & (A = not edge)) or ((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. And when the calculation formula when both of the above two condition judgments are not satisfied is shown,
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, since 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, the original pixels G, M, and S constituting these straight lines are used. To generate interpolation pixels a, e, and i. Also, the edge angle and line thickness of the original pixel are determined based on the luminance value of a predetermined pixel included in the 5 × 5 pixel area, and the interpolation pixels b, c, f, d, g, and The original pixel used for generating h is changed between “G, M, S” and “H, N”, or changed between “G, M, S” and “L, R”.

The process 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. This determination condition is shown in FIG. Note that the description enclosed by a broken line in the figure is the content intended by 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
And the calculation formula when process 2 is selected is:
b = (Gx5 + Mx10 + Sx4) / 19
c = (H + N) / 2
f = (Gx4 + Mx10 + Sx5) / 19
And the calculation formula when process 3 is selected is:
b = (Hx3 + Nx2) / 5
c = (H + N) / 2
f = (Hx2 + Nx3) / 5
It becomes. The calculation formula for the other pixels is
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
It becomes.

  When the pattern shown in FIG. 13E is found, an interpolation pixel shown in FIG. 13F is generated. That is, it seems that the straight lines (edges of 30 degrees) have a smaller angle than the straight lines shown in FIG. 9C, and the interpolation pixels a, b, c, and Generate e and f. Further, based on the luminance value of a predetermined pixel included in the 5 × 5 pixel area, the edge angle of the original pixel, the protruding pixel, and the like are determined, and the pixels are used for generating the interpolated pixels d, g, h, and i according to the determination. The original pixel to be changed is 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 by 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 process 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 process 4 is selected is:
d = (Lx15 + Rx10 + Sx6) / 31
g = (Lx2 + Rx2 + S) / 5
h = (Lx4 + Rx6 + Sx3) / 13
i = (Lx3 + Rx6 + Sx4) / 13
It becomes. The calculation formula for the other pixels is
a = (Gx4 + Mx6 + Nx3) / 13
b = (Gx3 + Mx6 + Nx4) / 13
c = (G + Mx2 + Nx2) / 5
e = (Gx4 + Mx6 + Nx3) / 13
f = (Gx2 + Mx5 + Nx5) / 12
It becomes.

The above-described edge pattern, 5 × 5 pixel area reference method, original pixel selection method, and the like are merely examples, and various modes can be adopted. As described above, in addition to generating pixels three times in the vertical and horizontal directions, various magnifications such as two times and four times can be adopted.
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 horizontal edges are 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, the pixel is not emphasized, and the target pixel is used as it is. More specific criteria are
((M = edge) &(R> M)) or ((M = not edge) & (R <M))
If this holds, the emphasis processing is performed. The calculation formula in that case is
a = b = M
c = d = R
In other cases, no emphasis processing is performed.
a = b = c = d = M
It becomes.

Next, FIG. 39 shows a case where a corner edge is found by pattern matching.
(((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)))
In order to emphasize that if this holds, it is a right angle,
b = (L + R) / 2
And if not,
b = (Gx2 + Hx3 + Nx3 + Sx2) / 10
It is calculated by the formula. Other pixel formulas are:
a = (Lx3 + Rx2) / 5
c = (L + R) / 2
d = (Lx2 + Rx3) / 5
It becomes.

FIG. 40 shows a case where a 45-degree edge is found by pattern matching. In order to determine whether the edge is a part of a 45-degree edge or a part of a 30-degree edge, the condition determination shown in FIG. The process branches to two processes. 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
It becomes. Other pixel formulas are:
a = (Gx2 + S) / 3
c = (G + S) / 2
d = (G + Sx2) / 3
It becomes.

FIG. 42 shows a case where a 30-degree edge is found by pattern matching, and branches to three processes according to the condition determination 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
And the calculation formula in the case of processing 3 is:
c = (Lx3 + Rx3 + Sx2) / 8
d = (G + Nx2) / 3
It becomes. Other pixel formulas are:
a = (Gx3 + Nx2) / 5
b = (Gx2 + Nx3) / 5
It becomes.

When it is not determined that the edge pattern prepared in advance in the 3 × 3 pixel region matches the binary pattern, the near-list method is executed without using such a pattern matching method as the first interpolation processing. You. In the near-list method, as shown in FIG. 14, the distance between four surrounding grid points Pij, Pi + 1j, Pij + 1, Pi + 1j + 1 and the point Puv to be interpolated is obtained, and the data of the closest grid point is transferred as it is. If this is represented by a general formula,
Puv = Pij
Here, i = [u + 0.5] and j = [v + 0.5]. [] Indicates that a Gaussian symbol takes an integer part.

  FIG. 15 shows a situation where the number of pixels is vertically and horizontally tripled by the near-list method. It is assumed that there are four corner pixels (□ 前 ●●) before the interpolation, and 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 the background as shown in FIG. 16 becomes oblique while the black pixels are enlarged three times vertically and horizontally 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 generating the interpolation pixel by the first interpolation processing in this way, if the rate is not "1", the second interpolation processing is further executed. In the present embodiment, an interpolation process based on the so-called cubic method is executed as the second interpolation process, and such a process 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, the weighted addition may be made in inverse proportion to the distance from the 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. Further, when focusing on the Y-axis direction, the distance from the interpolation point Puv to the 16 lattice points is a distance from the upper outer lattice point to y1, a distance from the upper inner lattice point to y2, and a lower inner lattice point. Similarly, the degree of influence can be expressed by a function f (y) while expressing the distance y3 to the point and the distance y4 to the lower outer grid point.

  Since the 16 grid points contribute to the interpolation point Puv with the degree of influence according to the distance as described above, the general method is to accumulate the respective degree of influence in the X-axis direction and the Y-axis direction on the data at all the grid points. The formula is as follows.

If the degree of influence according to the distance is represented by a third-order convolution function,
f (t) = {sin (πt)} / πt
It becomes. The above-described distances x1 to x4 and y1 to y4 are calculated as follows using the absolute value of the coordinate value (u, v) of the grid 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 |)
Expanding on P under the above assumptions,

It 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.

The cubic method has a feature that the gradual change gradually occurs as one grid point approaches the other grid point, and the degree of the change becomes a so-called cubic function.

FIG. 19 and FIG. 20 show specific examples 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 interpolation is shown as “Original” in the left column, and four pixels (P0, P1, P2, and P3) with the gradation value “64” are arranged, and the pixel with the gradation value “128” is displayed. (P4) is sandwiched by one point, and five pixels (P5, P6, P7, P8, P9) having a gradation value of “192” are arranged side by side. In this case, the edge is a pixel portion having a gradation value of “128”.

  Here, when three pixels (Pn1, Pn2, Pn3) are interpolated between each pixel, the distance between the interpolated pixels is “0.25”, and the above-mentioned x1 to x4 are each interpolated point. In the middle column of the table. 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” and “1.75”, f (t) corresponding thereto is approximately “−0.14”, “0.89”, “0.30”, “−0.05”. ". Further, when x1, x2, x3, and x4 are “1.50”, “0.50”, “0.50”, and “1.50”, respectively, f (t) is “−”. 0.125 "," 0.625 "," 0.625 ", and" -0.125 ". Further, when x1, x2, x3, and x4 are “1.75”, “0.75”, “0.25”, and “1.25”, respectively, f (t) is roughly described as “ −0.05 ”,“ 0.30 ”,“ 0.89 ”, and“ −0.14 ”. 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 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, and It can be calculated as follows using the degree of influence f (t) according to the distance to the grid point.
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
It becomes.

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 that 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 the hybrid bicubic method or the 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 the same hypothetical model 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. Regardless of which of the M cubic method and the ordinary cubic method is used, if the interpolation pixel is generated by the cubic method in the second interpolation processing, the variable of the superposition data calculation formula, that is, the first interpolation processing data , Rate, and the second interpolation processing data are all calculated, so the superimposition data of the interpolation pixels is calculated by substituting the values into the same equation.

  Hereinafter, the manner in which the superimposition processing is performed on the image data as shown in FIG. Here, in the 5 × 5 area around the pixel of interest in the description, it is assumed that the number of appearances of the luminance value Y is larger than 15, and the rate is not “0” or “1”. Therefore, after the rate is calculated in step S108, the first interpolation processing is executed in step S114. 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. Therefore, in the interpolation 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, an interpolated pixel shown in FIG. 22D is generated using the pixel data shown in FIG. Here, as described above, the surrounding 16 pixel data is used for generating the interpolation pixel. For example, in order to generate the upper right pixel shown as a black point in FIG. 16 pixels consisting of the solid line and dotted line areas shown are used. Due to the nature of the cubic method, the edges of the interpolated pixels are ambiguous compared to the pattern matching method, while reflecting the subtle changes between the pixels in the solid line and the 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 pixel is generated by the first interpolation processing and the second interpolation processing in this way, each interpolation pixel by the first interpolation processing is multiplied by rate, and each interpolation pixel by the second interpolation processing is (1 −rate) to generate a superimposed pixel as shown in FIG. As shown in FIG. 11E, when a superimposed interpolation pixel for the target pixel is generated, the interpolation process is performed for all the target pixels having the new coordinate values through the determination in step S122. Deliver to processing. However, the data amount of the interpolated image data may become extremely large depending on the interpolation magnification, and the memory area available to 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 an overview 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. If the number is smaller than a predetermined threshold value, it is determined that the nature of the image is a simple non-natural image, and the first interpolation is performed. 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 image is a mixed image of a natural image or a non-natural image. 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. The means for obtaining the superimposition ratio from the properties of the image in this way is the first superimposition ratio determination means C14.

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

Then, in step S120, the pixel data separately interpolated is weighted and added by the calculation formula (1) using the superimposition ratio (rate) determined as described above to generate an interpolation pixel. I do. 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 processing is performed for all pixels, an interpolated image is created. Therefore, in step S124, 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 embodiment 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 inkjet 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 comprising a print controller 21b, a printer controller 21e serving as an interface between the print head controller 21b, the print head digit 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 is formed with a fine conduit 21a3 extending from the color ink tank 21a1 to the nozzle 21a2, and an ink chamber 21a4 is formed at the end of the conduit 21a3. The wall surface of the ink chamber 21a4 is formed of a material having flexibility, 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 electro-mechanical energy conversion. The operation of distorting the crystal structure pushes the wall surface of the ink chamber 21a4, and the piezo element 21a5 presses the wall surface of the ink chamber 21a4. 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.

  In addition, two independent rows of nozzles 21a2 are formed in one print head unit, and color ink is independently supplied to the nozzles 21a2 of 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 to the maximum extent possible. In the example shown in FIG. 23, 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 The left and right two columns 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 spacing larger 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, if the paper is fed in eight steps between the nozzles 21a2 and the printing is performed by operating the print head 21a in the digit feed direction at each step, the resolution is improved. Of course, if the color ink is ejected at arbitrary intervals in the digit feed direction, it can be said that the resolution is the resolution, so it can be said that it depends on the timing control. In the strict sense, the dot diameter of the color ink can also be an element of the resolution, but is neglected here for the sake of easy understanding.

  In the present embodiment, based on the hardware system described above, printing is executed based on image data acquired by the image input device of the computer system 10. 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, a change in resolution and gradient when print data is output to the color printer 17b when the application 12d executes a printing process will be described.

  FIG. 26 shows the flow of image data. The application 12d issues a print request to the operating system 12a, and at that time, transfers an output size, a printing paper, a printing speed, an ink type, and RGB 256-gradation image data. Then, the operating system 12a transfers the output size, printing paper, printing speed, ink type, and image data to the printer driver 12c. Here, the operating system 12a 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, so that the printer driver 12c has the specified output size. The print data is generated by executing the image interpolation processing. 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 the present embodiment, the print control program is executed by the computer system 10 and the print data is output to the color printer 17b. However, the target printing apparatus is the ink jet type color printer 21 described above. It is not limited. For example, the color printer 21 is of an ink jet type employing a micropump mechanism, but it is also possible to employ a printer other than the micropump mechanism. As shown in FIG. 27, a heater 21a8 is provided on the wall surface of a pipe 21a7 near the nozzle 21a6, and a bubble jet (R) is generated by heating the heater 21a8 to generate bubbles and discharge color ink at the pressure. ) Type pump mechanism is also in practical use.

  FIG. 28 shows a schematic configuration of a main part of a so-called electrophotographic color printer 22 as another mechanism. A charging device 22b, an exposing device 22c, a developing device 22d, and a transfer device 22e are arranged on the periphery of the rotating drum 22a as a photoreceptor so as to correspond to the rotation direction, and the charging device 22b makes the circumferential surface of the rotating drum 22a uniform. Then, the image portion is removed from the image by the exposure device 22c, the toner is adhered to the uncharged portion by the developing device 22d, and the toner is transferred onto paper as a recording medium by the transfer device 22e. After that, the toner is melted by passing between the heater 22f and the roller 22g to be 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 illustrating a schematic configuration of the printing system. The print quality obtaining unit C21 obtains a print quality to be printed in the executed print job, and the first interpolation processing unit C22 and the second interpolation processing unit C23 perform interpolation to increase the number of constituent pixels in the image data. Perform processing. 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 determination unit C24 determines the superimposition ratio of the interpolated pixels by the first interpolation processing unit C22 and the second interpolation processing unit C23 based on the print quality acquired by the print quality acquisition unit C21. 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 unit C26 generates printing data based on the pixel data superimposed by the image data superimposing unit C25 and executes printing.

  FIG. 30 shows a software flow related to the printing process executed by the printer driver 12c described above. 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 print processing is executed by the application 12d, assuming that the operating system 12a provides a GUI environment, a window for a print operation is displayed as shown in FIG. Various parameters and the like can be adopted here, but in the present embodiment, there are “number of (printing)”, “start page”, “end page”, and the like. As the operation instruction buttons, a “printer setting” button is prepared in addition to an “OK” button and a “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, one of “360 dpi” and “720 dpi” can be selected as “(printing) resolution”. In addition, the size of “A4” or “B5” as “paper” and the quality of “plain paper” or “glossy paper”, whether “print speed” is “fast” or “slow”, or “ink” is “pigment” "Dye" can be selected. Of course, this print setting is merely an example, and it is not necessary to configure the user to select all of them, and various modes such as performing bidirectional communication with the color printer 17b and performing ink types and the like can be adopted. is there.

  In the present embodiment, the above-described setting contents are stored in a setting file under the control of the operating system 12a. If the setting has already been made, the setting file is read with reference to the setting file. When the operator changes the setting content in accordance with the printing operation, the changed setting content is read as the print setting. The processing in step S303 is the print quality acquisition step or print quality acquisition function when viewed as software. The various steps executed by the computer including the print quality acquisition step include the operating system 12a itself. 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 peripheral 5 × 5 pixel area around the target pixel is set as the reference pixel, and the histogram of the luminance value of the reference pixel is obtained. create. In step S306, the number of times that 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 one 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.

  If it is determined in step S306 that the number of appearances of the luminance value is not smaller than 15, the evaluation function F is determined in step S307 based on the print setting content 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 processing 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, and the evaluation function value when the print quality is high is used as the evaluation function when the print quality is low. It is smaller than the value. The evaluation function F is a function of the luminance value width of the reference pixel, that is, the difference between the maximum luminance value Ymax and the minimum luminance value Ymin in the reference pixel.

  In step S308, 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 S307, and rate is determined. Therefore, a series of processes shown in steps S304, S306, S307, S308, and S310 correspond to a second superposition ratio determination step or a second superposition ratio determination function, and these are organically integrated with hardware such as a CPU. In other words, the second superposition ratio determining means C24 is configured.

  When the rate is determined in step S308 or step S310, it is determined in step S312 whether or not the rate is "0". When the rate is "0", the first interpolation processing is not performed. ing. If it is not determined in step S312 that the rate is "0", a first interpolation process is executed in step S314. Accordingly, this processing corresponds to the first interpolation processing step or the first interpolation processing function. Considering that these are organically integrated with hardware such as a CPU, the first interpolation processing means C22 is used. Will be configured.

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. Accordingly, this processing corresponds to the second interpolation processing step or the second interpolation processing function. If these processings are organically integrated with hardware such as a CPU, the second interpolation processing means C23 Will be configured. After the rate is determined in this manner and the first interpolation processing or the second interpolation processing is executed, in step S320, pixel data is superimposed on the basis of the rate by the above-described equation (1) based on the rate to determine an interpolation pixel. Generate.
Superimposition data =
(First interpolation processing) × rate + (second interpolation processing) × (1-rate)
… (1)
Therefore, this processing corresponds to the above-mentioned image data superimposing step or the image data superimposing function, and if these are considered to be organically integrated with hardware such as a CPU, it constitutes the image data superimposing means C25. 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 is completed, and the process for all the target pixels is determined. Is repeated until the processing is completed.

  When the superimposition process 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, and these are hardware such as a CPU. Assuming that the print control processing means C26 is organically integrated with the ware, the print control processing means C26 is configured.

  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 graphics (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. It is made to let. Of course, it is possible to adopt a configuration in which the above-mentioned rate is not fixed to “1” or “0” in order to prevent an error in determining the interpolation method. It is emphasized that the image is an image and that 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 the luminance value histogram shown in FIG. 14. In step 104, a luminance value is obtained for each reference pixel in a 5 × 5 pixel area, and the number of pixels in a range where luminance can be obtained is determined. Tally the histogram of 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, if 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 one 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 evaluation function for high print quality. FIG. 33 shows an example of the evaluation function F (y). FIG. F1 shows an evaluation function for low print quality, and FIG. F2 shows an evaluation function for high print quality.

  The evaluation function F1 (y) has a value where “y” is in a range of “0 ≦ y ≦ 255” and fluctuates in a range of “0 ≦ F (y) ≦ 1”. Further, in the range of “0 ≦ y ≦ 64”, “F (y) = 0”, in the range of “192 ≦ y ≦ 255”, “F (y) = 1”, and in the range of “64 ≦ y ≦ 192”. , The F (y) increases linearly from “0” to “1”. In addition, the evaluation function F2 (y) has a value where “y” is in a range of “0 ≦ y ≦ 255” and fluctuates in 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”. In the range of ≤192, F (y) linearly increases from "0" to "0.7".

  Here, the luminance value width “Ymax−Ymin” in FIG. 14 is substituted for “y”. Therefore, the rate increases as the luminance value width increases, that is, as the relationship between the reference pixels is more like an edge. Further, the value of the evaluation function F1 is always equal to or larger than the value of the evaluation function F2 even if the luminance value width is the same. 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 impairing the gradation of the image is increased.

  When one or a combination of “high print resolution”, “high print paper quality”, and “low speed print” is selected, that is, when printing with high print quality, interpolation that does not impair the gradation of the original image is performed. For example, a subtle gradation change is reproduced as an effect visible to a user in a print result. 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 print result is not reproduced as an effect visible to the user. 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 this 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 determined unconditionally that any of them 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 ink reflects the print quality, and in the present embodiment, if an attempt is made to obtain the same level of print results with both inks, the second interpolation process is performed in the case of “pigment” as compared with the case of “dye” May be increased. 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 setting 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 with a smaller maximum value is selected each time an item set to high quality increases by one. It is possible to do.

  In addition, there is no need to prepare a plurality of evaluation functions in advance as described above, and one evaluation function F1 described above is prepared and the evaluation function is used as “0.7 times” at the time of high quality printing. It is also possible to adopt a configuration in which the rate is determined in advance, or a configuration in which the evaluation function is multiplied by “0.9” every time one high quality item is added. Further, the shape of the function need not be limited to the example shown in FIG. 33 as long as it is a function that increases monotonically with the luminance value width when the luminance value width is used as a variable, and the function value ranges from "0" to "255". It is also possible to employ a smoothly changing evaluation function.

  When the evaluation function to be used is determined in step S307 as described above, in step S308, the superimposition ratio 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 whose distribution number is not “0”, the process of step S307 is executed after the determination of step S306. As shown in FIG. 32, when the resolution is set to "720 dpi", the paper is set to "glossy paper", the printing speed is set to "fast", and the ink is set to "pigment". Since the print settings for selecting the evaluation function for high quality printing have been made in step S307, the evaluation function F2 is selected in step S307. Then, the rate is determined by the evaluation function F2 in step S308, and the first interpolation processing and the second interpolation processing are executed in steps S314 and S318, 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 ordinary 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 the resolution, “plain paper” for the paper, “fast” for the print speed, and “dye” for the ink. The effect of the second interpolation process is inconspicuous. In this state, the evaluation function F1 is used as described above, and the superimposition ratio of the second interpolation processing is lower than in the setting state shown in FIG. At this time, the superposition ratio of the pattern matching method or the near-list method that emphasizes the edge is higher than that of the cubic method that reproduces 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 display 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 in accordance 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 a 5 × 5 pixel area around the target pixel. In step S306, the number of times that different luminance values appear in the obtained histogram is obtained. If the number is smaller than a predetermined threshold value, the nature of the image is determined to be a simple non-natural image, and the first interpolation is performed. Although the superimposition ratio (rate) of the processing is set to 1, if the number is large, the evaluation function is determined in order to determine the rate 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. 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 edges are likely to be emphasized by the evaluation function F1 is increased; otherwise, the percentage of interpolation processing with a high computational load that emphasizes smoothness with the evaluation function F2 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 that tends to preserve edges is performed by a pattern matching method or a near-list method in a predetermined area centered on the target pixel, and this is the first interpolation processing unit C22.
On the other hand, in step S318, the interpolation processing is performed by the cubic method in which the edges are ambiguous as compared with the pattern matching method while reflecting a subtle change between pixels, and 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 to generate an interpolated pixel. I do. 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 the print control processing means C26.

  As described above, in the present invention, the first interpolation processing and the second interpolation processing are superimposed based on the 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 process more suitable for the print quality that directly affects the effect of the interpolation process is increased. As a result, the advantages of the individual interpolation processes become more noticeable. In addition, 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 the interpolation target image.

1 is a block diagram of an image data interpolation device according to an embodiment of the present invention. FIG. 3 is a block diagram of specific hardware of the image data interpolation device. It is a schematic diagram showing other examples of application of an image data interpolation device of the present invention. It is a schematic diagram showing other examples of application of an image data interpolation device of the present invention. It is a schematic diagram showing other examples of application of an image data interpolation device of the present invention. It is a schematic diagram showing other examples of application of an image data interpolation device of the present invention. 6 is a flowchart of a process related to resolution conversion executed by a printer driver. It is a first interpolation processing flowchart. It is a figure showing a histogram of a brightness value. FIG. 9 is a diagram illustrating a specific example of an evaluation function F (y). FIG. 3 is a diagram illustrating an example of a luminance pattern of 3 × 3 pixels. FIG. 3 is a diagram illustrating a 5 × 5 pixel area that is a reference pixel. FIG. 4 is a diagram illustrating a specific example of an edge pattern. It is a conceptual diagram of a near-list method. It is a figure showing the situation where data of each grid point is transferred by the near list method. It is the schematic which shows the situation before the interpolation of a near-list method. It is the schematic which shows the situation after the interpolation of a near-list method. It is a conceptual diagram of a cubic method. FIG. 9 is a diagram illustrating a data change situation when the cubic method is specifically applied. It is a figure which shows the specific application example of a cubic method. It is a figure which shows the specific application example of the M cubic method. FIG. 9 is a diagram illustrating a specific state in which superimposition processing is performed on image data. FIG. 1 is a schematic block diagram of an ink jet type color printer. FIG. 3 is a schematic explanatory view of a print head unit in the color printer. FIG. 4 is a schematic explanatory view showing a situation in which color ink is ejected by the print head unit. FIG. 2 is a flowchart illustrating a flow of image data in the printing system. FIG. 3 is a schematic explanatory view showing a situation in which color ink is ejected by a bubble jet (R) type print head. FIG. 1 is a schematic explanatory view of an electrophotographic printer. FIG. 1 is a block diagram illustrating a schematic configuration of a printing system. 6 is a flowchart of a process related to a printing process executed by a printer driver. FIG. 7 illustrates an operation window of a printing process. FIG. 4 illustrates an operation window for setting a printer. FIG. 9 is a diagram illustrating a specific example of an evaluation function F (y). FIG. 1 is a diagram showing a schematic configuration of the present invention. FIG. 1 is a diagram showing a schematic configuration of the present invention. It is a figure which shows the condition judgment with respect to a 45 degree edge. It is a figure which shows the condition judgment with respect to the edge of 30 degrees. FIG. 9 is a diagram illustrating a correspondence between an original pixel and an interpolated pixel when a horizontal edge is found in the case of double interpolation. FIG. 9 is a diagram illustrating the correspondence between original pixels and interpolated pixels when a right-angled edge is found in the case of double interpolation. FIG. 9 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. It is a figure which shows the condition judgment with respect to a 45 degree edge. FIG. 9 is a diagram showing the correspondence between original pixels and interpolated pixels when a 30-degree edge is found in the case of double interpolation. It is a figure which shows the condition judgment with respect to the edge of 30 degrees.

Explanation of reference numerals

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

Claims (33)

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 change of pixels for the image data,
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 for determining the superimposition ratio of the first and second interpolation processes based on the same property by determining a property of the image based on reference pixels around the pixel to be interpolated,
An image data superimposition function of superimposing the image data by the first interpolation processing function and the interpolation image data by the second interpolation processing function at the determined superimposition ratio;
An image data interpolation program for causing a computer to execute an image data output function of outputting the superimposed data as data after interpolation processing. 2. The image data interpolation program according to claim 1, wherein
The first interpolation processing function is capable of executing pattern matching interpolation for performing interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel and interpolation using nearest neighbor interpolation. Image data interpolation program. In the image data interpolation program according to any one of claims 1 and 2,
The image data interpolation program according to claim 1, wherein said first superimposition ratio determining function determines a superimposition ratio by an evaluation function dependent on data of said reference pixel. In the image data interpolation program according to any one of claims 1 to 3,
The image data interpolation program according to claim 1, wherein the first superimposition ratio determination function determines a superimposition ratio based on the number of appearances of different gradation values in the reference pixel. 5. The image data interpolation program according to claim 4, wherein
The first superimposition ratio determination function provides a superimposition ratio that uses only the first interpolation process when the number of appearances of different tone values in the reference pixel is smaller than a predetermined threshold. Image data interpolation program. In the image data interpolation program according to any one of claims 4 and 5,
The image data interpolation program, wherein 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. In the image data interpolation program according to any one of claims 4 to 6,
An image data interpolation program, wherein the gradation value of the reference pixel is a luminance value of the reference pixel. In the image data interpolation program according to any one of claims 1 to 7,
A print quality acquisition function for acquiring print quality when trying to print on a printing device using the image data,
A second superimposition ratio determination function that determines a superimposition ratio of the first and second interpolation processes based on the obtained print quality,
An image data interpolation program for causing a computer to execute a print control processing function of executing print control processing based on the superimposed data. 9. The image data interpolation program according to claim 8, wherein
The image data interpolation program according to claim 2, wherein the second superimposition ratio determination function determines the superimposition ratio using an evaluation function that depends on print quality. In the image data interpolation program according to any one of claims 8 and 9,
The image data interpolation program, wherein the second superimposition ratio determining function increases the superimposition ratio of the second interpolation process as the acquired print quality increases. In the image data interpolation program according to any one of claims 8 to 10,
The image data interpolation program according to claim 2, wherein the second superimposition ratio determination function is configured to prevent only the first interpolation process from being executed when the acquired print quality is high. In an image data interpolation method for performing pixel interpolation on image data in which an image is expressed in multiple gradations by dot matrix pixels,
An image data acquisition step of acquiring the image data,
A first interpolation processing step of performing interpolation on the image data without reducing the degree of pixel change,
A second interpolation processing step of performing interpolation on the image data without impairing the gradation of the image,
A first superimposition ratio determining step of determining a property of an image based on reference pixels around the pixel to be interpolated, and determining a superimposition ratio of the first and second interpolation processes based on the same property,
An image data superimposing step of superimposing the image data by the first interpolation processing step and the interpolated image data by the second interpolation processing step at the determined superimposition ratio;
An image data output step of outputting the superimposed data as data after the interpolation processing. The image data interpolation method according to claim 12,
The first interpolation processing step is characterized in that pattern matching interpolation for performing interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel and interpolation by nearest neighbor interpolation can be executed. Image data interpolation method. In the image data interpolation method according to any one of claims 12 and 13,
The image data interpolation method, wherein the first superimposition ratio determination step determines a superimposition ratio by an evaluation function depending on the data of the reference pixel. In the image data interpolation method according to any one of claims 12 to 14,
The image data interpolation method, wherein the first superimposition ratio determining step determines a superimposition ratio based on the number of appearances of different gradation values in the reference pixel. The image data interpolation method according to claim 15,
The first superimposition ratio determining step provides a superimposition ratio that uses only the first interpolation process when the number of appearances of different tone values in the reference pixel is smaller than a predetermined threshold. Image data interpolation method. In the image data interpolation method according to any one of claims 15 and 16,
The image data interpolation method, wherein the first superimposition ratio determining step increases the superimposition ratio of the first interpolation process as the gradation value width of the reference pixel increases. In the image data interpolation method according to any one of claims 15 to 17,
An image data interpolation method, wherein the gradation value of the reference pixel is a luminance value of the reference pixel. In the image data interpolation method according to any one of claims 12 to 18,
A print quality acquisition step of acquiring print quality when trying to print with a printing device using the image data,
A second superposition ratio determining step of determining a superposition ratio of the first and second interpolation processes based on the obtained print quality,
And a print control processing step of executing a print control processing based on the superimposed data. The image data interpolation method according to claim 19,
The image data interpolation method, wherein the second superimposition ratio determination step determines the superimposition ratio using an evaluation function that depends on print quality. In the image data interpolation method according to any one of claims 19 and 20,
The image data interpolation method, wherein the second superimposition ratio determining step increases the superimposition ratio of the second interpolation process as the acquired print quality increases. In the image data interpolation method according to any one of claims 19 to 21,
The image data interpolation method according to claim 2, wherein the second superimposition ratio determining step prevents the execution of only the first interpolation process when the acquired print quality is high. In an image data interpolation device that performs pixel interpolation on image data in which an image is represented by multi-gradation using pixels in a dot matrix,
Image data acquisition means for acquiring the image data,
First interpolation processing means for performing interpolation on the image data without reducing the degree of pixel change,
Second interpolation processing means for performing interpolation on the image data without impairing the gradation of the image,
A first superimposition ratio determining unit that determines a characteristic of an image based on reference pixels around a pixel to be interpolated, and determines a superimposition ratio of the first and second interpolation processes based on the same characteristic,
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 at the determined superimposition ratio,
Image data interpolating means for outputting the superimposed data as data after the interpolation processing. The image data interpolation device according to claim 23,
The first interpolation processing means is capable of executing pattern matching interpolation for performing interpolation according to a predetermined rule when a predetermined pattern exists in a reference pixel and interpolation using nearest neighbor interpolation. Image data interpolation device. In the image data interpolation device according to any one of claims 23 and 24,
The image data interpolation apparatus according to claim 1, wherein said first superimposition ratio determining means determines a superimposition ratio by an evaluation function depending on data of said reference pixel. The image data interpolation device according to any one of claims 23 to 25,
The image data interpolation device, wherein the first superimposition ratio determining means determines a superimposition ratio based on the number of appearances of different gradation values in the reference pixel. The image data interpolation device according to claim 26,
The first superimposition ratio determining means provides a superimposition ratio that uses only the first interpolation process when the number of appearances of different gradation values in the reference pixel is smaller than a predetermined threshold. Image data interpolation device. In the image data interpolation device according to any one of claims 26 and 27,
The image data interpolating apparatus, wherein the first superimposition ratio determining means increases the superimposition ratio of the first interpolation processing as the gradation value width of the reference pixel increases. In the image data interpolation device according to any one of claims 26 to 28,
The gradation value of the reference pixel is a luminance value of the reference pixel. The image data interpolation device according to any one of claims 23 to 29,
A print quality acquisition unit that acquires print quality when trying to print with a printing device using the image data,
A second superimposition ratio determining unit that determines a superimposition ratio of the first and second interpolation processes based on the obtained print quality,
An image data interpolation apparatus characterized by causing a computer to execute print control processing means for executing print control processing based on the superimposed data. The image data interpolation device according to claim 30,
The image data interpolation apparatus according to claim 2, wherein said second superimposition ratio determining means determines the superimposition ratio by an evaluation function depending on print quality. In the image data interpolation device according to any one of claims 30 and 31,
The image data interpolating apparatus, wherein the second superimposition ratio determining means increases the superimposition ratio of the second interpolation processing as the acquired print quality increases. The image data interpolation device according to any one of claims 30 to 32,
The image data interpolating apparatus, wherein the second superimposition ratio determining means prevents only the first interpolation processing from being executed when the acquired print quality is high.