JP3045134B2 - Image data interpolation apparatus, image data interpolation method, and medium recording image data interpolation program - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image data interpolation device, an image data interpolation method, and a medium on which an image data interpolation program is interpolated, for interpolating image data composed of dot matrix pixels.

[0002]

2. Description of the Related Art When handling images on a computer or the like,
An image is represented by pixels in a dot matrix, and each pixel is represented by a gradation value. For example, photographs and computer graphics are often displayed on a computer screen with 640 dots in the horizontal direction and 480 dots in the vertical direction.

On the other hand, the performance of a color printer has been remarkably improved, and its dot density is 720 dpi (dot / inc.).
h), the accuracy is extremely high. Then
If an attempt is made to print an image of 640 × 480 dots corresponding to each dot, the size of the image 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.

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 method) has been proposed. : Hereinafter referred to as the cubic method). Also, Japanese Patent Application Laid-Open No. 6-225140 discloses a technique of preparing a dot pattern in advance so as to have an enlarged form in which the edge is smoothed when smoothing the edge when the dots are interpolated. Have been.

[0005]

The conventional interpolation technique described above has the following problems. In the near-list method, the processing amount is small, but when the interpolation magnification is increased, the image quality is usually noticeable depending on the image. However, depending on the image, even if the near-list method is used, the roughness of the image quality is hardly noticeable. For example, if the image does not change, such as the sky, there is almost no concern.

On the other hand, in the cubic method, since the pixels to be interpolated change smoothly, the image quality does not deteriorate so much even if the interpolation magnification is increased. However, the amount of calculation becomes extremely large due to the multi-order calculation, and there may be little meaning in a portion where the image originally has little change. As described above, various methods such as the near-list method and the cubic method have their advantages and disadvantages, but it is difficult for the user to select them. In addition, the superiority may change partially in one image.

In the invention disclosed in JP-A-6-225140, the interpolation magnification must be fixed because a pattern is required in advance. The number is huge and it is difficult to prepare in advance. The present invention has been made in view of the above problems, and has as its object to provide an image data interpolation apparatus, an image data interpolation method, and a medium on which an image data interpolation program is recorded, which can perform efficient interpolation processing.

[0008]

According to a first aspect of the present invention, there is provided an image data acquiring means for acquiring image data in which an image is represented by pixels in a dot matrix, and an image data acquiring means for acquiring the image data based on the image data. In evaluating the degree of change in the gradation value of each pixel, if it is determined that one of the degree of change in the gradation value of the pixel and the degree of change in the gradation value of a neighboring pixel matches a predetermined condition. A pixel change degree evaluation means for evaluating the degree of change assuming that the pixel satisfies the same condition; and a plurality of interpolation processes corresponding to the predetermined condition in performing an interpolation process for increasing the number of constituent pixels in the image data. And an interpolation process for executing the pixel interpolation unit by selecting from the plurality of interpolation processes based on the evaluation of the pixel change degree evaluation unit. It is constituted comprising a selection means.

In the invention according to claim 1 configured as described above, when performing an interpolation process for increasing the number of constituent pixels of image data in which an image is represented by pixels in a dot matrix, a plurality of interpolation processes are performed by the pixel interpolation means. Can be selected and executed, and when the image data acquisition unit acquires the target image data, the pixel change degree evaluation unit determines the gradation value of each pixel based on the image data. Evaluate the degree of change. Here, the pixel change degree evaluation means evaluates not only the change degree of the gradation value of the pixel but also the change degree of the gradation value of the surrounding pixels. More specifically, if it is determined that one of the degree of change in the gradation value of the pixel and the degree of change in the gradation value of a neighboring pixel matches a predetermined condition, the condition is satisfied for the pixel. Then, the degree of change is evaluated.

The interpolation processing selection means selects an interpolation processing based on the evaluation by the pixel change degree evaluation means and causes the pixel interpolation means to execute the interpolation processing. In this case, an interpolation process that can obtain an optimal interpolation result is associated with each condition in advance, and the interpolation process is selected from the evaluation result based on the correspondence. That is, since the degree of change in the gradation value of a pixel is closely related to the specific method of the interpolation processing, the degree of change in the gradation value of the pixel is evaluated and the interpolation processing is actively changed, so that there is no waste. Implement interpolation processing.

On the other hand, the range of influence differs depending on the degree of change in the gradation value of the pixel depending on the processing content of the interpolation processing. For example, in some cases, the number of pixels required for executing the interpolation process is one, and in others, the interpolation process is performed based on a plurality of pixels. In the latter case,
The question is whether to change the interpolation processing if there is at least one pixel with a large degree of change, or whether to change the interpolation processing if there is at least one pixel with a small degree of change. When a plurality of pixels are required for the interpolation processing, and one of them has a large degree of change, two modes are conceivable. That is, it is not necessary to evaluate the remaining pixels in the target range in the interpolation processing, and conversely, even if the degree of change is small for the already evaluated pixels, the evaluation is unnecessary. That is, when there is a pixel having a large degree of change, the same interpolation processing as in the case where the degree of change is large is performed regardless of the original evaluation of the other pixels. Therefore, it is necessary to correct the original evaluation itself. .

In the present invention, the degree of change in the gradation value of the pixel is evaluated by using the degree of change in the gradation value of the surrounding pixels. Become. Here, the image data is a representation of the image by dot matrix pixels, and may be represented by data for each pixel, and may be a color image or a monochrome grayscale image. Good. The image data obtaining means obtains the image data, and may be any as long as it holds the target image data when the pixel interpolating means performs the interpolation processing for increasing the number of constituent pixels. Therefore, the acquisition method is not particularly limited, and various methods can be adopted. For example, the information may be obtained from an external device via an interface, or may be an image capturing device provided with an image capturing unit. Alternatively, a computer graphic application may be executed to perform input from a mouse or a keyboard.

The pixel change degree evaluation means determines whether or not the change degree of the gradation value of the pixel meets a predetermined condition, and the method and result of the determination are not particularly limited. For example, a condition that the degree of change in the gradation value is large or a condition that the gradation value is small can be set. Also,
As described above, not only the alternative determination of whether the value is large or small, but also the condition that the degree of change of the gradation value belongs to a predetermined range may be used. Further, the condition need not be limited to one kind, but may be plural. For example, a condition may be set by ranking the degree of change in the gradation value, and it may be determined which rank condition is met. Of course, by setting the multi-stage conditions, it is possible to support multi-stage interpolation processing. On the other hand, it is not necessary to determine whether the condition is met based on the evaluation result. This is because the neighborhood of a pixel that meets a predetermined condition is evaluated so that the same condition is met, and if this evaluation result is used, the condition is further satisfied up to the neighboring pixel. Of course, it is also possible to include a determination as to the extent to which the condition is to be met in the vicinity of the pixel when the condition is satisfied as the content of the determination.

Also, how to grasp the change in the gradation value of the pixel itself can be changed as appropriate. As an example, the invention according to claim 2 is based on the image data interpolation apparatus according to claim 1. , The pixel change degree evaluation means includes:
The brightness gradation value of each pixel is obtained, and the degree of change is calculated by comparison with the brightness gradation values of surrounding pixels. In the invention according to claim 2 configured as described above, the brightness of the pixel is used as a criterion for evaluation of the pixel, and the pixel change degree evaluation means obtains a gradation value of the brightness of each pixel. Then, the same gradation value is compared between the pixel and the surrounding pixels, and the comparison result is calculated as the degree of change.

Of course, it is possible to grasp the degree of change of the gradation value of the pixel in addition to this, but the gradation value of the brightness is used to uniformly grasp the pixel represented by the multi-element parameters. It is relatively easy to do. According to a third aspect of the present invention, in the image data interpolating apparatus according to any one of the first and second aspects, the pixel change degree estimating means includes the step of determining a change in the gradation value of each pixel and a predetermined threshold value. Are compared, and when it is determined that the degree of change in the gradation value of the pixel is larger, it is evaluated that the predetermined condition is satisfied, and the interpolation processing selecting means determines that all pixels surrounding the area to be interpolated When the degree of change is evaluated to be large, an interpolation process to be applied to pixels having a large degree of change is selected.

In the invention according to claim 3 configured as described above, it is determined whether or not a pixel has a large degree of change, such as an edge. It is determined that the degree of change in the gradation value is large. As described above, since the determination is made for each pixel including the degree of change in the gradation value of the surrounding pixels, it is determined that all surrounding pixels in each area have a large degree of change when performing the interpolation processing. Is determined, and only in that case, an interpolation process applied to pixels having a large degree of change is performed.

The pixel interpolating means only needs to be able to execute a plurality of interpolation processes related to the degree of change in the gradation value of the pixel, and various processes can be performed as the interpolation process itself. As an example, the invention according to claim 4 is an image data interpolating apparatus according to any one of claims 1 to 3, wherein the pixel interpolating means is preferably applied to an area having a small degree of change. In this configuration, an interpolation process using the image data of the nearest neighbor pixel before the interpolation process as the image data of a new constituent pixel can be executed.

In the fourth aspect of the present invention, the image data of the nearest pixel before the interpolation processing is used as the image data of the new constituent pixel as one interpolation processing. Even if it increases, there is no problem as long as it is an area where the degree of change is small, and it is preferable in that the processing amount is small. As another example, the invention according to claim 5 is the image data interpolation device according to any one of claims 1 to 4, wherein the pixel interpolation means is preferably applied to an area having a large degree of change. As a simple interpolation process, an interpolation process of calculating the image data of the interpolated pixel from the image data of the surrounding pixels by an arithmetic process so that the gradation value of the pixel to be interpolated smoothly changes can be executed.

In the invention according to claim 5 configured as described above, the gradation value of the pixel to be interpolated changes smoothly by performing arithmetic processing using the image data of the surrounding pixels. As described above, if the change is made gently, it is assumed that there is a row of pixels having a large change degree, and even if interpolation is performed between the pixels, the step is not conspicuous. Various calculation methods can be employed for the gradation value of the pixel to be interpolated to change gently, but the manner of change affects the image quality. Therefore, in a sense, the image quality can be adjusted by changing the calculation method. As an example in which the image quality can be adjusted, the invention according to claim 6 is the image data interpolation apparatus according to claim 5, wherein the pixel interpolating means calculates image data of an interpolated pixel between pixels having a large degree of change. In addition, the inclination of the image data is changed while the change mode of the image data is substantially S-shaped, and an undershoot is generated on the lower side and an overshoot is generated on the higher side at both ends to form a height difference. Is adjusted so that the degree of change of the image is optimized.

According to the sixth aspect of the present invention, when the gradation value of the pixel to be interpolated is smoothly changed, the image data is changed in a substantially S-shaped manner between pixels having a large change degree. I do. Therefore, although the change is gradual, the change mode can be made steeper than a gradient that is merely linearly connected, and the gradient can be adjusted to optimize the degree of change of the image. In addition, if an overshoot is generated on the high side while an undershoot is generated on the low side at both ends, the height difference becomes large, and by adjusting the height difference, the degree of change of the apparent image is optimized. It becomes possible. As an example of such arithmetic processing, a cubic convolution interpolation method of multi-dimensional arithmetic processing can be used, and the arithmetic processing that enables such adjustment is not limited to this, and another arithmetic technique is adopted. You can also.

Since the degree of change in the tone value of a pixel is not constant over the entire image, the interpolation processing selection means must select and switch the interpolation processing as appropriate. The frequency of such switching is not particularly limited, and various methods can be adopted. As an example thereof, the invention according to claim 7 is the image data interpolation device according to any one of claims 1 to 6, wherein the interpolation processing selecting means is configured to determine whether or not the pixel evaluated by the pixel change degree estimating means. The above-described interpolation processing is selected and executed in pixel units based on the degree of change in the gradation value.

In the invention according to claim 7 configured as described above, based on the degree of change of the gradation value of the pixel evaluated by the degree of pixel change evaluation, the interpolation processing selecting means performs the interpolation on a pixel-by-pixel basis. Select and execute a process. That is, since the degree of change is evaluated for each pixel, the interpolation processing is changed correspondingly. As another example, the invention according to claim 8 is the image data interpolation device according to any one of claims 1 to 6, wherein the interpolation processing selection unit is evaluated by the pixel change degree evaluation unit. The above-described interpolation processing is selected and executed for each predetermined small area including a plurality of pixels based on the degree of change in the gradation value of the pixel.

[0023] In the invention according to claim 8 configured as described above, the interpolation processing selecting means includes a plurality of pixels based on the degree of change in the gradation value of the pixel evaluated by the pixel change degree evaluating means. The above-described interpolation processing is selected and executed for each small area. As described above, the method of selecting the interpolation processing according to the degree of change in the image is not necessarily limited to a substantial device, and it can be easily understood that the method also functions as the method. Therefore, claims 9 to
The invention according to claim 16 has a configuration corresponding to an interpolation method performed by the image data interpolation device. In other words, there is no difference in that the present invention is not necessarily limited to a substantial device and is also effective as a method.

By the way, such an image data interpolation device may exist alone or may be used in a state of being incorporated in a certain device. It includes embodiments. Therefore, it can be changed as appropriate, such as software or hardware. When the software of the image data interpolation device is realized as an example of realizing the idea of the present invention, the software naturally exists on a recording medium on which such software is recorded, and it must be said that the software is used. In that sense, the invention according to claims 17 to 24 has a configuration corresponding to each step of causing the computer to execute the image data interpolation device.

Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any recording medium to be developed in the future. Also, the duplication stages of the primary duplicated product, the secondary duplicated product, and the like are equivalent without any question. It should be noted that, although different from the medium of the present invention, the present invention is not limited to the case where the supply is performed using a communication line. Further, even when a part is implemented by software and a part is implemented by hardware, there is no difference in the concept of the invention, and a part is stored on a recording medium and appropriately It may be in a form that can be read.

An image output device or the like is naturally included as one of the objects into which the entire image data interpolation device is incorporated. For example, a digital still camera or a video camera uses a digital zoom in addition to an optical zoom. However, the digital zoom may be incorporated in such a camera and used for zooming. In addition, although there are some high-performance televisions that can zoom the screen, in such a case, the zoom may be performed by interpolation processing according to the degree of change in the image. In other words, when the same color is painted in a certain range such as animation, the degree of change is low, and the degree of change is large only at the boundary portion. Therefore, interpolation processing according to each is appropriately selected and executed. do it.

[0027]

As described above, according to the present invention, the interpolation processing is changed in accordance with the degree of change in the image, and the degree of change in the gradation value of the surrounding pixels is considered when the degree of change in the image is evaluated. Therefore, the present invention provides an image data interpolation apparatus, an image data interpolation method, and a medium on which an image data interpolation program can be extremely easily obtained without complicating the determination of the interpolation processing. be able to.

[0028] Claims 2, 10, and 18 are also provided.
According to the invention, since the degree of change of the image is determined based on the gradation value of the brightness, the degree of change can be obtained relatively easily. Further, claim 3 and claim 1
According to the nineteenth aspect, the evaluation of pixels having a large degree of change is reflected in surrounding pixels in advance, and interpolation processing in each region is selected based on the reflection result. And an optimal interpolation result can be obtained.

Further, claim 4, claim 12, and claim 2
According to the aspect of the present invention, the processing amount can be reduced without affecting the image quality in an area where the degree of change is small.
Further, according to the fifth, thirteenth, and twenty-first aspects of the present invention, even if a pixel array having a large change degree is interpolated between the pixels, it is possible to prevent a step from being conspicuous and prevent deterioration in image quality. Further, claim 6 and claim 1
4. According to the invention according to claim 22, the image quality can be relatively easily adjusted by the inclination of the S-shaped curve and the height difference due to the undershoot and the overshoot.

Further, claim 7, claim 15, claim 2
According to the third aspect of the invention, the interpolation result can be finely improved by selecting the interpolation processing on a pixel-by-pixel basis.
Furthermore, according to the invention according to claims 8, 16, and 24, the interpolation processing is selected for each small area, so that the processing can be simplified.

[0031]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a claim correspondence diagram showing the image data interpolation device of the present invention. Assuming digital processing, an image is represented by pixels in a dot matrix, and image data is composed of a group of data representing each pixel. In a system that performs processing in units of pixels, the image is scaled up or down in units of pixels. The image data interpolating apparatus performs such an enlarging process in units of pixels, and includes an image data acquiring unit C1.
Obtains such image data, and executes pixel interpolating means C2.
Performs an interpolation process to increase the number of constituent pixels in this image data. Here, the pixel interpolation means C2 can execute a plurality of interpolation processes corresponding to the degree of change of the gradation value of the pixel as the interpolation processing, and the pixel change degree evaluation means C3 performs the pixel-by-pixel evaluation based on the image data. Evaluate the degree of change. However, at this time, the evaluation of the degree of change of the gradation value of the surrounding pixels is also used, and finally the degree of change of each pixel is evaluated. Then, the interpolation processing selection means C4 selects an interpolation processing capable of obtaining an optimum interpolation result in accordance with the degree of change in the gradation value of the pixel evaluated in this way, and executes the interpolation processing on the pixel interpolation means C2. Let it.

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

The computer main body 12 is connected to a floppy disk drive 13a, a hard disk 13b, and a CD-ROM drive 13c as external auxiliary storage devices, and the hard disk 13b stores main system-related programs. Necessary programs and the like can be read from a floppy disk or a CD-ROM as needed. The computer body 1
A modem 14a is connected as a communication device for connecting 2 to an external network or the like. The modem 14a is connected to the external network via the public communication line, and software and data can be downloaded and introduced.
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 are connected for operating the computer body 12.

Further, a display 17a and a color printer 17b are provided as image output devices.
The display 17a has a display area of 800 pixels in the horizontal direction and 600 pixels in the vertical direction, and each pixel can display the above-described 16.7 million colors. Of course, this resolution is only an example, 640x48
0 pixels, 1024 × 720 pixels, etc.
It can be changed as appropriate.

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

The application 12d is executed on the operating system 12a as the basic program. The processing contents of the application 12d are various, and include a keyboard 15a and a mouse 1 as operation devices.
5b is monitored, and when it is operated, various external devices are appropriately controlled to execute corresponding arithmetic processing and the like. Further, the processing result is displayed on the display 17a,
For example, the data is output to the color printer 17b.

In the computer system 10, image data is acquired by a scanner 11a or the like which is an image input device, a predetermined image processing is executed by an application 12d, and then output to a display 17a or a color printer 17b as an image output device. It is possible to In this case, focusing on the correspondence between pixels, if the pixel density of the color printer 17b and the pixel density of the scanner 11a match, the size of the scanned original image matches the size of the printed image.
If there is a difference between the two, the size of the image will be different.
In many cases, the pixel density of the scanner 11a is similar to the pixel density of the color printer 17b. 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, the display 17a
The display density is higher in each stage as compared with the display density described above, 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, the operating system 12
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 a. For example, if the resolution of the display 17a is 72d
pi, the operating system 1
If 2 dpi is based on 360 dpi, the display driver 12b performs resolution conversion between the two. In a similar situation, the resolution of the color printer 17b is 72
If it is 0 dpi, the printer driver 12c performs resolution conversion.

Since the resolution conversion corresponds to a process for increasing the number of constituent pixels in image data, it corresponds to an interpolation process.
c implements interpolation as one of its functions. Here, the display driver 12b and the printer driver 12c execute not only the above-described pixel interpolating means C2 but also the pixel change degree evaluating means C3 and the interpolation processing selecting means C4, as described below, to obtain the most balanced interpolation result in the resolution conversion. So that you can get it. 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, the program is recorded on a medium such as a CD-ROM or a floppy disk and installed. Therefore, these media constitute a medium on which the image data interpolation program is recorded.

In this embodiment, the image data interpolation device is realized as the computer system 10.
Such a computer system is not necessarily required, and any system that requires interpolation processing for similar image data may be used. For example, as shown in FIG. 3, a digital still camera 11b1 incorporates an image data interpolating device for performing interpolation processing, and displays the image data on the display 17a1 using the interpolated image data.
A system for printing on 7b1 may be used. As shown in FIG. 4, in a color printer 17b2 that inputs and prints image data without going through a computer system, image data input via a scanner 11a2, a digital still camera 11b2, a modem 14a2, or the like is automatically processed. It is also possible to adopt a configuration in which resolution conversion is performed and print processing is performed.

In addition, the color facsimile machine 18a as shown in FIG. 5 and the color copier 1 as shown in FIG.
It is naturally applicable to various devices that handle image data such as 8b. FIG. 7 shows a software flow relating to resolution conversion executed by the printer driver 12c described above. In step ST102, original image data is input. Scanner 1 with application 12d
When an image is read from the printer driver 1a and subjected to predetermined image processing and then subjected to print processing, print data of a predetermined resolution is transmitted to the printer driver 12 via the operating system 12a.
c), this stage of delivery corresponds. Of course, the image may be read by the scanner 11a, and in any case, the process corresponds to the image data acquisition unit C1.

Steps ST104 to ST108 are processes for evaluating the degree of change in the gradation value of the pixel in the read image data. The image data is represented by 256 gradations of RGB, and even if a change is made for each element color, a change degree for three colors occurs, and it is difficult to evaluate the change degree as it is. Therefore, in the present embodiment, attention is paid only to the luminance, and the degree of change is determined based on the luminance gradient. Here, if the image data has luminance as its component element, it is possible to calculate a luminance gradient using the luminance value. However, as described above, a book that performs gradation display for each of the RGB element colors is used. The image data does not have a direct component value. However, even in the case of image data whose luminance value is not a direct component value, the image data has an indirect component value representing the luminance, and the luminance value is calculated from the color space in which the luminance value is not a direct component value. By performing conversion to a color space in which is a direct component value, a luminance value can be obtained.

The color conversion between the different color spaces is not uniquely determined by the conversion formula. Relative correspondences are determined for the color spaces having their component values as coordinates, and the correspondences are stored. It is necessary to sequentially perform conversion with reference to the color conversion table obtained. Then, strictly speaking, it is necessary to have a color conversion table of 16.7 million elements. As a result of considering efficient use of storage resources, instead of preparing correspondences for all coordinate values, usually prepare correspondences for appropriate discrete grid points and use interpolation together Often do. 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 made to be a realistic size, the calculation processing amount becomes unrealistic. Often becomes the target. In view of such a situation, the present embodiment employs the following conversion formula for obtaining luminance from the three primary colors of RGB as used in a television or the like. That is, the luminance yp at the point P is R
From the GB component values (Rp, Gp, Bp), yp = 0.30Rp + 0.59Gp + 0.11Bp. In this way, a luminance value can be obtained only by three multiplications and two additions.

In the present embodiment, as a result of targeting RGB image data, such a conversion formula is employed. However, since each component value indicates the color brightness in the background, Has a property that when viewed alone, the component value linearly corresponds to the luminance. Therefore, more simply, it is not impossible to simply simplify to yp = (Rp + Gp + Bp) / 3 without considering each addition ratio.

FIGS. 8 and 9 show an edge detection filter for calculating a luminance gradient after obtaining the luminance in this manner. Since the image data is composed of pixels in the form of a dot matrix, the degree of change of the image should be evaluated between the pixel of interest and eight neighboring pixels centered on the pixel of interest. In this sense, as shown in FIG. 9, it is preferable to apply a filter by equally evaluating surrounding pixels while adding eight times the weight to the pixel of interest and summing them. However, empirically, it is possible to evaluate only the target pixel and the four surrounding pixels as shown in FIG. 8 without necessarily evaluating the eight surrounding pixels. Of course, there is a large difference in the amount of calculation between using four pixels and eight pixels, and thus reducing the number of evaluation targets can reduce the processing time.

If the result of using the edge detection filters shown in FIGS. 8 and 9 is called an edge amount E of each pixel, it is expected that the distribution will be a normal distribution as shown in FIG.
Whether or not the edge portion has a large degree of change in the image can be determined by comparing with the threshold value th. In the edge detection filters shown in FIGS. 8 and 9, threshold values of th = 32 and th = 64 are appropriate as threshold values, respectively. Therefore, whether or not the pixel is an edge pixel is evaluated from the following equation. (E <-th) or (th> E) The process of step ST106 performs this evaluation for all the pixels in the dot matrix, and the degree of change of the image is large like an edge pixel in each pixel unit. Evaluate whether the pixel is a pixel.

By the way, even if it is determined whether or not the degree of change of the image is large in each pixel unit, since the interpolation process is a process of generating a pixel for each fixed area, the degree of change of the image in each area unit is small. It is necessary to determine whether it is large. Since it is complicated to determine the degree of change for each area, a flag is set in advance in step ST108 by determining whether or not the pixel is an edge pixel. in this case,
As shown in FIG. 11, it is determined that the degree of change in the image is large in all pixels surrounding the edge pixels. More specifically, the degree of change in the gradation value of each pixel is shown in FIG.
If the threshold value is "32" in the case shown in (a), pixels exceeding the threshold value are (0, 0) (3, 0) ( 4,0) (1,
1) Even if it is (2, 1), a flag is set for a pixel adjacent to the edge pixel. Then, FIG.
As shown in (1), a flag is set for all pixels at y = 0, 1 and pixels at y = 2 except (4, 2). As a result, the interpolation process can be appropriately selected by referring to only the flag when moving the target block in each pixel unit in a later step. Here, the threshold is set to "3
The condition that the degree of change of the gradation value of the pixel is larger than 2 is set, and a judgment is made to compare the degree of change with the threshold value “32”. It is determined that this is large because (0, 0) described above.
Although there are five pixels (3, 0) (4, 0) (1, 1) (2, 1), if any one of the pixels adjacent to the pixel satisfies the condition, it is evaluated that the condition is met. Therefore, a flag is set for these adjacent pixels.

Of course, in the present embodiment, the processing of these steps ST104 to ST108 constitutes the pixel change degree evaluation means C3. Based on the flags set as described above, in step ST110 and subsequent steps, interpolation pixels are generated by loop processing. FIG. 13 schematically shows the arrangement of pixels generated by interpolating existing pixels. The coordinates of the existing pixels are temporarily displayed as (X, Y), and the coordinates of the pixels generated by the interpolation are displayed as <X, Y>.

One area surrounded by the existing four pixels is called a block, and an interpolation process of a pixel to be interpolated is selected for each block. In step ST108, since the flag is set for each pixel in consideration of the degree of change in the gradation value of the surrounding pixels, four pixels (0, 0) are used in each block.
If a flag is set for any of (1, 0), (0, 1), and (1, 1), an interpolation process in a case where the degree of change is large is selected, and any one of the flags is set. If not, an interpolation process in the case where the degree of change is small is selected. In step ST110, an interpolation process to be applied to the inside of the block is determined based on this condition. If the degree of change is small, the interpolation is executed by the near-list interpolation process in step ST112. Step ST1
At 14, interpolation is performed by an interpolation process using the cubic method. After one block is interpolated, the block to be processed is moved in steps ST116 and ST118, and when all blocks are completed, the interpolated image data is output in step ST120.

Although the flow of returning to step ST110 after the end of step ST118 is shown by a solid line in the figure, the process of counting edge pixels for each block may be repeated as shown by a broken line. . Needless to say, in this sense, the interpolation processing selecting means C4 includes the processing of step ST110 and the processing of steps ST116 and ST118. 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 the image data here means transferring the data to the next stage.

In the case of the present embodiment, the area surrounded by four pixels is called a block and interpolation processing is selected.
The criterion for changing the interpolation processing can be changed as appropriate according to the computing capacity, the interpolation processing, and the like. For example, as shown in FIG. 14, interpolation processing may be performed with reference to an area centered on a target pixel. In such a case, the interpolation process may be appropriately performed while scanning and moving the target pixel as indicated by an arrow. Here, a method of selecting an interpolation process while moving the pixel of interest will be described. In the above-described example, when determining whether the degree of change is large for each block, it is determined that the image has a large degree of change only when all the flags included in the area are “1”. . However, it can be said that not all the flags need to be "1". For example, assume that pixels are generated by interpolation processing in a region surrounded by four pixels as shown in FIG. In this case, since a flag is set to a pixel adjacent to an edge pixel in FIG. 11, only a case where flags are set to all four pixels as described above may be determined to be a region with a large degree of change. . However, when judging in this way, when the block is moved horizontally by one pixel, the judgment is duplicated each time for two vertical pixels because of the common vertical side, and when the block is moved in the vertical direction, the horizontal The judgment is duplicated each time for two horizontal pixels because of the common side. Such an overlapping situation is useless in the arithmetic processing. On the other hand, as shown in FIG. 3B, considering the adjacent state of the region, it is possible to associate one region with one pixel on the upper left as a representative, and at least the vicinity of a pixel that can be identified as an edge pixel. It can be said that there is no problem even if it is determined that the degree of change is large. In addition, it can be said that an area surrounded by adjacent pixels is sufficient in view of the fact that it is actually an extremely small area. Then, in this way, one pixel
If the regions are made to correspond, the pixel of interest is moved when the block is moved, and the degree of change of the region can be determined only by the edge amount of the pixel of interest, and the amount of calculation processing required for the determination is also reduced. Reduce.

It is also possible to form a block on the side of the pixel to be interpolated. FIG. 16 shows this example. In the figure, the grid points indicated by □ indicate the pixels to be interpolated.
Grid points indicate existing pixels. Now, it is assumed that one 5 × 5 block is used for the pixel to be interpolated, and it is determined whether or not the area has a large degree of change in the image based on the edge amount of the existing pixel included therein. In this case, one block is determined, existing pixels included in the block are extracted, the integrated value of the edge amount is obtained, and the pixel is generated by the same interpolation processing in the block.

Of course, in the above case, the interpolation processing may be selected by setting a block for each larger area.
For example, every 10 × 10 pixels can be a block. Further, it is also possible to determine the edge amount of the existing pixels surrounding each pixel to be interpolated without setting a block, and to select the interpolation processing. In the example of FIG. 16, the 3 × 3 □ grid points arranged inside are all included in the four existing grid points indicated by ○, and when generating each □ grid point, This means that the interpolation processing is selected based on the edge amounts of the four existing grid points indicated by the circles surrounding this. Of course,
What is necessary is just to implement | achieve when such a process is more convenient.
In other words, a method may be used in which a block to be interpolated is specified first and interpolation processing is determined, and then pixels are interpolated therein. Alternatively, the state of the block is determined for each pixel to be interpolated and interpolation processing is selected. You may.

Further, in the flow described above, step ST
At 108, a flag is set in advance for a pixel adjacent to the edge pixel, and the flag is referred to for each block. However, it is also possible to adopt a flow of moving a block as shown by a broken line in FIG. 7, and in this case, there is no need to set a flag, and the edge amount of pixels around the block is determined. What is necessary is just to select an interpolation process.

As described above, the processing of steps ST112 and ST114 having separate interpolation processing constitutes the pixel interpolation means C2. In this case, the pixel interpolation means C2 is realized by software, but an operating system is not indispensable for its configuration. That is, a predetermined function can be performed by calling a function of the operating system, or a predetermined function can be performed without calling the operating system. It goes without saying that the present invention is constituted alone in the process of recording and supplying the program to the medium. Of course, the same applies to other components realized by software.

Here, each interpolation process will be described in detail. As an interpolation process suitable for a case where the degree of change of an image is small and having a very small amount of computation, there is an interpolation process of a near-list method. In the near list method, as shown in FIG. 17, four surrounding grid points Pij, Pi + 1j, Pij +
The distance between 1, Pi + 1j + 1 and the point Puv to be interpolated is obtained, and the data of the closest lattice point is transferred as it is. When this is represented by a general formula, Puv = Pij where i = [u + 0.5] and j = [v + 0.5]. [] Indicates that a Gaussian symbol takes an integer part.

FIG. 18 shows that the number of pixels is 3 in the horizontal and vertical directions by the near-list method.
This shows a situation where interpolation is performed twice at a time. As the pixel to be interpolated, data of the closest pixel among the first four corner pixels is transferred as it is. Therefore, the original image in which black pixels are arranged obliquely with white pixels as the background as shown in FIG. 19 has a relationship in which black pixels are arranged in the oblique direction while being enlarged three times vertically and horizontally as shown in FIG. Is held.
The near-list method has a feature that an edge of an image is held as it is. Therefore, when the difference between adjacent pixels is large, jaggies become noticeable when enlarged.
However, in a portion such as the sky in the background, there is not much change in the arrangement of the adjacent pixels between them, and even when the image is enlarged, the jaggy is hardly noticeable. Further, if the amount of calculation is smaller than that, the effect is extremely large in reducing the processing time. Therefore, the merit of actively selecting the near-list method in a portion where the degree of change of the image is small is great.

On the other hand, there is a cubic interpolation process as an interpolation process which is suitable for a natural image such as a photograph while having a large amount of calculation processing. As shown in FIG. 21, 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. A general expression using a cubic convolution function is as follows.

[0060]

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

[0061]

(Equation 2) 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.

[0062]

(Equation 3) The cubic method has a feature that the gradual change gradually proceeds from one grid point to the other grid point, and the degree of the change becomes a so-called cubic function.

FIG. 22 and FIG. 23 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. 23 will be described. The gradation value of the pixel before interpolation is indicated as “Original” in the left column, and the gradation value “6” is displayed.
4 pixels (P0, P1, P2, P3) are arranged at four points, one pixel (P4) having a gradation value of "128" is interposed, and a gradation value of "1"
Five 92 pixels (P5, P6, P7, P8, P9) are arranged. In this case, the edge is a portion of the pixel having the gradation value “128”.

Here, three pixels (Pn1, Pn1) are located between each pixel.
n2, Pn3), the distance between the interpolated pixels is “0.25”, and the above-described x1 to x4
Is the value in the middle column of the table for each interpolation point. f (x1) to f (x4) are also uniquely calculated corresponding to x1 to x4. For example, x1, x2, x3, and x4 are "1.25" and "0.25", respectively. , "0.75",
When “1.75” is obtained, f (t) corresponding thereto is approximately “−0.14”, “0.89”, “0.3”
0 "and" -0.05 ". X1, x2, x
3, x4 are “1.50”, “0.50”,
When “0.50” and “1.50” are obtained, f (t) corresponding thereto is “−0.125” and “0.62”.
5 "," 0.625 ", and" -0.125 ". In addition, x1, x2, x3, and x4 are each "1.7.
5 "," 0.75 "," 0.25 ", and" 1.25 ", f (t) corresponding thereto is roughly expressed as"-
0.05 "," 0.30 "," 0.89 "," -0.1
4 ". The result of calculating the gradation 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 data in the vertical direction, the operation is simplified, and only data (P1, P2, P3, P4) of four grid points arranged in the horizontal direction are referred to.
Influence degree f according to distance from interpolation point to each grid point
Using (t), it can be calculated as follows. P = P1 ・ f (x1) + P21f (x2) + P3 ・ f (x3) + P4 ・ f (x4) Therefore, when calculating for the interpolation point P21, P21 = 64 * f (1.25) +64 * f (0.25) + 64 * f (0.75) + 128 * f (1.75) = 64 * (-0.14063) + 64 * (0.890625) + 64 * (0.296875) +128 * (-
0.04688) = 61.

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

FIG. 24 shows a specific example when interpolation is performed by the M cubic method, and shows the result of interpolation for a model on the same assumption as in the case of the cubic method. FIG. 22 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. In order to understand the characteristics of the above-described near-list method, cubic method, and M-cubic method, a bi-linear interpolation method (bilinear interpolation), which is another interpolation method, will be described.

The bilinear method is similar to the cubic method in that it gradually changes as one grid point approaches the other grid point as shown in FIG. 25, but the change is the data of the grid points on both sides. In that it is linear in that it only depends on That is, four lattice points Pij, Pi + 1j, Pij + 1, Pi + surrounding the point Puv to be interpolated
The area defined by 1j + 1 is divided into four sections by the interpolation point Puv, and the data at the diagonal positions is weighted by the area ratio. When this is represented by an equation, P = {(i + 1) -u} (j + 1) -v {Pij + {(i + 1) -u} v-j} Pij + 1 + {u-i} (j + 1) -v ｝ Pi + 1j + ｛u-i｝ ｛v-j｝ Pi + 1j + 1. Note that i = [u] and j = [v].

The two cubic methods and the bilinear method are common in that they gradually change as one grid point approaches the other grid point, and the change state is a cubic function or a linear function. The difference is large when viewed as an image. FIG. 26 is a diagram represented two-dimensionally to make it easier to understand the difference between the interpolation results in the near-list method, the cubic method, the M-cubic method, and the bilinear method. In the figure, the horizontal axis indicates the position, and the vertical axis indicates the interpolation function. Of course, this interpolation function corresponds to the degree of influence according to the distance described above. t = 0, t =
1, a lattice point exists at the position of t = 2, and the interpolation point is t = 0 to t = 0.
1 position.

In the case of the bilinear method, between two adjacent points (t
= 0 to 1), the boundary is smoothed because it changes only linearly, and the impression of the screen is blurred. That is, unlike the smoothing of the corners, if the boundary is smoothed, the contour which should be originally lost disappears, and the focus becomes weak in a photograph. On the other hand, in cubic, between two adjacent points (t = 0
In (1) to (1), not only is it gradually approached by drawing a mountain-shaped protrusion, but also has the effect of pushing down downward on the outside (t = 1 to 2) between the two points. That is, the edge portion is changed so as to have a large difference in height so that no step is formed, and in a photograph, there is a favorable effect that no step is formed while increasing sharpness. In addition, M cubic has an effect of increasing sharpness. Note that the cubic method requires a large amount of arithmetic processing if the interpolation magnification is large and the number of pixels to be interpolated is large.

If emphasis is placed on the image quality, a cubic function like the cubic method is likely to be selected. However, in computer processing, the balance between speed and image quality is large. That is, although the degree of reduction in the processing speed increases with the degree of improvement in the image quality, there is a case where the higher processing speed is preferred even if the image quality is slightly or slightly reduced. On the other hand, it is easier to understand by referring to FIGS. 22, 23, and 24 showing specific numerical values along with the comparison of the interpolation functions as described above. Referring to the example of FIG. 22, a pixel (P3) having a gradation value of “64”, a pixel (P4) having a gradation value of “128”, and a pixel (P5) having a gradation value of “192”, which are the original edge portions. )), The simple linear connection method corresponds to the bilinear method, whereas the cubic method has a specific S-shaped curve, and the M cubic method The S-shaped curve is steeper. Needless to say, the direction of the S-shaped curve makes the gradation value change of the pixel sharp, and the edge is emphasized. Also, regions (P2 to P2) adjacent to the edge pixels
3, P5 to P6), so-called undershoots and overshoots occur. Due to the undershoots occurring on the lower side and the overshoots occurring on the higher side, the height difference on both sides sandwiching the edge pixel increases. Therefore, it can be understood that the edge is emphasized by these two factors.

It can be easily understood that whether or not an image looks sharp depends on the inclination angle of the central portion of the S-shaped curve. In addition, it can be said that the height difference caused by the undershoot and the overshoot on both sides of the edge also has an effect. That is, using a higher-order function, the change mode of the image data is made substantially S-shaped, and when shifting from the lower side to the higher side of the image data, the image data temporarily decreases from the lowest value and then increases to increase the highest value. It is decreasing again. Then, the undershoot, the degree of inclination, and the overshoot at that time are adjusted by the parameters of the higher-order function to optimize the degree of change of the image.

On the other hand, in the interpolation function shown in FIG. 26, the slope becomes steeper in the interval from t = 0 to 1 and the negative side is increased so as to cancel the increased weight in the interval from t = 1 to 2. Occurs when the curve is drawn toward Therefore, when the sharpness is to be adjusted, an ideal slope serving as a reference for the sharpness is determined in the interpolation function, and a curve for generating the above slope is determined in a section from t = 0 to t = 1. This can be realized by determining a curve in which overshoot and undershoot are likely to occur while drawing to the negative side so as to offset the weighting increased by this curve in sections 1 and 2. Of course, in the subsequent work, the parameters of the multi-order operation function are determined so as to obtain the specified curve. However, since the determination methods of such parameters are extremely various, in a practical sense, the central portion of the S-shaped curve is determined. It is nothing less than adjusting the tilt angle and the undershoot and overshoot.

Each of the interpolation processes has the above-described difference in characteristics. In the block for which it is determined in step ST110 that the degree of change in the image is small, the interpolation process of the near-list method is executed in step ST112. Conversely, for a block determined to have a large degree of change, interpolation processing of the cubic method or the M cubic method is executed. When the interpolation process is performed by the cubic method, the calculation time is long. However, since the switching to the near-list method is performed in a portion where the degree of change of the image is small, the processing time as a whole is extremely reduced. In particular, when a certain area is painted in the same color as in computer graphics, there is no problem even if the near-list method is executed uniformly, so that the processing time is reduced. Also, even if it is a natural image, the part where the jaggy is conspicuous when enlarged is usually not so large even if it is the area ratio,
Thus, by sequentially switching the degree of change of the image, the processing amount can be reduced without deteriorating the image quality.

In the present embodiment, one of the two types of interpolation processing is executed according to the flag. However, a plurality of interpolation processing steps corresponding to the degree of change in the gradation value of the pixel are executed. You may do it. Also,
As shown in FIG. 27, the two interpolation processes may be performed in a superimposed manner, and the magnification may be made to correspond to the degree of change in the image. For example, assuming that the interpolation magnification is five times, if the degree of change of the image is small, the interpolation processing is performed five times by the nearlist method. If the degree of change of the image is large, the interpolation processing is performed five times by the cubic method. In these cases, it is the same as the above-described embodiment, but when the degree of change of the image is an intermediate value, the interpolation processing is performed twice by the cubic method, and the remaining 2.5 times is interpolated by the near-list method. To process. In this way, although two interpolation processes are performed, a plurality of interpolation processes can be substantially selected according to the degree of change of the image.

In the case of the cubic method which requires a large amount of interpolation processing, there is a merit that the interpolation magnification is intentionally set to an integral multiple such as twice. FIG. 28 shows an example of processing for interpolating twice in the horizontal and vertical directions. Assuming that a variable area for the interpolated image data is secured in advance, if the interpolation processing is an integer multiple, the image data of the original image is the image data of the pixel corresponding to the coordinate value multiplied by the integer. In the example shown in the figure, the old coordinate value (0,0) corresponds to the new coordinate value (0,0), the old coordinate value (1,0) corresponds to the new coordinate value (2,0), The old coordinate value (0, 1) corresponds to the new coordinate value (0, 2), and the old coordinate value (1, 1) corresponds to the new coordinate value (2, 2).

That is, even though the interpolation processing itself can be executed at an arbitrary magnification, if only the integral multiple interpolation processing is executed, the number of pixels to be interpolated is reduced, and the processing can be sped up. . As described above, in the computer system 10 having both the image input device and the image output device, the printer driver 12c inputs the original image data in step ST102, and then, in steps ST104 to ST108, determines the degree of change in the image around the original image data. By detecting and setting a flag while using the evaluation of the degree of change in the gradation value of the pixel, and referring to the flag in step ST110, a block having a small degree of change in the image is referred to as a near-list in step ST112. Interpolation processing is performed, and the interpolation processing by the cubic method is performed in step ST114 for a block having a large degree of change in the image, so that the determination of the interpolation processing is not complicated and the image quality is not degraded. Implement near-list methods whenever possible It is your, reduces the amount of computation while automatically select the optimum interpolation process.

[Brief description of the drawings]

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

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

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

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

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

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

FIG. 7 is a flowchart in the image data interpolation device of the present invention.

FIG. 8 is a diagram illustrating an example of an edge detection filter.

FIG. 9 is a diagram illustrating another example of the edge detection filter.

FIG. 10 is a diagram illustrating a relationship between a distribution of an edge amount and a threshold.

FIG. 11 is a diagram illustrating a relationship between a pixel of interest and a pixel targeted for flag setting determination.

FIG. 12 is a diagram illustrating a setting state of an edge amount and a flag.

FIG. 13 is a diagram illustrating a relationship between blocks formed by existing pixels and pixels to be interpolated.

FIG. 14 is a diagram illustrating a case where a block is formed based on a target pixel.

FIG. 15 is a diagram showing a correspondence between a flag status and an area.

FIG. 16 is a diagram illustrating a case where a block is formed by pixels to be interpolated.

FIG. 17 is a conceptual diagram of a near-list method.

FIG. 18 is a diagram showing a situation in which data of each grid point is transferred by the near list method.

FIG. 19 is a schematic diagram showing a situation before interpolation in a near-list method.

FIG. 20 is a schematic diagram showing a situation after interpolation in the near-list method.

FIG. 21 is a conceptual diagram of the cubic method.

FIG. 22 is a diagram showing a data change situation when the cubic method is specifically applied.

FIG. 23 is a diagram showing a specific application example of the cubic method.

FIG. 24 is a diagram showing a specific application example of the M cubic method.

FIG. 25 is a conceptual diagram of the bilinear method.

FIG. 26 is a diagram illustrating a change situation of an interpolation function.

FIG. 27 is a diagram illustrating a relationship in which interpolation magnifications of a plurality of interpolation processes are distributed according to a degree of change in an image.

FIG. 28 is a schematic diagram showing an integer multiple interpolation process.

[Explanation of symbols]

 10 Computer system 11a Scanner 11a2 Scanner 11b Digital still camera 11b1 Digital still camera 11b2 Digital still camera 11c Video camera 12 Computer body 12a Operating system 12b Display driver 12b Driver 12c Printer driver 12d Application 13a Floppy disk drive 13b Hard disk 13c CD-ROM drive 14a Modem 14a2 Modem 15a Keyboard 15b Mouse 17a Display 17a1 Display 17b Color printer 17b1 Color printer 17b2 Color printer 18a Color facsimile machine 18b: color copy machine

──────────────────────────────────────────────────続 き Continued on the front page (58) Field surveyed (Int.Cl. ⁷ , DB name) H04N 1/387-1/393 G06T 3/40

Claims (24)

(57) [Claims] An image data acquisition unit for acquiring image data representing an image by pixels in a dot matrix; and evaluating a gradation level of each pixel based on the image data. If it is determined that any one of the degree of change in the tonal value and the degree of change in the gradation value of the neighboring pixel matches a predetermined condition, the pixel for which the above-mentioned degree of change is evaluated is determined as that the pixel meets the condition. A change degree estimating means, a pixel interpolating means capable of executing a plurality of interpolation processes corresponding to the predetermined condition in performing the interpolation processing for increasing the number of constituent pixels in the image data, and based on the evaluation of the pixel change degree estimating means. And an interpolation processing selecting means for executing the pixel interpolation means by selecting from the plurality of interpolation processing. 2. The image data interpolating apparatus according to claim 1, wherein said pixel change degree evaluation means obtains a gradation value of brightness of each pixel and calculates a gradation value of brightness of surrounding pixels. An image data interpolation apparatus, wherein the degree of change is calculated by comparison. 3. The image data interpolation device according to claim 1, wherein the pixel change degree evaluating means compares the change degree of the gradation value of each pixel with a predetermined threshold value. When it is determined that the degree of change in the gradation value of the pixel is larger, it is evaluated that the predetermined condition is satisfied, and the interpolation processing selecting means performs the above-described change in all pixels surrounding the area to be interpolated. An image data interpolating apparatus which selects an interpolation process to be applied to a pixel having a large change degree when the degree is evaluated to be large. 4. The image data interpolating apparatus according to claim 1, wherein the pixel interpolating means performs a pre-interpolation process as an interpolation process suitable for an area having a small degree of change. An image data interpolation device capable of executing an interpolation process using image data of a nearest pixel as image data of a new constituent pixel. 5. The image data interpolating apparatus according to claim 1, wherein said pixel interpolating means is applied to an area having a large degree of change to perform interpolation of a pixel to be interpolated as a suitable interpolation process. An image data interpolating apparatus capable of executing an interpolation process of calculating image data of an interpolated pixel by arithmetic processing from image data of surrounding pixels so that a tone value changes smoothly. 6. The image data interpolating apparatus according to claim 5, wherein the pixel interpolating means uses a higher-order function to change the image data in a substantially S-shaped manner, and changes the image data from a lower side to a higher side. When moving to the side, once decrease below the minimum value, rise to exceed the maximum value and decrease again, and adjust the undershoot, inclination degree and overshoot at that time with the parameters of the higher-order function above An image data interpolating device that optimizes the degree of change of the image. 7. The image data interpolating apparatus according to claim 1, wherein said interpolation processing selecting means includes: a degree of change in a gradation value of a pixel evaluated by said pixel change degree evaluating means. An image data interpolating apparatus which selects and executes the above-mentioned interpolation processing in units of pixels based on. 8. The image data interpolating apparatus according to claim 1, wherein said interpolation processing selecting means includes: a degree of change in a gradation value of a pixel evaluated by said pixel change degree evaluating means. An image data interpolating apparatus which selects and executes the above-mentioned interpolation processing for each predetermined small area composed of a plurality of pixels based on the image data. 9. An image data interpolation method for increasing the number of constituent pixels of image data in which an image is represented by pixels in a dot matrix, comprising: a step of acquiring the image data; In evaluating the degree of change in the gradation value, if it is determined that any of the degree of change in the gradation value of the pixel and the degree of change in the gradation value of a neighboring pixel matches a predetermined condition, Comprises a step of evaluating the degree of change assuming that the same condition is met, a step of selecting from interpolation processing based on the evaluation, and a step of increasing the number of constituent pixels in the image data by the selected interpolation processing. A method for interpolating image data, comprising: 10. The image data interpolation method according to claim 9, wherein when evaluating the degree of change in the gradation value of a pixel, the gradation value of the brightness of each pixel is determined and the brightness of the surrounding pixels is determined. An image data interpolation method, wherein the degree of change is calculated by comparing with a gradation value. 11. The image data interpolation method according to claim 9, wherein the degree of change of the gradation value of each pixel is compared with a predetermined threshold value, and the gradation of the pixel is determined. When the degree of change of the value is determined to be larger, it is evaluated that the predetermined condition is met, and when the degree of change is evaluated to be larger in all pixels surrounding the region to be interpolated, the degree of change is larger. An image data interpolation method comprising selecting an interpolation process to be applied to pixels. 12. The image data interpolation method according to any one of claims 9 to 11, wherein the image data of the nearest neighbor pixel before the interpolation processing is used as image data of a new constituent pixel. An image data interpolation method characterized by being possible. 13. The image data interpolation method according to any one of claims 9 to 12, wherein a gradation value of a pixel to be interpolated as a suitable interpolation process applied to an area having a large degree of change gradually changes. An image data interpolation method, which can execute an interpolation process of calculating image data of an interpolated pixel from image data of peripheral pixels by arithmetic processing so as to perform image data interpolation. 14. The image data interpolation method according to claim 13, wherein the change mode of the image data is made substantially S-shaped using a higher-order function, and the image data is shifted from a lower side to a higher side of the image data. Once decreased from the lowest value, then increased to exceed the highest value and decreased again, and the degree of change in the image by adjusting the undershoot, the degree of inclination, and the overshoot at that time with the parameters of the higher-order function The image data interpolation method characterized in that: 15. The image data interpolation method according to claim 9, wherein the interpolation processing is selected and executed in pixel units based on the degree of change in the gradation value of the evaluated pixel. An image data interpolation method characterized by performing the following. 16. The image data interpolation method according to any one of claims 9 to 14, wherein the image data interpolation method includes the steps of: An image data interpolation method characterized by selecting and executing an interpolation process. 17. A medium recording an image data interpolation program for causing a computer to execute an interpolation process so as to increase the number of constituent pixels of image data in which an image is represented by pixels in a dot matrix. And evaluating the degree of change in the gradation value of each pixel based on the image data, wherein one of the degree of change in the gradation value of the pixel and the degree of change in the gradation value of a neighboring pixel is determined. If it is determined that the condition is satisfied, the step of evaluating the degree of change assuming that the pixel satisfies the same condition; a step of selecting from the interpolation processing based on the evaluation; Increasing the number of constituent pixels in the image data. . 18. A medium in which the image data interpolation program according to claim 17 is recorded, when evaluating the degree of change in the gradation value of a pixel, the gradation value of the brightness of each pixel is determined and the surrounding pixels are evaluated. A medium for recording the image data interpolation program, wherein the degree of change is calculated by comparing the brightness with a gradation value of the brightness. 19. A medium in which the image data interpolation program according to claim 17 or 18 is recorded, wherein a degree of change of a gradation value of each pixel is compared with a predetermined threshold value. When it is determined that the degree of change in the gradation value of the pixel is larger, it is evaluated that the predetermined condition is met, and when it is evaluated that the degree of change is larger in all the pixels surrounding the area to be interpolated. A medium in which an image data interpolation program is recorded, wherein an interpolation process to be applied to pixels having a large degree of change is selected. 20. A medium in which an image data interpolation program according to any one of claims 17 to 19 is recorded, wherein image data of a nearest pixel before interpolation processing is used as image data of a new constituent pixel. A medium recording an image data interpolation program, which is capable of executing an interpolation process. 21. A gradation value of a pixel to be interpolated as a suitable interpolation process by applying the image data interpolation program according to any one of claims 17 to 20 to a medium having a large degree of change. A medium storing an image data interpolation program, which is capable of executing an interpolation process of calculating image data of an interpolated pixel by arithmetic processing from image data of surrounding pixels so that the image data changes smoothly. 22. A medium on which the image data interpolation program according to claim 21 is recorded, wherein a change mode of the image data is made substantially S-shaped by using a higher-order function, and the image data is changed from a lower side to a higher side of the image data. At the time of transition, once decrease from the lowest value, then rise and exceed the highest value, decrease again,
A medium in which an image data interpolation program is recorded, wherein the undershoot, the degree of inclination, and the overshoot are adjusted by the parameters of the higher-order function to optimize the degree of image change. 23. A medium in which the image data interpolation program according to any one of claims 17 to 22 is recorded, wherein the interpolation processing is performed on a pixel-by-pixel basis based on the degree of change in the gradation value of the evaluated pixel. A medium storing an image data interpolation program characterized by being selectively executed. 24. A medium on which the image data interpolation program according to any one of claims 17 to 22 is recorded, wherein a predetermined small pixel composed of a plurality of pixels is determined based on a degree of change in the gradation value of the evaluated pixel. A medium on which an image data interpolation program is recorded, wherein the interpolation processing is selected and executed for each area.