JP3173496B2 - Image data interpolation apparatus, computer for image data interpolation processing, 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 interpolating apparatus for interpolating image data composed of pixels in a dot matrix, an image data interpolating method, and a medium on which an image data interpolating program is recorded.

[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 dots / inch (dp
As shown in i), the accuracy is extremely high. Then
If an attempt is made to print an image of 640 × 480 dots in correspondence with each dot, the image becomes extremely small. In this case, since the gradation value is different and the meaning of the resolution itself is different, it is necessary to convert between dots to print data by interpolating between dots.

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). : Hereinafter referred to as the cubic method). Japanese Patent Application Laid-Open No. 6-225140 discloses a technique in which a dot pattern is prepared 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. Various methods, such as the near-list method and the cubic method, have their own advantages and disadvantages, but it is difficult for users to select them,
If it is fixed to either one, the quality of the interpolation result for an image that is not good will be degraded. JP-A-6-225140
In the invention disclosed in Japanese Patent Laid-Open Publication No. H10-157, the interpolation magnification must be fixed because a pattern is prepared in advance, and the number of patterns becomes large assuming a color image. It is difficult to put it.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and provides an image data interpolation apparatus, an image data interpolation method, and a medium on which an image data interpolation program can be efficiently interpolated including a color image. With the goal.

[0007]

An image data interpolating apparatus provided by the present invention comprises: image data acquiring means for acquiring image data of an image represented by dot matrix pixels;
In performing the interpolation process for increasing the number of constituent pixels in the image data, a pixel interpolation unit that can be selected and executed from a plurality of interpolation processes, and a difference between peripheral pixels of the image data is greater than a predetermined threshold value. A statistical processing is performed on the large pixels, and a characteristic amount obtaining unit that obtains a characteristic amount related to the interpolation processing while expressing a characteristic of the entire image based on a result of the statistical processing, and a characteristic amount obtaining unit that obtains the characteristic amount. And an interpolating process selecting unit that selects an interpolating process based on the obtained feature amount and causes the pixel interpolating unit to execute the interpolating process. In the present invention configured as described above, in performing the interpolation process of increasing the number of constituent pixels of the image data in which the image is represented by the pixels in the dot matrix, the pixel interpolation unit selects one of the plurality of interpolation processes. When the image data acquisition unit acquires the target image data, the feature amount acquisition unit acquires the feature amount related to the above-described interpolation processing on the image data. Here, when acquiring the feature amount, the image data is subjected to statistical processing on pixels whose difference from peripheral pixels is larger than a predetermined threshold, and based on the result of the statistical processing, A feature amount related to the above-described interpolation processing is acquired while representing the entire feature. Then, the interpolation processing selecting means selects the interpolation processing based on the characteristic amount acquired by the characteristic amount acquiring means and causes the pixel interpolation means to execute the interpolation processing.

[0008]

As described above, the method of selecting the optimum interpolation processing according to the characteristics of the image is not necessarily limited to a substantial device, and it can be easily understood that the method also functions as the method. For this reason, the image data interpolation method provided by the present invention obtains image data of an image represented by pixels in a dot matrix, and the difference between the image data and peripheral pixels is smaller than a predetermined threshold value. Statistical processing is performed on large pixels, and a feature amount related to the interpolation process is acquired while representing features of the entire image based on the result of the statistical process. Based on the acquired feature amount, the image data is acquired. , An interpolation process for increasing the number of constituent pixels is selected, and the image data is processed by the selected interpolation process.

That is, there is no difference that the present invention is not necessarily limited to a substantial device but is also effective as a method. Such an image data interpolating device may exist alone or may be used in a state of being incorporated in a certain device. The idea of the present invention is not limited to this, but includes various modes. Therefore, it can be changed as appropriate, such as software or hardware. If the software of the image data interpolating device is realized as an example of realizing the idea of the present invention, the present invention naturally exists on a recording medium on which such software is recorded, and it cannot be said that the present invention is used.

As one example, a medium provided with an image data interpolation processing program for causing a computer to execute an interpolation processing according to the present invention is a medium for storing image data of an image represented by dot matrix pixels at a predetermined interpolation magnification. A medium storing an image data interpolation program for executing an interpolation process so as to increase the number of constituent pixels, the method comprising: acquiring the image data; and performing a plurality of interpolations in performing the interpolation process to increase the number of constituent pixels in the image data. A pixel interpolation step that can be selected and executed from the processing, and statistical processing is performed on the image data with respect to pixels having a difference from peripheral pixels greater than a predetermined threshold value, and based on the result of the statistical processing. A feature amount obtaining step of obtaining a feature amount related to the interpolation process while representing the feature of the entire image. An interpolation processing based on the feature acquired by the feature quantity acquiring step and the selection of it is constituted comprising an interpolation processing selection step to be executed by the pixel interpolation step.

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. Further, the duplication stages of the primary duplicated product, the secondary duplicated product and the like are equivalent without any question. In addition, the present invention is not limited to the case where the present invention is used even when the supply is performed using a communication line. further,
The concept of the invention is not completely different even when part is software and part is realized by hardware, and part is stored on a recording medium and read as needed as necessary. Such a form may be adopted. When the present invention is implemented by software, the present invention is naturally realized not only as a medium on which the program is recorded, but also as the program itself, and the program itself is also included in the present invention.

Here, the image data is an image represented by pixels in a dot matrix. The image data may be any data as long as each pixel is represented by data, and may be a color image or a monochrome image. Is also good. Further, the gradation value may be of two gradations or of multiple gradations. The image data acquiring means acquires 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 interpolating means may be any means which can select a plurality of interpolation processes by various methods, and may be any device which has options with substantially different interpolation processing results. Therefore, not all options need to be independent interpolation techniques. As one example, the pixel interpolating means may be configured to include an interpolation process in which a plurality of interpolation processes are performed in an overlapping manner as options of the interpolation process.
In the case of such a configuration, the pixel interpolating unit performs a plurality of interpolation processes as one of the options of the interpolation process. That is, after first interpolating a pixel in a certain interpolation process, the pixels are superimposed and interpolated in another interpolation process. Thus, even if there are two types of independent interpolation processing, there are three types of options including the processing of overlapping them.

Accordingly, as a corresponding example of the interpolation process, a plurality of interpolation processes can be executed in a superimposed manner, so that the variation of the interpolation process can be increased. Further, the interpolation processing to be executed does not need to be limited to a constant one for all pixels. As an example, the feature amount obtaining unit obtains a feature amount for each partial region of an image, and the interpolation processing selecting unit performs interpolation in the pixel interpolation unit based on the feature amount obtained for each of the regions. It is also possible to adopt a configuration in which a process is selected and executed.

In the case of such a configuration, the feature amount acquiring means acquires the feature amount for each partial area of the image.
Depending on the merits and demerits of the respective interpolation processes, some are preferably applied uniformly, while others are not. In addition, when it is not necessary to use uniform characteristics, it is preferable to utilize each feature. For this reason, the interpolation processing selecting means selects and executes the interpolation processing in the pixel interpolation means based on the characteristic amount acquired for each partial area.

For example, even if a simple interpolation process is adopted in a place where there is no change in the image, the deterioration of the image quality is difficult to understand. Would. Therefore, since the interpolation of the pixels increases the number of pixels between the existing pixels, the interpolation processing can be appropriately changed for the increased pixels. Therefore, it is not necessary to perform uniform interpolation processing on the entire image, and it is possible to obtain an optimal result in a comprehensive sense. However, even in this case, since interpolation processing has been performed, uniform interpolation processing is applied to each area.

The reason why the interpolation process is selected is that the interpolation result differs depending on the interpolation process, and there are various feature amounts which are keys for selecting the interpolation method. As an example, the pixel interpolating unit can execute an interpolation process suitable for each of a natural image and a non-natural image, and the feature amount acquiring unit determines that the image represented by the image data is a non-natural image The interpolation processing selecting unit causes the pixel interpolation unit to execute an interpolation process suitable for a natural image when the image is determined to be a natural image based on the characteristic amount. In addition, when it is determined that the image is a non-natural image, the pixel interpolating means may execute an interpolation process suitable for the non-natural image.

In such a configuration, assuming that the pixel interpolating means can execute an interpolation process suitable for each of a natural image and a non-natural image, it is assumed that the image represented by the image data is a natural image. The feature amount obtaining unit obtains a feature amount capable of determining whether the image is a non-natural image, and the interpolation process selecting unit determines whether the image is a natural image based on the feature amount. The interpolation means performs an interpolation process suitable for a natural image, and when it is determined that the image is a non-natural image, causes the pixel interpolation means to execute an interpolation process suitable for a non-natural image.

Therefore, it is possible to select an optimal interpolation process according to a natural image and a non-natural image that are familiar objects to be processed. The natural image means something like a so-called live-action image, and the non-natural image means computer graphics represented by a business graph. Characteristically, it can be considered that a natural image uses multiple colors, and a non-natural image uses a small number of colors. Of course, it may correspond to other features found,
The pixel interpolation means can execute optimal interpolation processing for each of a natural image and a non-natural image having such a characteristic difference.

More specifically, the pixel interpolating means executes an interpolation process by a nearest neighbor interpolation method on a non-natural image and executes an interpolation process by a third-order convolution interpolation method on a natural image. Configuration. In this way, in the case of a non-natural image, an advantage of high-speed processing by the nearest neighbor interpolation method is obtained, and in the case of a natural image, an advantage of maintaining sharpness by the third-order convolution interpolation method is obtained. There is often a slight difference in the amount of expression per pixel between non-natural images and natural images, and the nearest neighbor interpolation method is simple, so high-speed processing is possible. This can lead to unnatural feelings. On the other hand, the third-order convolution interpolation method can maintain sharpness, but the processing is complicated, the calculation load required for the interpolation processing is large, and the processing time is long.

On the premise of such advantages and disadvantages, interpolation processing by the nearest neighbor interpolation method is performed on a non-natural image, so that it is possible to expect a higher processing speed, and to perform tertiary convolution on a natural image. Since the interpolation processing by the interpolation method is executed, sharpness is secured. The feature amount acquiring means acquires a feature amount of the image data, and the feature amount itself has a property related to the interpolation processing. As described above, as an example, it can be obtained by determining whether the image is a natural image or a non-natural image, but there are various acquisition methods.

As one example, the above-mentioned feature amount acquiring means includes:
It is also possible to adopt a configuration in which the data of each pixel in the acquired image data is totaled based on a predetermined standard, and a feature amount for determining the type of the image represented by the image data is acquired. In the case of such a configuration, the feature amount obtaining means totalizes the data of each pixel based on a predetermined standard. That is, a feature amount for determining the type of the content of the image is acquired from the image data itself. Of course, various statistical methods and the like can be adopted as the totaling method, and a histogram and the like are one example.

In this way, since the type of image is determined by summing up the image data, it is possible to reliably select the optimal interpolation processing even when there is no other information. Examples of the data to be aggregated include the number of colors used. As described above, in computer graphics, considering that a person who draws specifies colors, not many colors can be used. In particular, such a tendency appears strongly in business graphs and the like. On the other hand, if it is a natural image, even a single color image can be multicolored by adjusting the number of light rays, and the number of colors used becomes extremely large. Therefore, if the number of colors used for image data is totaled, it can be determined whether the image is a natural image or a non-natural image.

In this case, it is not necessary to strictly count the number of colors used. For example, the histogram of the luminance of each pixel may be totaled, and the number of colors used may be determined from the frequency of use of each luminance. Although it is difficult to find the relationship between the luminance histogram and the number of colors used for a natural image, it can be easily understood by assuming a business graph or the like. A business graph or the like uses a small number of colors, and often uses only a few colors in consideration of a filled graph or the like.
In this case, the luminance histogram appears as a spectrum. Since the luminance may be the same for different colors, the frequency of use of the luminance does not always match the number of colors used, but it is easy to understand whether the frequency is high or low. For example,
Assuming that the image is a natural image, the brightness used surely increases because of shading and the like, and the histogram should appear as a continuous curve rather than a spectrum.

In this case, it is apparent from the same point that the luminance itself does not have to be accurate. For example,
Accurate luminance for RGB image data is not usually obtained by calculation alone. However, in television broadcasting and the like, luminance is obtained from RGB by simple weighted addition. That is, such a simple method can be used. Whether the histogram has a spectral shape or a curved shape is also derived from the aggregation result, and it can be determined as a feature amount by determining whether the histogram is a natural image or a non-natural image based on the result. . Further, in addition to the viewpoint of whether or not the image is a natural image, it is also possible to acquire a feature amount such as whether or not the image is bright. That is, interpolation processing suitable for a bright image,
If there is a suitable interpolation process for a dark image, these can be selected.

It is also possible to sum up the degree of change in the data of each pixel. When a picture is drawn with computer graphics, the degree of change is only the amount that the user has drawn, but for a natural image that captures a landscape, the amount of information is large in each row, which is reflected in the magnitude of the degree of change in pixels. Come. Therefore, it is possible to determine the type of image from the degree of change between pixels. On the other hand, since the purpose of the feature amount obtaining means is to determine an image, it can be said that image data need only be aggregated to an extent necessary for the determination. In this sense, the feature amount obtaining means may be configured to extract the data for some of the pixels constituting the image data and total the data.

In the case of such a configuration, the above-mentioned gradation values are extracted and tabulated only for some of the pixels constituting the image data. Here, various methods can be adopted as the method of extraction. It is also possible to simply select pixels as a simple one and sum them up, or to find a specific object and sum them up. Alternatively, the tendency of the tallying result may be predicted while tallying. If it is an example to determine whether it is a natural image or a non-natural image,
If it is found that the used color gently includes the surroundings, it can be determined that the image is a natural image at that time. If it is found that the number of colors is extremely large, the determination may be made at that time.

In such a case, all the image data is not totalized, so that the processing can be sped up. Further, assuming a computer or the like, image data is processed as a file, and the type of image can often be determined from the file format. Therefore,
It is also possible to adopt a configuration in which a feature amount is obtained based on the format of the image data. For example, JPEG for natural images
It is often compressed by a method. Therefore, if the format of the image data is the JPEG format, it can be determined that the image is a natural image. In addition, a business graph often has an extension of a file indicating an application that outputs the business graph. Therefore, it is possible to determine the type of the image by determining that such an extension is also one type.

In this way, since the type of image is determined based on the format of the image data, the processing amount can be reduced as compared with the case where the image data is analyzed. On the other hand, the feature quantity related to the interpolation processing does not necessarily have to be of a kind such as an image type. Therefore, the feature amount acquiring unit may be configured to acquire the interpolation magnification to be executed and use the obtained interpolation magnification as the feature amount. The interpolation magnification also relates to the interpolation result. For example, an image may not be rough while the interpolation magnification is small, but may be rough when the interpolation magnification is increased. In this case, in the latter case, it can be said that the interpolation process must be complicated. For this reason, the feature amount obtaining means obtains the interpolation magnification.

Of course, in this case, the threshold value of the interpolation magnification changes depending on the selected interpolation processing.
When shifting from one interpolation process to another interpolation process, it is also possible to change stepwise. By doing so, it is possible to select an optimal interpolation process based on factors other than the type of image data.

As described above, claims 1, 3,
According to the invention of claim 5, based on the empirical fact that the image is sharper than the other parts, the object finds a pixel whose difference from the surrounding pixels is larger than a predetermined threshold value. Interpolation processing is selected by using the feature amount obtained by performing statistical processing on pixels, and interpolation processing is performed according to the characteristics of the image, so that it is extremely easy to obtain an optimal interpolation result. , An image data interpolation method, an image data interpolation method, and a medium recording an image data interpolation program.

According to the second, fourth, and sixth aspects of the present invention, the object is often located at the center of the composition, and a structure in which many pixels are extracted from the center. This makes it easier to extract the pixels of the object.

[0034]

[0035]

[0036]

[0037]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a main configuration of an image data interpolation device according to the present invention. Assuming digital processing, an image is represented by 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 apparatus performs such an enlarging process in pixel units. The image data acquiring unit C1 acquires such image data, and the pixel interpolating unit C2 determines the number of constituent pixels in this image data Is performed. Here, the pixel interpolation unit C2 can execute a plurality of interpolation processes as the interpolation process. When the feature amount acquisition unit C3 acquires the feature amount related to the interpolation process for the image data, the interpolation process selection unit C4 selects an interpolation process capable of obtaining an optimal interpolation result in accordance with the characteristic amount, and causes the pixel interpolation means C2 to execute the interpolation process.

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 form and outputting the image data to the computer main unit 12, where 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 with 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 the 2 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.
In this example, the modem 14a accesses the outside via a telephone line. However, a configuration in which a network is accessed via 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 only an example, 640 × 48
0 pixels, 1024 x 768 pixels,
It can be changed as appropriate.

The color printer 17b is an ink jet printer, and is capable of printing an image with dots on printing paper as a recording medium using four color inks of CMYK. The image density can be printed at a high density 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 output device while inputting an image using such an image input device.
Among them, an operating system (OS) 12a is operating as a basic program, and the operating system 12a has a display driver (DSP DR) for performing display on the display 17a.
V) 12b and a printer driver (PRT DRV) 12c for causing the color printer 17b to perform print output are incorporated. These drivers 12b and 12c depend on the models of the display 17a and the color printer 17b, and can be additionally changed to the operating system 12a according to each model. In addition, additional functions beyond standard processing can be realized depending on the model. That is, it is possible to realize various additional processes within an allowable range while maintaining a common processing system on the standard system of the operating system 12a.

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

Here, the application 12d is executed on the operating system 12a as a basic program. The processing contents of the application 12d are various, and 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 and the like. Further, the processing result is displayed on the display 17a or output to the color printer 17b.

In the computer system 10, image data is acquired by a scanner 11a or the like which is an image input device, 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, which is improved in pixel density 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, and if the display on the display 17a is made to correspond to each pixel and printed, an extremely small image may result.

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

Since the resolution conversion corresponds to a process for increasing the number of constituent pixels in the image data, it corresponds to an interpolation process.
c implements interpolation as one of its functions. Here, the display driver 12b and the printer driver 12c execute not only the above-described pixel interpolating unit C2 but also the feature amount acquiring unit C3 and the interpolation processing selecting unit C4 as described below so that the image quality is not deteriorated by the resolution conversion. ing.

The display driver 12b
The printer driver 12c is stored in the hard disk 13b, and is read and operated by the computer 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 the present embodiment, the image data interpolation device is implemented 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.
The image data interpolating device for performing the interpolation processing may be incorporated in the apparatus 1 and the system may be such that the image data subjected to the interpolation processing is displayed on the display 17a1 or printed on the color printer 17b1. Also, as shown in FIG.
In a color printer 17b2 that inputs and prints image data without going through a computer system, the image data input via the scanner 11a2, the digital still camera 11b2, the modem 14a2, or the like is automatically subjected to resolution conversion and printed. It is also possible to configure so that

In addition, the color facsimile machine 18a shown in FIG. 5 and the color copying machine 1 shown in FIG.
It is naturally applicable to various devices that handle image data such as 8b. FIGS. 7 and 8 show a software flow relating to the resolution conversion executed by the printer driver 12c described above. Here, the former shows a general-purpose flow, and the latter shows a specific flow of the present embodiment.

In step ST102, original image data is obtained. If the application 12d reads an image from the scanner 11a and performs predetermined image processing and then performs print processing, print data of a predetermined resolution is obtained by the printer driver 12c via the operating system 12a. . Of course, scanner 11
The image may be read at a. This processing is an image data acquisition step when viewed as software, but various steps executed by the computer including the image data acquisition step are understood as not directly including the operating system 12a itself or hardware. can do. 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 means C1.

Step ST104 is a process for extracting a characteristic amount in the read image data. Details of the feature amount extraction processing will be described later. In step ST106, an optimum interpolation process for the image data is selected based on the obtained feature amount, and a flag indicating the interpolation process is set. Then, in step ST108, each interpolation process 1 in steps ST110, ST112, and ST114 is performed with reference to the same flag.
To N. Therefore, step ST1
10, interpolation processing 1 to N shown in ST112 and ST114
Specifically corresponds to the image interpolation step, and the step S
Since T106 and ST108 select the interpolation processing based on the feature amount, this corresponds to an interpolation processing selection step. Of course,
If these are considered to be organically integrated with hardware such as a CPU, they constitute an image interpolation means C2 and an interpolation processing selection means C4.

Steps ST116 and ST118 show processing for moving blocks until the interpolation processing is completed for all blocks. The interpolation process does not necessarily have to be a uniform process over the entire image, and it is also possible to change the interpolation process for each region in block units. Therefore, when the optimal interpolation processing is to be performed for each block, when the interpolation processing for each block is completed, the processing is again executed from the processing for extracting the feature amount of the next block in step ST104. On the other hand, when processing is performed uniformly over the entire image data, the processing from step ST108 is repeated.

When all blocks have been completed, the image data interpolated in step ST120 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. Next, more specific processing for the above-described general-purpose flow will be described. In the present embodiment, the original image is computer graphics (non-natural image)
Or a photograph (natural image), and an interpolation process is selected based on the result of the determination. Step ST
In step 202, the original image data is input as in step ST102.

There are several methods for determining the type of the original image, but in the present embodiment, a totaling process is performed using image data.
More specifically, the number of colors used in the image data is determined. If the number is large, the image is determined to be a natural image, and if the number is small, the image is determined to be a non-natural image. In the case of a photograph, even if an object of one color is reflected, the width is made from a bright part to a dark part due to the adjustment and shading of light rays, and the number of colors increases. Because of these characteristics, it is possible to determine whether the image is a natural image or a non-natural image by looking at the number of colors. However, it is not efficient in the processing by the program to count how many colors among the 16.7 million colors are actually used. Also, even natural pictures often use only a part of them, making it difficult to distinguish them from non-natural pictures.

For this reason, in the present embodiment, the luminance of each pixel is obtained, and a histogram of the number of pixels is totaled in a range in which the luminance can be obtained, and the tendency of the number of colors used is determined. 16
It is natural that there are a plurality of colors having the same luminance among 700,000 colors. However, if attention is paid only to the comparison with the non-natural image, it is compared whether the color or the luminance is large or small. Is possible. Furthermore, if it is considered that the non-natural image uses at most about 64 colors, the range in which the luminance can be taken is 2
It is considered that the determination can be made sufficiently even with 56 gradations.

On the other hand, the totalization of the luminances merely determines the general tendency of the image data as described above, and therefore does not necessarily need to be totalized for all the pixels. That is, a thinning process is performed to select a pixel to be counted. As shown in FIG. 9, in the case of a bitmap image, a two-dimensional dot matrix including predetermined dots in the vertical direction and predetermined dots in the horizontal direction is established. It is necessary to check the brightness for. However, it need not be accurate here. Therefore, it is possible to perform the thinning to the extent that it falls within a certain error range. According to the statistical error, the error with respect to the number of samples N can be approximately expressed as 1 / (2 ** (1/2)). Here, ** indicates a power. Therefore, in order to perform processing with an error of about 1%, N = 10000.

Here, the bit map screen shown in FIG. 9 has the number of pixels of (width) × (height), and the sampling period ratio is ratio = min (width, height) / A
+1. Here, min (width, height)
ht) is the smaller of width and height, and A is a constant. The sampling period ratio here indicates how many pixels are sampled, and the pixels marked with a circle in FIG.
The case where atio = 2 is shown. That is, sampling of one pixel every two pixels in the vertical and horizontal directions,
Sampling is performed every other pixel. When A = 200, the number of sampling pixels in one line is as shown in FIG.

As can be seen from the figure, except for the case where the sampling period ratio = 1 when sampling is not performed, when the width is 200 pixels or more, the number of samples is at least 100 pixels or more. Therefore,
In the case of 200 pixels or more in the vertical and horizontal directions, (1
(00 pixels) × (100 pixels) = (10000 pixels) is ensured, and the error can be reduced to 1% or less. Where min
(Width, height) is based on the following reason. For example, assuming that width >> height as in the bitmap image shown in FIG. 12A, if the sampling period ratio is determined by the longer width, as shown in FIG. In addition, it may happen that only two lines, the upper and lower lines, are extracted in the vertical direction. However, if min (width, height) is used to determine the sampling period ratio based on the smaller one, it is possible to perform the thinning including the middle part even in the smaller vertical direction as shown in FIG. Will be able to

In this example, thinning is performed at an accurate sampling cycle for pixels in the vertical and horizontal directions. This is suitable when processing is performed while thinning out sequentially input pixels. However, when all pixels have been input, pixels may be selected by designating coordinates randomly in the vertical direction and the horizontal direction.
In this way, when the minimum necessary number of pixels, such as 10,000 pixels, is determined, the process of randomly extracting until 10,000 pixels is repeated is repeated.
It suffices to stop the extraction at the point when it becomes a pixel.

If the pixel data of the selected pixel has luminance as its component element, the distribution can be obtained using the luminance value. However, even in the case of image data whose luminance value is not a direct component value, a component value representing luminance is provided indirectly.
Therefore, a luminance value can be obtained by performing 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 the different color spaces is not uniquely determined by the conversion formula. Mutual correspondences are obtained for the color spaces having their component values as coordinates, and this correspondence is stored. It is necessary to sequentially perform conversion by referring to the color conversion table obtained. Then, strictly speaking, it is necessary to have a color conversion table of 16.7 million elements. As a result of considering efficient use of storage resources, instead of preparing correspondences for all coordinate values, usually prepare correspondences for appropriate discrete grid points and use interpolation together I am trying to do it. However, since such an interpolation operation can be performed through several multiplications and additions, the amount of operation processing becomes enormous.

That is, if a full-size color conversion table is used, the processing amount is reduced, but the table size becomes an unrealistic problem. 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 the RGB color space, such a conversion equation is employed. However, since each component value indicates the color brightness on the background, There is a property that when each component value is viewed independently, it linearly corresponds to luminance. Therefore, more simply, it is not impossible to simply simplify to yp = (Rp + Gp + Bp) / 3 without considering each addition ratio.

In step ST204, the luminance of the pixels subjected to the thinning processing as described above is converted into a histogram. After the aggregation, the number of colors is counted in step ST206. If the number of colors is small, the distribution of luminance is sparse, and a non-natural image such as a business graph appears as a line spectrum as shown in FIG. It is expected to be a gentle curve. For this reason, in step ST206, the number of luminance values having a distribution number other than “0” out of the 256 gradation luminances is counted, and in step ST208, it is determined that the image is not a natural image when the number is less than “64”. And
It is determined that the image is a natural image when the number of colors is “64” or more.
On the other hand, it is also possible to determine whether the distribution is in the form of a linear spectrum or not by the adjacency ratio of luminance values whose distribution number is not “0”. That is, it is determined whether or not the number of distributions is a luminance value other than “0” and there is a distribution number in an adjacent luminance value. If at least one of the two adjacent luminance values is adjacent, nothing is performed, and if both are not adjacent, counting is performed, and as a result, the ratio between the number of non-zero luminance values and the count value Judge it. For example, if the number of luminance values other than “0” is “80” and the number of non-adjacent luminance values is “80”, it is understood that the luminance values are distributed in a linear spectrum. Of course, the number of colors used corresponds to the feature amount.

It should be noted that the method of acquiring the feature amount is not limited to these, and other methods can be realized. First, in thinning out the pixels to be counted, the present invention is not limited to the thinning-out of the uniform intervals as described above. For example, it is also possible to find an original object portion in an image and to sum up feature amounts of the pixels. Based on the empirical fact that the image of such an object is sharper than other parts, it is determined that the sharp part is a pixel of the object. When the image data is composed of pixels in a dot matrix, the difference of the same data between adjacent pixels becomes large at the edge portion of the image. This difference is a luminance gradient, which is called an edge degree, and the edge degree at each pixel is determined. When considering the XY orthogonal coordinates as shown in FIG. 15, the vector of the degree of change of the image can be calculated by obtaining the X-axis direction component and the Y-axis direction component, respectively.
In a digital image composed of pixels in a dot matrix, pixels are adjacent to each other in a vertical axis direction and a horizontal axis direction as shown in FIG. f (x + 1, y) -f (x, y) fy = f (x, y + 1) -f (x, y) Therefore, the magnitude | g (x, y) | of a vector having these as components is | g (x, y) | = (fx ** 2 + fy ** 2) ** (1 /
2) is expressed as Of course, the degree of edge is | g (x,
y) | Note that the pixels are originally arranged in a grid shape vertically and horizontally as shown in FIG. 17, and there are eight adjacent pixels when focusing on the central pixel. Therefore, similarly, the difference between the image data and each adjacent pixel may be represented by a vector, and the sum of the vectors may be determined as the degree of change of the image.

Since the edge degree is obtained for each pixel as described above, basically, a pixel having a larger edge degree than a certain threshold value may be determined as a pixel of an object. However, considering empirical facts, the object is often located in the center of the composition. This fact supports that the pixel of the object is more easily extracted by adopting a mechanism in which many pixels are extracted from the central portion.

Therefore, as shown in FIG. 18, the threshold values Th1, Th2, and Th3 to be compared for each part in the image can be made different. Needless to say, in this example, there is a relationship of Th1 <Th2 <Th3, and the threshold value is lower at a portion closer to the center,
Even if the edge degree is relatively low, it is determined that the object is an object. Obviously, the image data of the pixels determined to be the object in this way is totaled, and the feature amount corresponding to the interpolation processing is obtained.

On the other hand, the feature quantity does not necessarily need to be obtained by summing up image data. What is necessary is just to associate whether or not the interpolation result is good depending on the interpolation process. Whether or not the image of the image data is a natural image can also be determined from the format of the image file serving as the printing source. FIG. 19 shows the printer driver 1
2c shows a situation in which a system function prepared in the operating system 12a is used. When the printer driver 12c uses a function for querying a file name,
The operating system 12a returns the corresponding file name. In this case, if it is “XXXX.XLS”, it is known from the extension that it is a business graph, and it can be determined that it is a non-natural image. In addition, "XXXX.J
If "PG", the extension indicates that it is a compressed file of a photographic image, and it can be determined that the file is a natural image.
Of course, it is possible to determine whether the file structure is a draw-based file structure or a bitmap-based file structure from the information contained in the beginning of the data file, not from the extension. It will be possible to obtain an indication of whether or not. That is, a feature amount is configured as a standard that enables the content of such an image to be inferred.

As described above, if it is determined in step ST202 whether the input original image data is a natural image or a non-natural image, an appropriate interpolation process is executed according to each. Here, each method of the interpolation processing executed in the present embodiment will be described. As an interpolation process suitable for a non-natural image such as computer graphics, a near-list interpolation process can be executed in step ST210. In the near list method, as shown in FIG. 20, four surrounding grid points Pij, Pi + 1j, Pij + 1, P
The distance between i + 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. 21 shows that the number of pixels is set to 3
This shows a situation where interpolation is performed twice at a time. Assuming that there are four corner pixels (□ に は ●●) before the interpolation, the data of the closest pixel among these pixels is directly transferred to the pixels generated by the interpolation. That is, in this example, the pixels adjacent to the four corner pixels are copied. When such processing is performed, the original image in which black pixels are arranged obliquely with white pixels as a background as shown in FIG. 22 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 the edge of an image is held as it is. Therefore, when enlarged, jaggies are noticeable but edges are retained as edges. On the other hand, in other interpolation processing, the pixel to be interpolated is changed smoothly using data of surrounding pixels. Therefore, while the jaggy becomes inconspicuous, the information of the original image is cut off, and the edge disappears, making it unsuitable for non-natural images such as computer graphics.

On the other hand, in step ST212, a cubic interpolation process is executed as an interpolation process suitable for a natural image such as a photograph. As shown in FIG. 24, 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 by those influences. For example, if an attempt is made to interpolate using a linear expression, weighted addition may be performed 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, While expressing the distance x3 to the grid point and the distance x4 to the right outer grid point,
The degree of influence corresponding to such a distance is represented by a function f (x). Further, when focusing on the Y-axis direction, the distance from the interpolation point Puv to the 16 grid points is a distance y1 from the upper outer grid point, a distance y2 from the upper inner grid point, and a lower inner grid 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.

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

[0073]

(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 |)

[0074]

(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.

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

FIG. 25 and FIG. 26 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. 26 will be described. The gradation value of the pixel before the interpolation is shown as “Original” in the left column, and the gradation value “6” is displayed.
4 pixels (P0, P1, P2, P3) are arranged at four points, one pixel (P4) having a gradation value of "128" is sandwiched, and a gradation value of "1"
Five 92 pixels (P5, P6, P7, P8, P9) are arranged. In this case, the edge is a portion of the pixel having the gradation value “128”.

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

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

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

FIG. 27 shows a specific example when interpolation is performed by the hybrid bicubic method, and shows the result of interpolation for a model on the same assumption as in the case of the cubic method. FIG. 25 also shows the result of the interpolation processing by the hybrid bicubic method.
The quadratic curve becomes slightly steep, and the entire image becomes sharper. In order to understand the characteristics of the above-described near-list method, cubic method, and hybrid bicubic method, a bilinear interpolation method (bilinear interpolation: hereinafter, referred to as a bilinear method) 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. 28, 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 grid points Pij, Pi + 1j, Pij + 1, and Pi + surrounding the point Puv to be interpolated.
The area defined by 1j + 1 is divided into four sections at the interpolation point Puv, and the data at the diagonal positions is weighted by the area ratio. When this is expressed by an equation, P = {(i + 1) −u} (j + 1) −vεPij +
｛(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. The difference is large when viewed as an image. FIG. 29 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 hybrid bi-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.
Grid points exist at positions of t = 0, t = 1, and t = 2, and interpolation points are at positions of t = 0 to 1.

In the case of the bilinear method, between two adjacent points (t
= 0 to 1), and 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 in computer graphics disappears, and the focus becomes weak in a photograph. On the other hand, in the cubic, not only the two points adjacent to each other (t = 0 to 1) draw a mountain-shaped protrusion but gradually approach each other, and further, the outer side (t = 1 to 2) between the two points lowers. It has the effect of pushing down. That is, a certain edge portion is changed so as to have a large difference in height such that no step is formed, and this has a favorable effect in a photograph that no step is formed while increasing sharpness. In addition, the hybrid bicubic has an effect of increasing sharpness. Note that the cubic method requires a large amount of arithmetic processing, and 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, but 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, referring to FIG. 25, FIG. 26, and FIG. 27 showing specific numerical values together with the comparison of the interpolation functions as described above, it is easier to understand. Referring to the example of FIG. 15, 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 original edge portions. Focusing on three points, the simple linear connection method corresponds to the bilinear method, whereas the cubic method has a specific S-shaped curve, and the hybrid bicubic method. In the figure, the S-shaped curve is steeper. Needless to say, the direction of the S-shaped curve is a direction in which the change in the gradation value of the pixel is steep, and therefore the edge is emphasized. Further, in the regions (P2 to P3, P5 to P6) adjacent to the edge pixels, so-called undershoots and overshoots occur, and the undershoots occurring on the low side and the overshoots occurring on the high side cause both sides sandwiching the edge pixels. The height difference becomes larger. Therefore, it can be understood that the edge is emphasized by these two factors.

It can be easily understood that whether or not the image looks sharp depends on the inclination angle of the central portion of the S-shaped curve. In addition, it can be said that a difference in height caused by undershoot and overshoot on both sides of the edge also has an effect. Each of the interpolation processes has a difference in the characteristics as described above. If it is determined in step ST208 that the image is a non-natural image based on the number of colors obtained in step ST206, the interpolation process by the near-list method in step ST210 is performed. Then, if it is a natural image, interpolation processing by the cubic method is executed.

The interpolation process itself can be executed at an arbitrary magnification. However, in order to speed up the process in the printer driver 12c, an interpolation process of an integral multiple is accepted.
FIG. 29 shows an example of processing for interpolating twice in the horizontal and vertical directions. If a variable area for the interpolated image data is secured in advance, the image data of the original image will be the image data of the pixel corresponding to the coordinate value multiplied by the integer if the interpolation processing is an integral multiple. 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), and 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). Therefore,
Image data is generated only for the remaining coordinate values in accordance with the above-described interpolation processing. In this case, scanning can be performed sequentially with the width direction of the image data as the main scanning direction and the length direction as the sub-scanning direction, or for each block surrounded by four grid points having image data. It is also possible to perform interpolation processing of the internal coordinate values and fill them.

When all the new coordinate values have been subjected to the interpolation processing, the interpolated image data is transferred to the next processing in step ST214. However, the data amount of the interpolated image data may be extremely large depending on the interpolation magnification, and the memory area available for the printer driver 12c may not be so large in the first place. In such a case, the data may be output separately for each fixed data amount. As described above, steps ST116 and ST11
Reference numeral 8 corresponds to a method in which the interpolation process changes the interpolation process for each block-unit area. This will be described in correspondence with the specific software processing of the printer driver 12c described above. As shown in FIG. The feature amount is obtained for each block surrounded by the squares and interpolation processing is selected. In the example described above, since the number of colors used is totaled, such a feature amount cannot be used. However, it is possible to acquire another feature amount and change the interpolation process.

For example, when a difference between four grid points is obtained, the difference often occurs in a natural image. However, in a case where a character is embedded in a natural image, if the character is a single color, the four grid points match and no difference occurs. Needless to say, the pixels to be interpolated in the region where no difference occurs need only be the same data as the four grid points, and may be interpolated by the nearest method which requires the least amount of calculation. Also,
It is not necessary that the difference does not occur. In a region such as the sky where there is not much change, image quality degradation cannot often be determined even by using a near list. Therefore, when the difference between the four grid points is small, interpolation may be performed using the near list method.
In the above example, a block is formed in the smallest unit as an area, but the interpolation process may be changed for a larger block.

It is not necessary to limit the selectable interpolation processing to either the near list method or the cubic method. For example, in the case of interpolating four times, first, the interpolation processing is performed twice by the cubic method, and then the interpolation processing is performed by the near-list method.
It is also meaningful to perform interpolation processing twice. This is because, before the number of pixels is increased by the interpolation processing, the interpolation processing with a large amount of calculation is executed, and thereafter the interpolation processing with a small amount of calculation is executed.

In other words, it can be said that the interpolation magnification affects the interpolation result in such a case. Therefore, the printer driver 12c can select the interpolation processing when the magnification can be determined in the comparison between the reference resolution in the operating system 12a and the resolution of the color printer 17b. in this way,
In the computer system 10 having the scanner 11a and the like as the image input device and the color printer 17b and the like as the image output device, the printer driver 12c acquires the original image data in step ST202, and uses the colors used in the image in steps ST204 and ST206. By counting the number, a feature amount for determining whether the image of the image data is a natural image or a non-natural image is extracted. If it is determined that the image is a non-natural image, the near-list interpolation process of step ST210 is executed if it is a non-natural image, and if it is a natural image, step ST2
Since the twelve cubic interpolation processes are executed, the interpolation process according to the characteristics of the image is performed, and the optimum interpolation result can be obtained very easily.

The computer 12 is assumed to execute such a program.
An AM 12f, a ROM 12g, an I / O 12h, and the like are provided. The RAM 12f functions as an image memory for storing image data in which an image is represented by pixels in a dot matrix. Further, the RAM 12f acquires a feature amount related to the interpolation process for the image data stored in the image memory, and obtains an optimal interpolation result from a plurality of interpolation processes corresponding to the feature amount. A possible interpolation process is selected, and a processing program for causing the CPU 12a to execute the interpolation process and writing the result into the image memory is stored. Further, the I / O 12h functions as an interface for inputting and outputting the image data.

Of course, while using the RAM 12f as a temporary work area, a setting storage area, and a program area, the CPU 12e appropriately executes the basic program written in the ROM 12g, and outputs unprocessed image data via the I / O 12h. Input and output the processed image data.
Next, an embodiment in which the degree of change of the image data described above is used as a feature amount will be described. FIG. 31 is a block diagram illustrating the image data interpolation apparatus. The present image data interpolating apparatus performs such an enlarging process in pixel units. The image data acquiring unit D1 acquires such image data, and the pixel interpolating unit D2 determines the number of constituent pixels in the image data. Is performed. Here, the pixel interpolation means D2 can execute a plurality of interpolation processes according to the degree of change of the pixels as the interpolation processing, and the pixel change degree evaluation means D3 evaluates the degree of change for each pixel based on the image data. I do. Then, the interpolation processing selection means D4 selects an interpolation processing capable of obtaining an optimum interpolation result in accordance with the degree of change of the pixel evaluated in this way, and causes the pixel interpolation means D2 to execute the interpolation processing.

In the present embodiment, the display driver 12b and the printer driver 12c execute not only the above-described pixel interpolating means D2 but also the pixel change degree evaluating means D3 and the interpolation processing selecting means D4 as described below. In the conversion, the most balanced interpolation result can be obtained. FIG. 32 shows a software flow relating to the resolution conversion executed by the printer driver 12c described above.

In step ST302, original image data is obtained. Steps ST304 to ST308 are processing for evaluating the degree of change of pixels in the read image data. FIG. 33 and FIG. 34 show an edge detection filter for calculating a luminance gradient after obtaining the luminance by the above-described simplified calculation. 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 that sense, FIG.
As shown in (a), it is preferable to apply a filter by equally evaluating surrounding pixels while adding eight times the weight to the pixel of interest and adding them together. However, empirically, it is not necessary to evaluate the surrounding eight pixels.
As shown in (a), evaluation can be made only from the target pixel and four surrounding pixels. Of course, there is a large difference in the amount of calculation between using four pixels and eight pixels. If the number of evaluation targets is reduced in this way, the processing time can be reduced.

FIGS. 33 (b) and 34 (b) show examples of actual image data (luminance).
FIG. 34 (c) shows an example of calculation when the filter shown in FIG. 34 (a) is applied to the arrangement of the image data shown in FIG. The image data is generally at the upper left of the image data "10
There is an area “0”, and the image data “7”
The case where there are areas of “0” and “60” is shown.
In the example of FIG. 33, a weight of “−1” is added to each of four pixels (image data “100”, “100”, “70”, “70”) on the upper, lower, left, and right of the center pixel, and the center pixel (image data “100”) is assigned a weight of “4”. Then, weighted addition is performed for these five pixels. The weighted addition result is “60”, which exceeds the threshold value (th) “32”.

On the other hand, in the example of FIG. 34, a weight of “−1” is added to each of the eight pixels surrounding the central pixel.
The weight of “8” is added to the center pixel. The weighted addition result is “100”, which exceeds the threshold value (th) “64”. If the result of using the edge detection filters shown in FIGS. 33 and 34 is called an edge amount E of each pixel, the distribution is expected to be a normal distribution as shown in FIG. 35, and the degree of change of the image is large. Whether it is an edge portion can be determined by comparing with a threshold th. In the edge detection filters shown in FIGS. 33 and 34, the threshold values of the edge amounts 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) It is the process of step ST306 that this evaluation is performed 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, the interpolation process is a process of generating a pixel for each fixed area. 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 ST308 by determining whether or not the pixel is an edge pixel. in this case,
As shown in FIG. 36, it is determined that the degree of change of the image is large in all the pixels surrounding the edge pixels. More specifically, when the degree of change of each pixel is as shown in FIG.
, Pixels exceeding the threshold value are represented by (0,0) (3,0) (4,0) (1,1) (2,
Even if it is 1), a flag is set for a pixel adjacent to the edge pixel. Then, flags are set for all pixels at y = 0, 1 and pixels at y = 2 except (4, 2), as shown in FIG. As a result, when the target block is moved in pixel units in a later step, the interpolation process can be appropriately selected with reference to only the flag.

Of course, in the present embodiment, the processing of steps ST304 to ST308 corresponds to a pixel change degree evaluation step. Of course, if these are considered to be organically integrated with hardware such as a CPU, the pixel change degree evaluation means D3 is configured. Based on the flag set as described above, step ST310
Hereinafter, interpolation pixels are generated by loop processing. FIG.
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>. In the example shown in the figure, an interpolation process of about 2.5 × 2.5 is performed.

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 ST308, a flag is set for each pixel in consideration of the degree of change of surrounding pixels, and therefore, in each block, four pixels (0, 0) (1, 0)
If the flag is set for any of (0, 1) and (1, 1), the interpolation processing for the case where the degree of change is large is selected. If none of the flags is set, the degree of change is selected. Is selected when the value of is small. In step ST310, an interpolation process to be applied inside the block is determined based on this condition,
If the degree of change is small, interpolation is performed by an interpolation process using a near-list method in step ST312. If the degree of change is large, interpolation is performed by an interpolation process using a cubic method in step ST314. After one block is interpolated, the blocks to be processed are moved in steps ST316 and ST318, and when all the blocks are completed, the interpolated image data is output in step ST320.

Although the flow of returning to step ST310 after the end of step ST318 is shown by a solid line in the figure, the process of totalizing the edge pixels for each block may be repeated as shown by the broken line. . Needless to say, in this sense, the processing corresponds to an interpolation processing selection step including the processing of steps ST316 and ST318 centering on the processing of step ST310. Of course, if these are considered to be organically integrated with hardware such as a CPU, the interpolation processing selecting means D4 will be configured. In addition,
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 present embodiment, the interpolation process is selected by calling the area surrounded by the four pixels a block.
The criterion for changing the interpolation process can be changed as appropriate according to the calculation capability, the interpolation process, and the like. For example, as shown in FIG. 39, 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 the interpolation processing while moving the pixel of interest will be described. In the above-described example, when determining whether or not 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 an area surrounded by four pixels as shown in FIG. In this case, since a flag is set to a pixel adjacent to the edge pixel in FIG. 36, only a case where the flag is set to all four pixels as described above may be determined to be a region with a large change degree. . 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 at 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 the area surrounded by adjacent pixels is sufficient even in view of the fact that it is actually a very small area. Then, in this way, one pixel
If the area is made to correspond, the pixel of interest is moved when the block is moved, and the degree of change of the area can be determined only by the edge amount of the pixel of interest, and the amount of computation required for the determination is also reduced. Reduce.

It is also possible to form a block on the pixel side to be interpolated. FIG. 41 shows this example. In the figure, grid points indicated by □ indicate 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 based on the edge amount of the existing pixel included therein whether or not the area has a large degree of change in the image. 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 pixels are generated in the block by the same interpolation processing.

Needless to say, in the above case, the interpolation processing may be selected by setting a block for each larger area.
For example, it is also possible to make a block of 10 × 10 pixels. 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. 41, the 3 × 3 □ grid points arranged inside are all included in the four existing grid points indicated by ○, and when generating the respective □ grid points, 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 subjected to interpolation processing is specified first and interpolation processing is determined, and then pixels are interpolated therein. You may.

Further, in the flow described above, step ST
At 308, a flag is set in advance for the 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. 32. 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 the interpolation processing.

As described above, the processing of steps ST312 and ST314 having separate interpolation processing corresponds to a pixel interpolation step. Of course, if these are considered to be organically integrated with hardware such as a CPU, the pixel interpolation means D2 will be configured. Here, each interpolation process will be described in detail. On the other hand, such a relationship is a curve in which the slope becomes steep in the section of t = 0 to 1 in the interpolation function shown in FIG. 29, but is drawn to the negative side so as to cancel the increased weight in the section of t = 1 to 2. Occurs when

Therefore, when the sharpness is to be adjusted, an ideal slope serving as a reference for the sharpness in the interpolation function is determined, and a curve for generating the above slope is determined in the section of t = 0 to 1. , T = 1 to 2, it 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. 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 methods for determining such parameters are extremely diverse, the central part of the S-shaped curve in a practical sense is substantially 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 ST310 that the degree of change of the image is small, the interpolation process of the near list method is executed in step ST312. Conversely, for blocks determined to have a large degree of change, interpolation processing of the cubic method or the hybrid bi-cubic method is executed. When the interpolation process is performed by the cubic method, the calculation time is long. However, in a portion where the degree of change of the image is small, the process is switched to the near-list method, so that the processing time as a whole is extremely reduced. In particular, in the case where 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 area where the jaggy is conspicuous when enlarged is usually not so large even if the area ratio is large, and thus the image quality is obtained by sequentially switching the degree of change of the image in this way. The processing amount can be reduced without deterioration.

In the present embodiment, one of the two types of interpolation processing is executed according to the flag. However, a plurality of interpolation processings corresponding to the degree of change of the pixel in a stepwise manner are executed. Is also good. Further, as shown in FIG. 42, two interpolation processes may be performed in a superimposed manner, and the magnification may be made to correspond to the degree of change of the image. For example, assuming that the interpolation magnification is 5 times, if the degree of change of the image is small, the interpolation processing is performed five times by the near-list method, and 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, it is possible to select a plurality of interpolation processes according to the degree of change of the image, although the two interpolation processes are performed.

As described above, the interpolation magnification is set to an integral multiple for a large calculation amount of the interpolation processing such as the cubic method. 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 ST302, and detects the degree of change of the image in steps ST304 to ST108. By setting a flag and referring to the flag in step ST310, an interpolation process by the near-list method is executed in step ST312 for a block having a small image change degree, and a block having a large image change degree in step ST312. Since the interpolation processing by the cubic method is executed in step ST314, it is controlled so that the near-list method is executed as much as possible without deteriorating the image quality. Reduce.

As described above, according to the present invention, an image data acquiring means for acquiring image data representing an image by dot matrix pixels, and a pixel change degree for evaluating a pixel change degree based on the image data are provided. An evaluation unit, a pixel interpolation unit that can be selected from a plurality of interpolation processes to perform an interpolation process to increase the number of constituent pixels in the image data, and a pixel change degree evaluated by the pixel change degree evaluation unit. And an interpolation process selecting means for selecting an interpolation process capable of obtaining an optimum interpolation result in accordance with the degree of change based on the interpolation process and causing the pixel interpolation means to execute the selected interpolation process.

In the present invention configured as described above, in performing the interpolation processing for increasing the number of constituent pixels of the image data in which the image is represented by the pixels in the dot matrix, the pixel interpolating means selects one of the plurality of interpolation processing. When the image data acquisition unit acquires the target image data, the pixel change degree evaluation unit evaluates the pixel change degree based on the image data. The interpolation processing selecting means selects an interpolation processing capable of obtaining an optimal interpolation result corresponding to the degree of change based on the degree of change of the pixel evaluated by the degree of pixel change evaluation, and Cause the interpolation means to execute.

That is, since the degree of change of a pixel is closely related to the specific method of the interpolation processing, the interpolation processing is realized by evaluating the degree of change of the same pixel and actively changing the interpolation processing. I do. As described above, the present invention can provide an image data interpolation apparatus that can obtain an optimum interpolation result extremely easily by changing the interpolation processing according to the degree of change of an image. The pixel change degree evaluation means evaluates the degree of change in pixels, and the evaluation method and result are not particularly limited.
Further, it can be relatively changed in accordance with the manner of use of the evaluation result by the interpolation processing selecting means. For example, if a specific degree of change is required as a numerical value, a numerical value may be output, and if it is sufficient to simply determine whether the degree of change is large, the numerical value may be output in accordance with the numerical value.

Further, how to grasp the change of the pixel itself can be changed as appropriate. As an example, the pixel change degree evaluation means may be configured to calculate the brightness degree of each pixel and calculate the change degree by comparing with the parameters of surrounding pixels. In such a case, the brightness of the pixel is used as a criterion for evaluating the pixel. And the same parameter is compared, and the comparison result is calculated as the degree of change.

Of course, other than this, it is possible to grasp the degree of change of the pixel, but the brightness parameter is relatively easy to uniformly grasp the pixel represented by the multi-element parameters. With this configuration, the degree of change in the image is determined based on the brightness parameter, so that the degree of change can be obtained relatively easily.
On the other hand, it can be said that the range in which the degree of change of the pixel affects the processing content of the interpolation processing also differs. 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 particular, in the latter example, the question is whether to change the interpolation processing if there is at least one pixel having a large degree of change, or whether to change the interpolation processing if at least one pixel has a small degree of change.

As an example of such a situation, the pixel change degree evaluating means may be configured to use the change degree obtained for each pixel also for evaluating the change degree of surrounding pixels. When a plurality of pixels are required for the interpolation processing, and one of them has a large degree of change, two modes can be considered. That is, there is no need 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.

In such a two-way sense, the degree of change obtained for each pixel is also used for evaluating the degree of change of surrounding pixels. In this way, the amount of calculation can be reduced by using the degree of change of one pixel also in the surrounding pixels, and the optimal interpolation result can be obtained by sharing in an appropriate range affected by the interpolation processing. Obtainable. In the pixel interpolation means, it is sufficient that a plurality of interpolation processes related to the degree of change of the pixel can be executed,
Various processes are possible as the interpolation process itself. As an example, the pixel interpolating means executes an interpolation process using the image data of the nearest neighbor pixel before the interpolation process as image data of a new constituent pixel as a suitable interpolation process applied to an area having a small degree of change. Possible configurations are also possible.

In the case of such a configuration, 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. It is preferable that the area is small without any problem and the amount of processing is small. In this way, the processing amount can be reduced without affecting the image quality in an area where the degree of change is small. Further, as another example, the pixel interpolating means performs arithmetic processing from image data of surrounding pixels so that the image data of the pixel to be interpolated changes smoothly as a suitable interpolation process applied in an area having a large degree of change. It is also possible to adopt a configuration capable of executing an interpolation process for calculating image data of an interpolation pixel.

In such a configuration, the image data of the pixel to be interpolated changes gently 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. Therefore, even if the pixel sequence having a large degree of change is interpolated between them, the step is not conspicuous and deterioration in image quality can be prevented.

Various arithmetic methods for smoothly changing the image data of the pixel to be interpolated can be adopted, but the manner of change affects the image quality. Therefore, in a sense, it can be said that the image quality can be adjusted by changing the calculation method. As an example in which the image quality can be adjusted, the pixel interpolation unit calculates the image data of the interpolated pixel between pixels having a large degree of change, and adjusts the inclination of the image data while making the change mode of the image data substantially S-shaped, At both end portions, an undershoot is generated on the lower side and an overshoot is generated on the higher side to form a height difference, and the height difference is adjusted, so that the degree of change of the image is adjusted to be optimal. A configuration can also be used.

In the case of such a configuration, when the image data of the pixel to be interpolated is smoothly changed, the change mode of the image data between the pixels having a large change degree is substantially S-shaped. Therefore, although the change is gradual, the change can be made steeper than a gradient that is linearly connected, and the gradient can be adjusted to optimize the degree of change of the image. Also, if an overshoot is generated on the high side while generating an undershoot 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.

In this way, the slope of the S-shaped curve and
The image quality can be relatively easily adjusted by the difference in height due to the undershoot and the overshoot. Since the degree of pixel change is not constant over the entire image, the interpolation processing selecting means must appropriately select and switch the interpolation processing. The frequency of the switching is not particularly limited, and various methods can be adopted. As an example, the interpolation processing selection means may be configured to select and execute the interpolation processing on a pixel-by-pixel basis based on the degree of pixel change evaluated by the pixel change degree evaluation means.

In the case of such a configuration, the interpolation processing selecting means selects and executes the interpolation processing on a pixel-by-pixel basis on the basis of the pixel change degree evaluated by the pixel change degree evaluation means. That is, since the degree of change is evaluated for each pixel, the interpolation processing is changed correspondingly. Therefore, the interpolation result can be finely improved by selecting the interpolation processing for each pixel. Further, as another example, the interpolation processing selection means selects and executes the interpolation processing for each predetermined small area composed of a plurality of pixels based on the degree of change of the pixel evaluated by the pixel change degree evaluation means. A configuration can also be used. In this way, the interpolation processing is selected for each small area, so that the processing can be simplified.

Next, a description will be given of an embodiment in which sharpness is corrected together with interpolation processing. FIG. 43 is a block diagram showing such an image data interpolation device. Assuming that the original image is a natural image, some images may lack sharpness. For example, a photograph with a soft focus corresponds to this. Further, in a case where the image itself is to be enlarged and output in addition to the enlargement for matching the resolutions between the devices, the image lacking in sharpness may be further defocused. This image data interpolation apparatus adjusts the sharpness when enlarging the image data in units of pixels. The image data acquisition unit E1 acquires the image data, and the pixel interpolation unit E2 acquires the image data. An interpolation process for increasing the number of constituent pixels in the data is performed. Here, the pixel interpolating means E2 can change the sharpness of the image accompanying the interpolation processing.
The sharpness evaluation means E3 evaluates the sharpness of the image based on the image data. Then, the interpolation processing control means E4 controls the pixel interpolation means E2 so as to execute an interpolation processing to increase the sharpness evaluated in this way if the sharpness is low.

As described above, when the resolution managed by the operating system 12a does not match the resolution of the color printer 17b, the printer driver 12c executes the processing for matching the resolutions. Usually, since the resolution of the color printer 17b is finer than the resolution managed by the operating system 12a, an interpolation process for increasing the number of pixels is performed to match the resolutions. As described above, the interpolation processing is performed when the enlargement processing is performed by the application 12d and when the resolution is matched by the printer driver 12c. These interpolation processings can affect the sharpness of the image.

It can be said that the sharpness of an image is a comprehensive evaluation of the degree of change between adjacent pixels. An image lacking in sharpness means that pixels change gently at an original edge portion, and a sharp image has a steep change between adjacent pixels at an original edge portion. This is because the interpolation process generates a new pixel between existing pixels, and the sharpness of the image changes depending on the value of the new pixel.

In this sense, in the image data interpolation device of the present invention, the application 12d and the printer driver 12
c constitutes not only the above-described pixel interpolation means E2 but also the sharpness evaluation means E3 and the interpolation processing control means E4 as described below. FIG. 44 shows a software flow relating to resolution conversion executed by the printer driver 12c as an example of executing the interpolation processing. Step S
T402 acquires original image data. Step ST40
Steps 4 to ST408 are processes for evaluating the sharpness of the image from the degree of change of each pixel in the read image data.

If the result of using the edge detection filters shown in FIGS. 33 and 34 is called an edge amount E of each pixel, it is expected that the distribution will be a normal distribution as shown in FIG. The processing of step ST406 is performed for all the pixels in the dot matrix in this manner, and the absolute values of the edge amounts are totaled in step ST408. The aggregation may be a simple average value, but it can be said that the aggregation is easily affected by the area ratio of the background portion. For example, FIG.
In FIG. 46, the subject image is large and the background portion is small, but in FIG. 46, the subject image is small and the background portion is large. Since the degree of pixel change tends to be small in the background portion, the area ratio of the background portion is large.
6, the average value tends to be lower than that shown in FIG. In this sense, a certain threshold value may be provided, and an average of only the threshold value or more may be calculated.

On the other hand, the main purpose of this tallying stage is to determine the sharpness of the image, but it is necessary to determine from the tallying result whether or not the image requires sharpness like a natural image in the first place. Is also possible. In the case of a natural image, even if it is just a background part, the same pixels are not arranged according to the brightness of the color or the shape of the real thing as the background, so the absolute value of the edge amount is totaled The result is as shown in FIG. 47, and the edge amount tends to be large. On the other hand, in an image such as a business graph, a certain area is often filled with the same color. Therefore, the totaling result of the absolute value of the edge amount is as shown in FIG. 48, and the edge amount is lower.

Therefore, if the average value (av) of the tally results is lower than a certain threshold Th, it can be said that the image does not need to be processed to increase sharpness, and an interpolation process which does not affect sharpness is executed. What should I do? As described above, in steps ST404 to ST408, the degree of change of each pixel constituting the image is totalized, and whether or not the image can be said to be a sharp image has been evaluated. Will constitute the sharpness evaluation means E3.

On the basis of this evaluation result, step ST41
In the case of 0, an interpolation process according to the sharpness of the image is selected. In the present embodiment, interpolation processing by a cubic method is performed on a sharp image, and interpolation processing by a hybrid bi-cubic method is performed on an image lacking sharpness. Therefore, in this sense, the step ST410 constitutes the interpolation processing control means E4,
Steps ST412 and ST having separate interpolation processes
The processing of 414 constitutes the pixel interpolation means E2. Here, each interpolation process will be described in detail.

Each of the interpolation processes has a difference in the characteristics as described above. If it is determined in step ST410 that the image is sharp, the interpolation process of the cubic method is executed in step ST412. If it is determined that there is no interpolation processing, the interpolation processing of the hybrid bicubic method is executed. When performing the interpolation processing by the hybrid bicubic method, the curve to be interpolated becomes sharp and the sharpness can be increased. Such interpolation processing is selected because the sharpness of the target image is evaluated and the evaluation is performed. It is based on the results. For this reason, the operator can sharpen an unsharp image without making any special judgment.

In the present embodiment, one of the two types of interpolation processing is executed. However, a plurality of interpolation processing steps corresponding to the degree of change of pixels may be executed. . FIG. 49 shows the evaluation of sharpness as 4
An example is shown in which four cubic methods are implemented in which the parameters of the third-order interpolation method are changed in stages. In the figure, “0” indicates a curve of the cubic method applied to an image having normal sharpness, and a curve of “+1” indicates a cubic method applied to an image slightly lacking sharpness. The "+2" curve shows the cubic method applied to images that are significantly less sharp. For an image that is too sharp, a cubic method with a curve of “−1” is applied. Of course, these are realized by adjusting the inclination angle, undershoot, and overshoot of the central portion of the S-shaped curve as in the case of the hybrid bicubic method.

Next, the flow of the procedure in this case is shown in FIG. Step ST510 based on the sharpness of the image
Then, these parameters are set, and the cubic method using these parameters is executed in step ST514. Also, in this flow, taking into account that the sharpness of the image is partially different, the image is divided into blocks, which are small areas, and the optimal interpolation processing is executed for each block.
That is, in order to evaluate the sharpness of each block and select an interpolation process, in step ST508, the edge amounts of each block are totaled, and steps ST516 and ST518 are performed.
The interpolation processing is executed while sequentially moving the blocks.

When the interpolation processing is completed for all the image data, the image data interpolated in step ST420 or ST520 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 the image data here means transferring the data to the next stage. Thus, in the computer system 10 having the image input device and the image output device, the printer driver 12c
After inputting the original image data at 402, step ST4
In steps 04 to 108, the sharpness of the image is evaluated and counted. In step ST410, if the image lacks sharpness based on the result of the counting, the flow proceeds to step ST414.
In step ST412, a normal interpolation process is executed if the image is sharp, so that the operator does not need to select an image process for increasing sharpness. Also, a sharp image can be obtained only through the interpolation processing.

As described above, in the present embodiment, the image data acquiring means for acquiring the image data representing the image by the pixels in the dot matrix form, and the sharpening means for evaluating the sharpness of the image based on the image data. A degree evaluator, a pixel interpolator capable of executing an interpolating process for changing the sharpness of the image in performing the interpolating process to increase the number of constituent pixels in the image data, and a sharpness of the image evaluated by the sharpness evaluator. If is not appropriate, the image processing apparatus is provided with interpolation processing control means for causing the pixel interpolating means to execute interpolation processing so that the sharpness is changed and becomes appropriate.

In such a configuration, 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, the pixel interpolation means performs an interpolation process for changing the sharpness of the image. When the image data acquisition unit acquires the target image data, the sharpness evaluation unit evaluates the sharpness of the image based on the image data. Then, based on the sharpness evaluated by the sharpness degree evaluation means, the interpolation processing control means performs an interpolation process on the pixel interpolation means so as to change the sharpness if the image sharpness is not appropriate so as to be appropriate. Let it run.

That is, when the sharpness of the image is low, the sharpness is increased by the interpolation processing, and when the image is too sharp, the sharpness is reduced. In this way, since the sharpness is adjusted by the interpolation processing according to the sharpness of the image, an image data interpolating device capable of easily improving the image quality without complicating the operation is provided. can do. As a method of changing the sharpness of an image while performing interpolation, the inclination of the image data is adjusted while changing the image data in a substantially S-shaped manner,
At both ends, an undershoot is generated on the lower side and an overshoot is generated on the higher side to form a height difference, and by adjusting the height difference, the degree of change of the image is adjusted to be optimal. To change the sharpness of the image.

As an example of taking such an S-shaped curve,
The pixel interpolating means may be configured to adjust the parameters in the cubic convolution interpolation method to change the sharpness of the image. In the case of such a configuration, by adjusting the parameters of the cubic convolution interpolation method used as the interpolation processing, the pixel interpolated between adjacent pixels in the original image becomes a cubic function. By adopting it, it draws an S-shape, and it has both gentle and steepness. By adjusting the degree of the S-shaped bend with a parameter, the steepness changes and the sharpness of the image changes.

In this way, the multi-order arithmetic processing is performed in 3
By using the convolution interpolation method, the sharpness can be adjusted relatively easily by adjusting the S-shaped curve. In changing the sharpness, it is not always necessary to employ only one operation method, and it is also possible to execute a plurality of interpolation processes that affect the sharpness. As an example of such a configuration, the pixel interpolating means can execute a plurality of interpolation processes having different degrees of change in the sharpness of the image, and adjust the sharpness of the image by changing the ratio of each interpolation magnification. It can also be.

In the case of such a configuration, the degree of change in the sharpness of the image differs in each of the plurality of interpolation processes, and the plurality of interpolation processes are executed to obtain a necessary interpolation magnification. Therefore, the sharpness can be adjusted by changing the share ratio of the interpolation magnification. For example, when there is an interpolation process with a low degree of change in sharpness and an interpolation process with a high degree of change in sharpness, if the sharing ratio of both is changed, an intermediate point between the two degrees of change can be selected.

In this case, since only the interpolation magnification assigned to the plurality of interpolation processes is changed, the parameter setting is simplified. The sharpness of an image does not always have to be high. Therefore, it may not be necessary to increase the sharpness. In this case, it is possible for the operator to make a decision, but it is also possible to adopt a configuration that realizes such decision at the same time. As an example, the interpolation processing control means controls the pixel interpolation means to change the sharpness of the image when the sharpness of the image is evaluated as exceeding a predetermined threshold value. It can also be.

In such a case, the interpolation processing control means compares the sharpness of the image with a predetermined threshold value. Control is performed by the pixel interpolation means to change the sharpness of the image. However, if the sharpness of the image does not exceed the threshold value, no control is made to change the sharpness of the image. For example, when comparing the sharpness of an image when it is a natural image with the sharpness of an image when it is a non-natural image, it can be said that the former is generally higher in sharpness. Needless to say, the sharpness of an image is not determined only by the classification of a natural image or a non-natural image, and therefore, it is possible to add another judgment factor. For example, if a classification of an image is acquired and the threshold is changed after the classification, more flexible handling is possible.

In this way, in order to prevent the sharpness from being changed up to a certain range, the inconvenience of automatically adjusting the sharpness to an image for which changing the sharpness is inappropriate is obtained. Disappears. Since the sharpness of the screen is not constant over the entire image on the basis of individual pixels, it is not necessarily limited to a constant interpolation process. For this reason, it is also possible to adopt a configuration in which the sharpness of an image is evaluated on a pixel-by-pixel basis, and control is performed so as to change the sharpness of the image on a pixel-by-pixel basis based on the sharpness of the evaluated image. In addition, the sharpness of the image is evaluated for each predetermined small area, and the image is controlled so as to change the sharpness of the image for each predetermined small area based on the evaluated sharpness of the image.

Next, an embodiment in which image processing can be selected will be described. FIG. 51 is a block diagram showing such an image data interpolation device. This image data interpolating apparatus simultaneously performs enlargement processing and sharpness change processing as interpolation processing when performing image processing in pixel units on image data, and obtains the same image data with image data obtaining means F1. In both cases, the image processing to be performed by the image processing selecting means F2 is selected. Simultaneous processing determination means F3 determines whether the image processing to be performed is to simultaneously perform the enlargement processing and the sharpness change processing, and
If it is necessary to perform the processing simultaneously, the pixel interpolating means F4 changes the sharpness of the image accompanying the execution of the interpolation processing for increasing the number of constituent pixels in the image data. Then, the image data after the enlargement processing is output to the image data output unit F5.
Output.

In the computer system 10, image data is acquired by the image input device, such as the scanner 11a, and the application 12d executes predetermined image processing. There are various types of image processing, such as enlargement / reduction, sharpness, contrast, and color correction. As described above, in order to display and output on the display 17a or the color printer 17b as an image output device, it is necessary to match the resolutions. Is performed. This interpolation process may be executed by the printer driver 12c or by the application 12d.

Here, in the application 12d, enlargement processing and sharpness change processing can be selected as image processing, and the sharpness is changed along with the enlargement processing when matching the resolution of the printer. It is also possible. Therefore, in these cases, enlargement processing and sharpness adjustment processing are performed. In this sense, the image data interpolation device of the present invention is realized as the application 12d in the computer system 10 described above. And application 1
2d includes not only the above-described pixel interpolating means F4 but also an image processing selecting means F2 and a simultaneous processing determining means F as described below.
Constituting No. 3. Further, since data input / output is involved, the image data acquisition means F1 and the image data output means F5 are configured in the sense of file input, file output or print data output.

FIG. 52 shows an outline of a software flow of the application 12d. The application 12d can execute various image processing by menu selection as shown in FIG. 53, and is not necessarily limited to the software flow as shown in FIG. 52.
For simplicity of understanding, the display is simplified. In step ST602, original image data is obtained. Scanner 1 in file menu etc. in application 12d
For example, a process of reading an image from 1a corresponds. In the present embodiment, since image data to be used for image processing described later may be generated, it can be said that processing for generating an image file by selecting creation of a new file or the like is also acquisition of original image data. . Acquisition processing of the original image data excluding the configuration of the operating system 12a and hardware corresponds to image data acquisition. Of course, if these are considered to be organically integrated with hardware such as a CPU, they correspond to the image data acquisition means F1.

In step ST604, image processing is selected. In steps ST606 and ST608, the selected image processing is determined. To select image processing, see FIG.
As shown in FIG. 54, when the character portion of "image" is clicked with the mouse 15b from the menu bar displayed on the window frame on the screen, various image processes that can be executed are displayed as shown in FIG. . In the example shown in FIG.
“Enlargement” processing, “sharpness” adjustment processing, “contrast” adjustment processing, and “brightness” adjustment processing can be executed. A “button” switch is prepared for each image processing, and a plurality of image processings can be selected at the same time. Each process is displayed in gray until it is selected by the "button" switch, so that the non-display state can be distinguished.

When the desired image processing button switch is set to the selected state, the respective parameters are set, and the “OK” button is clicked, the selection is processed as input processing, and the selection is made in steps ST606 and ST608. Judge the processing. In this example, step ST6
At 06, it is determined whether or not enlargement processing has been selected. If enlargement processing has not been selected, image processing corresponding to each is performed at step ST610. On the other hand, if enlargement processing has been selected, it is determined in step ST608 whether or not sharpness adjustment processing has been selected. As a result, if both the enlargement process and the sharpness adjustment process are selected, the process proceeds to step ST612,
If the enlargement process has been selected but the sharpness adjustment process has not been selected, the process proceeds to step ST614. Steps 112 and ST614 will be described later.

In this software flow, the process is performed in two branch processes of steps ST606 and ST608 for easy understanding, but a large number of branches may be actually selected in the case process. In the present embodiment, since the image processing is selected in step ST604, it corresponds to the image processing selecting step, and corresponds to steps ST606 and ST60.
In step 8, it is determined whether the enlargement processing and the sharpness adjustment processing are both selected, which corresponds to a simultaneous processing determination step. Of course, if these are considered to be organically integrated with hardware such as a CPU, they correspond to the image processing selecting means F2 and the simultaneous processing determining means F3.

Here, a modified example of the image processing selecting means F2 and the simultaneous processing determining means F3 will be described. FIG. 55 to FIG.
Numeral 7 indicates an example of this. It is assumed that the application 12d does not have explicit sharpness adjustment processing as shown in FIG. 54, and that enlargement processing can be selected. In this example, a parameter input window for enlargement processing as shown in FIG. 56 is displayed as selection of image processing in step ST704. Here, it is possible to select and input the enlargement ratio in%.
Click the "K" button to proceed to the next step.
Normally, by inputting the enlargement ratio, the interpolation process corresponding to the magnification may be executed. In this example, if it is determined in step ST706 that the enlargement process has been selected, the process proceeds to step ST708 in FIG. A window for inquiring about sharpness adjustment is displayed as shown in FIG. In this window, "NO", "Low", "High"
Are prepared, and buttons that can be selected alternatively are assigned to each of the three options. Default is "N
O ", and the operator can select" Low "or" Hi "as necessary.
gh ”can be selected. These are inquiries about sharpness emphasis. "NO" does not emphasize, "Low" implies a little emphasis, and "High" means emphasis.

Then, based on the selected result, steps ST712, ST714, and ST71, which are interpolation processes, are performed.
6. Execute any of 6. At the time of the enlargement processing, the sharpness can be changed by selecting the interpolation processing as described later, so that the inquiry is automatically made when the enlargement processing is selected. In this example, step S
T704 and ST708 correspond to an image processing selection step, and step ST710 corresponds to a simultaneous processing determination step.

FIGS. 58 to 60 show a case where the image processing is not directly selected, but the enlargement processing is actually performed internally. Shows the case of the printing process. When print is selected from a file menu (not shown), a print menu as shown in FIG. 59 is displayed in step ST804. Various parameters can be set in this print menu, and one of them is a selection box for “print resolution”. Regardless of the resolution handled internally by the application 12d, a resolution matching operation is required depending on which resolution is used for printing at the time of printing. Assuming that the resolution of the color printer 17b2 is 720 dpi, when printing at a print resolution of 720 dpi, one dot of image data corresponds to one dot at the time of printing, so that resolution conversion is unnecessary. However, when printing at 300 dpi, the correspondence with print data must be matched, and resolution conversion in this sense is required.

For this reason, in step ST808, the parameter of the print resolution box is compared with the resolution of the color printer 17b2 managed by the operating system 12a to determine whether or not it is necessary to perform resolution conversion. If conversion is required, step ST81
At 0, a sharpness inquiry window as shown in FIG. 60 is displayed. In this example, as in the case of FIG. 54, the degree of sharpness adjustment is selected and input in%, and processing is selected in step ST812 according to the input parameters. That is, when the adjustment process is not requested, the process proceeds to step ST814, and when the adjustment process is requested, the process proceeds to step ST816. Thereafter, in a state where the resolutions match, the printing process is executed in step ST818.

In this example, steps ST804, ST8
10 corresponds to the image processing selection step, and the step S
T812 corresponds to the simultaneous processing determination means step. It goes without saying that the image processing selecting means F2 and the simultaneous processing judging means F3 can be realized by other methods. The interpolation process is executed after the above determination. Specifically, when only the enlargement processing is selected and the sharpness enhancement processing is not selected, the interpolation processing by the near list method is executed (steps ST614, ST712, and ST814), and the enlargement processing and the sharpness enhancement processing are performed. Is selected, the interpolation processing by the hybrid bicubic method (steps ST612 and ST816) and the interpolation processing by the cubic method (step ST714) are executed. Therefore, the latter steps ST612, ST816 and ST714 constitute the pixel interpolation means F4.

As described in the present embodiment, the ability to adjust the sharpness during the enlargement process is superior to simply performing the enlargement process and the sharpness adjustment process separately. For example, even if the enlargement process is performed after emphasizing the sharpness, if the enlargement process is of a near-list method, jaggies may be conspicuous and a sharp feeling may not be maintained. If the sharpness is emphasized after the enlargement by the near-list method, the sharpness will be sharpened in a conspicuous state, and the image quality cannot be improved. On the other hand, if the sharpening is also performed in the integrated enlargement processing, such an adverse effect hardly occurs.

When the interpolation processing and other image processing are completed for the image data, the image data is output in step ST66. Outputting the image data here has a broad meaning, and is not limited to processing such as outputting to the color printer 17b2 or writing to the hard disk 13b, and displaying the image data on the display 17b1 while retaining the data. Alternatively, it may be prepared for the next image processing. Of course, in the present embodiment, this step ST616 constitutes the image data output means F5.

As described above, in the computer system 10 having an image input device, an image output device, and the like, the application 12d can execute various image processing, and in step ST604, the user selects the image processing to be executed. If the enlargement process and the sharpness change process are specified at the same time, step ST60
In step ST612, the interpolation processing for increasing the sharpness is performed after the determination of step 8, and when only the enlargement processing is selected, the normal interpolation processing that does not affect the sharpness is performed in step ST614. Since the enlargement processing and the sharpness change processing are not separately performed and the both are executed integrally,
The sharpness can be adjusted reliably.

As described above, according to the present invention, the image data obtaining means for obtaining the image data representing the image by the pixels in the dot matrix form, and changing the image data of each pixel with respect to the image data. Image processing selecting means for displaying image processing that can be executed to execute various image processing and inputting a selection, and the image processing selecting means selects both image enlargement processing and image sharpness changing processing Means for judging whether or not image processing has been performed, and when the simultaneous processing judging means determines that both image enlargement processing and image sharpness change processing have been selected, the number of constituent pixels in the image data When the image is enlarged by increasing the number of pixels, the degree of change of the interpolated image data is adjusted to It is constituted comprising a viable pixel interpolation means interpolating process so that To is, and an image data output means for outputting the generated image data.

In the present invention having such a configuration,
After the image data obtaining means obtains the image data expressing the image by the pixels of the dot matrix, the image processing is selected to execute various types of image processing by changing the image data of each pixel with respect to the image data. The image processing executable by the means is displayed and a selection is input. Here, the simultaneous processing determination means determines whether or not the image enlargement processing and the image sharpness change processing are both selected by the image processing selection means, and if it is determined that both processings are selected, The pixel interpolation means increases the number of constituent pixels in the image data to enlarge the image,
By adjusting the degree of change of the image data to be interpolated, an interpolation process is performed so that the selected image becomes sharp. Then, the image data output means outputs the generated image data.

That is, if it is necessary to simultaneously execute the process of enlarging the image and changing the sharpness, the sharpness is changed while enlarging the image data by adjusting the image data generated by the interpolation process. As described above, according to the present invention, the sharpness of an image is changed by the interpolation process required in the enlargement process. Therefore, it is not necessary to perform the enlargement process and the sharpness change process separately, and the processing time is shortened. It is possible to provide an image data interpolating device capable of performing such operations.

Also, since they are performed simultaneously, there is no possibility that a good result is not obtained when the sharpness is subsequently changed depending on the enlargement process. Of course, the enlargement process is performed after the sharpness is changed. The processing for changing the sharpness is not wasted. The image processing selection means displays executable image processing and inputs a selection, and the simultaneous processing determination means determines whether both the image enlargement processing and the image sharpness change processing have been selected. Things. As a result, it suffices to determine whether it is necessary to simultaneously execute both processes, and the mode of selection and the like can be appropriately changed.

As an example, the image processing selecting means can individually select the enlargement processing and the sharpness change, and the simultaneous processing determining means allows the image processing selecting means to perform the enlargement processing. It is also possible to adopt a configuration in which it is determined whether or not the selection and the selection of the sharpness change are simultaneously selected. In such a configuration, since the selection of the enlargement processing and the selection of the sharpness change can be individually selected, only the enlargement processing or only the sharpness change may be selected. Then, the simultaneous processing determining means determines whether or not the selection of the enlargement processing and the selection of the sharpness change are simultaneously selected on the premise of such a situation.

This is suitable for the case where the enlargement process and the sharpness change process can be individually selected. Further, as another example, the image processing selecting means can select an enlargement processing, and the simultaneous processing determining means determines a degree of change in sharpness when the enlargement processing is selected by the image processing selecting means. May be selected. In such a configuration, only the enlargement processing can be selected by the image processing selection means, and no input is made until the selection for changing the sharpness is made. However, when the enlargement processing is selected by the image processing selection means, the simultaneous processing determination means independently determines the degree of sharpness change. Of course, it is also possible to choose not to change the sharpness, in which case only the enlargement processing will be performed, and conversely, if the choice is made to change the sharpness, the enlargement processing will be performed. It is determined that the processing for changing the sharpness has been selected at the same time.

Therefore, even when only enlargement processing can be selected on the surface, the sharpness can be changed together. Furthermore, in the above-described example, the enlargement processing is explicitly selected, but the enlargement processing itself does not need to be limited to an explicit one. As an example,
The simultaneous processing determination means may be configured to select the degree of change in sharpness when the image processing selection means performs resolution conversion processing in conjunction with image processing.

In the case of such a configuration, there is a case where it becomes necessary to perform a resolution conversion process accompanying the image processing selected by the image processing selecting means.
In this case, the simultaneous processing determination means allows the user to select the degree of change in sharpness. Then, when changing the sharpness is selected, it is determined that the enlargement processing and the processing for changing the sharpness are simultaneously selected. In this way, the sharpness can be changed in the case where the enlargement process is performed incidentally.

The display and input operations in these cases may be realized by screen display or mouse operation under a GUI, or may be implemented by hardware switches. It can be changed as appropriate.

[Brief description of the drawings]

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

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

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

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

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

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

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

FIG. 8 is a more specific flowchart in the image data interpolation device of the present invention.

FIG. 9 is a diagram illustrating the size of an original image.

FIG. 10 is a diagram showing a sampling cycle.

FIG. 11 is a diagram showing the number of sampling pixels.

FIG. 12 is a diagram illustrating a relationship between an original image and pixels to be sampled.

FIG. 13 is a diagram illustrating a histogram of luminance for a non-natural image.

FIG. 14 is a diagram illustrating a histogram of luminance for a natural image.

FIG. 15 is an explanatory diagram in the case where the degree of change of an image is represented by each component value of rectangular coordinates.

FIG. 16 is an explanatory diagram in a case where the degree of change of an image is obtained by a difference value between adjacent pixels in a vertical axis direction and a horizontal axis direction.

FIG. 17 is an explanatory diagram in a case where the degree of change of an image is obtained between all adjacent pixels.

FIG. 18 is a diagram showing an area where a threshold value is changed.

FIG. 19 is a diagram illustrating a situation where a printer driver makes an inquiry to an operating system.

FIG. 20 is a conceptual diagram of a near-list method.

FIG. 21 is a diagram showing a situation in which data of each grid point is transferred by the near list method.

FIG. 22 is a schematic diagram showing a situation before interpolation in the near-list method.

FIG. 23 is a schematic diagram showing a situation after interpolation by the near-list method.

FIG. 24 is a conceptual diagram of the cubic method.

FIG. 25 is a diagram illustrating a change state of data when the cubic method is specifically applied.

FIG. 26 is a diagram showing a specific application example of the cubic method.

FIG. 27 is a diagram showing a specific application example of the hybrid bicubic method.

FIG. 28 is a conceptual diagram of the bilinear method.

FIG. 29 is a diagram illustrating a change state of an interpolation function.

FIG. 30 is a schematic diagram showing an integer multiple interpolation process.

FIG. 31 is a schematic block diagram of an image data interpolation device according to an embodiment of the present invention.

FIG. 32 is a flowchart in the image data interpolation device of the present invention.

FIG. 33 is a diagram illustrating an example of an edge detection filter.

FIG. 34 is a diagram illustrating another example of the edge detection filter.

FIG. 35 is a diagram illustrating a relationship between a distribution of edge amounts and a threshold.

FIG. 36 is a diagram illustrating a relationship between a pixel of interest and a pixel targeted for flag setting determination.

FIG. 37 is a diagram illustrating an edge amount and a setting state of a flag.

FIG. 38 is a diagram illustrating a relationship between blocks formed by existing pixels and pixels to be interpolated.

FIG. 39 is a diagram illustrating a case where a block is formed based on a target pixel.

FIG. 40 is a diagram showing a correspondence between a flag status and an area.

FIG. 41 is a diagram illustrating a case where a block is formed by pixels to be interpolated.

FIG. 42 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. 43 is a schematic block diagram of an image data interpolation device according to an embodiment of the present invention.

FIG. 44 is a flowchart in the image data interpolation device of the present invention.

FIG. 45 is a diagram showing a small image of a background portion.

FIG. 46 is a diagram illustrating an image having a large background portion.

FIG. 47 is a diagram illustrating a result of counting the edge amounts of a natural image.

FIG. 48 is a diagram showing a result of counting the edge amounts of the business graph.

FIG. 49 is a diagram illustrating a change state of the interpolation function.

FIG. 50 is a diagram showing a flow for selecting an interpolation function.

FIG. 51 is a schematic block diagram of an image data interpolation device according to an embodiment of the present invention.

FIG. 52 is a flowchart in the image data interpolation device of the present invention.

FIG. 53 is a diagram showing a display of a menu for executing image processing.

FIG. 54 is a diagram showing a screen for selecting image processing.

FIG. 55 is a flowchart showing a modification of the image data interpolation device.

FIG. 56 is a diagram showing a screen for instructing enlargement processing.

FIG. 57 is a diagram showing a screen for instructing sharpness.

FIG. 58 is a flowchart of a printing process.

FIG. 59 is a diagram showing a screen for instructing parameters at the time of printing.

FIG. 60 is a diagram showing a screen for instructing sharpness.

[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 front page (58) Field surveyed (Int.Cl. ⁷ , DB name) G06T 3/40 H04N 1/387 101

Claims (6)

(57) [Claims] 1. An image data acquisition means for acquiring image data of an image represented by pixels in a dot matrix form, and a plurality of interpolation processes selected from a plurality of interpolation processes for increasing the number of constituent pixels in the image data. Executable pixel interpolation means, for the image data, perform statistical processing on pixels whose difference from peripheral pixels is larger than a predetermined threshold value, and characterize the entire image based on the result of the statistical processing. A feature amount obtaining unit that obtains a feature amount related to the interpolation process while representing the interpolation process; and an interpolation process selection unit that selects an interpolation process based on the feature amount obtained by the feature amount obtaining unit and causes the pixel interpolation unit to execute the interpolation process. An image data interpolation apparatus comprising: 2. The image data interpolating apparatus according to claim 1, wherein said characteristic amount obtaining means lowers the threshold value in a portion closer to the center of the image. 3. Acquiring image data of an image represented by pixels in a dot matrix form, and performing statistical processing on the image data with respect to pixels whose difference from peripheral pixels is greater than a predetermined threshold value. Based on the result of the statistical processing, a feature amount related to the interpolation process is acquired while representing the feature of the entire image, and an interpolation process for increasing the number of constituent pixels in the image data is selected based on the acquired feature amount. And an image data interpolation method characterized by processing the image data by the selected interpolation processing. 4. The image data interpolation method according to claim 3, wherein the threshold value is set lower in a portion closer to the center of the image. 5. A medium recording an image data interpolation program for executing an interpolation process on image data of an image expressed by dot matrix pixels so as to increase the number of constituent pixels at a predetermined interpolation magnification. And a pixel interpolation step that can be selected and executed from among a plurality of interpolation processes in performing the interpolation process for increasing the number of constituent pixels in the image data. A difference between peripheral pixels of the image data is predetermined. Performing a statistical process on a pixel larger than the threshold value of, and acquiring a feature amount related to the interpolation process while expressing a feature of the entire image based on a result of the statistical process; An interpolation process that selects an interpolation process based on the feature amount acquired in the feature amount acquisition step and causes the pixel interpolation step to execute the interpolation process Medium recording an image data interpolation program characterized by comprising a selection step. 6. The image data interpolation program according to claim 5, wherein in the feature amount obtaining step, the threshold value is set lower in a portion closer to the center of the image. Medium on which interpolation program is recorded.