JP2008017526A - Interpolation of image data - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To solve the problem that specific sharpness should be adjusted uniformly, so that there may be incurred cases preferable and non-preferable for images. <P>SOLUTION: When interpolating image data representing a color natural image with pixels in a dot matrix shape, a chromaticity representative value is acquired for a block of interest that is a region to be referenced for interpolating and generating pixels (step 110) and when a skin-color region or sky-color region is judged from the relevant chromaticity (steps 115, 120), pixels are interpolated by a bi-linear technique (step 130), so as to smoothly expand the image or for regions of other color tones, pixels are interpolated by an M-cubic technique (step 125), so as to sharply expand the image. Thus, for the entire image, a portion of human skin or blue sky can be prevented from getting rough while maintaining sharpness. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

The present invention relates to interpolation of image data .

  When an image is handled by a computer or the like, the image is expressed by a dot matrix pixel, and each pixel is expressed by a gradation value. For example, photographs and computer graphics are often displayed with pixels of 640 dots in the horizontal direction and 480 dots in the vertical direction on a computer screen.

On the other hand, the performance of color printers is remarkably improved, and the dot density is extremely high, such as 720 dpi. Then, if an image of 640 × 480 dots is printed in correspondence with each dot, the image becomes extremely small. In this case, since the gradation values are different and the meaning of the resolution itself is different, it is necessary to interpolate between dots and convert the data into printing data. Conventionally, as a method for interpolating dots in such a case, nearest neighbor interpolation (nearlist neighbor interpolation: hereinafter referred to as the nearlist method) or cubic convolution interpolation (cubic convolution interpolation: hereinafter) A method such as a cubic method is known. Further, Patent Document 1 discloses a technique for preparing a dot pattern so as to obtain an enlarged form in which an edge becomes smooth when performing edge smoothing when dots are interpolated.
JP-A-6-225140

  In the case of a natural picture, a sharper one is generally preferred. In particular, it is not preferable that the sharpness deteriorates due to pixel interpolation. Therefore, the sharpening process is basically executed. However, it is not always necessary that everything is sharp, and a rough feeling appears by applying sharpening, which is not preferable. In the above-described conventional method, the degree of influence on the sharpness is different, and specific sharpness is uniformly adjusted by executing interpolation processing on certain image data. Therefore, depending on the image, there are cases where it is preferable or not preferable, and in order to avoid this, there is a problem that it is necessary to select an interpolation process on the operation side. Even if the interpolation process is selected, the desired sharpness differs depending on the portion of the image, so that a compromise has to be made after all.

The present invention has been made in view of the above problems, and an object of the present invention is to provide interpolation of image data that can be interpolated with good sharpness regardless of the type of image or subject.

In order to achieve the above object, the invention according to claim 1 selects image data acquisition means for acquiring image data representing an image with a plurality of pixels, and whether to prevent roughness of a predetermined color area in the image. And a first pixel interpolation process for smoothing the predetermined color area is executed when it is selected as a result of the selection by the selection means to prevent roughness of the predetermined color area. In addition, when the second pixel interpolation process for sharpening the color area other than the predetermined color area is performed and it is not selected to prevent the predetermined color area from being roughened, the predetermined color area and the predetermined color area and color corresponding pixel interpolating means for executing the second pixel interpolation process on predetermined color region other than the color areas, leaving the image data pixel interpolation processing is executed by the tone corresponding pixel interpolation means An image data output means for, is configured to have a.

According to a second aspect of the present invention, in the image data interpolation apparatus according to the first aspect, the first and second pixel interpolation processes include a plurality of pixel interpolation processes having different degrees of sharpening the image. This is executed a plurality of times, and the respective interpolation magnifications of the pixel interpolation processing executed a plurality of times are adjusted so that the predetermined color region and the color region other than the predetermined color region have a desired sharpness.

Furthermore, the invention according to claim 3 is the image data interpolation device according to claim 1 or 2, wherein the first and second pixel interpolation processes have different degrees of sharpening the image depending on parameters. Pixel interpolation processing is executed, and the parameters are adjusted so that the predetermined color region and the color region other than the predetermined color region have a desired sharpness.

According to a fourth aspect of the present invention, in the image data interpolating apparatus according to the first aspect, the first pixel interpolation process is a pixel interpolation process using a bilinear interpolation, and the second This pixel interpolation processing is configured as pixel interpolation processing using a cubic convolution interpolation method.

Further, the invention according to claim 5 is the image data interpolation device according to any one of claims 1 to 4, wherein the predetermined color region is at least one of a skin color region and a sky blue color region. It is as a configuration.

According to a sixth aspect of the present invention, in the image data interpolating apparatus according to any one of the first to fifth aspects, the tone-corresponding pixel interpolating means uses a region pixel used when generating an interpolated pixel. Based on the average value of the chromaticity and luminance, it is determined whether or not it is the predetermined color region .

Furthermore, the invention according to claim 7 is the image data interpolation device according to any one of claims 1 to 5, wherein the color-corresponding pixel interpolation unit is configured to adjust chromaticity and luminance of a predetermined target pixel. Based on this, it is configured to determine whether or not it is the predetermined color region.

Such an image data interpolating apparatus may be implemented independently, or may be implemented together with other methods in a state of being incorporated in another apparatus such as an image output apparatus. The present invention is not limited to this and includes various aspects. Therefore, it can be changed as appropriate, such as software or hardware. In the case of software that implements the image data interpolation method as an embodiment of the idea of the invention, it naturally exists on a recording medium that records such software, and it must be used. Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any recording medium that will be developed in the future. In addition, the duplication stages such as the primary duplication product and the secondary duplication product are equivalent without any question. In addition, even when the communication method is used as a supply method, the present invention is not changed. Further, even when a part is software and a part is realized by hardware, the idea of the invention is not completely different, and a part is stored on a recording medium and is appropriately changed as necessary. It may be in the form of being read.

  As described above, according to the present invention, it is possible to obtain a good image by appropriately adjusting the sharpness according to the portion while making the whole image sharp by selecting the interpolation processing with reference to the color tone. An image data interpolation method can be provided.

  FIG. 1 is a flowchart showing the image data interpolation method of the present invention, and FIG. 2 is a block diagram showing a computer system 10 for executing the image data interpolation method.

  The computer system 10 includes a scanner 11a, a digital still camera 11b, and a video camera 11c as image input devices, and is connected to a computer main body 12. Each input device can generate image data in which an image is expressed by a dot matrix pixel and output the image data to the computer main body 12. Here, the image data is displayed in 256 gradations for each of the three primary colors of RGB. About 16.7 million colors can be expressed.

  The computer main body 12 is connected with a flexible disk drive 13a, a hard disk 13b, and a CD-ROM drive 13c as external auxiliary storage devices. The hard disk 13b stores main system-related programs. Necessary programs and the like can be read from a CD-ROM or the like as appropriate. In addition, a modem 14a is connected as a communication device for connecting the computer main body 12 to an external network or the like, and it can be connected to an external network via the public communication line, and can be installed by downloading software and data. It has become. In this example, the modem 14a accesses the outside via a telephone line, but it is also possible to adopt a configuration where the network is accessed via a LAN adapter. In addition, a keyboard 15a and a mouse 15b are also connected for operating the computer main body 12.

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

  The color printer 17b is an ink jet printer, and can print an image by adding dots on a printing paper as a recording medium using four color inks of CMYK. The image density is capable of high-density printing such as 360 × 360 DPI or 720 × 720 DPI, but the gradation table limit is expressed in two gradations such as whether or not color ink is applied. On the other hand, in order to display or output to the image output device while inputting an image using such an image input device, a predetermined program is executed in the computer main body 12. Among them, an operating system (OS) 12a is operating as a basic program, and the operating system 12a performs a print output to a display driver (DSP DRV) 12b for displaying on the display 17a and a color printer 17b. A printer driver (PRT DRV) 12c is installed. These types of drivers 12b and 12c depend on the models of the display 17a and the color printer 17b, and can be added to and changed from the operating system 12a according to the respective models. In addition, depending on the model, additional functions beyond standard processing can be realized. That is, various additional processes within an allowable range can be realized while maintaining a common processing system on the standard system called the operating system 12a.

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

  In such a computer system 10, image data is acquired by a scanner 11 a as an image input device, and predetermined image processing is executed by an application 12 d, and then displayed on a display 17 a or a color printer 17 b as an image output device. Is possible. In this case, simply focusing on the correspondence between the pixels, when the pixel density in the color printer 17b and the pixel density of the scanner 11a match, the size of the scanned original image and the size of the printed image match. If there is a deviation, the size of the image will be different. In the case of the scanner 11a, there are many things that approximate the pixel density of the color printer 17b, but the pixel density of the color printer 17b in which the pixel density is improved in order to improve the image quality is a general image input device. It is often higher than the pixel density. In particular, the display density is higher than the display density of the display 17a, and if the display on the display 17a is made to coincide with each other on a pixel basis and printed, an extremely small image may be obtained.

  Therefore, resolution conversion is performed in order to eliminate the difference in pixel density for each actual device while determining the reference pixel density in the operating system 12a. For example, when the resolution of the display 17a is 72 DPI, if the operating system 12a uses 360 DPI as a reference, the display driver 12b performs resolution conversion between the two. In the same situation, if the resolution of the color printer 17b is 720 DPI, the printer driver 12c performs resolution conversion.

  The resolution conversion corresponds to an interpolation process because it is a process of increasing the number of constituent pixels in the image data, and the display driver 12b and the printer driver 12c implement the interpolation process as one of its functions. Here, the display driver 12b and the printer driver 12c are main components for realizing the above-described tone-corresponding pixel interpolation step A2, and the input / output process thereof realizes the image data acquisition step A1 and the image data output step A3. As described in detail below, the resolution conversion technique is appropriately changed according to the color tone to improve the image quality. The display driver 12b and the printer driver 12c are stored in the hard disk 13b and are read and operated by the computer main body 12 at the time of activation. At the time of introduction, it is recorded on a medium such as a CD-ROM or a flexible disk and installed. Therefore, these media constitute a medium in which an image data interpolation program is recorded.

  In the present embodiment, the image data interpolation apparatus is realized as the computer system 10, but such a computer system is not necessarily required, and any system that requires interpolation processing for similar image data may be used. . For example, as shown in FIG. 3, an image data interpolation device for performing interpolation processing is incorporated in the digital still camera 11b1, and the interpolated image data is displayed on the display 17a1 or printed on the color printer 17b1. Also good. As shown in FIG. 4, in the color printer 17b2 that inputs and prints image data without using a computer system, the image data input through the scanner 11a2, the digital still camera 11b2, the modem 14a2, or the like is automatically processed. It is also possible to perform a print process after performing resolution conversion.

  In addition, the present invention can naturally be applied to various apparatuses that handle image data such as a color facsimile apparatus 18a as shown in FIG. 5 and a color copy apparatus 18b as shown in FIG. The flowchart shown in FIG. 1 is a procedure based on a general concept, and it is not always necessary to clearly separate the program in an actual program. FIG. 7 shows a software flow related to resolution conversion executed by the printer driver 12c described above. The display driver 12b can be executed in the same manner. However, when priority is given to the processing speed, the display driver 12b may not need to execute the process.

  Step 100 inputs original image data. When the application 12d reads an image from the scanner 11a, performs predetermined image processing, and performs print processing, print data having a predetermined resolution is transferred to the printer driver 12c via the operating system 12a. Applicable. Of course, the image may be read by the scanner 11a, and in any case, this processing corresponds to the original image data acquisition step A1.

  All images are scanned based on the read image data, and pixel interpolation processing is executed in the process. In that sense, in step 105, the initial position of the block of interest is set. In step 110 and subsequent steps, as shown in FIG. 8, the sub-scan is performed by sequentially moving the scanning lines in the vertical direction (height direction) while performing main scanning in the horizontal direction (width direction) of the image. . Since the pixel interpolation process interpolates pixels based on a certain range of pixels, in this embodiment, a 4 × 4 pixel region as shown in FIG. Move this block. The target block for processing at this time is called a target block.

In step 110, when the process proceeds with respect to the target block, first, the chromaticity and luminance of the pixel that is interpolated based on the target block are calculated. Luminance Y is
Y = 3.0R + 0.59G + 0.11B
And the chromaticity is calculated as
r = R / (R + G + B)
b = B / (R + G + B)
Represented as: Here, the attention area is composed of 16 pixels. In this case, the above calculation may be to obtain an average value for all the pixels, or to obtain an average value for the four pixels on the inner circumference side. Alternatively, it may be obtained for one specific pixel in the four pixels on the inner peripheral side. A method for obtaining an average value from a plurality of pixels is appropriate as a guideline for pixels generated by interpolation, but the number of operations increases by the amount for obtaining the average value. On the other hand, even if one pixel is represented, there is an advantage that the amount of calculation can be reduced considering that the pixel to be interpolated is unlikely to greatly deviate. Note that when obtaining a representative value from a plurality of pixels, it is not limited to the average value, and various calculation methods such as using the median as a representative value may be employed.

FIG. 10 shows a sampling result of image data representing human skin. That is, the three data on the left are the values of (R, G, B) of the pixels constituting the skin, and the sum (sum_rgb) of (R + G + B) is shown on the right, and the color based on the above calculation is shown on the right. Degrees r, b and luminance Y are shown. FIG. 11 shows a graph when each pixel is plotted in the rb space. As shown in the figure, even if it is difficult to find unity as RGB data, it is found that there is regularity when plotted on a graph as chromaticity. In other words, human skin may appear dark or bright, but nevertheless, it is distributed linearly as shown in FIG. The linear distribution shown in the figure is
Y> 128
0.33 <r <0.51
| 0.74r + b−0.57 | <0.1
Therefore, if the representative value of the target block as described above is applied to this relational expression, it can be said that the target block belongs to the skin color region. This determination corresponds to the determination of whether or not the skin color region in step 115.

FIG. 12 similarly shows the sampling result of image data representing the blue sky. In this case, considering that the fluctuation range is larger than that of the skin color,
Y> 128
0.17 <r <0.30
| 1.11r + b−0.70 | <0.2
It can be said that the following relational expression holds. Therefore, if it is determined in step 115 that the region is not a skin color region, it is determined in step 120 whether or not this relational expression is established.

  Whether or not the region is a skin color or sky blue region can be determined by steps 110 to 120, but these are examples of images that are preferred if the interpolation result is not so sharp. For example, when a person's face is shown large, the image becomes sharp and a rough feeling is generated, which is not preferable. Also, even if there is a slight color change for the blue sky, it is preferable to blur the change rather than sharpen it and make it look stunning. In consideration of such a favorite element, the image is slightly blurred only in the case of skin color or sky blue while performing the interpolation processing for sharpening the image through the branch processing of step 115 and step 120. Interpolation processing that makes you feel is done.

The former interpolation process employs the M cubic method, and the latter interpolation process employs the bilinear method. Here, these interpolation processes will be described. The simplest of the interpolation processes is an interpolation process of the near list method. As shown in FIG. 14, the distance between the surrounding four grid points Pij, Pi + 1j, Pij + 1, Pi + 1j + 1 and the point Puv to be interpolated is obtained. The data of the nearest grid point is transferred as it is. This can be expressed as a general formula:
Puv = Pij
It becomes. Here, i = [u + 0.5] and j = [v + 0.5]. In addition, [] has shown taking an integer part with a Gauss symbol.

となる。これをＰについて展開すると、

となる。なお、

と置換可能である。 In the near list method, since the edge of the image is maintained as it is, the edge of the jaggy is noticeable as an edge when enlarged. On the other hand, an interpolation process that is suitable for a natural image such as a photograph but has a large calculation processing amount is a cubic method interpolation process. As shown in FIG. 15, the cubic method uses data of a total of 16 lattice points including not only four lattice points surrounding the point Puv to be interpolated but also surrounding lattice points around the point. A general expression using a cubic convolution function is as follows.

It becomes. Expand this for P,

It becomes. In addition,

Can be substituted.

としたものをＭキュービック法と呼ぶことにする。 This cubic method has a feature that it gradually changes from one lattice point to the other lattice point, and the degree of change becomes a so-called cubic function. Since the cubic method can be expressed in 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,

Will be referred to as the M cubic method.

  FIG. 16 shows an interpolation function f (t) in the M cubic method and the cubic method. In the figure, the horizontal axis indicates the position, and the vertical axis indicates the interpolation function. There are lattice points at the positions of t = 0, t = 1, and t = 2, and the interpolation point is at the position of t = 0 to 1. When the cubic method and the M cubic method are compared, the cubic function curve is slightly steeper in the M cubic method, and the entire image becomes sharper.

Next, the bilinear method (bilinear interpolation) interpolation method will be described. As shown in FIG. 16, the cubic method is such that it gradually changes from one lattice point to the other lattice point. The change is a linear function that depends only on the data of the grid points on both sides. That is, as shown in FIG. 14, an area defined by four grid points Pij, Pi + 1j, Pij + 1, and Pi + 1j + 1 surrounding a point Puv to be interpolated is divided into four sections at the interpolation point Puv, and the area ratio is Weight the corner position data. This can be expressed as an expression:
P = {(i + 1) -u} {(j + 1) -v} Pij
+ {(I + 1) -u} {v-j} Pij + 1
+ {U−i} {(j + 1) −v} Pi + 1j
+ {U−i} {v−j} Pi + 1j + 1
It becomes. Note that i = [u] and j = [v].

  The cubic method and the bilinear method differ in whether the change state is a cubic function or a linear function, and the difference when viewed as an image is large. In the case of the bilinear method, since it changes only linearly between two adjacent points (t = 0 to 1), the boundary is smoothed, and the impression of the screen is blurred. That is, unlike the smoothing of the corners, when the boundary is smoothed, the outline that should be originally obtained is lost in computer graphics, and the focus is soft in the photograph.

  Since there is such a difference, pixel interpolation using the M cubic method is performed in step 125 in the region where the image is to be sharpened as described above, and bilinear is performed in step 130 in the region where roughness is not desired. By executing pixel interpolation employing the method, a sharp image is changed for each region, and a preferable image is generated. After interpolating the pixels to be generated in one target block, it is determined in step 135 whether all blocks have been completed. If not, the target block is moved to the next block in step 140, and the processing is completed. In step 145, the corrected image data is output. Of course, this output may simply be transferred to the next process while the data is stored in a predetermined area.

  In steps 105 to 145, interpolation processing is performed to obtain an appropriate sharpness in accordance with the color tone of the image. As a premise, the skin tone color tone and the sky blue color tone are determined by the bilinear method in steps 115 and 120. The interpolation process is associated with the other color tone, and the other color tones are associated with each other so that the M cubic method interpolation process is performed at step 125. Then, since the predetermined interpolation processing to be handled in step 125 and step 130 is executed by this association, it can be said that the execution of the association and interpolation processing constitutes the tone-corresponding pixel interpolation step A2.

  In this example, the determination of the color tone is directly linked to the selection of the interpolation process, but the realization method can be easily achieved by changing the software. For example, it is possible to specify the interpolation process having the corresponding relationship by determining the color tone in step 115 or 120, and to perform the corresponding interpolation process by referring to the specified result in the next step. Will not change. On the other hand, it is clear from FIG. 16 that the sharpness differs between the cubic method and the M cubic method even in the interpolation process using the same cubic function. That is, in the case of pixel interpolation using the cubic convolution interpolation method, the characteristics of the interpolation curve can be appropriately changed depending on how the parameters are given. Therefore, if the shape of the S-curve is changed as shown in FIG. Can be set to smooth or smooth.

  FIG. 18 shows a method of controlling the interpolation processing result by changing the parameters in this way. If it is determined in step 215 that the region is a skin color area, the parameter for skin color is changed to step 220 in step 223. If it is determined that the region is a sky blue region, the parameter for sky blue is set at step 222, and the default parameter is set at step 225 at other times. That is, in step 225, a parameter for executing the M cubic method is set, in step 222 and step 223, a parameter for executing the cubic method is set, and in step 226, pixel interpolation is performed by a third-order convolution interpolation method according to the parameter. Is running.

In this example, the interpolation process is distinguished depending on whether it is a skin color area or a sky blue area, but if the sharpness can be gradually changed according to the parameters, the skin color It is also possible to increase or decrease the sharpness according to the degree of separation from the area or the degree of separation from the sky blue area. In addition, the method that can gradually change the sharpness is not only to give a parameter to one interpolation process, but when performing multiple interpolation processes with different sharpness, It is also possible to change the burden. FIG. 19 specifically shows this, showing the original image P1 on the left side and the final enlarged image P2 on the right side. The enlargement ratio in this case is represented by (P2 / P1), and the interpolation processing is performed by executing two interpolation processing with different sharpness. In this case, when the enlargement ratios α1 and α2 are set for the first stage interpolation process and the enlargement ratios β1 and β2 are set for the second stage interpolation process,
P2 / P1 = α1 × β1 = α2 × β2
Thus, the combination of α1, α2, β1, and β2 can be appropriately changed. Of course, this change in the interpolation magnification burden ratio will affect the sharpness of the image, so the smoother interpolation processing burden ratio should be increased in the case of flesh-colored areas and sky-blue areas. What is necessary is just to make it the burden ratio of the interpolation process which becomes sharp about an area | region large.

  In step 321 to step 323 in FIG. 20, the interpolation magnification of the M cubic method that is the first stage interpolation processing and the interpolation magnification of the bilinear method that is the second stage interpolation processing are determined from such a viewpoint. Referring to the determination result, in step 327, the first-stage interpolation processing is executed by the M cubic method, and in step 328, the remaining second-stage interpolation processing is executed by the bilinear method. In this way, the final interpolation magnification is constant, but the image quality can be adjusted by changing the burden ratio of the process.

  By the way, in this example, the skin color area and sky blue area are always instructed to be smooth. However, although there is a sky blue part, it may be the color of a specific object instead of the blue sky. May be the color of certain other objects rather than human skin. In this case, it can be said that the smooth interpolation process is not flexible. Accordingly, in step 107 shown in FIG. 7, the user may be allowed to select the correspondence using the setting screen shown in FIG. In this setting screen, the user selects whether to prevent skin color roughness by operating the mouse 15b, or to select whether to prevent sky blue roughness. Then, as shown in FIG. 22, before determining whether it is a skin color area in step 115, it is determined whether or not the above-mentioned option is selected in step 114, and it is selected to prevent skin color roughness. Sometimes the determination of step 115 is performed. Similarly, before determining whether it is a sky blue area in step 120, it is determined whether or not the above option is selected in step 117, and if it is selected so as to prevent sky blue roughness, the step is performed. 120 determinations are performed.

  Needless to say, it is possible to make it possible to select the prevention of roughness for other color tones, and it is also possible to select the degree of smoothness or sharpness. In this way, when interpolating image data representing a color natural image with pixels in a dot matrix, a representative value of chromaticity is acquired for a target block which is an area to be referred to in order to interpolate and generate pixels (steps). 110) If it is determined from the chromaticity that the region is a skin color region or a sky blue region (steps 115 and 120), pixel interpolation is performed by the bilinear method (step 130), and the region is smoothly enlarged. Since the pixel interpolation is performed by the cubic method (step 125), it is enlarged sharply, and it is possible to prevent the human skin and the blue sky from being rough while maintaining the sharpness of the entire image.

It is a schematic flowchart of the image data interpolation method concerning one Embodiment of this invention. It is a block diagram of the computer system as an image data interpolation apparatus which performs the image data interpolation method. It is a schematic block diagram which shows the other application example of the image data interpolation method of this invention. It is a schematic block diagram which shows the other application example of the image data interpolation method of this invention. It is a schematic block diagram which shows the other application example of the image data interpolation method of this invention. It is a schematic block diagram which shows the other application example of the image data interpolation method of this invention. It is a flowchart of the image data interpolation method performed with a computer system. It is a figure which shows the condition which moves a pixel of interest with a dot matrix-like image. It is a figure which shows the pixel area | region used as an attention block. It is a figure which shows the image data, chromaticity, and brightness | luminance of a skin color pixel. It is a figure which shows the pixel of skin color with the graph of chromaticity. It is a figure which shows the image data, chromaticity, and brightness | luminance of a sky blue pixel. It is a figure which shows a sky blue pixel with the graph of chromaticity. It is a figure which shows the pixel produced | generated by basic pixel interpolation. It is a figure which shows the pixel interpolation method of a cubic method. It is a figure which shows the change of the interpolation function utilized by pixel interpolation. It is a figure which shows the method of adjusting sharpness with a cubic interpolation function. It is a flowchart which adjusts the parameter of a cubic interpolation function and adjusts sharpness. It is a figure which shows the method of adjusting the sharpness by changing the burden ratio of interpolation magnification, utilizing two interpolation methods. It is a flowchart which adjusts the sharpness by changing the burden ratio of the interpolation magnification. It is a figure which shows the option screen of the interpolation process corresponding to a color tone. It is a part of flowchart which respond | corresponds to the option of the interpolation process corresponding to a color tone.

Explanation of symbols

DESCRIPTION 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 main body 12a ... Operating system 12b ... Display driver 12b ... Driver 12c ... Printer driver 12d ... Application 13a ... Flexible 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 copying machine

Claims (12)

  Image data acquisition process that acquires image data that represents natural color images with dot-matrix pixels, and color tone and interpolation processing that achieves an appropriate sharpness depending on the color tone, assuming multiple interpolation processes that affect sharpness And a tone-corresponding pixel interpolation step for executing a predetermined interpolation process selected from the correspondence relationship based on the tone of the pixel to be interpolated, and an image data output step for outputting the interpolated image data. An image data interpolation method characterized by being executed.   2. The image data interpolation method according to claim 1, wherein, in the color-tone-corresponding pixel interpolation step, the correspondence between the color tone and the interpolation processing so that an appropriate sharpness is obtained according to the color tone on the premise of a plurality of interpolation processing affecting the sharpness. An interpolation process selection reference acquisition process for acquiring a relationship, and a pixel interpolation process for acquiring and executing a corresponding interpolation process in the interpolation process selection reference acquisition process based on the color tone of the pixel to be interpolated Image data interpolation method.   3. The image data interpolation method according to claim 1 or 2, wherein in the tone-corresponding pixel interpolation step, a plurality of interpolation processes that affect sharpness are executed in an overlapping manner, and interpolation is borne by each process. An image data interpolation method characterized by adjusting a magnification to obtain a desired sharpness.   The image data interpolation method according to any one of claims 1 to 3, wherein in the color-corresponding pixel interpolation step, an interpolation process having different degrees of influence on the sharpness is executed according to parameters, and the parameters are adjusted to be desired. An image data interpolation method characterized in that the image data is sharpened.   The image data interpolation method according to any one of claims 1 to 4, wherein in the color-tone-corresponding pixel interpolation step, an interpolation process is performed to make the image feel smooth with respect to a color region in which a rough feeling is not preferred. An image data interpolation method characterized by being executed.   6. The image data interpolation method according to claim 5, wherein in the tone-corresponding pixel interpolation step, the image of the skin color region is made smooth.   7. The image data interpolation method according to claim 5, wherein, in the color tone-corresponding pixel interpolation step, an image of a blue sky color region is made smooth.   8. The image data interpolation method according to claim 1, wherein in the color-tone-corresponding pixel interpolation step, an interpolation process is determined based on an average color tone of area pixels necessary to generate the interpolation pixel. An image data interpolation method characterized by:   In the image data interpolation method according to any one of claims 1 to 7, in the tone-corresponding pixel interpolation step, an interpolation process is performed while scanning the image data by determining a pixel of interest, and the pixel of interest An image data interpolation method characterized by determining an interpolation process based on a color tone.   The image data interpolation method according to any one of claims 1 to 9, wherein in the tone-corresponding pixel interpolation step, it is possible to specify a correspondence between a color region and an interpolation process to be executed. Data interpolation method.   Image data acquisition means for acquiring image data expressing natural color images with pixels in a dot matrix, and color tone and interpolation processing that makes the color tone moderately sharp based on multiple interpolation processes that affect sharpness Interpolated with the interpolation processing selection reference acquisition means for acquiring the correspondence relationship with the pixel interpolation means for acquiring and executing the corresponding interpolation processing in the interpolation processing selection reference acquisition step based on the color tone of the pixel to be interpolated An image data interpolation apparatus comprising: image data output means for outputting image data.   A medium on which an image data interpolation program for performing pixel interpolation by a computer based on image data representing a color natural image by dot matrix pixels is recorded, and an image data acquisition step for acquiring the image data, and sharpness Assuming a plurality of interpolation processes that affect the color tone, a color tone that is moderately sharp depending on the color tone is associated with the interpolation process, and a predetermined interpolation process that is selected from this correspondence based on the color tone of the pixel to be interpolated A medium on which an image data interpolation program is recorded, comprising: a color-tone-corresponding pixel interpolation step for executing the image data; and an image data output step for outputting the interpolated image data.

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