JPH11341264A - Mosaic image generation method and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide visually natural mosaic images by using luminance information or a coordinate in an uniform color matching space as an evaluation reference at the time of selecting an optimum image. SOLUTION: An image 701 to be the base of image constitution is divided into M×N pieces of tiles, the respective average luminance values of R, G and B are calculated and obtained for the respective tiles and the smallest one of the square sums of the errors of RGB tristimulus values is selected as the image optimum for a specified tile, variable-power-processed to an appropriate size and stuck. At the time, for the P sheets of material images 703, average luminance is obtained in advance and compared with the average luminance of the tile unit of a first image 701 and the closest one is selected. In such a manner, by calculating a luminance average based on the luminance value of color-mixing the RGB tristimulus values by an appropriate ratio, the mosaic of excellent color matching is generated for an object tile image. Also, by selecting the one of the minimum square error of the difference of the average stimulus value of the material image, a halftone part is appropriately expressed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to a mosaic image generating method and a recording medium.

[0002]

2. Description of the Related Art Conventional mosaics are "stones of various colors.
It is widely known as "a technique that combines small pieces of glass, marble, etc., and fits them into floors, walls, etc., and uses them as a pattern, or a technique based on it" (Sanseido Modern Japanese Dictionary). Using this technique, it is possible to construct a design or one photographic image by combining a number of photographic images.

[0003]

However, when an image imitating a certain image is formed using this mosaic technique, there has not been a sufficient study on the formation of an image that reflects the image well. It is an object of the present invention to provide a visually natural mosaic image generation method and a computer-readable recording medium made in view of the above points.

[0004]

In order to solve the above-mentioned problems, the present invention is characterized in that luminance information or coordinates in a uniform color space are used as evaluation criteria when selecting an optimum image. That is, the mosaic image generation method of managing a plurality of material images, combining a plurality of the plurality of material images, and forming a second image that imitates the first image of a design or a photographic image, A dividing step of dividing the image into two-dimensional tile areas; an average luminance value calculating step for calculating an average luminance value by color stimulation of each tile area divided in the dividing step; and an averaging of the plurality of material images A material image average brightness value calculating step of calculating a brightness value, an average brightness value calculated by the in-region average brightness value calculating step, and a material image average brightness value calculated by the material image average brightness value calculating step. And an optimal image selecting step of selecting an optimal image from the plurality of material images based on

The color stimulus is a luminance value calculated based on the superposition of red, green and blue RGB tristimulus values of each pixel. Further, the optimal image selecting step is to select, as the optimal material image, an image in which the sum of the square of the difference between the average luminance value in the area of each tile region and the average luminance value of the material image is the minimum. And

Further, a mosaic image generating method for managing a plurality of material images and combining a plurality of the plurality of material images to form a second image imitating a first image of a design or a photographic image, A dividing step of dividing the first image into two-dimensional tile areas; an in-area parameter calculating step of calculating L * a * b * spatial parameters by color stimulation of each tile area divided in the dividing step; A material image parameter calculating step of calculating L * a * b * spatial parameters of a plurality of material images; an L * a * b * spatial parameter calculated by the in-region parameter calculating step; and a material image parameter calculating step. L of the calculated material image
an optimal image selecting step of selecting an optimal image from the plurality of material images based on the * a * b * spatial parameters.

The L * a * b * spatial parameters include L * indicating a lightness index, and a * and b * indicating perceived chromaticity.
And a parameter that describes an equivalent perceived color space. Further, the optimal image selecting step is an optimal image selecting step in which a sum of squares of a difference between an L * a * b * spatial parameter of each tile area and an L * a * b * spatial parameter of the material image is minimized. It is characterized in that it is selected as a natural material image.

[0008] The image processing apparatus may further include an image attribute input step for inputting an attribute of whether the first image is monochrome or color,
According to the input attribute, a mosaic image is formed based on the average luminance value for a black-and-white image and based on the L * a * b * spatial parameters for a color image. Further, a mosaic image generation method for managing a plurality of material images, combining a plurality of the plurality of material images, and forming a second image imitating a first image by a design or a photographic image, the A dividing step of dividing the image into two-dimensional tile areas; an area average luminance value calculating step of calculating an average luminance value of red, green, and blue tristimulus value components of each tile area divided in the dividing step; A material image average luminance value calculating step of calculating an average luminance value of the plurality of material images, an average luminance value calculated in the area average luminance value calculating step, and a material image average luminance value calculating step. And selecting an optimum image from the plurality of material images based on the average luminance value of the material images thus obtained.

The computer-readable recording medium may further include a dividing step of dividing the image into two-dimensional tile areas;
An area average luminance value calculation step of calculating an average luminance value by color stimulation of each tile area divided in the division step, and a material image average luminance value calculation step of calculating an average luminance value of the plurality of material images, An optimal image is selected from the plurality of material images based on the average luminance value calculated in the area average luminance value calculation step and the average luminance value of the material image calculated in the material image average luminance value calculation step. And a program for executing the optimum image selecting step.

The computer-readable recording medium further comprises a dividing step of dividing the image into two-dimensional tile areas;
An in-region parameter calculating step of calculating an L * a * b * spatial parameter by a color stimulus of each tile area divided in the dividing step, and a material for calculating an L * a * b * spatial parameter of the plurality of material images L * a * b calculated in the image parameter calculation step and the in-area parameter calculation step
Performing an optimal image selecting step of selecting an optimal image from the plurality of material images based on the spatial parameters and the L * a * b * spatial parameters of the material images calculated in the material image parameter calculating step. A program for causing the program to be recorded.

[0011]

[Embodiment 1] A mosaic image generating method according to the present embodiment will be described below with reference to the drawings. FIG. 7 illustrates the relationship between a plurality of types of images used in the mosaic method. In FIG. 7, a first image 701
Is a design or an image which is a base when an image is constructed using the mosaic method. The second image 702 is an image formed using a plurality of small images by a mosaic method. The material image 703 is a material image used to compose the second image 702. The number P of the material images is generally large enough to prepare the types of colors and textures necessary to compose the second image. Here, the size of the P images is assumed to be the same as the size of the tile for the sake of explanation, but this does not necessarily have to match the size of the tile, and it is not necessary that all the P images have the same size. In such a case, it is necessary to convert the size of the material image into a tile size when pasting the material image on the corresponding tile portion.

Next, a method for constructing a mosaic image will be described with reference to FIG. In FIG. 8, in step S801, the first image is divided into M × N tiles. As a result, M × N rectangular tiles TL (0,0), TL (0,1), TL
(0,2),..., TL (4,2), TL (4,3) are generated. FIG. 9 illustrates a first image divided into M × N = 4 × 5 rectangles.

In FIG. 9, X and Y are the numbers of pixels of the first image 701 in the horizontal and vertical directions, respectively. p and q represent the first image as M × N rectangular tiles TL (0,0), T
L (1,0), TL (2,0), ... TL (M, N) ...,
This is the number of pixels in the horizontal and vertical directions of each tile when divided into TL (2, 4) and TL (3, 4). Therefore,
The relationship of X = p × M and Y = q × N is established.

FIG. 10 shows the three primary colors of each tile. Each tile is composed of p × q pixels and is separated into three primary colors, red (R), green (G), and blue (B). Assume that the separated RGB components of each pixel are Ri, Gi, and Bi. Step S802 in FIG. 8 is the M divided in step S801.
For each of the × N tiles, the average luminance of each of R, G, and B is calculated according to the following equation. Subscript d of R, G, B on the left side is dest
It means ination.

Rd-av = {1 / (p · q)} · {R i (1) Gd-av = {1 / (p · q)} · ΣG i (2) Bd-av = {1 / (p Q)}} Bi (3) In step S803, the average luminance of R, G, and B is calculated for each of the P material images according to the following equation. The suffix s of R, G, B on the left side means source.

Rs-av = {1 / (p · q)} · {R i (4) Gs-av = {1 / (p · q)} · ΣG i (5) Bs-av = {1 / (p) · Q)} · ΣBi (6) Step S804 is a counter X_Pos (0 ≦ X_Pos ≦ M−1) indicating the position of the tile being processed, Y-Pos
(0 ≦ Y−Pos ≦ N−1) is initialized to 0 as an initial value. Here, (X-Pos, Y-Pos) =
(0,0) indicates the upper left tile of the first image.

Step S805 is a position counter X_Po.
A process is performed to select the most appropriate (optimal) image from the material image for the tile specified by s, Y_Pos. The selection method calculates the sum of squares (の E) of the errors of the RGB tristimulus values (Equation (7)), and selects the one with the smallest value.
The evaluation formula is shown below. ΔE = (Rs−av−Rd−av) ² + (Gs−av−Gd−av) ² + (Bs−av−Bd−av) ² (7) When pasting the selected image to the tile portion If the sizes do not match, the magnification is changed to an appropriate size.

This process is sequentially performed in the horizontal and vertical directions, and is continued until the process is performed on all tiles (S806, S807). According to the first embodiment, when extracting an optimal image for each tile, each component of the RGB tristimulus values is extracted as an independent parameter, and a material that minimizes a luminance error for each component stimulus value is pasted. To form a mosaic image.

However, even if an optimal image having a wavelength near the luminance is selected for each stimulus value, the superimposition does not always result in an excellent configuration as a tile image. Therefore, depending on the color, the color difference between the tile portion of the first image and the image selected from the material image is varied, or the halftone portion is roughly expressed, and it is difficult to reproduce human skin and the like finely. . This problem is solved by the second or third embodiment described below.

[Embodiment 2] Hereinafter, a mosaic image generating method according to Embodiment 2 will be described with reference to the drawings. FIG.
Is a hardware configuration diagram for implementing the present embodiment. 1
01 is a CPU, 102 is a memory, 103 is a hard disk, and 104 is a keyboard and a mouse. 105 is a monitor, 106 is a network interface, 107
Denotes a CD-ROM, 108 denotes a scanner, 109 denotes a printer, 110 denotes a database connected via a network, and 111 denotes a bus.

The CPU 101 controls the above configuration,
The memory 102 stores an execution module of software for realizing the present embodiment or image data in the hard disk 10.
Load from 3. The keyboard and mouse select and acquire an image to be processed from a hard disk or a database via a network via a user interface displayed on a monitor.

The monitor 105 displays a first image, a second image, and a material image. Further, the image may be an image previously recorded on a CD-ROM. In this case, the data is read from the CD-ROM drive 107. The image formats recorded on the CD-ROM 107 include PhotoCD, FlashPix, JFI
F and the like are widely known.

Further, the image can be read by the scanner 108 as needed. The printer 109 prints an image on recording paper. A bus 111 exchanges data and control signals. FIG. 2 is a first flowchart showing the processing in the present embodiment. In this flowchart, a process of calculating the average luminance of P material images in advance is performed.

In step S201, a counter variable Count for controlling the number of times of processing is initialized. In step S202, a material image (ImageID = i) having an image identification number equal to the counter value (Count = i) is selected and acquired. However, although the embodiment is described here assuming that the Count and the image identification number are the same, the present invention is not limited to this example.
It does not matter if t corresponds to one-to-one. This material image is not only stored on the hard disk of FIG.
It may be read from. To calculate the average luminance of each material image (step S203), specifically, the flowchart of FIG. 3 is followed.

In step S301, a variable sum for holding a total value of luminance is initialized. Step S302, S30
3, variables y and y indicating the vertical and horizontal pixel positions of the image.
Initialize x respectively. In S304, the luminance Y (x, y) of each pixel is calculated from the RGB information of the pixel located at (x, y) according to the following equation (8). Y (x, y) = 0.3R + 0.59G + 0.11B (8) The luminance given by the expression (8) realizes the color matching to the light stimulus in which the R, G, and B wavelengths are constant by additive color mixture. Things.

In step S305, a cumulative addition value of the luminance value is obtained. In step S306, the counter indicating the pixel position in the x direction is incremented by 1. If it is determined in step S307 that the number of pixels in the horizontal direction of the image does not exceed xsize, step S3 is performed again.
Returning to step 04, the brightness is calculated, and the calculation results are added.
This process is repeated until the number of divided horizontal pixels is reached.

In step S307, the number x of pixels in the horizontal direction
If the size has reached (S307-Yes), the vertical pixel position counter y is incremented by one in step S308, and the process proceeds to the next pixel row. If the number of pixels in the vertical direction ysize of the image does not exceed ysize in step S309, S
Returning to step 303, the same processing is repeated. Step S30
9, when the number of pixels in the vertical direction of the image becomes ysize (S
309 Yes), the cumulative sum of the luminance values sum is divided by the cumulative number of pixels xsize × ysize to obtain an average luminance value (step S310). The average luminance value for each material image unit is calculated in the same manner.

After obtaining the average value of the luminance of the material image, the process returns to step S204. In S204, the obtained average luminance value is recorded in association with the image identification number. Thereby, the average luminance value of the material image and the identification number correspond one-to-one.
Step S205 is a control variable for performing repetitive control.
The value of the count is incremented. If the number of divisions (P) of the material image has not been reached in step S206 (S20)
-No), returning to step S202 and repeating the same processing. If the control variable count has reached the number of divisions P of the material image in step S206 (S206-Yes), the process ends. With the above processing, the average luminance value for each material image has been calculated.

Next, the processing of the mosaic image generating method according to this embodiment will be described with reference to FIG. Step S401
To obtain an image to be a mosaic image from the first image. The first image may be one stored in the hard disk 103, one stored in the CD-ROM 107, one stored in the database 110 via a network, , Scanner 108, or an image obtained by a digital camera (not shown).

At the time of selection, a plurality of candidate images are displayed, and an instruction is selected using the keyboard or the mouse 104. The displayed image is reduced in size depending on the size of the stored image. The format of the image is PhotoCD, FlashPix, JPEG, or the like. After selecting and acquiring the first image, in step S402, the first image is divided into M × N regions. The result is M × N
Rectangular tiles TL (0,0), TL (0,1), TL
(0,2), ..., TL (4,2), TL (4,3) ...,
TL (M, N) is generated. FIG. 9 illustrates a first image divided into 4 × 5 rectangles.

In FIG. 9, X and Y are the numbers of pixels of the first image 701 in the horizontal and vertical directions, respectively. p and q represent the first image as M × N rectangular tiles TL (0,0), T
L (1,0), TL (2,0), ... TL (M, N) ...,
This is the number of pixels in the horizontal and vertical directions of each tile when divided into TL (2, 4) and TL (3, 4). Therefore, X =
The following relationship holds: p × M, Y = q × N. This tile image is stored in the memory 102.

Next, in steps S403 and S404, the position variable X-Pos,
Initialize Y-Pos. In step S405, the average luminance Y-TL of the tile at the position indicated by the position variables X-Pos and Y-Pos is calculated. The method of calculating the average luminance is shown in FIG.
In the flowchart shown in, the variables xsize, y
It can be calculated by setting size to p and q, respectively.

In step S406, the calculated average luminance Y-TL in tile units is compared with the average luminance of the P material images. A detailed flowchart of the average luminance comparison method will be described with reference to FIG. In FIG. 5, in step S501, a variable Min_ID indicating an image having the smallest difference in average luminance is initialized. In S502, an initial value Y-Diff_min of the difference in average luminance is initialized, and in S503, a variable i for controlling the number of repetitions is initialized.

In step S504, the i-th image (the image indicated by the counter i) is extracted. In step S505, the average luminance Y of the material image indicated by the identification number i obtained in S301 to S310 in FIG. Calculate the square Y-Diff of the difference from the average luminance (Y-TL). Although the square of the difference is used here, the mean square may be used.

In step S506, Y-Diff and Y-Diff
Diff_min is compared, and Y-Diff is compared with Y-Dif.
Only when it is smaller than f_min, Y_Di in S507
Update ff_min and MinID. Step S50
In step S508, the ID indicating the image to be compared is incremented (S508), and a comparison is made between the magnitudes of all P material images (S509). By this processing, it is possible to specify the material image having the minimum average luminance value error with respect to the tile of interest.

Returning to FIG. 4, the image ID obtained in step S407 is associated as an image of the corresponding tile. In step S408, the horizontal pointer indicating the tile position (for example, a control variable for identifying and displaying the i-th pixel in FIG. 9; 0 ≦ i ≦ M in TL (i, y)) is incremented, and reaches M in S409. If not, the process returns to S405 and the same processing is repeated. Also, in step S409, M
If the processing of the tiles has been completed, step S4
At 10, the vertical pointer Y-Pos is incremented,
If the number has not reached N in S411, the process returns to S404,
The same processing is repeated. If the number has reached N in S411, the process ends (S411). By the above processing,
This means that the material image having the minimum average luminance value error has been specified for all the divided tiles.

[Effect of Second Embodiment] By calculating the average of the luminance values based on the luminance values obtained by mixing the RGB stimulus values at an appropriate ratio, it is possible to generate a mosaic with excellent color matching for the target tile image. Make it possible. By selecting the average color stimulus value in the area of each tile area and the one that minimizes the square error of the difference between the average color stimulus values of the material image, it is possible to appropriately express the halftone part, When the mosaic image is printed in black and white or in sepia, the luminance change can be faithfully reproduced.

[Embodiment 3] Uniform perceived color space (L * a * b)
It is also possible to associate the divided tiles with the material images using the parameters described in *). In other words, the process of step S304 in which the average luminance value is calculated in the first embodiment is replaced and calculated according to the following calculation formula. This calculation formula is based on the L * a * b * display system. Here, L * is a lightness index, and a * and b * are perceived chromaticities. Tristimulus values X, Y, Z of XYZ display system of object color
Is given by the following equations.

(L * when Y / Y0> 0.008856) L * = 116 (Y / Y0) ^1/3 −16 (9a) (L * when Y / Y0 ≦ 0.008856) L * = 903.3 (Y / Y0) (9b) a * = 500 ((X / X0) ^1/3 − (Y / Y0) ^1/3 ) (10) b * = 200 ((Y / Y0 ) ^1/3 − (Z / Z0) ^1/3 ) (11) X0, Y0, Z0 are standard light sources used for illumination or tristimulus values of standard light. X, Y, and Z are (1
2) It can be obtained by the expansion formula of the matrix of the formula. However, the coefficient aij is a parameter that varies depending on how the image input device handles RGB.

X = a11R + a12G + a13B (12) Y = a21R + a22G + a23B Z = a31R + a32G + a33B According to the above equations (9a), (9b), (10) to (12), the lightness index L * of the material image and the tile image, and the perceived chromaticity a
* And b * are calculated. In this process, the calculation process of the luminance average is L in the flowchart of FIG. 3 described in the first embodiment.
It can be obtained by replacing the process of calculating *, a *, b *. The correspondence between the tile image and the material image is
It is determined based on the color difference ΔE-Lab in equation (13).

ΔE−Lab = {(ΔL *) ² + (Δa *) ² + (Δb *) ² } ^1/2 (13) That is, in the processing of the flowcharts of FIGS. The process of selecting a material image in which the average error of the average luminance value is the smallest is defined as a material image in which the error (ΔL *, Δa *, Δb *) of each parameter of L *, a *, b * is minimized. By replacing the process with pasting on the tile,
A mosaic image configuration based on the L * a * b * display system becomes possible.

[Effects of Third Embodiment] L * a * is used for comparing and associating the divided tile image of the first image with the material image.
By using b * color system parameters (brightness index and perceived chromaticity), it is possible to specify a material image based on uniform representation of perceived color, thereby forming a mosaic image more natural for human eyes. Becomes possible.

[Embodiment 4] A third embodiment will be described with reference to FIG. In FIG. 6, a step S601 outputs a message to the monitor 105 asking whether the printing object is color or black and white. In step S602, color or black and white is selected by the keyboard or the mouse 104. In step S603, according to the selection result, the process of S604 is performed if the image is color, and the process of S605 is performed if the image is monochrome. The processing in step S604 is a method using the display system based on the L * a * b * space as the evaluation criterion shown in the second embodiment, and S605 is the first embodiment.
Is a method using the average luminance value shown in FIG.

[Effect of Embodiment 4] The optimum mosaic image according to the attribute of the output image is obtained by inquiring in advance the attribute (color or black and white) of the output image and selectively changing the processing method according to the result. Is possible.

[0045]

[Other Embodiments] The present invention is applicable to a system including a plurality of image forming apparatuses (for example, a host computer, an interface apparatus, a reader, a printer, etc.), and is not limited to a single apparatus (for example, an apparatus). , A copying machine, a facsimile machine, etc.).

Another object of the present invention is to provide a storage medium storing a program code of software for realizing the functions of the above-described embodiments to a system or apparatus, and to provide a computer (or CPU) of the system or apparatus.
And MPU) read and execute the program code stored in the storage medium.

In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiment, and the storage medium storing the program code constitutes the present invention. Examples of a storage medium for supplying the program code include a floppy disk, hard disk, optical disk, magneto-optical disk, and C
A D-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, and the like can be used.

When the computer executes the readout program code, not only the functions of the above-described embodiment are realized, but also an OS (Operating System) running on the computer based on the instruction of the program code. ) May perform some or all of the actual processing, and the processing may realize the functions of the above-described embodiments.

Further, after the program code read from the storage medium is written into a memory provided on a function expansion board inserted into the computer or a function expansion unit connected to the computer, based on the instructions of the program code, It goes without saying that the CPU included in the function expansion board or the function expansion unit performs part or all of the actual processing, and the processing realizes the functions of the above-described embodiments.

When the present invention is applied to the storage medium, the storage medium stores program codes corresponding to the above-described flowcharts. Each module shown will be stored in a storage medium. That is, at least “division module 1101”, “intra-region average luminance value calculation module 1102”, “material image average luminance value calculation module 1103”, “optimum image selection module 110”
4), the program code of each module of “area parameter calculation module 1105”, “material image parameter calculation module 1106”, and “image attribute input module 1107” may be stored in the storage medium.

[0051]

The average of the luminance values is calculated based on the luminance values obtained by mixing the RGB stimulus values at an appropriate ratio, thereby making it possible to generate a mosaic with excellent color matching for the target tile image. By selecting the average color stimulus value in the area of each tile area and the one that minimizes the square error of the difference between the average color stimulus values of the material image, it is possible to appropriately express the halftone part, When the mosaic image is printed in black and white or in sepia, the luminance change can be faithfully reproduced.

By using the L * a * b * color system parameters (brightness index, perceived chromaticity) for comparing and associating the divided tile image of the first image with the material image, an even The material image can be specified based on the expression of the perceived color, and a mosaic image more natural for human eyes can be formed. By inquiring in advance the attributes (color or black and white) of the output image and selectively changing the processing method according to the result, it is possible to configure an optimal mosaic image according to the attributes of the output image.

[Brief description of the drawings]

FIG. 1 is a diagram showing a hardware configuration according to an embodiment of the present invention.

FIG. 2 is a flowchart of a process of calculating a luminance average from a plurality of material images.

FIG. 3 is a flowchart of a process of calculating a luminance average from one material image.

FIG. 4 is a flowchart of a process for forming a mosaic image when a first image is provided.

FIG. 5 is a flowchart of a process of selecting an optimum image by associating a divided tile image with a material image.

FIG. 6 is a flowchart illustrating a fourth embodiment.

FIG. 7 is a diagram illustrating a configuration concept of a mosaic image.

FIG. 8 is a flowchart for forming a mosaic image.

FIG. 9 is a diagram in which a first image is divided into M × N tile areas.

FIG. 10 is a diagram showing correspondence between each pixel and RGB.

FIG. 11 is a diagram showing a memory map of a recording medium.

[Explanation of symbols]

 101 CPU 102 Memory 103 Hard Disk 104 Input Device (Keyboard, Mouse) 105 Monitor 106 Network Interface 107 CD-ROM 108 Scanner 109 Laser Printer

Claims (10)

[Claims] 1. A mosaic image generating method for managing a plurality of material images and combining the plurality of material images to form a second image imitating a first image of a design or a photographic image. A dividing step of dividing the first image into two-dimensional tile areas; an area average luminance value calculating step of calculating an average luminance value by a color stimulus of each tile area divided in the dividing step; A material image average brightness value calculating step of calculating an average brightness value of the material image of the source image; an average brightness value calculated by the in-region average brightness value calculating step; and a material image calculated by the material image average brightness value calculating step. A mosaic image generation method, comprising: selecting an optimum image from the plurality of material images based on the average luminance value of the mosaic image. 2. The color stimulus is a red, green, and blue R of each pixel.
2. The mosaic image generating method according to claim 1, wherein the luminance value is calculated based on the superposition of the GB tristimulus values. 3. The optimum image selecting step selects an image in which the sum of squares of the difference between the average luminance value in each tile area and the average luminance value of the material image is minimum as the optimum material image. 2. The mosaic image generating method according to claim 1, wherein: 4. A mosaic image generating method for managing a plurality of material images and combining the plurality of material images to form a second image imitating a first image of a design or a photographic image, A dividing step of dividing the first image into two-dimensional tile areas; and an in-area parameter calculating step of calculating an L * a * b * spatial parameter by a color stimulus of each tile area divided in the dividing step. A material image parameter calculating step of calculating L * a * b * spatial parameters of the plurality of material images; and L * calculated by the in-region parameter calculating step.
an a * b * space parameter and an L * a * b * space parameter of the material image calculated in the material image parameter calculating step, and an optimum image selecting step of selecting an optimum image from the plurality of material images. A mosaic image generation method, comprising: 5. The L * a * b * space parameters are L * indicating a lightness index, and parameters indicating a * and b * indicating a perceived chromaticity, and describe an equivalent perceived color space. 5. The mosaic image generation method according to claim 4, wherein the method is a parameter. 6. The optimal image selecting step includes: an L * a * b * spatial parameter of each tile region; and an L * of the material image.
5. The mosaic image generation method according to claim 4, wherein the one that minimizes the sum of the square of the difference from the a * b * spatial parameter is selected as the optimal material image. 7. An image attribute inputting step for inputting an attribute of whether the first image is black and white or color, and in the case of a black and white image, a color based on the average luminance value according to the input attribute. 5. The mosaic image generation method according to claim 1, wherein a mosaic image is formed based on the L * a * b * spatial parameters in the case of an image. 8. A mosaic image generating method for managing a plurality of material images and combining the plurality of material images to form a second image imitating a first image of a design or a photographic image, A dividing step of dividing the first image into two-dimensional tile areas; and an average in a region for calculating an average luminance value of red, green, and blue tristimulus value components of each tile area divided in the dividing step. A brightness value calculating step, a material image average brightness value calculating step of calculating an average brightness value of the plurality of material images, an average brightness value calculated by the in-region average brightness value calculating step, and the material image average brightness value A mosaic image generating method, comprising: selecting an optimum image from the plurality of material images based on the average luminance value of the material images calculated in the calculating step. 9. A dividing step of dividing an image into two-dimensional tile areas; an area average luminance value calculating step of calculating an average luminance value of each tile area divided in the dividing step by a color stimulus; A material image average brightness value calculating step of calculating an average brightness value of the material image of the source image; an average brightness value calculated by the in-region average brightness value calculating step; and a material image calculated by the material image average brightness value calculating step. A computer-readable recording medium having recorded thereon a program for executing an optimum image selecting step of selecting an optimum image from the plurality of material images based on the average luminance value of the plurality of material images. 10. A dividing step of dividing an image into two-dimensional tile areas, and an in-area parameter calculating step of calculating L * a * b * spatial parameters by color stimulation of each tile area divided in the dividing step. A material image parameter calculating step of calculating L * a * b * spatial parameters of the plurality of material images; and L * calculated by the in-region parameter calculating step.
an a * b * space parameter and an L * a * b * space parameter of the material image calculated in the material image parameter calculating step, and an optimum image selecting step of selecting an optimum image from the plurality of material images. A computer-readable recording medium having recorded thereon a program for executing the program.