JPH0296879A - Picture processor - Google Patents

Abstract

PURPOSE:To obtain a picture in which color is used properly so as to suit to the purpose of processing by converting original picture data stored in a first storage means into color data stored in a second storage means. CONSTITUTION:The first storage means 16 to 18 store respectively the original picture data of the three primary colors of R (red), G (green), B (blue) read in through a picture data I/O 27, and besides, the second storage means 2 stores respectively plural pieces of color data determined beforehand according to the kinds of the processing. Then, in a color converting means 8, the original picture data stored in the first storage means 16 to 18 is converted into the color data stored in the second storage means 2 according to the selected kind of the processing. Thus, the picture in which the color is used properly so as to suit to the purpose of the processing can be obtained.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an image processing device, and for example, to an image processing device that converts original image data into aesthetic image data with a painterly touch.

[Prior Art] Conventionally, mosaic processing is an example of creating an aesthetic image by applying image processing technology. This can be done, for example, in the X direction and in the Y direction.
Image mosaic processing is performed using blocks of 5 pixels in each direction, totaling 25 pixels. Specifically, if the original image data at address (x, y) is a (x, y), then mosaic processing is performed. The subsequent image data a (x, y) is (
1) Determined by the formula.

a' (5m-i, 5n-j) = a (5m-3, 5n-3) ... (1) Here, i, j = pixel number (1, 2, 3, 4, 5) m, n block number (1, 2, 3,...), that is, in the above equation (1), the center pixel data a (5m-3, 5n
-3) is the representative value, and all pixel data B (5m-i, 5n-j) in the block is replaced with the representative value and subjected to mosaic processing. Note that this representative value is not limited to the center pixel data, but may be represented by any pixel data within the block, and an average value within the block may be used.

Furthermore, as an application of mosaic processing to pictorial expression, there are Japanese Patent Application Laid-open Nos. 179059/1982, 179060/1982, and 179061/1982. These generate the mosaic pattern at random positions using a random function, or change the size of the mosaic pattern according to the contrast and spatial frequency characteristics of the original image data.

[Problem to be Solved by the Invention] However, in the above conventional example, the color of the generated mosaic pattern is the same color extracted from a part of the original image, or the average value of several pixels, so the processing is difficult. The resulting image's color tone was dependent on the original image, and was often severely desaturated when compared to an actual painting.

The present invention was made to solve the above problems, and
An object of the present invention is to provide an image processing device that converts original image data into color data according to processing.

[Means for Solving the Problem] In order to achieve the above object, the image processing device of the present invention has the following features:
It has the following configuration. That is, an image processing apparatus that performs the processing selected by the selection means, a selection means for selecting the type of image processing, a first storage means for storing original image data, and a first storage means for storing the original image data, and a first storage means for storing the original image data, and a first storage means for storing original image data, a second storage means for respectively storing a plurality of predetermined color data; and a conversion means for converting the original image data stored in the first storage means into color data stored in the second storage means. Equipped with

[Operation] The above configuration operates to convert the original image data stored in the first storage means into color data stored in the second storage means according to the selected type of processing.

[Embodiment] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

[Description of the Apparatus (Fig. 1)] Fig. 1 is a block configuration diagram of an image processing apparatus according to an embodiment of the present invention. In Fig. 0, 1 is a control processor unit (CPU); A CPU memory 2 performs main control, and stores, for example, the painting processing program shown in FIG. 2, which is executed by the CPU 1, and parameters necessary for the processing in a ROM or RAM (not shown). 3 is an I10 interface of the CPU 1, to which an unillustrated input device such as a keyboard and a mouse is connected. 4 is a CPU bus, which consists of an address bus, a data bus, and a control bus for the CPU 1.

Reference numeral 27 denotes image data I10, and by connecting an image reading device or an image output device (not shown) to this, image data before and after painting processing can be input and output. 16 to 18 are image memories with 8 bits per pixel, and the above image data l
R (red), G (green), B (
Original image data of three primary colors (blue) are stored respectively. Reference numeral 19 denotes a texture memory of 8 bits per pixel, which stores image data of a canvas (knit pattern) such as that used in oil painting, for example. Reference numeral 20 denotes a work memory of 8 bits per pixel, which temporarily stores the results of arithmetic processing performed on the image data in the image memories 16 to 18.

21 is a 16-bit memory with 16 bits per pixel;
Similarly, the product-sum results etc. performed on the image data in the image memories 16 to 18 are temporarily stored.

Reference numerals 22 to 24 are look-up tables (LUTs) consisting of high-speed RAM, each having a storage capacity of 256 x 8 bits that allows the gradation conversion table to be rewritten by CPtJl, and each of the LUTs 22 to 24 has eight address lines ( 0
~255 addresses), and the address data is the image data (0~255 gradation) of the image memory 16~18 output, respectively.
given by. On the other hand, each of the LUTs 22 to 24 has eight read data lines, which are connected to the video bus 25. Furthermore, the CPU bus 4 is directly connected to each LUT 22 to 24, and the CPU
U1 can read and write the contents of LUTs 22-24 at any time. Reference numeral 26 denotes a video controller, and by connecting a CRT display device or a video printer (not shown) to this controller, image data before and after the painting process can be monitored.

5 is an edge detection section, and the image memories 16 to 1 are
The edge portion in the original image is detected from the image data obtained by color-compositing each of the image data in 8, and is binarized, and the resulting binarized edge data is output. This is to make the properties and shapes of brush patterns generated different between edge portions (contrast portions) and flat portions in the original image data. Reference numeral 6 denotes a thick line processing unit, which performs pattern thickening processing on the binarized edge data output from the edge detection unit 5. Reference numeral 7 denotes a garbage processing unit, which extracts isolated lines from the thick line pattern that has been subjected to the thick line processing. Noise patterns with small areas are removed and only the necessary edge pattern portions are left. 8 is a color conversion unit which converts original image data into color data.

Reference numeral 13 denotes an edge direction detection section, which enables the brush pattern selection section 10 described later to select an appropriate brush pattern by detecting the presence or absence of directionality of the detected edge portion and its direction. 9 is a drawing start position generating section;
A random function generating means (not shown) is used to generate a random function so that the drawing position of brush pattern data, which will be described later, is random. Reference numeral 10 is a brush pattern selection section, which is stored in advance in a ROM (not shown).
A plurality of types of small multivalued (or binary) brush pattern data are stored therein, and from among the plurality of small multivalued (or binary) brush pattern data, or an RO (not shown) in the brush pattern rotation unit 11, which will be described later, is stored.
The large multi-valued (or binary) brush pattern data stored in M and sequentially generated by pattern rotation processing is selected based on the above-mentioned detection results such as the presence or absence of edges.

Reference numeral 11 denotes a brush pattern rotation unit, which stores a single large multivalued (or binary) basic brush pattern data in a ROM (not shown) and sequentially rotates the basic brush pattern. By applying a rotation effect, substantially multiple types of brush pattern data are generated. Reference numeral 12 denotes a drawing section, which draws (synthesizes) the brush pattern data selected by the brush pattern selection section 10 at the randomly generated drawing positions on the original image data, that is, for example, the brush pattern data is composed of multi-values. In this example, the original image data in the vicinity of the generated drawing position is converted into uneven, glossy image data that looks as if it were drawn with a real brush of a picture of that color. 14
is an image synthesis unit, which further synthesizes a texture image such as a canvas on the image data for which drawing processing has been completed. 1
Reference numeral 5 denotes an image memory controller, which synchronizes the image memories 16 to 18 and the LUT when each section from the edge detection section 5 to the image composition section 14 performs respective processing.
22 to 24 necessary controls are performed.

[Description of Processing Procedure (FIG. 2)] FIG. 2 is a flowchart of the painting processing procedure of the embodiment. In the following explanation, the original image (density) data at address (x, y) is referred to as al (x.

However, when the subscript i represents R, G, and B image data individually, they are written as R, G, and B, respectively. Furthermore, the image data al (x, y) is 8 bits (0 to 255 The maximum density data (darkest data) is the gradation FIO, and the minimum density data (the brightest data) is the gradation 255.

Step Sl> The CPU l takes in original image data R, G, and B from the outside via the image data l1027, and stores them in the image memories 16 to 18, respectively.

At this point, the contents of the LUTs 22 to 24 have standard conversion characteristics in which input and output are equal, as shown in FIG.

<Step S2> The edge detection unit 5 extracts edge portions based on the original image data R, G, and B from the image memory 16 to 1 day. This edge extraction process first calculates the human visual sensitivity according to equation (2). Create image data that matches the curve.

a (x, y) = - (3a * (x, y) + 6 a a (x, y) +
a a (x, y))... (2) In the above equation (2), the original image data R, G, B is set to 0.3:0.
．． They were synthesized at a ratio of 6:0.1.

Since paintings are meant to be appreciated with the eyes, the original image data is first synthesized according to the human visual sensitivity curve, and edges are evaluated. At this time, each product-sum result is temporarily stored in the 16-bit memory 21 in order to perform the product-sum calculation in parentheses on the right side. Then, the product-sum result in parentheses (l/IOL) is stored in the work memory 2o.

Next, for example, the image data in the work memory 20 (
3X3) Edge extraction processing is performed using a matrix differential operator. Figure 4 shows an example of the differential operator employed in this example. This differential operator extracts edges that increase in brightness toward the right of the image. (contrast).

Next, the differential operator in FIG. 4 is rotated counterclockwise by π/4 to obtain the differential operator in FIG. 5. This differential operator detects edges whose brightness increases toward the upper right of the image. This edge detection result is then compared with the detection result stored in the work memory 20 described above, and the larger one is stored in the work memory 20. Thereafter, the differential operator is similarly rotated counterclockwise by π/4 to obtain edge detection results viewed from a total of eight directions and stored in the work memory 20. As a result, the maximum edge component for all pixels of the image data converted into a visible image is extracted into the work memory 2o.

Next, the work memory 20 is binarized using a predetermined threshold, and pixels determined to be edges (larger than the threshold) have a bit of “1”.
, pixels determined to be less than that are replaced with bit "0". In this way, the work memory 20 stores edge pattern data regarding edge components of the original image.

This series of processing is performed on a plane-by-plane basis rather than on a point-by-point basis, so it is executed at high speed.

Step S3> The edge pattern generated in step S2 is too thin to perform the processing described below. Therefore, the edge pattern is thickened. This thickening process is performed as shown in (3) when the edge pattern data of interest in the image memory 16 is a (x, y).
When a (x, y) = 1, a (x+i,
y+J)=1.

Here, 13≦i≦3. An integer of −3≦J≦3.Then, the result of this thick line processing is stored in the image memory 1, for example.
Stored in the lower 1 bit of 7.

Step S4> The thickened edge pattern data obtained in step S3 above usually contains many small isolated noise patterns. The dust processing section 7 calculates the area of all the edge pattern data stored in the image memory 17 based on the determination of the presence or absence of connectivity, and erases the edge pattern data having a predetermined area or less as a noise pattern. This determination of the presence or absence of connectivity is based on the edge pattern data of interest in the image memory 17 a (x,
y), it is performed according to equation (4).

That is, when a (x, y) = 1, a (x+i,
Check y+j)=1.

... (4) Here, 11≦i≦1. If at least one of the conditions -1≦j≦1, that is, a (x+i, y+j)=1 is satisfied, it is considered that there is continuity.

Step S5> Step S5 is a process of converting the entire original image into colors of a pre-selected palette, and in this embodiment, the color conversion unit 8 performs the process described below.

Note that this process is the process that best represents the features of the present invention. Further, in this embodiment, the palette for color conversion is used for oil painting, and "256" representative colors of oil paint are selected and stored in the CPU memory 2. When color information obtained from natural images and oil painting images was quantized and the saturation of the entire image was measured, oil painting images had higher saturation on average than natural images. This is because the number of colors used is limited. Therefore, the frequency of appearance of a plurality of oil painting images in R, G, and B space was measured, and the "256" colors were selected from those with the highest frequency of appearance.

In step S5, all pixels in the image memories 16 to 18 are replaced using the color data stored in the CPU memory 2 described above. Assuming that the data at the position (χ, y) before replacement is (Rio, G rfi, B in) and the first value of the representative color is (R+, Gt, B+), the gap D+ is expressed by equation (5). be able to.

D+ = +n-r +
In+B, fi-B, ”...
(5) Calculate Di from the 1st color to the 256th color, and
The color that minimizes , is the optimal color, so with that color
Replace the point (χ, y). By performing this processing on all pixels, the original image is converted into an image composed of colors suitable for an oil painting.

Step S6> In step S6, the drawing start position generating section 9 generates drawing position information of the brush pattern. If the drawing positions of the brush patterns are generated regularly and sequentially, the naturalness of the pictorial expression will be lost.

Therefore, the drawing position in the embodiment is generated at a random position.

The generation of this drawing position information is first performed on the original image data R.The drawing start position generating section 9 is internally equipped with a random number generating means (not shown), and the random number generating means is, for example, a CPU.
1 to three random number generation parameters (an integer that gives a random number generation sequence in the row direction, an integer that gives a random number generation sequence in the column direction, and the number of random numbers to be generated), the receiver generates random numbers in the corresponding mode. The drawing start position generating section 9 determines the drawing start position (Xs, y
Determine a).

In this example, the random number generation parameter is set to the original image data G.
Since the processing of and B is the same, the drawing position and the number of drawings are also the same as those of the original image data R.

Step S7> In step S7, it is determined whether or not edge pattern data exists at the generated drawing position 7. If there is no edge pattern at the drawing position (edge data = 0), drawing is performed using the large brush pattern data from step S8 onwards. Proceed to the routine, and if there is an edge pattern at the drawing position (edge data = 1), proceed to the routine that draws with small brush pattern data from step S12 onwards. This means that the image of the edge part is drawn in detail, and the other parts are This is for drawing roughly.

On the other hand, the brush pattern selection unit 10 first selects large basic pattern data or small basic pattern data in accordance with the determination in step S7. (binary image data in other embodiments), and substantially multiple types are prepared.

In this embodiment, a ROM (not shown) in the brush pattern rotating section 11
One type of large base pattern data (hereinafter, multi-value and binary are collectively referred to simply as base pattern data) is stored in the brush pattern selection unit 10, and three types of small base pattern data are stored in a ROM (not shown) in the brush pattern selection unit 10. I remember. As small base pattern data, in order to appropriately draw the edge portion of the original image data, a pattern that is close to a circle with no directionality, a vertical pattern suitable for vertical edges, and a horizontal pattern suitable for horizontal edges are stored. There is.

6(A) to 6(D) show examples of multivalued brush pattern data of the embodiment, and FIGS. 6(E) to 6(H) show examples of binary brush pattern data. )(E) is an example of large base pattern data, FIGS. 6(B) and (F) are examples of non-directional base pattern data, and FIGS. 6(C) and (G) are examples of vertically long brush pattern data. , and FIG. 6 (CD) (H) respectively show examples of horizontally long brush pattern data. These base pattern data are literally composed of base pattern data as if drawn on paper by applying paint to a large or thin brush (however, in the embodiment, an achromatic color representing the shape) is used. In other words, when the direction in which the light beam is irradiated is taken into consideration in the output image, a feeling of bulge in brightness, a feeling of thickness, or a feeling of unevenness along the direction of the brush stroke will appear accordingly, and the pattern shape will have a tail at the end. In this embodiment, a representative example of such base pattern data is read from an actual painting sample image or generated by image processing, and is stored in advance as brightness information such as the raised shape of a brush pattern. be.

<Step S8> Step S8 is input to a routine that uses large base pattern data. In step S8, it is first determined whether or not the rotation process of the large base pattern data has been completed. If the 0 rotation process has been completed, the process proceeds to step SIO, where the drawing process of the large base pattern data is performed. If not completed, proceed to step S9,
Performs rotation processing of the base pattern data.

Step S9> In step S9, rotation processing of large basic pattern data is performed.If multiple types of large basic pattern data are stored in advance in the ROM, access is faster.However, in this embodiment, the purpose is to save the ROM capacity. Therefore, by rotating one base pattern data, substantially the same effect as preparing multiple types of base pattern data can be obtained. This rotation process is performed by the brush pattern rotation unit 11. For example, if the basic position of the basic pattern data is the vertical direction, the main 2
0de Perform rotation processing sequentially. In addition, the rotation range is mainly 2
This takes the 0de direction into consideration. Moreover, within this range, there is no practical problem even if the shadow effect does not change depending on the direction of the irradiated light beam.

Specifically, the coordinates (K
, L) are determined according to equation (6).

Margin below... (6) Here, (1, J): Coordinates of input brush pattern data (xo, y
o) Center coordinate of two rotations 01 Rotation angle Here, the rotation angle θ changes sequentially by ldeg, but since the drawing position generated in step S6 is random, it ends up being a random position on the original image data R. This means that brush pattern data in random directions has appeared. Moreover, the main 20de as a whole retains a certain degree of directionality, making it possible to express the brush strokes (habits) unique to paintings.

Step SIO> The drawing unit 12 draws large brush pattern data at the drawing position generated in step S6.

FIG. 7(A) shows the generated drawing start position (X@).

y. ) and the center position of the multivalued brush pattern data (xc,
yc ). That is, the original image data and the multivalued brush pattern data are not aligned so that the relationship shown in FIG. is calculated according to equation (7).

CI (X, y')... (7) Here, i: R, G. B al (Xc + yc): Original image data P (x, y) corresponding to the center position of the brush pattern data. Multivalued brush pattern data at the near address (x.y) n: Number of gradations of the brush pattern data (x .y'') position of the original image data corresponding to the near address (x, y), that is, equation (7) is the original image data a l (Xc * : J c ) corresponding to the center position of the multivalued brush pattern data.
Therefore, the color of the brush pattern area (i=R.

G, B) and brightness are represented, and the surrounding area is filled with paint bulge information P (x.y) of multi-valued brush pattern data.
It is drawn with some changes.

Note that the reason for dividing by (n-1) in equation (7) is to make the calculation result 8 bits. In this way, the calculation of equation (7) is performed sequentially from the upper left of the multi-value brush pattern data P (x, y), and when writing corresponding to one pixel of the multi-value brush pattern data is completed, the process advances to step Sll.

FIG. 7(B) shows a case where the brush pattern data is binary as another example. In this case, the above equation (7) can be expressed as equation (7)'.

Cr (x, y') = P (x, y)Xa+ (xc, yc)...
(7) Here, P (x.y) is the binary brush pattern data of the near address (x, y), that is, in equation (7)', the original image data ai (Xc .

yc) and the color of the binary brush pattern area (i=R
, G, B) and the brightness, and the surrounding P (x,
The portion where y)=1 is drawn using the above-mentioned representative value.

Note that in the above embodiment, the original image data at (xcyc
) is extracted and multi-valued or binary brush pattern data P (x, y) is drawn around it, but this is not limited to this, and the average around the original image data a+ (Xs, ye) value or use multi-value brush pattern data P (
x, y) is greater than or equal to a predetermined value or binary brush pattern data P (
The average value of the original image data corresponding to the position where x, y) is "1" may be used, or the maximum value or minimum value of those original image data may be used.

<Step St 1> It is determined whether drawing has been completed for all pixels of the brush pattern data, and if drawing has not been completed, the process returns to step S7. Therefore, if edge data is encountered in the middle of drawing large brush pattern data, the drawing of the large brush pattern data is ended at that point and the process proceeds to the drawing process of small brush pattern data from step SL2 onward.Drawing of edge portions takes priority over others. Because it does.

Step S12> The edge direction detection unit 13 detects the direction of the edge data recorded in the lower 1 bit of the image memory 16, and in response to this, the brush pattern selection unit 10 selects the brush pattern data suitable for the detected direction. . To detect the edge direction, for example, calculate the AND of the one-dimensional operator and edge data as shown in Figures 8 or 9, compare the number of pixels for which the result is true in the vertical direction and the horizontal direction, and find out the difference between them. When it is larger than a certain value, the direction with the larger number of pixels is determined to be the edge direction. Specifically, the edge direction signal S is obtained according to equation (8).

S=F (T (x, y) nE (x, y)) − F (
Y (x, y) nE (x, y)) Here, E (x, y): Edge data T (x, y): Vertical operator Y (x, y): Horizontal operator F (): Logical product Based on this edge direction signal S, the functional brush pattern selection unit 10 that calculates the number of pixels for which is true, sets d to a predetermined number, and if -d≦S≦d, a "round pattern" is selected.
Figure 6 (B) or (F), if S<-d, the "horizontal pattern" Figure 6 (D) or (H), if d<S, the "vertical pattern" Figure 6 (C) or (G ).

Step S13> The drawing unit 12 draws the brush pattern data selected in step S12. As a result, the edge portion is drawn along the direction using small brush pattern data or elongated brush pattern data, resulting in a sharp painterly expression. can be done.

Step S14> It is determined whether drawing has been completed for all pixels of the small brush pattern data. If not completed, step S
Return to step S13, and if the process has been completed, proceed to step S15.

<Step S15> The CPU 1 determines whether drawing processing has been performed for a set number of randomly generated drawings. If the set number of items has not been completed, the process returns to step S6, and if the process has been completed, the process proceeds to step S16.

Step S16> The CPU 1 determines whether or not the drawing process has been completed for the three sides of the image data R and G%B. If the processing has not been completed, the process returns to step SS6 to start processing the remaining surfaces. If the drawing process for all surfaces has been completed, the process advances to step S17.

<Step S17> Finally, the canvas image data in the texture memory 19 and the image data in the image memories 16 to 18 are combined. Specifically, the image data G+ (x, y) after composition is (
9) Obtain according to the formula.

G+ (x, y) = aA+ (x, y) + bT (x, y)... (
9) Here, a, b: constants and a+b=1 i: R, G, B A+ (x, y): Image data of image memories 16 to 18 T (x, y): Image data of texture memory 19 , in the texture memory 19 in advance (bXT(x,y)
) can be stored.

In addition, the operation of (aXA+ (x, y)) is, for example, the first
This can be easily done by writing the conversion table shown in Figure 0 into the LUTs 22-24. Then, since the addition of the two image data can be performed in each memory brain R, G, and B as a whole, step S
Processing No. 17 is also executed in real time.

Incidentally, in the description of the above-mentioned embodiment, all the drawing processing was performed by digital calculation, but the present invention is not limited to this.

For example, a robot can be equipped with several types of real brushes,
Further, the brush pattern data may be drawn at the randomly generated drawing position with the selected rotation angle.

Further, in the above embodiment, the brush pattern data is selected depending on the presence or absence of edges and the direction of the edges, but the invention is not limited to this.For example, the brush pattern data may be selected by analyzing the spatial frequency components of the original image data. good.

Further, in the above embodiment, a 3×3 pixel differential operator is used for edge extraction, but the present invention is not limited to this.

For example, the size and contents of the differential operator may be changed depending on the pixel size of the original image, the size of the brush pattern data, etc.

Although the brush pattern data and parameters necessary for processing are stored in the ROM, they may be stored in the RAM so that these data can be changed before or during the processing. Furthermore, the size of the image memory and the number of gradations that can be expressed may be changed arbitrarily.

In addition, we have one set of palettes for color conversion for oil paintings, but we also have one set of palettes for watercolor paintings and one for illustration paintings that suit individual tastes, and the palettes used differ depending on the image. Therefore, if you prepare a palette that corresponds to the artist's color usage, such as Van Gogh's or Bicaso's, you can obtain more realistic and unique results.

[Effects of the Invention] As explained above, by preparing a palette suitable for the processing content and ensuring that all pixels of the original image are optimally replaced with the colors of the prepared palette, it is possible to You can obtain images with different colors.

[Brief explanation of drawings]

FIG. 1 is a block configuration diagram of an image processing apparatus according to an embodiment of the present invention, FIG. 2 is a flowchart of a painting processing procedure of the embodiment, and FIG. 3 is a gradation conversion table in the initial state of LUTs 21 to 23 of the embodiment. Figure 4 is a diagram showing an example of the characteristics. Figure 4 is a diagram showing an example of the differential operator employed in the example. Figure 5 is the differential operator when the differential operator in Figure 4 is rotated counterclockwise by π/4. Figures 6(A) to 6(D) are diagrams showing examples of multivalued brush pattern data of the embodiment, and Figures 6<E) to (H) are examples of binary brush pattern data of other embodiments. Figures 7 (A) and (B) show the drawing start position (X, y+-) and the center position (xc) of the selected brush pattern data.
, yc); FIG. 8 is a diagram showing an example of an operator for detecting edges in the vertical direction according to the embodiment; FIG. 9 is a diagram showing an example of an operator for detecting edges in the horizontal direction according to the embodiment; The figure is a diagram showing an example of LUT conversion characteristics during texture image synthesis. In the figure, l... CPU, 2... CPU memory, 3...
・CPU10.4...CPU bus, 5...Edge detection section, 6...Thick line processing section, 7...Trash processing section,
8... Color conversion section, 9... Drawing start position generation section, 10
... Brush pattern selection section, 11... Brush pattern rotation section, 12... Drawing section, 13... Edge direction detection section, 1
4... Image composition unit, 15... Image memory controller, 16-21... Image memory, 22-24...
-LUT, 25...Video bus, 26...Video controller, 27...Image data I10. 7 IS completed Figure 3

Claims (2)

[Claims] (1) A selection means for selecting a type of image processing; an image processing apparatus for performing the processing selected by the selection means; a first storage means for storing original image data; and a first storage means for storing original image data; a second storage means for storing a plurality of color data respectively determined in advance; and a conversion means for converting the original image data stored in the first storage means into color data stored in the second storage means. An image processing device comprising: (2) The image processing apparatus according to claim 1, wherein the conversion means converts the color data stored in the second storage means to color data closest to the original image data.