JPH0296880A - Picture processor - Google Patents

Abstract

PURPOSE:To obtain the picture of color (tint) suitable for the contents of processing by preparing beforehand a pallet for color conversion suitable for the contents of the processing by combining two attributes among three attributes of the color into a group, and color-converting an original picture most suitably by using said pallet. CONSTITUTION:First storage means 16 to 18 store respectively the original picture data of three primary colors of R (red), G (green), B (blue) read in through a picture data I/O 27. Besides, a second storage means 2 stores respectively plural conversion data in which two attributes, at least, among three attributes of the lightness, the hue and the saturation of the color are combined according to the kind of the processing. Then, in a color converting means 8, the original picture data is converted into color data in conformity with plural conversion data according to the contents of the processing. Thus, the picture of the color suitable for the contents 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 more particularly 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, 1, J = pixel number, 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 a' (5m-i, 5n-j) in the block is replaced with the representative value for 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 or spatial frequency characteristics of the original image data.

Problem to be solved by invention C] However, in the above conventional example, the color of the mosaic pattern to be generated is the same color extracted from a part of the original image or the average value of several pixels, so the processing result is The color tone of the image depends on the original image, and the color saturation is often extremely low when compared to actual paintings.

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 suitable for processing contents.

[Means for Solving the Problem] In order to achieve the above object, the image processing device of the present invention has the following features:
Prepare the following configuration. That is, an image processing apparatus that performs the processing selected by the selection means, including a selection means for selecting a type of image processing, a first storage means for storing original image data, and a first storage means for storing original image data, and a first storage means for storing original image data, and a first storage means for storing original image data; Color brightness 1 hue.

a second storage means for storing a plurality of conversion data each combining at least two of the 3i characteristics consisting of saturation;
The original image data stored in the first storage means is stored in the second storage means.
and conversion means for converting according to the conversion data stored in the storage means.

[Operation] In the above configuration, original image data is converted into color data according to a plurality of conversion data that combine at least two of the three attributes of color, brightness, hue, saturation, stored according to the processing content. Works to convert.

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

[Description of Apparatus (FIG. 1)] FIG. 1 is a block diagram of an image processing apparatus according to an embodiment of the present invention. In the figure, reference numeral 1 denotes a control processor unit (CPU), which performs main control of the apparatus of this embodiment. Reference numeral 2 denotes a CPU memory, which 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). Reference numeral 3 denotes a ■/○ interface of the CPLII, to which an intention input device such as a keyboard and a mouse (not shown) is connected. 4 is a CPU bus, which consists of an address bus, a data bus, and a control bus for the CPU 1.

27 is image data I10, and by connecting an image reading device or an image output device (not shown) to this, input/output of image data before and after painting processing is performed. 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, and each LUT 22 to 24 has a storage capacity of 256 x 8 bits, which allows the CPU 1 to write and change the gradation conversion table.The LUTs 22 to 24 each have 8 address lines. (O~
255 address), and the address data is given by image data (0 to 255 gradations) output from the image memories 16 to 18, respectively. On the other hand, each of the LUTs 22 to 24 has eight read data lines, which are connected to the video bus 25. In addition, each LUT22-24 has a C
PU bus 4 is directly connected, and the CPU
1 can read and write the contents of LUTs 22 to 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 in edge portions (foot rest portions) and flat portions of the original image data different. Reference numeral 6 denotes a thick line processing section, which performs pattern thick line processing on the binary edge data output from the edge detection section 5. Reference numeral 7 denotes a garbage processing section, which removes isolated noise patterns with small areas from the thick line pattern that has been subjected to the thick line processing, and leaves only the necessary edge pattern portions. Reference numeral 8 denotes a color conversion unit, which performs a process of converting 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 occurrence part,
A random function generating means (not shown) is provided, and the drawing position of brush pattern data, which will be described later, is generated at 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 selection is made based on the above-mentioned detection results such as the presence or absence of edges, which is multivalued (or binary) large brush pattern data stored in M and sequentially generated by pattern rotation processing.

Reference numeral 11 denotes a brush pattern rotation unit 5. In this embodiment, a single large multivalued (or binary) basic brush pattern data is stored in a ROM (not shown), and the basic brush pattern data is sequentially rotated with respect to 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 1o at the randomly generated drawing position 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.
6. [Explanation 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 a, (x).

y). However, when the subscript i represents R, G, and B image data individually, it is written as R, G, and B, respectively. Furthermore, the image data al (x, y) is 8 bits (0 to 255 gradations can be expressed), the value of maximum density data (darkest data) is set to gradation O, and the value of minimum density data (brightest data) is is the gradation level 255.

<Step SL> The CPU 1 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 in the image memories 16 to 18. In this edge extraction process, first, image data matching the human visibility curve is created according to equation (2).

a (x, y) = - (3a * (x, y) + 6 a a (x, y) +
a e (x, y))... (2) In the above equation (2), the original image data R, G, B is 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 contents obtained by multiplying the product-sum result in parentheses by 1/IOL are stored in the work memory 20.

Next, for example, (
3×3) Edge extraction processing is performed using a matrix differential operator. FIG. 4 shows an example of the differential operator employed in this embodiment. This differential operator detects edges (contrast) that increase in brightness toward the right of the image.

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 that increase in brightness 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 20.

Next, the work memory 20 is binarized using a predetermined threshold value, and pixels determined to be edges (larger than the threshold value) have a bit “1”.
, pixels determined to be less than that are replaced with bit "O". 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 follows (3):
Do it according to the formula. That is, 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 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
,y+j)=1.

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

Step S5> Step S5 is a process of color converting the entire original image according to the type of processing, 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. In addition, in the case of normal oil painting processing, the color conversion palette is used for oil painting, and the representative colors of oil paint are "256".
A color is selected and stored in the CPU memory 2. this is,
When we quantized the color information obtained from natural images and oil painting images and measured the saturation of the entire image, we found that, on average, oil painting images had higher saturation than natural images. This is because the number of colors available is limited. Therefore, we measured the appearance frequency of multiple oil painting images in R, G, and B space,
"256" colors are selected from those with the highest frequency of appearance.

However, using the above data, the image memories 16 to 1
Even if you perform a conversion process that replaces all pixels of 8, it is sufficient to express the coloring of oil paint, but it is not suitable for works by artists who create realistic expressions or works by artists who use highly saturated colors. The error may be too large to represent. In such a case, the problem can be solved by widening the range of selection without limiting the representative colors to "256" colors. In other words,
By selecting two of the three attributes (brightness, hue, and saturation) of the representative color as color feature quantities, a color usage suitable for processing can be expressed. In the case of oil painting processing, it has been confirmed through subjective evaluation that hue and saturation are the dominant characteristics that characterize the color usage. The specific processing procedure for oil painting processing in this embodiment will be explained below.

In addition, the representative colors (256 colors) in R, G, B space are H, S
, into L space, and write the result of the conversion into the CPU memory 2. Further, since this conversion formula is common, its explanation will be omitted.

First, the original images in the image memories 16 to 18 are converted to H, S, and L spaces based on the conversion data in the CPU memory 2,
The conversion results are stored in image memories 16-18, respectively.

Next, regarding the conversion result, H and S are replaced. The H, S data of the position (x, y> before replacement is (Hlo, SL
. ) The i-th data of one representative color is (Hl, SL
), then the distance, DI, is D+= 111
−1+II+... (5) It can be expressed as follows. Calculate Dl from the 1st color to the 256th color, and find the optimal H, when D is the minimum.
, S, so the image memory i6.17)(,
Replace S with the optimal value.

With the above processing, H and S are “256” specified by the representative color.
Among the combinations of ``-'' and ``, the values of the original image are maintained.As a result, it is possible to effectively express the characteristics of color usage in oil painting processing.

Finally, the contents of image memories 16 to 18 are inversely converted to RlG and B space, and the results are converted back to image memories 16 to 18.
8 and ends the process.

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.

This drawing position information is first generated for the original image data H. 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.
By taking three random number generation parameters from I (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), random numbers are generated in the corresponding mode. The drawing start position generating unit 9 determines the drawing start position (xs,
ys).

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, and if there is no edge pattern at the drawing position (edge data = 0), the routine draws with large brush pattern data from step S8 onwards. Proceed to
There is also an edge pattern at the drawing position (edge data =
If it is 1), the process advances to a routine for drawing using small brush pattern data starting from step S12. This is because the edge portion of the image is drawn in detail, and the other portions are drawn roughly.

On the other hand, the brush pattern selection unit 10 first selects large brush pattern data or small brush pattern data in accordance with the determination in step S7. Examples of any brush pattern data ^
For example, it is composed of Oyn gradation multivalued image data (binary image data in other embodiments), and a plurality of types are substantially prepared.

In this embodiment, a ROM (not shown) in the brush pattern rotating section 11
One type of large brush pattern data (hereinafter, multivalued and binary data are collectively referred to as brush pattern data) is stored in the ROM, and three types of small brush pattern data are stored in a ROM (not shown) in the brush pattern selection section 10. I remember. As small brush 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 vertically long pattern suitable for vertical edges, and a horizontally long pattern suitable for horizontal edges are stored. There is.

FIGS. 6(A) to 6(D) show examples of multi-valued brush pattern data of the embodiment, and FIGS. 6(E) to 6(H) show examples of binary brush pattern data. Figure 6 (A) (E) shows an example of large brush pattern data, Figure 6 (B) (F) shows an example of non-directional brush pattern data, and Figure 6 (C) (G) shows an example of vertical brush pattern data. Examples of brush pattern data are shown in FIGS. 6(D) and 6(H), respectively. These brush pattern data are literally composed of brush pattern data that looks like a large or thin brush coated with paint and drawn on paper (however, in the embodiment, it is an achromatic color representing the shape). 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 brush 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 the brush pattern. be.

<Step S8> Step S8 is the input to the routine that uses large brush pattern data. In step S8, it is first determined whether the rotation process of the large brush pattern data has been completed. If the rotation process has been completed, the process advances to step Sl◯, where large brush pattern data is drawn. Furthermore, if the rotation process has not been completed, the process advances to step S9;
Performs rotation processing of brush pattern data.

<Step S9> In step S9, large brush pattern data is rotated. If a plurality of types of large brush pattern data are stored in advance in the ROM, access is faster. However, in this embodiment, for the purpose of saving the ROM capacity, by rotating one piece of brush pattern data, substantially the same effect as preparing a plurality of types of brush pattern data is obtained. This rotation process is performed by the brush pattern rotation unit 11. For example, if the basic position of the brush pattern data is in the vertical direction,
0de Perform rotation processing sequentially. In addition, the rotation range is upper 2
This takes the 0de direction into account. 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).

... (6) Here, (I, J): Coordinates of input brush pattern data (xo.)
'o) 'Rotation center coordinate 01 Rotation angle Here, the rotation angle θ changes sequentially by ldeg, but since the drawing position generated in step S6 is random, there is a random position on the original image data R after all. This means that brush pattern data in random directions appears. Moreover, a certain degree of directionality remains in the upper 20de as a whole, and it is 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.

ya) and the center position of the multivalued brush pattern data (xc, yc
) is a diagram showing the relationship between That is, the original image data and the multivalued brush pattern data are aligned so that they have the relationship shown in FIG. 7(A), and specifically, new writing data C+ (x, yi Calculate according to formula (7).

C+ (x', y') ... (7) Here, i: R, G, B an (xc, ye): Original image data P (x, y) corresponding to the center position of the brush pattern data Multivalue brush pattern data n at address (x, y): Number of gradations of brush pattern data (x', y') Position of original image data corresponding to near address (x, y), that is, equation (7) is The color (i = RG, B) and brightness of the brush pattern area are represented by the original image data a+ (Xc, yc) corresponding to the center position of the multivalued brush pattern data, and the surrounding area is the multivalued brush pattern. The data is drawn with changes based on the paint buildup information P (x, y).

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 (a) can be expressed as equation (7)'.

C+ (x, y') = P (x, y)Xa, (xc, 3/c)...
(7) Here, the binary brush pattern data of P (x, y) near address (x, y), that is, the original image data al (xc .

ye) 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.

In the embodiment described above, the original image data at(xcyc) is extracted and multivalued or binary brush pattern data P (x,y) is drawn around it, but the present invention is not limited to this. In addition, the average value around the original image data at (xe, ye) may be used, or the multivalued brush pattern data P (x, y
) is greater than or equal to a predetermined value or the binary brush pattern data P (x, y
) may be used as the average value of the original image data corresponding to the position where is "1", or the maximum value or minimum value of those original image data may be used.

Step S11> It is determined whether drawing has been completed for all pixels of the brush pattern, and if the drawing has not been completed, the process returns to step S7. Therefore, if edge data is encountered during the drawing of large brush pattern data, drawing of the large brush pattern data is terminated at that point, and the process proceeds to drawing processing of small brush pattern data from step S12 onwards. This is because drawing of edge portions takes priority over other drawings.

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))... (8) Here, E (x, y): Edge data T (x, y): Vertical operator Y (x, y) Horizontal operator F(): The function brush pattern selection unit 10 that calculates the number of pixels for which the logical product is true is based on this edge direction signal S, where d is a predetermined number, and if -d≦S≦d, a “round pattern” is selected.
Figure 6 (A) or (E), if S < -d, use the "horizontal pattern" Figure 6 (D) or (H), if dos, use the "vertical pattern" Figure 6 (C) or (G). select.

Step S13> The drawing unit 12 draws the brush pattern data selected in step SL2. 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 pictorial expression.

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 816> The CPU 1 determines whether the drawing process for the three sides of the image data R, G, and B has been completed. 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 LUs 722-24. 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,
In addition, the brush pattern data with the selected or rotation angle may be drawn at the randomly generated drawing position.

Further, in the above embodiment, the brush pattern data is selected depending on the presence or absence of an edge and the direction of the edge, but the present 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.

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.

In addition, the ROM stores pattern data and parameters necessary for processing, but it is also possible to store them in the RAM so that they can be changed before or during processing. The size and the number of gradations that can be expressed may also be changed arbitrarily.

In addition, in this embodiment, one set of palettes for color conversion is prepared for oil painting, but palettes for watercolor painting, illustration painting, and palettes that suit individual tastes are prepared, and can also be used by artists. There are differences in palettes, so 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.

Furthermore, in this embodiment, a set of saturation and hue is used as a set of color attributes for color conversion, but depending on the type of processing,
There are also processes where brightness and saturation are dominant, such as watercolor paintings on colored paper, and the two dominant attributes change depending on the type of process.

Furthermore, in this example, information on brightness, which is a non-dominant attribute, is obtained directly from the original image, but depending on the artist,
In the case of a painter like Monet who creates paintings with high brightness overall, by preparing a brightness LUT as shown in Figure 11, the characteristics of the painter's color usage can be reflected in the brightness of the remaining attributes. It is possible to reflect

[Effects of the Invention] As explained above, a color conversion palette suitable for the processing content is prepared using two of the three color attributes as strings, and is used to optimize the original image. By performing color conversion, it is possible to obtain an image with a color scheme (hue) appropriate for the processing content.

[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 floor diagram of LUTs 21 to 23 in the initial state of the embodiment. Figure 4 is a diagram showing an example of the key conversion table characteristics. Figure 4 is a diagram showing an example of the differential operator employed in the example. Figure 5 is the differential when the differential operator in Figure 4 is rotated counterclockwise by π/4. Figures 6(A) to 6(0) are diagrams showing examples of multivalued brush pattern data of the embodiment. Figures 7(A) and (B) are drawing start positions (X, ya). and the center position of the selected brush pattern data (×.,
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. FIG. , FIG. 11 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, 1o
... 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.

Claims (1)

[Scope of Claims] An image processing apparatus comprising: a selection means for selecting a type of image processing; 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 conversion data each combining at least two of the three attributes consisting of color brightness, hue, and saturation according to the type; and an original image stored in the first storage means. data in the second
An image processing apparatus comprising: conversion means for converting according to conversion data stored in the storage means.