JPH04143873A - Graphic processor - Google Patents

Abstract

PURPOSE:To quickly obtain an approximate area rate without dividing a picture element to subpixels neither counting the number of painted-out picture elements by using the number of picture elements in the edge part to obtain the approximate area rate of picture elements in the edge part by interpolation of the area rate in a picture part and that in a non-picture part. CONSTITUTION:Coordinate values Xa and Xb of two intersections where vector data traverses scan lines Y0 and Y1 are inputted to calculate the number of picture elements in the edge part, and this number of picture elements is used to approximate area rates of plural picture elements in the edge part at a time by interpolation of the area rate of the picture part and that of the non- picture part. That is, approximate area rates of (m) picture elements in the edge part are calculated at a time based information indicating that the edge of the picture is extended over (m) picture elements ((m) is a positive integer) on scan lines Y0 and Y1. Thus, the approximate area rate is quickly obtained without dividing a picture element to subpixels neither counting the number of painted-out picture elements.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a graphic processing device that performs anti-aliasing processing to remove jagged edges of an output image.
More specifically, the present invention relates to a graphic processing device that can perform anti-aliasing processing at high speed.

[Conventional technology]

In the field of computer graphics, a technique called anti-aliasing processing is used to display images on a CRT, which is an output medium, and to make the displayed images more beautiful. This process is performed on the jagged part (called alias) on the stairs as shown in Fig. 25(a).
Figure 25(b) visually displays the displayed image by applying brightness modulation to
It is used to smooth the surface as shown in the figure.

In conventional graphic processing apparatuses, (1) uniform averaging method, (2) weighted averaging method, (2) convolution integral method, etc. are generally applied as anti-aliasing processing methods.

■The uniform averaging method calculates each pixel by N*M (N,
After decomposing the image into sub-pixels (M is a natural number) and performing rask calculation at high resolution, the brightness of each pixel is determined by taking the average of N*M sub-pixels. Figure 26(a)
, (b), anti-aliasing processing using the uniform averaging method will be specifically explained. If the edge of the image overlaps a certain pixel (here, it is assumed that the image is connected to the lower right of the diagonal line), and when anti-aliasing processing is not performed, as shown in Figure (a), The brightness kid of this pixel is assigned the highest brightness of the displayable gradations (for example, kid·255 for 256 gradations). When performing anti-aliasing processing on this pixel using the uniform averaging method with N=M=7, as shown in the same figure (b), the pixel is decomposed into 7*7 subpixels, and the pixels covered by the image are divided into 7*7 subpixels. Count the number of sub-vixels. The count number (28) is divided by the total number of sub-vixels in one pixel (49 in this case) and normalized (averaged)
The brightness of that pixel is calculated by multiplying the brightness by the maximum brightness (255). In this way, in the uniform averaging method, the brightness of each pixel is determined by taking into consideration how the image covers each pixel.

■Weighting is an averaging method The weighting and averaging method is a partial modification of the uniform averaging method. In contrast to the weighted averaging method, which simply counts subpixels, weighting gives each subpixel a weight, and the influence on the brightness kid of that subpixel depends on which subpixel the image falls on. are different. Note that the weight at this time is given using a filter.

With reference to FIGS. 27(a) and (b), FIG. 26(a)
An example is shown in which the same image data is weighted using the same dividing method (N=M=7) and the averaging method.

FIG. 27(a) shows a filter (here, Conef
ilter), and the corresponding sub-pixel is given the same weight as this characteristic. For example, the weight of the upper right corner subpixel is 2. When an image is applied to each subpixel, the weight value given by the filter characteristics becomes the count value of that subpixel. Same figure (b)
This figure shows how the display pattern of the image is changed depending on the weight of the sub-pixels. In this case, if we count the weighted subpixels of the image, we get
It becomes 199. This value is divided by the sum of the filter values (336 in this case) corresponding to uniform averaging, averaged, and multiplied by the maximum brightness to calculate the brightness of this pixel. In addition, as a filter, Fig. 28 (a), (
Filters shown in b), (C), and (d) are known.

■Convolution Integration Method The convolution integration method is a method that refers to the appearance of surrounding pixels when determining the brightness of one pixel. That is, the area around one pixel whose brightness is to be determined is N
It is assumed that the 'XN' pixel corresponds to the pixel of the equal-averaging method or the weighting/averaging method. 29th
The figure shows a convolution method with 3x3 pixel references. In this figure, the pixel whose brightness is to be determined is 2901
Indicated by The image continues below and to the right of the diagonal line, and the subpixels painted black are the subpixels that are counted.

Each pixel is divided into 4*4. Therefore, in this case, a 12*12 filter will be used. This method has the effect of removing high frequency components contained in a vector image.

On the other hand, a publishing system using a personal computer,
With the spread of so-called DTP (desk top publishing), systems for printing vector images, such as those used in computer graphics, have become widely used. A typical example is a system using Atoto Co.'s Post Script. PostScript is a page description language (Page Des
criptionLanguage: Hereafter, PDL
It belongs to a language genre called ``Japanese language'', and describes the form of the contents of a single document, including the text (letter part), graphics, and their arrangement and appearance. It is a novel programming language, and such systems use vector fonts as character fonts. Therefore, even if the characters are scaled, the print quality can be significantly improved compared to systems using bitmap fonts (for example, conventional word processors).
Another advantage is that character fonts, graphics, and images can be mixed and printed.

However, the resolution of the laser printers used in these systems is at most 240dpi to 400dp.
There are many i, computer graphics C
Similar to RT display, there is a problem in that aliasing occurs due to the low resolution. For this reason, even in printing using a laser printer, there is a need to perform anti-aliasing processing to improve the quality of the printed image.

[Problem to be solved by the invention]

However, according to a graphic processing device that applies a conventional anti-aliasing processing method, one pixel is divided into multiple sub-pixels (for example, 49 sub-pixels) and the number of sub-pixels to be filled is counted. Since the approximate area ratio is calculated, there is a problem in that it takes time to calculate the area ratio, which hinders improvement in display speed or printing speed. In particular, the convolution method has the problem that it requires a large amount of calculation and affects multiple pixels, making it difficult to improve the processing speed.

The present invention has been made in view of the above, and an object of the present invention is to quickly obtain an approximate area ratio without dividing subpixels or counting the number of filled pixels.

[Means to solve the problem]

In order to achieve the above object, the present invention adjusts the output of pixels at the edges of vector data based on the area ratio to be filled, and performs anti-aliasing processing to smoothly express jaggedness (alias) at the edges of the output image. In the graphic processing device that executes, input the coordinate values of the two intersection points when the vector data crosses the scan line,
Edge portion pixel number calculation means for calculating the number of edge portion pixels;
Provided is a graphic processing device equipped with an approximate area ratio determining means for determining an approximate area ratio of pixels in an edge part by interpolating an area ratio of an image part and an area ratio of a non-image part using the number of pixels of an edge part. It is.

[Effect]

In the graphic processing device of the present invention, the edge portion pixel number calculation means is configured to calculate
Input the coordinate values of the two intersection points and calculate the number of edge pixels. Subsequently, the approximate area ratio determination means determines the approximate area ratio of the pixels in the edge part by interpolating the area ratio of the image part and the area ratio of the non-image part using the number of pixels in the edge part.

[Example] Hereinafter, an image forming system incorporating the graphic processing device of the present invention as a PDL controller will be described as an example. (device) configuration and operation.

A detailed explanation will be given in the following order: 1) the configuration of the image processing device, 2) the configuration and operation of the multi-valued color laser printer, and 2) the multi-valued drive of the driver.

■Overview of anti-aliasing processing The graphic processing device of the present invention (hereinafter referred to as PDL controller) calculates the number of pixels at the edge by inputting the coordinate values of two intersection points when vector data crosses a scan line. , the approximate area ratio of a plurality of pixels in the edge part is obtained at once by interpolating the area ratio of the image part and the area ratio of the non-image part using the number of pixels of the edge part. That is, based on the information that the edge of the image spans m pixels (m is a positive number) on one scan line,
This method derives the approximate area ratio of each edge pixel at once.

As shown in Figure 1(a), the vector data is Yo and Y.
When crossing the I scan line, the intersection point of the X coordinate value Xa is on the side closer to the image area (Y0 side in the figure).

Two intersections of X coordinate values xb are formed on the side far from the image area (Yl side in the figure). This means that the scan line is 1
This is because it has the thickness of a pixel (that is, the thickness of Y + Yo).Hereafter, among the two intersections of the vector data and the scan line, the X coordinate value of the intersection closest to the image area is calculated from the image area Xa and il. The X coordinate value of the difference intersection is written as xb.

Therefore, the value of the integer part of lXa - the integer part i+1 of xb is
Yo.Y1 The number of edge pixels based on vector data crossing the scan line is m.

In the present invention, the approximate area ratio is determined by gradually decreasing it in accordance with the slope of the vector data from the image part side to the non-image part side (from pixel A to the next pixel in FIG. 3A). In reality, the slope information of vector data is included in the number m of edge pixels, and is constant for consecutive edge pixels, so it is interpolated from the image area to the non-image area. What is necessary is to find the approximate area ratio of the intermediate pixels (edge portion pixels).

Hereinafter, with reference to FIG. 1(b), an explanation will be given of how to obtain the approximate area ratio by interpolation.

Yo ・Intersection X of Y1 scan line and vector data
If m is the value obtained from a and Xb using the above-mentioned formula (l - the integer part of

Here, the area ratio is expressed in n stages from the highest value (n-1) to the lowest value 0, and is expressed by multiplying it by (n-1) to simplify the explanation. Therefore, the area ratio when the image covers the entire pixel is originally "1", but from now on, the area ratio of the image part is expressed as (n-1), and the area ratio of the non-image part is expressed as 0. do.

This corresponds to the approximate area ratio obtained by dividing into (n-1) subpixels.

First, the approximate area ratio of m edge pixels is determined by interpolating (n-1) and O. That is, the approximate area ratio of the edge part is (a++1), (m+1), , , in order from the pixel where Xa exists to the pixel where xb exists.
(m+1) is rounded off to the nearest whole number.

For example, FIG. 1(C) shows an example of calculation of the approximate area ratio in five stages from the lowest value 0 to the highest value 4, which corresponds to the case of subpixel division 2*2. The approximate area ratio of the pixel is 4, and the approximate area ratio of the non-image area is O.

An approximate area ratio is determined from an edge pixel A having an X coordinate value Xa closer to the image portion to an edge pixel having an X coordinate value xb. Here, since Xa = 6.5°Xb = 3.6 and the difference in the integer parts is 3, it becomes m -4. Therefore, from the approximate area ratio of edge pixel A to the approximate area ratio of edge pixel A, (4X4) rounded value of 15 = 3 (3x4) rounded value of 15 = 2 (2X4) rounded value of 15 = 2 ( IX4) The rounded value of 15 = 1, and the edge part pixel A is the approximate area ratio 3, the edge part pixel B is the approximate area ratio 2, the edge part pixel C is the approximate area ratio 2, and the edge part pixel 1 is the approximate area ratio 1. Desired.

By using the above-mentioned interpolation method, scan line processing for each subpixel and subsequent subpixel counting processing are not required, so processing can be performed at a correspondingly high speed.

Also, since multiple edge pixels are gradually modulated,
There is almost no difference in image quality compared to when accurate modulation is performed.

Note that when there is an endpoint of a vector between the Yo and YI scan lines, the following processing is performed for two cases: a case where the endpoint is on the xb side and a case where the endpoint is on the Xa side.

First, if Xa exists as usual and the xb side is an endpoint as shown in FIG.

Next, as shown in Figure 1(e), if xb exists as usual and the endpoint is on the Xa side, the X coordinate of the endpoint is considered to be Xa, and almost the same processing as when there is no endpoint is performed. , Xa is defined as the highest value of the interpolation method.

Specifically, the number m of pixels to be interpolated is determined as (l integer part of Xa - integer part 1 of xb), and the area ratio of edge pixels where Xa exists when Xa is an end point is determined in advance. , and replaces that value with the highest value (n-1) of the interpolation formula described above to obtain an approximate area ratio from the pixel next to the pixel Xa to the pixel where xb exists. Here, if we consider that on average the endpoint exists in the center of the pixel, then when Xa is the endpoint,
The area ratio of the edge pixel where a exists can be set to (n-1)/2.

Furthermore, if there are two or more pieces of vector data for one edge pixel, their approximate area ratios are averaged.

By performing the anti-aliasing process as described above, it becomes possible to easily and quickly obtain the approximate area ratio of the edge portion pixels. Also, the accuracy of the above interpolation method is considered to be quite good when the vector data intersects the Yo/Y1 scan line (does not include end points), and in fact, in most cases it does not include end points. Considering this, it is almost equivalent to the sub-vixel division method.

■Block diagram of the image forming system The image forming system of this embodiment uses the Page Description Language output from DTP (Desk Top Publishing).
e: hereinafter referred to as PDL language)) and image information read by an image reading device. The configuration of the image forming system of this embodiment will be described below with reference to FIG.

The image forming system includes a host computer 100 that creates a document written in PDL language (Bost script language is used in this embodiment);
A PDL controller (graphic processing device of the present invention) that applies anti-aliasing processing to the PDL language sent page by page from ) 200, an image reading device 300 that reads image information via an optical system unit, and a PDL controller 200. Or.

An image processing device 400 that inputs the image output from the image reading device 300 and performs image processing (details will be described later), and a multi-value color laser printer that prints the multi-value image data output from the image processing device 400. printer 50
0, PDL controller 2002 image reading device 30
0. ii image processing device 400. and a system control section 600 that controls the multivalued color laser printer 500.

■Configuration and operation of PDL controller FIG. 3 shows the configuration of the PDL controller 200, which includes a receiving device 201 that receives PDL language sent from the host computer 100, storage control of the PDL language received by the receiving bag 2201, and A CPU 202 that executes anti-aliasing processing, an internal system bus 203, a RAM 204 that stores PDL language to be transferred from the receiving device 201 via the internal system bus 203, and a ROM 205 that stores anti-aliasing programs and the like.
, a page memory 206 that stores multivalued ROB image data subjected to anti-aliasing processing, a transmitting device 207 that transfers the RGB image data stored in the page memory 206 to the image processing device 400 , and a system control unit 600 . It is composed of an I10 device 208 that performs transmission and reception.

Here, the CPU 202 receives the P received by the receiving device 201.
The DL language is stored in the RAM 204 via the internal system bus 203 according to the program stored in the ROM 205. After that, receive 1 page of PDL language,
Once stored in the RAM 204, the graphic elements in the RAM 204 are subjected to anti-aliasing processing based on a flowchart to be described later, and the multivalued RGB image data is stored in the brain memory section of the page memory 206 (the page memory 206 is , B's brain memory section and a feature information memory section).

The data in the page memory 206 is then transferred to the transmitter 2
07 to the image processing device 400.

Hereinafter, with reference to FIGS. 4(a) and (b), PDL:
7) The operation of the roller 200 will be explained.

FIG. 4(a) shows a flowchart of processing performed by the CPU 202. As described above, the PDL controller 200 performs anti-aliasing processing on the PDL language sent page by page from the host computer 100, and converts it to red (R).

It is developed into three-color images: green (G) and blue (B).

In the PDL language, graphics and characters are all described using vector data, and as the name "page description language" indicates, image information is processed in units of pages. Further, one page is made up of at least one bus, each of which is made up of one or more elements (graphic elements and character elements).

First, when PDL language is input, it is determined whether the element is a curved vector, and if it is a curved vector, it is approximated to a straight line vector and registered as a straight line element (line) in the work area. This is performed for all graphic and character elements within one bus, and linear elements are registered in the work area for each bus (processing 1).

Then, the linear elements of the work area registered in this bus unit are sorted by the starting X coordinate of the straight line (Processing 2
).

Next, in process 3, while updating the X coordinate one by one,
Performs filling processing using scanning lines. For example, in Figure 4 [
When performing the bus filling process shown in
A scan line that has a thickness of one pixel, such as one scan line, is described as a scan line, and a scan line that indicates a straight line with no thickness is described as a scan line), and its scanning. The real value of the X coordinate across the line yc (XI Xz Xz X4 shown in Figure 4) is
T (Active Edge Table: a table that records the X coordinate of an edge portion appearing on a scanning line). Here, the order of the elements registered in the work area is the order in which they were registered in process 1, so they are not necessarily registered in order of decreasing X coordinate across the scanning line yc. For example, in process 1, if a straight line element passing through scanning lines yc and x3 in FIG. be registered. Therefore, after the AET is registered, the elements on each side within the AET are sorted in descending order of X coordinate. Then, the first two elements of AET are made into a pair, and the space between them is filled in (specifically, for example, a filling process is performed using a scan line formed by scanning line yc and scanning line yc+1).

Anti-aliasing processing is achieved by adjusting the density and brightness of pixels in the edge portion in accordance with the approximate area ratio in this filling processing. Then, remove the processed edge from the AET, update the scanline (update the Repeat the process.

Above processing 1. Processing 2. The work in process 3 is executed for each bus, and is repeated until all buses for one page are completed.

Next, the anti-aliasing process executed during the scan line filling process of process 3 described above will be described in detail with reference to the flowchart of FIG. 4(C).

Here, for example, in process 1 of FIG. 4(a),
), this figure has the following elements.

(B) Five line vectors AB, BC, CD, DE, and EA (represented by real numbers) (II) Color and brightness values inside the figure As shown in Figure 5 (b), this figure is , into seven straight line vectors (expressed as real numbers) extending in the main scanning direction. At this time, in this embodiment, the following information is added to the starting points and ending points of the seven straight line vectors. That is, (c) Starting point coordinate values (real number expression) of the vector elements ((a) above) forming the starting point and ending point of the straight line vector (d) Slope information (near ) Characteristic information of the start point and end point of a straight line vector (right edge, left edge, end point of a figure, line of 1 dot or less, intersection of straight lines, etc.) When an edge pixel is detected in the scan line filling process, the The anti-aliasing process shown in the flowchart of FIG. 4(C) is executed.

First, it is necessary to determine whether the edge pixel is a left edge or a right edge (5401), and if it is a left edge, Yo
Among the intersections of Y, scan line and vector data, set the larger one as Xa and the smaller one as xb5402
), in the case of the right edge, the smaller one of the intersections of the Yo/Y1 scan line and the vector data is set as Xa, and the larger one is set as xb (5403). In other words, Yo
- Of the two intersections of the Y, scan line and vector data, set the X coordinate value of the intersection near the image area to Xa, and set the X coordinate value of the intersection far from the image area to xb.

Next, it is determined whether the end points of the vector are included in the edge pixels (S404).
). If there are no end points of the vector, that is, the intersections Xa and Xb
If both exist, the number m of pixels in the edge portion is calculated using the following equation (2) (S405).

m=(integer part of lXa - integer part 1+1 of xb
kmax=n 1 : Indicates the highest value when the area ratio is set to n stages; in this example, the area ratio is set to 10 stages, so kmax is 9), and the edge part pixel where Xa exists is set to 1. xb force direction αth (α is l-m
) edge pixel β is defined as β=α (5407
).

On the other hand, if the vector has endpoints, the endpoints are the intersections Xa and X
Determine which side of b it is on 5408), and if it is on the xb side, let the X coordinate value of the end point be xb5409), 54
Proceed to 05. If the end point is on the Xa side, the X coordinate value of the end point is set to Xa (5410), and the number m of pixels in the edge portion is calculated using the following equation (2) (5411).

m = integer part of IXa - integer part 1 of
x=n -1/2: indicates the area ratio of the end point when the area ratio is set to n stages, and in this example, the area ratio is set to 10 stages, so kmax is 9/2) is set, and Xa is xb force direction αth (
The edge pixel β of α is 1 to m is defined as β=α−1 (5413).

Thereafter, in step 5414, approximate area ratios of the 1st to βth edge pixels are determined using the second interpolation method of the following equation (2).

Furthermore, it is determined whether other vector data exists in the edge pixel being processed (5415), and if so, the approximate area ratios of all vector data are averaged to obtain the approximate area ratio of the edge pixel (5415). 5416). If it does not exist, the approximate area ratio k obtained in step 5414 is used.

The CPU 202 repeats the above processing up to the last pixel of the scanning line (y coordinate), and at the same time updates the contents of (c) above using the information of (=) above. Approximate area ratio k of the figure in FIG.
(expressed as n-1) times) has a value as shown in FIG.

Here, if the figure in FIG. 5(a) has a white background color (
The figure color is red (maximum brightness: 25) on top of (maximum brightness: 255)
5), from the approximate area ratio k (see Figure 6), the brightness values for each color of the figure are (red), (red), (
Kb (green) and Kb (blue) are obtained based on the following formula.

Note that in the following equation, a value that is not multiplied by (n-1) is used for the approximate area ratio.

K, = K□xk + K,zx(1k)K* =
Kc+Xk + KczX(1-k)K,=□Xk
+ KRIX (1k) However, KRI, K-1, K
□ indicates the brightness value of the color (red, green, blue, respectively) of the figure given by (El) above, KHz, Kcm
, 0 indicates the brightness value of each previously painted color. In addition, Kl
! , , , , refer to data in each brain memory section corresponding to RGB of the page memory 206.

The brightness value Kl'1KflK obtained in this way is stored as RGB image data in the corresponding brain memory section of the page memory 206 as shown in FIGS. 7(a), (b), and (C). Stored. Here, for comparison, RGB image data without antialiasing processing is shown in Figures 8 (a), (b),
Shown in (C).

(2) Configuration of Image Processing Apparatus The configuration of the image processing apparatus 400 will be explained with reference to FIG. 9.

The image processing bag W400 is a CC in the image reading device 300.
Black (BK) necessary for recording the three-color image signals read by D7r, 7g, and 7b.

Yellow (Y), magenta (M), and cyan (C)
Convert to each recording signal. In addition, the RGB image data given from the PDL controller 200 described above are similarly applied to blank (BK), yellow (Y), magenta (M
) and cyan (C) recording signals. Here, the mode for inputting the image signal from the image reading device 300 is the copying machine mode, and the mode for inputting the image signal from the PDL controller 200 is set to the RG mode.
The mode in which B image data is input is called a graphics mode.

The image processing device 400 includes CCDs 7r, 7g, and 7b.
The color gradation data obtained by A/D converting the output signal of
A shading correction circuit 4 that performs correction for variations in the degree of anger of internal terminal elements of 7r, 7g, and 7b.
01, and a multiplexer 4 that selectively outputs either the color gradation data output from the shading correction circuit 401 or the color gradation data (RGB image data) output from the PDL controller 200 according to the above-described mode.
02, a γ correction circuit 403 that inputs the 8-bit data (color gradation data) output from the multiplexer 402, changes the gradation according to the characteristics of the photoreceptor, and outputs it as 6-bit data, and a γ correction circuit. Output from 403 (R)
, green (G), and blue (B) are used as complementary colors, cyan (C) and magenta (M).
) A complementary color generation circuit 405 that converts into yellow (Y) gradation data (6 bits), and a masking processing circuit 406 that performs predetermined masking processing on each Y, M, and C gradation data output from the complementary color generation circuit 405. and a UCR processing/black generation circuit 4 that inputs each Y, M, and C gradation data after masking processing and executes UCR processing and black generation processing.
07, and Y and M output from the UCR processing/black generation circuit 407.

Gradation processing that converts each 6-bit gradation data of C2 and BK into 3-bit gradation data Yl, MLCL, and BKI, and outputs the data to the laser drive processing unit 502 inside the multivalued color laser printer 500. It is composed of a circuit 408 and a synchronization control circuit 409 for synchronizing each circuit of the image processing apparatus 400.

Although details will be omitted, the γ correction circuit 403 has a configuration in which the gradation can be arbitrarily changed using an operation button on the console 700.

Further, as an algorithm used in the gradation processing circuit 408, a multi-value dither method, a multi-value error diffusion method, etc. can be applied. For example, a dither matrix of the multi-value dither method is
x3, multilevel color laser printer 500
The number of gradations is the product of 3×3 area gradations and 3-bit (that is, 8 steps) multivalue level, and is 3×3×8=72 (gradations).

Next, the processing of the masking processing circuit 406 and the OCR processing/black generation circuit 407 will be explained.

Generally, the arithmetic expression for the masking process of the masking process circuit 406 is as follows: Y,,M,,C,: Data before masking process Y,,M,
, C0: Data after masking processing Also, the arithmetic expression for UCR processing of the NCR processing/black generation circuit 407 is generally expressed as follows.

Therefore, in this embodiment, new coefficients are obtained using the product of both coefficients.

In this embodiment, a new coefficient (such as "all") that performs this masking processing and UCR processing at the same time is calculated in advance, and
Furthermore, the masking processing circuit 40
6 scheduled input values Y,,M,.

Output value (yoo, etc.) corresponding to Ci (6 bits each)
A value that is the calculation result of the UCR processing/black generation circuit 407 is obtained and stored in a predetermined memory in advance.

Therefore, in this embodiment, the masking processing circuit 406 and U
The CR processing/black generation circuit 407 is composed of a set of ROMs, and the data at the address specified by the inputs Y, M, and C of the masking processing circuit 406 is given as the output of the OCR processing/black generation circuit 407.

In addition, for boat fishing, the masking processing circuit 406 performs Y and M according to the characteristics of the spectral reflection wavelength of the toner for forming a recorded image.
, C signals, and the UCR processing/black generation circuit 407 performs correction for color balance in overlapping toners of each color. UCR processing/black generation circuit 4
07, black component data BK is generated by combining the input three color data of Y, M, and C, and the output Y,
The M and C color component data are corrected to values obtained by subtracting the black component data BK.

In the above configuration, the γ correction circuit 403 executes the process based on the γ correction conversion graph shown in FIG. 10, and the complementary color generation circuit 405 executes the process as shown in FIG. 11(a).

Assuming that processing is executed based on the conversion graphs for complementary color generation shown in (b) and (C), and then the masking processing circuit 406 and the UCR processing/black generation circuit 407 execute processing based on the following equation. Figure 7(a).

The RGB image data shown in (b) and (C) is γ
Correction circuit 403. Complementary color generation circuit 405. Masking processing circuit 406. 12(a), (b), (C), (d)(
7) It is converted as follows.

Furthermore, if the gradation processing circuit 408 uses a Bayer type 3×3 multivalued dither matrix shown in FIG. 13, Y in FIGS. ,M
.

The data for C and BK are shown in Figure 14 (a) and (b), respectively.
, (C) is converted into the data shown in (C).

For comparison, when data without anti-aliasing processing (data in FIGS. 8(a), (b), and (C)) is processed by the image processing device 400, the first
The transformation shown in Figure 5 (a), (b), (cl, and (d)) is performed.

■Configuration of multivalued color laser printer First, the 16th
The schematic configuration of the multivalued color laser printer 500 will be described with reference to the control block diagram shown in the figure.

A photoreceptor development processing unit 501 uniformly charges the surface of a photoreceptor drum (described later), exposes the charged surface to a laser beam to form a latent image, develops the latent image with toner, and transfers it to recording paper. The details will be explained later, but the development and development of BK data is
A blank development/transfer section 501bk that performs transfer, a cyan development/transfer section 501c that develops and transfers C data,
It includes a magenta developing/transfer section 501m that develops and transfers M data, and a yellow developing/transfer section 501y that develops and transfers Y data.

The laser drive processing unit 502 includes the image processing device 4 described above.
It outputs a laser beam by inputting the 3-bit data of Y, M, C, and BK output from 00 (in this case, image density data). Input buffer memory 503y, 5
03m, 503c, and a laser diode 50 that outputs laser beams corresponding to Y, M, C, and BK, respectively.
Drivers 505y, 505m, 505 drive the laser diodes 504y, 504m, 504c, and 504bk, respectively.
c, 505b.

In addition, the black developing/transfer section 5 of the photoreceptor development processing section 501
01bk, the laser drive processing unit 502, the laser diode 504bk, and the driver 505bk are called a black recording unit BKU (see FIG. 17). Similarly, the cyan developing/transfer section 501c, laser diode 504c, driver 505c, and buffer memory 503c are combined into a cyan recording unit C.
U (see Figure 17), magenta developing/transfer section 501m,
Laser diode 504m, driver 505
The combination of the buffer memory 503m and the magenta recording unit MU (see Fig. 17) and the yellow developing unit MU (see Fig. 17)
Transfer section 501y, laser diode j504y
, driver 505y, and.

The combination of buffer memories 503y is called a yellow recording unit YU (see FIG. 17). As shown in the figure, each of these recording units is connected to a conveyor belt 506 that conveys the recording paper.
Black recording unit B from the recording paper conveyance direction
KU, cyan recording unit CU, magenta recording unit MU, and yellow recording unit YU are arranged in this order.

Due to this arrangement of each recording unit, the laser diode 5 for black exposure starts exposure first.
04bk, and the laser diode 504y for yellow exposure starts exposure last. Therefore,
There is a time difference in the order of exposure start between each laser diode,
Data recorded during the time difference (output of the image processing device 400)
In order to hold the data, the laser drive processing unit 502 includes the three sets of buffer memories 503y, 503m, and 503c described above.
is provided.

Next, the configuration of the multivalued color laser printer 500 will be specifically explained with reference to FIG.

The multilevel color laser printer 500 includes a conveyor belt 506 that conveys recording paper, and recording units YU, MU, and YU arranged around the conveyor belt 506 as described above.
Paper cassette 507 containing CU, BKU, and recording paper
a, 507b, and paper feed rollers 508a, 508b that feed recording paper from paper feed cassettes 507a, 507b, respectively.
The recording units (BKU, CU, M
A fixing roller 510 transports U and YU sequentially and fixes the transferred image onto the recording paper, and the recording paper is transferred to a predetermined ejection section (
(not shown). Here, each recording unit YU, MU, CU, BKU has a photosensitive drum 512)', 512m, 512c, 51
2bk, and photoreceptor drums 512y and 512m, respectively.
Charger 513y that uniformly charges 512c and 512bk
, 513m, 513c, 513bk, and photosensitive drum 5
Polygon mirror 514)', 514m for guiding the laser beam to 12y, 512m, 512c, 512bk
, 514c, 514bk and motor 515y, 515m
, 515c, 515bk, and photosensitive drums 512y, 5
Toner developing devices 516)', 516m, 516c, and 516bk that develop the electrostatic latent images formed on 12m, 512c, and 512bk using toners of corresponding colors, respectively.
and a transfer charger 5 that transfers the developed toner image onto recording paper.
17y, 517m, 517c, 517bk, and photosensitive drums 512y, 512m, 512c, 512b after transfer.
Cleaning device 1J for removing toner remaining on k
518y, 518m.

It consists of 518c and 518bk. In addition, 519y
, 519m, 519c, and 519bk indicate CCD line sensors for reading predetermined patterns provided on the photoreceptor drums 512)', 512m, 512c, and 512bk, respectively. Although details are omitted, this allows multi-value color - Detecting the process status of the laser printer 500.

In the above configuration, the operation will be explained by taking the exposure, development, and transfer of the yellow recording unit 7 YU as an example.

Figures 18(a) and 18(b) show the yellow recording unit YU.
The configuration of the exposure system is shown. In the same figure, a laser beam emitted from a laser diode 504y is reflected by a polygon mirror 514y, passes through an f-theta lens 520y, and further passes through a mirror 521y.

522y and is irradiated onto the photosensitive drum 512y through the dustproof glass 523y. At this time, since the polygon mirror 514y is rotated at a constant speed by the motor 515y, the laser beam moves in a direction along the axis of the photosensitive drum 512y (main scanning direction). Furthermore, in this embodiment, a photosensor 524y is provided to detect the base point for tracking the scanning position of the main scan, so that the laser beam at the non-exposed position is detected. The laser diode 504y outputs recording data (
Since the photoreceptor drum 504y is activated to emit light based on the 3-bit data from the image processing device 400, multivalue exposure corresponding to the recording data is performed on the surface of the photoreceptor drum 504y. The surface of the photosensitive drum 504y is uniformly charged in advance by the charger 513y as described above, and an electrostatic latent image corresponding to the original image is formed by the exposure. The electrostatic latent image is developed by a yellow developing device 516y to become a yellow toner image. This toner image is transferred from the cassette 507a (or 507b) to the paper feed roller 5, as shown in FIG.
08a (or 508b), and is transferred by registration rollers 509 onto a recording sheet conveyed by a conveyor belt 506 in synchronization with the toner image formation in blank recording unit BKU.

The other recording units BKU, CU, and MU have similar configurations and perform similar operations, but the black recording unit BKU is equipped with a black toner developing device 516bk and forms and transfers a blank toner image, and the cyan recording unit C
U is equipped with a cyan toner developing device 516C, which forms and transfers a cyan toner image, and a magenta recording unit) M
U includes a magenta toner developing device 516m, which forms and transfers a magenta toner image.

(2) Multi-value drive drivers 505)', 505m, 505c, and 505b are Y sent from the image processing device 400.

Based on the 3-bit data of M, C, BK, the corresponding laser diodes 504)', 504m, 504c, 5
It performs control for multi-value driving of 04bk,
As the driving method, power modulation, pulse width modulation, etc. are generally used.

Hereinafter, multi-value drive by power modulation applied in this embodiment will be explained in detail with reference to FIGS. 19(a), (b), (C), and (d). Furthermore, the driver 505)', 505
m.

505c, 505b, and laser diode 504
y, 504m, 504c, and 504bk each have the same configuration, so here, the driver 505y and laser diode 504y will be explained as an example.

As shown in FIG. 19(a), the driver 505y turns the laser diode 504y on and off based on a predetermined LD drive clock.
n/Of f circuit 550, a D/A converter 551 that converts 3-bit image density data (here, Y data) into an analog signal, and an analog signal based on the image density value inputted from the D/A converter 551. The current that drives the laser diode 504y (LD drive current)
A constant current circuit 552 supplies Id to a laser diode on10ff circuit 550.

Here, the LD drive clock is defined as "1" to be on and "0" to be off, as shown in FIG. 19(b).
The laser diode on10ff circuit 550 turns off the laser diode 504y accordingly.

Furthermore, since the LD drive current Id and the laser beam power are in a proportional relationship, by generating the LD drive current 1d based on the image density data value, the laser beam power output corresponding to the image density data value can be obtained. become. For example, as shown in FIG. 19(b), when the image density data value is "4°" (data N-1 in the same figure), the corresponding LD drive current Id is supplied by the constant current circuit 552. , the laser beam power of the laser diode 504y is level 4. Also, the image density data value is “7”.
(Data N in the figure), the constant current circuit 552 supplies the corresponding LD drive current 1d, and the laser beam power of the laser diode 504y is level 7.
becomes.

Next, referring to FIG. 19(C), laser diode on10ff circuit 550. A specific circuit configuration of the D/A converter 551 and the constant current circuit 552 is shown. The laser diode oH10ff circuit 550 includes TTL inverters 553 and 554, differential switching circuits 555 and 556 that perform an onloff toggle operation, and a VGl
> When VG2, the differential switching circuit 555 is on.
, the differential switching circuit 556 is off, V(1,1
<When VG2, the differential switching circuit 555 is off.
.

Resistors R2 and R form a voltage dividing circuit that generates VG2 that satisfies the conditions for the differential switching circuit 556 to turn on.
It consists of 3. Therefore, when the LD drive clock is "1", the output of the inverter 554 generates VGI, the above condition (VG 1 > VG2) is satisfied, the differential switching circuit 555 is turned on, and the differential switching circuit 556 is turned off. and turn on the laser diode 504y.
Do n.

Conversely, when the LD drive clock is "0", there is no output from the inverter 554, so the above condition (VGI<
VG2) and the differential switching circuit 555 is
ff, the differential switching circuit 556 turns on and turns off the laser diode 504y.

The D/A converter 551 includes a latch 557 that latches the input image density data while the LD drive clock is "'1", a V ref generator 558 that provides the maximum output value V ref, and a V ref generator 558 that latches the input image density data and the maximum output value. V re
3-bit D outputting analog data Vd based on f
/A converter 559. Furthermore, here V
The relationship between d, image density data, and maximum output value V ref is expressed by the following equation.

The constant current circuit 552 includes the laser diode 504 in the laser diode ON10ff circuit 550 as described above.
y current, and the transistor 560
and resistors R4 and Rs. The output Vd from D/A converter 551 is applied to the base of transistor 560 to determine the voltage applied to resistor R4. In other words, since the current flowing through the resistor R4 is approximately equal to the collector current of the transistor 560, the current 1d flowing through the laser diode 504y is controlled by Vd.

FIG. 19(d) shows the output of the latch 557 mentioned above.

5 is a timing chart showing the relationship between VGI, Vd, and Id. Here, Vd is Vrey based on image density data (3 bit data: 8 gradation data from 0 to 7).
X takes values in 8 levels from O/7 to 7/7, and Id indicates levels in 8 levels from 10 to I based on this VdO value. The laser diode 504y has eight levels of this Id (I.-level 0°I, = level...T7=
According to Leher 7), the second
A latent image as shown in Figure 0 is formed.

In the image forming system to which the anti-aliasing processing and the apparatus of the present invention are applied, the toner image shown in FIG. 21 is finally created for the pentagon ABCDE shown in FIG. 5(a) by the above-described configuration and operation. Formed on recording paper. -Resolution of laser printer for boat fishing is 240~
Considering that it is 400 dpi, the density of the edge portion of the figure is visually reduced by anti-aliasing processing. FIG. 22 shows a toner image of pentagon ABCDE without anti-aliasing processing;
As is clear from a comparison between the figure (toner image of the present invention) and FIG.
becomes visually smoother.

Further, in this embodiment, multi-value driving using power modulation is applied, but it goes without saying that similar effects can be obtained by using multi-value driving using pulse width modulation.

For reference, FIG. 23 shows the change in latent image form depending on the level of pulse width modulation, and also shows the toner image when pulse width modulation is applied to the pentagon ABCDE shown in FIG. 5(a). It is shown in FIG.

〔Effect of the invention〕

As explained above, the graphic processing device of the present invention adjusts the output of pixels at the edges of vector data based on the area ratio to be filled, and smoothly expresses the jaggedness (alias) at the edges of the output image. In a graphic processing device that performs anti-aliasing processing, an edge portion pixel number calculating means calculates the number of edge portion pixels by inputting the coordinate values of two intersection points when vector data crosses a scan line; Since the present invention includes an approximate area ratio determining means for determining an approximate area ratio of pixels in the edge part by interpolating the area ratio of the image part and the area ratio of the non-image part using Approximate area ratios can be obtained quickly and easily.

[Brief explanation of the drawing]

FIGS. 1(a) to (e) are explanatory diagrams showing the principle of antialiasing processing in the graphic processing apparatus of the present invention, FIG. 2 is an explanatory diagram showing the configuration of the image forming system of this embodiment, and FIG. FIG. 4(a) is a flowchart showing the operation of the PDL controller; FIG. 4(c) is an explanatory diagram showing the path filling process; FIG. Figure (C) is a flowchart showing anti-aliasing processing, Figure 5 (a),
(b) is an explanatory diagram showing linear vector division of a figure, the sixth
The figure is an explanatory diagram showing the approximate area ratio after performing anti-aliasing processing, Figure 7 (a) [Yes]), and (C) is an explanatory diagram showing RGB image data stored in the plain memory section of the page memory. , Figure 8(a), ■), (C)
is an explanatory diagram showing the RGB image data stored in the plain memory section of the page memory when anti-aliasing processing is not performed, Fig. 9 is an explanatory diagram showing the configuration of the image processing device, and Fig. 10 is an explanatory diagram showing the configuration of the T correction circuit. Explanatory diagram showing the conversion graph for T correction, Fig. 11 (a), (b), (C)
are explanatory diagrams showing conversion graphs for complementary color generation used in the complementary color generation circuit;
D is Fig. 7(a),(b). An explanatory diagram showing the state in which the RGB image data shown in (C) is output from the UCR processing/black generation circuit, FIG. 13 is an explanatory diagram showing a Bayer type 3×3 multilevel dither matrix, and FIG. Figures (a), (b), (C), and (b) are Y and M in Figure 12 (a), (b), (C), and (d).
, C, BK data converted by the gradation processing circuit.
C), (d) is Fig. 8(a). Φ), (C) is an explanatory diagram showing the state in which the data is processed by the image processing device. Fig. 16 is a control block diagram showing a multi-value color laser printer. Fig. 17 is a control block diagram of a multi-value color laser printer. FIGS. 18(a) and 18(b) are explanatory diagrams showing the configuration of the exposure system of the yellow recording unit, and FIGS. 19(a) and (b) are explanatory diagrams showing the configuration of the exposure system of the yellow recording unit. (C) and (d) are explanatory diagrams showing multi-level driving by power modulation, Figure 20 is an explanatory diagram showing the state of latent images depending on the level of power modulation, and Figure 21 is the diagram shown in Figure 5 (a). An explanatory diagram showing the final toner image of pentagon ABCDE, FIG. 22 is pentagon A without anti-aliasing processing.
An explanatory diagram showing the toner image of BCDE, Fig. 23 is an explanatory diagram showing the state of the latent image depending on the level of pulse width modulation, and Fig. 24 is an explanatory diagram showing the state of the latent image depending on the level of pulse width modulation. An explanatory diagram showing a toner image in the case of FIG. 25 (
a), (b) are explanatory diagrams showing conventional anti-aliasing processing, FIGS. 26 (a), et al.) are explanatory diagrams showing anti-aliasing processing using the uniform averaging method, and FIG.
), (b) is an explanatory diagram showing anti-aliasing processing using the weighted averaging method, and Fig. 28 (a), (b),
(C) and (d) are explanatory diagrams showing examples of filters used in the weighted averaging method, and FIG. 29 is an explanatory diagram showing a convolution method with reference to 3×3 pixels. Explanation of symbols 100-1...-Host computer 200---
----- P D L controller 201---m
-Receiving device 202・----CP U203---
---1 Internal system bus 204 --- RAM 205 ----RO
M206----Page memory 207----
--- - Transmitting device 208 --- I/O device 300 --- Image reading device 400 --- Image processing device 500 --- Multi-level color laser printer 60
0---System control section

Claims (1)

[Claims] Graphic processing that adjusts the output of pixels at the edge of vector data based on the area ratio to be filled, and performs anti-aliasing processing to smoothly express jaggedness (alias) at the edge of the output image. In the apparatus, an edge part pixel number calculating means calculates the number of edge part pixels by inputting the coordinate values of two intersection points when the vector data crosses a scan line; 2. A graphic processing device comprising: approximate area ratio determination means for determining an approximate area ratio of pixels in the edge portion by interpolating the area ratio of the non-image area and the area ratio of the non-image area.