JP2004054635A - Picture processor and its method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a picture processor and its method capable of obtaining effective plotting results by small calculation quantity. <P>SOLUTION: The picture processor is provided with a vertex unit processing part 21 for calculating a filter processing parameter on the basis of a normal vector and a sight line vector provided in each vertex, a rasterizing part 22 for rasterizing vertex information as a fragment unit and rasterizing the filter processing parameter, a fragment unit processing part 23 for performing arithmetic processing on the basis of the information rasterized as the fragment unit, a memory part 24 for storing the rasterized filter processing parameter in each pixel and storing the output information from the fragment unit processing part 23, and a picture processing part 25 for finding a suitable filter kernel on the basis of the value of the filter processing parameter stored in the memory part 24 and applying filter processing to the information stored in the memory part 24 on the basis of the found filter kernel. <P>COPYRIGHT: (C)2004,JPO

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an image processing apparatus and method for realizing three-dimensional computer graphics.
[0002]
[Prior art]
The computer graphics (CG) technology, which creates and processes graphics and images using computer resources, has been actively researched and developed in conjunction with the recent increase in computational speed and the enhancement of drawing functions in computer systems. It has been put to practical use.
[0003]
For example, three-dimensional graphics expresses an optical phenomenon when a three-dimensional object is illuminated by a predetermined light source by using a mathematical model, and based on this model, shades, shades, and even patterns on the object surface. By pasting, a more realistic three-dimensional two-dimensional high-definition image is generated.
Such computer graphics have been increasingly used in CAD / CAM and other various application fields in development fields such as science, engineering, and manufacturing.
[0004]
The three-dimensional graphics generally includes a “geometry subsystem” positioned as a front end and a “raster subsystem” positioned as a back end.
[0005]
The geometry subsystem is a process of performing a geometric operation such as a position and a posture of a three-dimensional object displayed on a display screen.
In the geometry subsystem, an object is generally treated as an aggregate of a large number of polygons, and geometric calculation processing such as “coordinate conversion”, “clipping”, and “light source calculation” is performed in units of polygons.
[0006]
On the other hand, the raster subsystem is a process of painting each pixel constituting an object.
The rasterizing process is realized by, for example, interpolating the image parameters of all the pixels included in the polygon based on the image parameters obtained for each vertex of the polygon.
The image parameters referred to here include color (rendering color) data expressed in a so-called RGB format and the like, z values indicating a distance in the depth direction, and the like.
In recent high-definition three-dimensional graphics processing, f (fog: fog) for creating perspective and texture t (texture) for expressing reality by expressing the texture and pattern of the surface of an object, It is included as one of the image parameters.
[0007]
Here, the process of generating pixels inside the polygon from the vertex information of the polygon is often performed using a linear interpolation method called DDA (Digital Differential Analyzer).
In the DDA process, the inclination of data in the direction of the side of the polygon is obtained from the vertex information, the data on the side is calculated using this inclination, and then the inclination in the raster scanning direction (X direction) is calculated. The internal pixel is generated by adding the parameter change obtained from the above to the parameter value of the scanning start point.
[0008]
FIG. 1 is a block diagram illustrating a configuration example of a conventional image processing apparatus in the field of real-time three-dimensional computer graphics, and FIG. 2 is a flowchart illustrating an image processing operation of the image processing apparatus of FIG.
[0009]
As shown in FIG. 1, the image processing apparatus 10 includes a vertex unit processing unit 11, a rasterizing unit 12, a fragment unit processing unit 13, and a memory unit 14.
[0010]
Hereinafter, the drawing process in the image processing apparatus 10 of FIG. 1 will be described with reference to the flowchart of FIG. 2 together with the function of each component.
[0011]
The vertex unit processing unit 11 receives the vertex information VI, performs a process on a per-vertex basis, and outputs the result to the rasterizing unit 12 as a signal S11 (step ST1).
The rasterizing unit 12 inputs the processed vertex information S11, converts it into information in fragment units by interpolating, and outputs the result to the fragment unit processing unit 13 as a signal S12 (step ST2).
The fragment unit processing unit 13 receives the fragment information S12, performs fragment unit processing on the fragment information S12, and outputs the result to the memory unit 14 as a signal S13 (step ST3).
The memory unit 14 receives the processed fragment information S13, and holds a result obtained by adding necessary tests and calculations to the fragment information S13 (step ST4).
Finally, when the rendering of all the primitives included in the scene is completed, the process ends. On the other hand, if the drawing is not completed, the process is repeated from step ST1 (step ST5).
[0012]
In typical three-dimensional computer graphics, the vertex unit processing in step ST1 includes processing such as coordinate conversion and color calculation. Further, the fragment unit processing in step ST3 includes processing such as texture mapping. The drawing process in step ST4 includes processes such as alpha blending and a depth test.
[0013]
Any of the above processes can realize high throughput by using a pipeline configuration, and is suitable for real-time computer graphics. Hereinafter, these may be called a drawing pipeline.
[0014]
On the other hand, in recent three-dimensional computer graphics, a method for improving image quality by using image processing other than a drawing pipeline has been proposed.
[0015]
FIG. 3 is a block diagram showing a configuration example of an image processing apparatus adopting a method of improving image quality by using image processing other than a drawing pipeline in the field of conventional real-time three-dimensional computer graphics, and FIG. 13 is a flowchart illustrating an image processing operation of the image processing apparatus of No. 3;
[0016]
The image processing apparatus 10A includes an image processing unit 15 in addition to the vertex unit processing unit 11, the rasterizing unit 12, the fragment unit processing unit 13, and the memory unit 14, which are the components of FIG.
Further, in the flowchart of FIG. 4, a step ST6 for image processing in the image processing unit 15 is added in addition to the processing of steps ST1 to ST5 of FIG.
[0017]
Basically, in the image processing apparatus 10A, after drawing of the entire scene is completed, some calculation is performed on the entire screen.
The portion described as the entire screen above may be a part of the screen depending on the drawing method. For example, in a case where the entire screen is divided into a plurality of partial areas and each is separately drawn, image processing is performed for each of the partial areas. Hereinafter, image processing including such a case will be described.
[0018]
In the fur drawing process, there is a method of improving the image quality by performing a filtering process on the entire screen based on the direction of the fur in the window coordinate system.
There is also a method of realizing a depth of field based on a depth value held for the entire screen.
These are techniques for improving the image quality by using an image processing approach without greatly increasing the information flowing through the drawing pipeline.
[0019]
In drawing fur, there has been proposed a method of expressing a texture at high speed by overlapping and drawing a plurality of shells as a translucent object as shown in FIG.
However, there is a problem that discontinuity due to shell drawing increases near an edge, and hairs appear discontinuous as shown in FIG.
On the other hand, by applying a filter considering the direction of the hair, discontinuity of the hair can be eliminated as shown in FIGS. 7A and 7B.
[0020]
In realizing Depth Of Field, filtering is performed using a larger kernel for pixels whose depth value deviates from a certain value. Thereby, the blurring effect can be expressed well.
[0021]
As a similar method, there is a method of realizing simple anti-aliasing by applying a fixed filter to the entire screen.
This uses a filter as shown in FIG. 8, for example, for the result drawn in the frame buffer. Here, anti-aliasing is realized by weighting and adding the color information of one pixel to a plurality of neighboring pixels.
[0022]
All of these methods improve image quality by using image processing performed on the entire screen in combination with conventional drawing processing using a drawing pipeline.
In the case of fur drawing or depth of field, parameters for selecting a kernel for filter processing have been calculated using a drawing pipeline. On the other hand, in the case of realizing the simple anti-aliasing, the filter kernel is often fixed.
[0023]
[Problems to be solved by the invention]
When image processing is performed on the entire screen in addition to the drawing processing using the drawing pipeline as described above, there are the following problems.
[0024]
In the case of filter processing used for drawing fur, a filter is often required near the edge of an object. Filtering with a large kernel size is necessary near the edge, but discontinuity is not noticeable outside the edge, so a small kernel size is sufficient for filtering.
[0025]
Generally, in the filtering process, the calculation amount increases as the kernel size increases. Filtering with a large kernel size other than at edges results in high-cost calculations with little effect on image quality. This leads to reduced performance. Further, when image processing is used for the anti-aliasing, there is a problem in image quality. When performing the filter processing, it is possible to satisfactorily blur the vicinity of the edge. However, when performing the filter processing, there is a problem that an image other than the edge is excessively blurred because the entire screen is uniformly blurred.
[0026]
The present invention has been made in view of such circumstances, and an object of the present invention is to make it possible to change the content of image processing for an edge portion of an object and other regions, and to obtain a good drawing result with a small amount of calculation. To provide an image processing apparatus and a method for obtaining the image processing apparatus.
[0027]
[Means for Solving the Problems]
In order to achieve the above object, an image processing apparatus according to a first aspect of the present invention includes a vertex unit processing unit that performs a calculation in units of vertices, and information calculated by the unit of vertices is rasterized into fragments. A rasterizing unit, a fragment unit processing unit that performs arithmetic processing based on the information in fragment units, a memory unit that holds the information processed by the fragment unit processing unit, and an image An image processing unit for performing processing, wherein the image processing unit performs different filter processing according to predetermined filter processing parameters.
[0028]
In the present invention, the filter processing parameter is a parameter indicating the degree of the vicinity of the edge.
[0029]
In the present invention, the vertex unit processing unit calculates a filter processing parameter and supplies the filter processing parameter to the image processing unit.
[0030]
In the present invention, the fragment unit processing unit calculates a filter processing parameter and supplies it to the image processing unit.
[0031]
In the present invention, the filter processing parameter is calculated based on the normal vector and the line-of-sight vector.
[0032]
According to the present invention, the image processing section performs a filter process that produces an anti-aliasing effect.
[0033]
In the present invention, the image processing unit performs a filter process in drawing a shell of fur.
[0034]
An image processing apparatus according to a second aspect of the present invention performs a calculation in units of vertices, and calculates a filter processing parameter based on a normal vector and a line-of-sight vector given to each vertex, and a vertex unit processing unit. A rasterizing unit that rasterizes the information calculated by the vertex unit processing unit into fragment units and rasterizes the filter processing parameters; a fragment unit processing unit that performs arithmetic processing based on the information in fragment units; A memory unit that holds the processed filter processing parameters for each pixel, and that holds the information processed by the fragment unit processing unit, and a filter that is suitable based on the values of the filter processing parameters stored for each pixel in the memory unit. Calculate the kernel and make the above memo based on the obtained filter kernel. And an image processing unit that performs filter processing on information held in the section.
[0035]
An image processing apparatus according to a third aspect of the present invention includes: a rasterizing unit that rasterizes vertex information into fragments and rasterizes a normal vector and a line-of-sight vector given to each vertex; A fragment unit processing unit that calculates a filter processing parameter based on the line-of-sight vector and performs an arithmetic process based on the information in the fragment unit, and a filter unit that holds the filter processing parameter for each pixel, A memory unit for storing the information obtained by the arithmetic processing, a filter kernel is obtained based on the value of the filter processing parameter stored for each pixel in the memory unit, and the information stored in the memory unit based on the obtained filter kernel And an image processing unit that performs filter processing on .
[0036]
An image processing method according to a fourth aspect of the present invention includes a first step of rasterizing vertex unit information of an object into fragment units and a second step of performing an arithmetic process based on the information in fragment units. And a third step of performing different filter processing on the arithmetic processing information in accordance with a filter processing parameter indicating a degree of edge proximity.
[0037]
According to the present invention, for example, the vertex information and the normal vector N and the line-of-sight vector E given to each vertex are input to the vertex unit processing unit.
In the vertex unit processing unit, a filter processing parameter indicating the degree of the vicinity of the edge is calculated based on the normal vector N and the line-of-sight vector E given to each vertex, and output to the rasterizing unit.
Further, the vertex unit processing unit performs processing per vertex based on the input vertex information, that is, contents to be normally processed other than calculation of filter processing parameters, for example, vertex coordinate conversion, and the like. Output the processing result to the rasterizing unit.
[0038]
In the rasterizing unit, for example, interpolation processing is performed on the processed vertex information, and conversion into information in fragment units is performed. Then, the processing result is output from the rasterizing unit to the fragment unit processing unit.
The filter processing parameters supplied to the rasterizing unit are rasterized and stored in the memory unit.
In the fragment unit processing unit, fragment unit processing such as texture mapping is performed on the fragment information by the rasterizing unit. Then, the fragment information is output from the fragment unit processing unit to the memory unit and held.
Then, in the image processing unit, the filter processing parameter and the fragment information, which are stored in the memory unit and indicate, for example, the ratio near the edge, are read. In the image processing unit, a filter kernel is selected based on a filter processing parameter indicating a ratio in the vicinity of the edge, and a filter process is performed using the selected filter kernel.
As described above, according to the present invention, by selecting a filter kernel based on the filter processing parameter indicating the ratio of the vicinity of the edge, a good drawing result can be obtained with a small amount of calculation as a whole.
[0039]
BEST MODE FOR CARRYING OUT THE INVENTION
First Embodiment FIG. 9 is a block diagram showing a first embodiment of an image processing device according to the present invention.
[0040]
As shown in FIG. 9, the image processing apparatus 20 includes a vertex unit processing unit 21, a rasterizing unit 22, a fragment unit processing unit 23, a memory unit 24, and an image processing unit 25.
[0041]
The vertex unit processing unit 21 calculates a filter processing parameter p based on the normal vector N and the line-of-sight vector E given for each vertex. The filter processing parameter calculated by the vertex unit processing unit 21 is a parameter indicating a degree near the edge.
The reciprocal of the inner product of the normal vector N and the line-of-sight vector E is used for calculating the filter processing parameter.
Further, the vertex unit processing unit 21 receives the vertex information VI, performs a process on a per-vertex basis, and outputs the result (information on which the arithmetic processing is performed) to the rasterizing unit 22 as a signal S21.
[0042]
FIG. 10 is a diagram for explaining specific processing of the vertex unit processing unit 21.
[0043]
As shown in FIG. 10, the vertex unit processing unit 21 receives a normal vector N and a line-of-sight vector E for each vertex from a higher-level device (not shown) (step ST21). Based on this, the filter processing parameter p is calculated as in Equation 1 (Step ST22). This equation takes a larger value as the normal and the line of sight are closer to vertical.
[0044]
(Equation 1)
p = 1− | N · E |
[0045]
Note that Equation 1 is an example of a calculation method, and any calculation method may be used as long as it is possible to distinguish the periphery of the edge and the rest from the values of the normal vector N and the line-of-sight vector E.
For example, Equation 2 shown below takes a larger value as the normal and the line of sight are closer to vertical.
[0046]
(Equation 2)
p = | sin (NE) |
[0047]
The vertex unit processing section 21 outputs the calculated filter processing parameter p to the subsequent stage, that is, to the rasterizing section 22 (step ST23).
Also, the vertex unit processing unit 21 performs processing other than the calculation of the filter processing parameters, such as the contents to be normally processed, for example, the coordinate conversion of the vertices (step ST24).
[0048]
The rasterizing unit 22 receives the processed vertex information S21, converts it into information on a fragment basis by interpolating the same, and outputs the result to the fragment unit processing unit 23 as a signal S22.
The rasterizing unit 22 rasterizes the filter processing parameter p calculated by the vertex unit processing unit 21, and causes the memory unit 24 to store the pixel by pixel through the fragment unit processing unit 23.
[0049]
Fragments unit processing section 23 inputs the fragment information S22 by rasterizing unit 22, performs processing of fragments units such as texture mapping to the input information, in the memory unit 24 the fragment information d _in a result as signal S23 Output and hold.
[0050]
Memory unit 24 receives the fragment information d _in the processed, holds the result of adding the test or operations required thereto.
The memory unit 24 holds, for each pixel, a filter processing parameter p calculated by the vertex unit processing unit 21 and rasterized by the rasterizing unit 22, and also holds fragment information d _out after image processing by the image processing unit 25. .
[0051]
The image processing unit 25 selects a filter kernel based on the filter processing parameter indicating the ratio of the vicinity of the edge held in the memory unit 24, performs a filter process using the selected filter kernel, and processes the fragment information after the process. Is output to the memory unit 24 and stored.
[0052]
FIG. 11 is a diagram for explaining specific processing of the image processing unit 25.
[0053]
The image processing unit 25, as shown in FIG. 11, enter the filter parameter p stored in the memory unit 24 (step ST31), and inputs the fragment information _{d in} (step ST32).
Then, the image processing unit 25 performs a filter process (step ST33).
The image processing unit 25 outputs the fragment information d _out after the filter processing to the memory unit 24 (Step ST34).
The image processing unit 25 repeats the processing of steps ST31 to ST34 for the entire screen (step ST35).
[0054]
As the filter processing performed by the image processing unit 25, there are combinations of single / plural input d _in and output d _out . In the present embodiment, the case where the kernel of the filter processing is changed by the filter processing parameter p also includes any of these cases.
[0055]
Hereinafter, a case where an input of a single pixel is output to a plurality of pixels will be described. Equation 3 shows a case where the input d _in of a single pixel is set as the output d _out [i] to a plurality of pixels.
[0056]
[Equation 3]
d _out [i] = a [i] * d _in
[0057]
Here, [i] expresses a plurality of pixels in an array format. a [i] is a coefficient indicating how much the input contributes to the output, and can be considered as a filter kernel.
In general, the filter kernel a [i] does not depend on the filtering parameter p.
[0058]
FIG. 12 is a diagram illustrating an example of a filter kernel a [i] related to fur drawing.
[0059]
Typically, the filter kernel a [i] is provided in the table by the image processing unit 25, and is given by referring to the table using the direction of hair as an index.
By the way, the example in FIG. 12 is a 5 × 5 filter kernel, and there is a case where it is not necessary to calculate a filter kernel of this size unless it is near an edge. In this case, the amount of calculation can be reduced by subtracting a small filter kernel a [i] s of 3 × 3 based on the filter processing parameter p as shown in FIG.
[0060]
FIG. 14 is a diagram illustrating an example of a filter kernel when performing anti-aliasing.
As shown in the example of FIG. 14, by using a filter kernel in which the coefficient value at the center is large and the periphery is small, it is possible to blur the whole.
However, there is a disadvantage that using such a filter kernel is too blurry except for the vicinity of the edge.
Therefore, it is effective to set the filter kernel not to a fixed value but to variable values al and a2 that change depending on the filter processing parameter p as shown in FIG. al and a2 can be calculated as in Equation 4, for example.
[0061]
(Equation 4)
a1 = 1/2 + 1/2 * (1-p)
a2 = 1/16 * p
[0062]
According to Equation 4, when p = 1 (that is, near the edge), al and a2 are 、 and 1/16, respectively. On the other hand, when p = 0, a1 = 1 and a2 = 0.
In other words, blurring occurs in the vicinity of the edge, but hardly occurs in other areas, and a better image can be obtained.
[0063]
Next, the operation of the image processing apparatus of FIG. 9 will be described.
[0064]
The vertex information VI and the normal vector N and the line-of-sight vector E given for each vertex are input to the vertex unit processing unit 21.
The vertex unit processing unit 21 calculates a filter processing parameter p indicating the degree of the vicinity of the edge based on the normal vector N and the line-of-sight vector E given to each vertex, and outputs it to the rasterizing unit 22.
In addition, the vertex unit processing unit 21 performs processing per vertex based on the input vertex information VI, that is, contents to be normally processed other than calculation of the filter processing parameter, for example, coordinate conversion of the vertex. The processing result is output from the vertex unit processing unit 21 to the rasterizing unit 22 as a signal S21.
[0065]
In the rasterizing unit 22, interpolation processing is performed on the processed vertex information S21, and conversion into information in fragment units is performed. Then, the processing result is output from the rasterizing unit 22 to the fragment unit processing unit 23 as a signal S22.
The filtering parameter p supplied to the rasterizing unit 22 is stored in the memory unit 24 through the fragment unit processing unit 23.
[0066]
In the fragment unit processing unit 23, fragment unit processing such as texture mapping is performed on the fragment information S 22 by the rasterizing unit 22. The fragment information _{d in} the fragment unit processing unit 23 is output as signal S23 to the memory unit 24, is held.
[0067]
Then, the image processing unit 25, filter parameter p and the fragment information d _in showing the percentage of edge periphery held in the memory unit 24 is read out. In the image processing unit 25, a filter kernel is selected based on a filter processing parameter p indicating a ratio near the edge, and a filter process is performed using the selected filter kernel. Then, the processed fragment information is output to the memory unit 24 and held.
[0068]
As described above, according to the first embodiment, a vertex unit processing unit 21 that calculates a filter processing parameter based on a normal vector and a line-of-sight vector given for each vertex, and vertex information is rasterized and fragmented. A rasterizing unit 22 that rasterizes filter processing parameters as a unit, a fragment unit processing unit 23 that performs arithmetic processing based on information in fragment units, and holds a rasterized filter processing parameter for each pixel, and performs fragment unit processing. A memory unit 24 for holding the output information of the unit 23, and a suitable filter kernel based on the value of the filter processing parameter stored in the memory unit 24, which is stored in the memory unit 24 based on the obtained filter kernel. Image processing unit 25 that performs filter processing on information Since the provided, it is possible to change the contents of the image processing on the edge portion and the other area of the object, it is possible to obtain a good drawing results with a small amount of calculation.
[0069]
Second Embodiment FIG. 16 is a block diagram showing a second embodiment of the image processing device according to the present invention.
[0070]
The difference between the second embodiment and the first embodiment is that the filter processing parameters are calculated by the fragment unit processing unit 23A instead of being calculated by the vertex unit processing unit 21A.
[0071]
In this case, the normal vector N and the line-of-sight vector E are rasterized by the rasterizing unit 22A and provided to the fragment unit processing unit 23A.
[0072]
Other configurations and functions are the same as those of the above-described device of FIG.
[0073]
When the calculation of the filter processing parameter p is performed for each fragment as in the second embodiment, higher quality filter processing can be expected.
[0074]
【The invention's effect】
As described above, according to the present invention, the content of image processing can be changed for an edge portion of an object and other regions, and a good drawing result can be obtained with a small amount of calculation.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration example of a conventional image processing apparatus in the field of real-time three-dimensional computer graphics.
FIG. 2 is a flowchart illustrating an image processing operation of the image processing apparatus of FIG. 1;
FIG. 3 is a block diagram illustrating a configuration example of an image processing apparatus employing a method of improving image quality by using image processing other than a drawing pipeline in the field of conventional real-time three-dimensional computer graphics.
FIG. 4 is a flowchart illustrating an image processing operation of the image processing apparatus of FIG. 3;
FIG. 5 is a diagram for describing fur shell drawing.
FIG. 6 is a diagram for explaining a problem that discontinuity due to shell drawing increases near an edge and hairs appear discontinuous.
FIG. 7 is a diagram for explaining that the discontinuity of the hair can be eliminated by applying a filter in which the direction of the hair is added.
FIG. 8 is a diagram for explaining a method of realizing simple anti-aliasing by applying a fixed filter to the entire screen.
FIG. 9 is a block diagram illustrating a first embodiment of the image processing apparatus according to the present invention.
FIG. 10 is a flowchart illustrating a specific process of a vertex unit processing unit according to the first embodiment;
FIG. 11 is a flowchart illustrating a specific process of an image processing unit according to the first embodiment.
FIG. 12 is a diagram illustrating an example of a filter kernel a [i] related to fur drawing.
FIG. 13 is a diagram for explaining an example in which the amount of calculation is suppressed using a small filter kernel based on a filter processing parameter p.
FIG. 14 is a diagram illustrating an example of a filter kernel when performing anti-aliasing.
FIG. 15 is a diagram for describing an example in which a filter kernel is not a fixed value but a variable value that changes according to a filter processing parameter p.
FIG. 16 is a block diagram illustrating a second embodiment of the image processing apparatus according to the present invention.
[Explanation of symbols]
20, 20A image processing device, 21, 21A vertex unit processing unit, 22, 22A rasterizing unit, 23, 23A fragment unit processing unit, 24 memory unit, 25 image processing unit.

Claims (20)

A vertex unit processing unit that performs an operation per vertex;
A rasterizing unit that rasterizes the information calculated by the vertex unit processing unit and sets it as a fragment unit;
A fragment unit processing unit for performing arithmetic processing based on the information in fragment units,
A memory unit for holding information processed by the fragment unit processing unit;
An image processing unit that performs image processing on the content of the memory unit;
Has,
An image processing apparatus, wherein the image processing unit performs different filter processing according to a predetermined filter processing parameter. The image processing apparatus according to claim 1, wherein the filter processing parameter is a parameter indicating a degree near an edge. The image processing apparatus according to claim 1, wherein the vertex unit processing unit calculates a filter processing parameter and supplies the calculated parameter to the image processing unit. The image processing apparatus according to claim 1, wherein the fragment unit processing unit calculates a filtering parameter and supplies the calculated parameter to the image processing unit. The image processing device according to claim 1, wherein the filter processing parameter is calculated based on a normal vector and a visual line vector. The image processing apparatus according to claim 3, wherein the vertex unit processing unit calculates the filter processing parameter based on a normal vector and a line-of-sight vector. The image processing apparatus according to claim 4, wherein the fragment unit processing unit calculates the filter processing parameter based on a normal vector and a line-of-sight vector. The image processing apparatus according to claim 1, wherein the image processing unit performs a filter process that produces an anti-aliasing effect. The image processing device according to claim 1, wherein the image processing unit performs a filter process in drawing a fur shell. A vertex unit processing unit that performs an operation for each vertex, and calculates a filter processing parameter based on a normal vector and a line-of-sight vector given for each vertex;
A rasterizing unit that rasterizes the information calculated by the vertex unit processing unit into fragments and rasterizes the filter processing parameters;
A fragment unit processing unit for performing arithmetic processing based on the information in fragment units,
A memory unit that holds the rasterized filter processing parameters for each pixel, and holds information that has been subjected to arithmetic processing by the fragment unit processing unit;
Image processing for obtaining a suitable filter kernel based on a value of a filter processing parameter held for each pixel in the memory unit, and performing a filter process on information held in the memory unit based on the obtained filter kernel Image processing apparatus having a unit. The image processing apparatus according to claim 10, wherein the filter processing parameter is a parameter indicating a degree near an edge. The image processing device according to claim 10, wherein the vertex unit processing unit calculates a filter processing parameter based on a normal vector and a visual line vector. A rasterizing unit for rasterizing the vertex information into fragments and rasterizing a normal vector and a line-of-sight vector given for each vertex;
A fragment unit processing unit that calculates a filter processing parameter based on the rasterized normal vector and the line of sight vector, and performs an arithmetic process based on the information in the fragment unit.
A memory unit that holds the filter processing parameter for each pixel, and holds information processed by the fragment unit processing unit;
An image processing unit that obtains a filter kernel based on a value of a filter processing parameter held for each pixel in the memory unit, and performs a filtering process on information held in the memory unit based on the obtained filter kernel; An image processing apparatus having: 14. The image processing apparatus according to claim 13, wherein the filter processing parameter is a parameter indicating a degree near an edge. 14. The image processing apparatus according to claim 13, wherein the rasterizing unit calculates a filter processing parameter based on a normal vector and a visual line vector. A first step of rasterizing the vertex unit information of the object into fragment units,
A second step of performing an arithmetic process based on the information in fragment units;
Performing a different filtering process on the calculation processing information in accordance with a filtering parameter indicating a degree of edge proximity. 17. The image processing method according to claim 16, wherein the filtering parameter is based on a normal vector and a line-of-sight vector. 17. The image processing method according to claim 16, wherein the calculation of the filter processing parameter is performed at a time of a vertex unit process. 17. The image processing method according to claim 16, wherein the calculation of the filter processing parameter is performed at the time of fragment unit processing. 17. The image processing method according to claim 16, wherein the filtering includes a filtering that produces an anti-aliasing effect.

Images