JP2006011539A - Program, information storage medium, and image generating system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a program, an information storage medium and an image generating system capable of carrying out efficient blurring treatment. <P>SOLUTION: The image generating system comprises an image reducing part that reduces an area to be blurred, and generates a reduced image, a blurring part that carries out blurring of the reduced image and generates a blurred reduced image, and an enlarging part that enlarges the area of the blurred reduced image. The image reducing part generates a first reduction image based on the information of the first sampling point group of the image to be blurred, generates a second reduction image based on the information of the second sampling point group of the image to be blurred, and generates a Jth reduction image based on the information of the Jth sampling point group of ..... the image to be blurred (J is an integer no less than 2). For the reduction image, the system increases luminance of a pixel of high luminance and decreases luminance of a pixel of low luminance using a given threshold value of luminance, and generates a soft focus image. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a program, an information storage medium, and an image generation system.

  Conventionally, an image generation system (game system) that generates an image that can be viewed from a virtual camera (a given viewpoint) in an object space (virtual three-dimensional space) in which an object such as a character is set is known. It is popular as a place to experience so-called virtual reality.

  In such an image generation system, a blurred image may be generated for various image expressions such as a glare effect, a depth of field, and a soft focus effect. As a technique for generating such a blurred image, for example, there is a conventional technique disclosed in Japanese Patent Laid-Open No. 2001-160153.

  However, the processing capacity of the image generation system hardware is limited, and the processing time is also limited. Therefore, realization of an efficient blurring process with a high blurring effect is desired.

For example, when generating an image of a game scene illuminated by the moonlight, if the soft focus effect used in the field of photography can be expressed realistically, the virtual reality of the player can be further enhanced.
JP 2001-160153 A

  The present invention has been made in view of the above problems, and an object thereof is to provide a program, an information storage medium, and an image generation system capable of realizing efficient blurring processing.

  The present invention is an image generation system for generating an image, and performs a process for reducing the area of a blur target image to generate a reduced image, and a blur process for the reduced image. And an image generation system including a blur processing unit that generates a blurred reduced image and an enlargement processing unit that performs processing to enlarge the area of the blurred reduced image. The present invention also relates to a program that causes a computer to function as each of the above-described units. The present invention also relates to a computer-readable information storage medium that stores (records) a program that causes a computer to function as each unit.

  According to the present invention, the blurring target image is reduced, the reduced image is subjected to the blurring process, and the reduced image subjected to the blurring process is enlarged. In this way, since the blurring process only needs to be performed on a reduced image with a small area, the load of the blurring process can be reduced. In the present invention, since the reduced image subjected to the blurring process is enlarged, the blurring length can be increased, and an efficient blurring process can be realized with a small processing load.

  In the image generation system, the program, and the information storage medium according to the present invention, the reduction processing unit generates a first reduced image based on information in the first sampling point group of the blur target image, and the blur target image. A second reduced image is generated based on the information in the second sampling point group, and ... based on the information in the Jth sampling point group (J is an integer of 2 or more) of the blurring target image Thus, a Jth reduced image may be generated.

  In this way, it is possible to minimize the loss of information that the blur target image has, and the quality of the generated blur image can be improved.

  In the image generation system, the program, and the information storage medium according to the present invention, the blur processing unit combines the first to Jth reduced images, performs blur processing on the combined reduced image, and blurs the image. A composite reduced image may be generated, and the enlargement processing unit may perform an enlargement process of the blurred composite reduced image.

  In the image generation system, the program, and the information storage medium according to the present invention, the blur processing unit performs blur processing on the first to Jth reduced images, and the first to Jth blurred reduced images are obtained. The enlargement processing unit may perform the enlargement process and the composition process of the first to Jth blurred reduced images.

  In the image generation system, the program, and the information storage medium according to the present invention, the reduction processing unit may generate a reduced image by bilinear interpolation texture mapping.

  However, the reduction process may be realized by texture mapping other than bilinear sampling (for example, point sampling).

  In the image generation system, the program, and the information storage medium according to the present invention, the brightness of the reduced image is higher for a high pixel and lower for a low pixel with a given value as a threshold. A video filter processing unit that performs video filter processing (the computer functions as a video filter processing unit), and the blur processing unit performs blur processing on the reduced image that has been subjected to the video filter processing. Also good.

  In this way, a real image with a so-called soft focus effect can be generated.

  In the image generation system, the program, and the information storage medium according to the present invention, the video filter processing unit may perform video filter processing that emphasizes a given color component of the reduced image.

  In this way, it is possible to generate a soft focus image in which a specific color is emphasized, and increase the diversity of the generated image.

  In the image generation system, the program, and the information storage medium according to the present invention, when the blur processing unit performs the blur processing a plurality of times on the reduced image, the Kth of the plurality of blur processings. A conversion process for increasing the pixel value of the blurred image obtained by the blurring process may be performed, and the next K + 1th blurring process may be performed on the blurred image after the conversion process.

  According to the present invention, blurring processing is performed a plurality of times on a reduced image (first to Jth reduced images). Then, a conversion process for increasing the pixel value of the blurred image obtained by the Kth blurring process is performed, and the next K + 1th blurring process is performed on the blurred image subjected to the conversion process. In this way, it is possible to solve the problem that the actual blur length is shorter than the blur length expected from the number of blur processes, and to realize efficient blur processing.

  Further, the image generation system, the program, and the information storage medium according to the present invention include a conversion table storage unit that stores a conversion table for the conversion process (a computer functions as the conversion table storage unit), and the blur processing unit includes The conversion process for increasing the pixel value of the blurred image may be performed using the conversion table.

  In this way, the conversion process can be realized using an appropriate conversion table according to the number and contents of the blur processes.

  In the image generation system, the program, and the information storage medium according to the present invention, the conversion table is a look-up table for index color / texture mapping, and the blur processing unit is the blur obtained by the Kth blur process. The pixel value of the image is set as the index number of the lookup table, and the conversion process for increasing the pixel value of the blurred image is performed by performing index color / texture mapping using the lookup table. Also good.

  In this way, for example, a blurred image conversion process can be realized by a single texture mapping, so that the process can be made more efficient.

  In the image generation system, the program, and the information storage medium according to the present invention, the blurring processing unit shifts the texture coordinates and performs the index color / texture mapping by a bilinear interpolation method, so that the conversion processing and the first storage are performed. Both K + 1 blurring processes may be performed simultaneously.

  In this way, for example, both the blur image conversion process and the blur process can be realized by a single texture mapping, so that the process can be further improved in efficiency.

  In the image generation system, the program, and the information storage medium according to the present invention, the blur processing unit may perform the conversion process using a conversion table having a conversion characteristic corresponding to the number of blur processes. . The blur processing unit may perform the conversion process using a conversion table that linearly changes the pixel value of the blurred image from the contour of the blur target image toward the blur boundary.

  Hereinafter, this embodiment will be described. In addition, this embodiment demonstrated below does not unduly limit the content of this invention described in the claim. In addition, all the configurations described in the present embodiment are not necessarily essential configuration requirements of the present invention.

1. Configuration FIG. 1 shows an example of a functional block diagram of an image generation system (game system) of the present embodiment. Note that the image generation system of the present embodiment may have a configuration in which some of the components (each unit) in FIG. 1 are omitted.

  The operation unit 160 is for a player to input operation data, and the function can be realized by a lever, a button, a steering, a microphone, a touch panel display, a housing, or the like. The storage unit 170 serves as a work area for the processing unit 100, the communication unit 196, and the like, and its function can be realized by a RAM (VRAM) or the like.

  The information storage medium 180 (computer-readable medium) stores programs, data, and the like, and functions as an optical disk (CD, DVD), hard disk, memory card, memory cassette, magnetic disk, or memory ( ROM) or the like. The processing unit 100 performs various processes of the present embodiment based on a program (data) stored in the information storage medium 180. That is, the information storage medium 180 stores a program for causing a computer to function as each unit of the present embodiment (a program for causing a computer to execute processing of each unit).

  The display unit 190 outputs an image generated according to the present embodiment, and its function can be realized by a CRT, LCD (Liquid Crystal Display), touch panel display, HMD (Head Mount Display), or the like. The sound output unit 192 outputs the sound generated by the present embodiment, and its function can be realized by a speaker, headphones, or the like.

  The portable information storage device 194 stores player personal data, game save data, and the like. Examples of the portable information storage device 194 include a memory card and a portable game device. The communication unit 196 performs various controls for communicating with the outside (for example, a host device or other image generation system), and functions thereof are hardware such as various processors or communication ASICs, programs, and the like. It can be realized by.

  Note that a program (data) for causing a computer to function as each unit of this embodiment is distributed from the information storage medium of the host device (server) to the information storage medium 180 (storage unit 170) via the network and communication unit 196. May be. Use of the information storage medium of such a host device (server) can also be included in the scope of the present invention.

  The processing unit 100 (processor) performs processing such as game processing, image generation processing, or sound generation processing based on operation data and programs from the operation unit 160. Here, as the game process, a process for starting a game when a game start condition is satisfied, a process for advancing the game, a process for placing an object such as a character or a map, a process for displaying an object, and a game result are calculated. There is a process or a process of ending a game when a game end condition is satisfied. The processing unit 100 performs various processes using the storage unit 170 as a work area. The functions of the processing unit 100 can be realized by hardware such as various processors (CPU, DSP, etc.), ASIC (gate array, etc.), and programs.

  The processing unit 100 includes an object space setting unit 110, a movement / motion processing unit 112, a virtual camera control unit 114, a drawing unit 120, and a sound generation unit 130. Note that some of these may be omitted.

  The object space setting unit 110 includes various objects (primitive surfaces such as polygons, free-form surfaces, and subdivision surfaces) representing display objects such as characters, cars, tanks, buildings, trees, columns, walls, and maps (terrain). (Object) is set in the object space. That is, the position and rotation angle (synonymous with direction and direction) of the object (model object) in the world coordinate system are determined, and the rotation angle (X, Y, Z axis around the position (X, Y, Z)) is determined. Arrange objects at (rotation angle).

  The movement / motion processing unit 112 performs a movement / motion calculation (movement / motion simulation) of an object (such as a character, a car, or an airplane). That is, an object (moving object) is moved in the object space or an object is moved based on operation data input by the player through the operation unit 160, a program (movement / motion algorithm), various data (motion data), or the like. Perform processing (motion, animation). Specifically, a simulation process for sequentially obtaining object movement information (position, rotation angle, speed, or acceleration) and motion information (position or rotation angle of each part object) every frame (1/60 second). I do. A frame is a unit of time for performing object movement / motion processing (simulation processing) and image generation processing.

  The virtual camera control unit 114 performs a virtual camera (viewpoint) control process for generating an image viewed from a given (arbitrary) viewpoint in the object space. Specifically, a process for controlling the position (X, Y, Z) or the rotation angle (rotation angle about the X, Y, Z axes) of the virtual camera (process for controlling the viewpoint position and the line-of-sight direction) is performed.

  For example, when an object (eg, character, ball, car) is photographed from behind using a virtual camera, the position or rotation angle of the virtual camera (the direction of the virtual camera is set so that the virtual camera follows changes in the position or rotation of the object. ) To control. In this case, the virtual camera can be controlled based on information such as the position, rotation angle, or speed of the object obtained by the movement / motion processing unit 112. Alternatively, the virtual camera may be controlled to rotate at a predetermined rotation angle or to move along a predetermined movement path. In this case, the virtual camera is controlled based on the virtual camera data for specifying the position (movement path) or rotation angle of the virtual camera.

  The drawing unit 120 performs drawing processing based on the results of various processing (game processing) performed by the processing unit 100, thereby generating an image and outputting the image to the display unit 190. In the case of generating a so-called three-dimensional game image, first, geometric processing such as coordinate transformation (world coordinate transformation, camera coordinate transformation), clipping processing, or perspective transformation is performed, and drawing data ( The position coordinates, texture coordinates, color data, normal vector, α value, etc.) of the vertexes of the primitive surface are created. Then, based on the drawing data (primitive surface data), the object (one or a plurality of primitive surfaces) after the perspective transformation (after the geometry processing) is stored in the drawing buffer 172 (frame buffer, work buffer, etc.) in pixel units. Can be drawn in a VRAM). Thereby, an image that can be seen from the virtual camera (given viewpoint) in the object space is generated.

  The drawing unit 120 can perform texture mapping processing and hidden surface removal processing.

  Here, the texture mapping process is a process of mapping a texture (texel value) stored in the texture storage unit 174 to an object. Specifically, the texture (surface properties such as color and α value) is read from the texture storage unit 174 using the texture coordinates set (given) at the vertices of the object (primitive surface). Then, a texture that is a two-dimensional image or pattern is mapped to the object. In this case, processing for associating pixels and texels, bilinear interpolation (texel interpolation), and the like are performed.

  The hidden surface removal process is realized, for example, by a Z buffer method (depth comparison method, Z test) using a Z buffer 176 (depth buffer) in which the Z value (depth information) of each pixel is stored. That is, when drawing each pixel of the primitive surface of the object, the Z value stored in the Z buffer 176 is referred to. Then, the Z value of the referenced Z buffer 176 is compared with the Z value at the drawing target pixel on the primitive surface, and the Z value at the drawing target pixel is the front side when viewed from the virtual camera (for example, a small value). Z value), the drawing process for the pixel is performed and the Z value in the Z buffer 176 is updated to a new Z value.

  The drawing unit 120 includes a reduction processing unit 122, a blur processing unit 124, an enlargement processing unit 126, a video filter processing unit 128, and an α blending unit 129. Note that some of these may be omitted.

  The reduction processing unit 122 performs image reduction processing. Specifically, a reduced image is generated by performing a process of reducing the area of a blurring target image (an original image or the like) that is a target of the blurring process. The reduction process in this case may be realized by bilinear interpolation texture mapping or may be realized by point sampling texture mapping. The original image, which is the image to be blurred, is subjected to geometry processing (coordinate transformation processing such as perspective transformation) on the object, and the object (polygon) after geometry processing is drawn in the frame buffer (drawing area in a broad sense) It can be generated by (rendering).

  Further, the reduction processing unit 122 generates a first reduced image based on the information in the first sampling point group of the blurring target image, and the second based on the information in the second sampling point group of the blurring target image. Are generated, and a Jth reduced image is generated based on information in the Jth sampling point group (J is an integer of 2 or more) of the blurring target image. Here, the information of the first to Jth sampling point groups is information obtained from the pixel values of the blurring target image, such as a pixel value or an average value of a plurality of pixel values.

  The blur processing unit 124 performs a blur process on the reduced image reduced by the reduction processing unit 122 to generate a reduced blur image. Specifically, blur processing is performed a plurality of times (M times) on the reduced image. For example, the first blurred process is performed on the reduced image, the second blurred process is performed on the blurred image obtained by the first blurred process, and the blurred image obtained by the Kth blurred process. The (K + 1) -th blurring process is performed on the blurred image obtained by the (M-1) -th blurring process (K is a positive integer, M is an integer of 2 or more). .

  In this case, the blur processing unit 124 increases (raises) the pixel value (α value, color information, etc.) of the blurred image obtained by the K-th blur processing among a plurality of blur processing ( It is also possible to perform (K + 1) th blur processing on the blurred image that has been subjected to the conversion processing.

  Specifically, the storage unit 170 includes a conversion table storage unit 171 (main storage unit) that stores a conversion table for conversion processing (such as a lookup table for index color / texture mapping). The conversion table storage unit 171 can store a conversion table having conversion characteristics corresponding to the number of blurring processes.

  Then, the blur processing unit 124 reads the conversion table from the conversion table storage unit 171 and performs a conversion process for increasing the pixel value of the blurred image using the conversion table. Specifically, the blur processing unit 124 sets the pixel value (α value, color information, etc.) of the blurred image obtained by the Kth blur processing as the index number of the lookup table for index color / texture mapping. Yes (assumed to be an index number). Then, by performing index color / texture mapping using this look-up table (conversion table in a broad sense), a conversion process for increasing the pixel value of the blurred image obtained in the Kth blur process is performed. This index color / texture mapping is performed by shifting the texture coordinates (0.5 texel shift) using the bilinear interpolation method (texel interpolation method), so that both conversion processing and K + 1-th blur processing can be performed. Thus, the processing efficiency can be improved.

  Note that the conversion process can be performed on all pixels or a part of the pixels. In addition, a blurred image obtained by a plurality of blurring processes may be output as a blurred image of the original image, or the obtained blurred image and the original image are combined to produce, for example, a glare effect or a depth of field. Alternatively, an image in which an image effect such as a soft focus effect is applied to the original image may be output.

  The enlargement processing unit 126 performs image enlargement processing. Specifically, a process for enlarging the area of the reduced blurred image obtained by the blurring process in the blurring processing unit 124 is performed.

  For example, when the reduction processing unit 122 generates the first to Jth reduced images based on the information in the first to Jth sampling point groups, the blurring processing unit 124 uses the first to Jth reduction points. The reduced images are combined, and the combined reduced image is subjected to blurring processing to generate a blurred combined reduced image. Then, the enlargement processing unit 126 performs enlargement processing of the blurred combined reduced image. Alternatively, the blur processing unit 124 performs blur processing on the first to Jth reduced images to generate first to Jth blurred reduced images, and the enlargement processing unit 126 performs these first to first reduced images. You may make it perform the enlargement process and synthetic | combination process (process to synthesize | combine while expanding) of J blurring reduction image.

  The video filter processing unit 128 performs various video filter processes. For example, the video filter processing unit 128 performs video filter processing for luminance correction on the reduced image generated by the reduction processing unit 122 (an image obtained by combining the first to Jth reduced images). Specifically, video filtering is performed to increase the luminance of pixels whose luminance is higher than a given value. Alternatively, in addition to this, video filter processing for color correction may be performed. Specifically, video filter processing for enhancing a given color component (at least one color component of RGB) of the reduced image is performed. Then, the blur processing unit 124 performs a blur process on the reduced image that has been subjected to the video filter process.

  The video filter processing can be realized by using index color / texture mapping, for example. Specifically, the information of the reduced image (the image obtained by combining the first to Jth reduced images) is set as the index number of the index color / texture mapping lookup table stored in the conversion table storage unit 171 (index). Considered a number). Then, by performing index color / texture mapping using this lookup table (conversion table in a broad sense), a conversion process for correcting the luminance and tone of the reduced image is realized.

  The α blending unit 129 performs α blending processing (normal α blending, α addition blending, α subtraction blending, or the like) based on the α value (A value). For example, in the case of normal α blending, the following processing is performed.

R _Q = (1−α) × R ₁ + α × R ₂
G _Q = (1−α) × G ₁ + α × G ₂
B _Q = (1−α) × B ₁ + α × B ₂
On the other hand, in the case of addition α blending, the following processing is performed.

R _Q = R ₁ + α × R ₂
G _Q = G ₁ + α × G ₂
B _Q = B ₁ + α × B ₂
Here, R ₁ , G ₁ , B ₁ are RGB components of an image (original image) already drawn in the drawing buffer 172, and R ₂ , G ₂ , B ₂ should be drawn in the drawing buffer 172. This is the RGB component of the image. R _Q , G _Q , and B _Q are RGB components of an image obtained by α blending. The α value is information that can be stored in association with each pixel (texel, dot), for example, plus alpha information other than color information. The α value can be used as translucency (equivalent to transparency and opacity) information, mask information, or bump information.

  The sound generation unit 130 performs sound processing based on the results of various processes performed by the processing unit 100, generates game sounds such as BGM, sound effects, or sounds, and outputs the game sounds to the sound output unit 192.

  Note that the image generation system of the present embodiment may be a system dedicated to the single player mode in which only one player can play, or may be a system having a multiplayer mode in which a plurality of players can play. Further, when a plurality of players play, game images and game sounds to be provided to the plurality of players may be generated using one terminal, or connected via a network (transmission line, communication line) or the like. Alternatively, it may be generated by distributed processing using a plurality of terminals (game machine, mobile phone).

2. Next, the method of this embodiment will be described with reference to the drawings.

2.1 Blur Processing for Reduced Image When generating a blurred image, in order to increase the blur length, the number of times of performing blur processing may be increased. That is, the first blurring process is performed on the blurring target image, and the second blurring process is performed on the image obtained by the first blurring process .... obtained by the (N-1) th blurring process. The Nth blurring process is performed on the obtained image. However, increasing the number of blur processing increases the processing load.

  Therefore, in the present embodiment, as shown by A1 in FIG. 2, a process for reducing the area of the blurring target image (1 / L × 1 / L times reduction) is performed to generate a reduced image. Next, as shown in A2, this reduced image is subjected to blurring processing one or more times to generate a blurred reduced image. And as shown to A3, the process (LxL expansion) which expands the area with respect to this reduced image is performed, and the blurring image of the blurring target image is obtained. The blurred image may be output as a final game image, or an image obtained by combining the blurred image and the original image (such as α blending) may be output as the final game image. FIG. 2 shows an example in which the vertical and horizontal reduction / enlargement magnifications are equal (1 / L × 1 / L times, L × L times), but the vertical and horizontal reduction / enlargement magnifications are different (1). / M × 1 / N times, M × N times) is also possible.

  For example, the blurring process can be realized by texture mapping of a bilinear interpolation (texel interpolation) method performed by shifting texture coordinates. The processing load of bilinear interpolation texture mapping is basically proportional to the processing area (the size of the processing region). Therefore, as shown in A1 and A2 of FIG. 2, if the blurring process is realized by reducing the blurring target image and performing the texture mapping of the bilinear interpolation method using the obtained reduced image as the texture, the load of the blurring process Can be greatly reduced. In particular, when the number of blurring processes is large, the degree of reduction of the processing load increases. Further, in A3 of FIG. 2, the reduced blur image is enlarged so as to be the size of the original blur target image, so that the blur length can be sufficiently increased even if the number of blur processes is not so large. There are advantages.

  For example, by performing bilinear interpolation texture mapping that shifts texture coordinates by 0.5 texels only once (0.5 texel shift and -0.5 texel shift), the blur length of 1 pixel is ideally achieved. Obtainable. If N round-trip blurring is performed on the blurring target image without performing reduction / enlargement processing, the blurring length becomes N pixels.

  On the other hand, as shown in FIG. 2, the blurring target image is reduced to 1 / L × 1 / L times, and the obtained reduced image is subjected to N round-trip blurring processing. It is assumed that a process of enlarging to × L times is performed. In this case, the blurring effect is L times larger than the length by reducing the blurring target image to 1 / L × 1 / L times and enlarging to L × L times after the blurring process. Further, the blur length of several pixels (K pixels, for example, K = 2) is added by reduction / enlargement. Therefore, according to the present embodiment, the blur length can be, for example, (L × N + K) pixels. When the reduction / enlargement is performed by point sampling instead of bilinear sampling, the blur length can be set to L × N pixels, for example.

  As described above, according to the present embodiment, it is possible to sufficiently increase the blur length while greatly reducing the load of blur processing, and to express images such as a soft focus effect, a glare effect, and a depth of field. Optimal blurring can be realized.

2.2 Generation of a plurality of reduced images with different sampling point groups In FIG. 2, based on the information of the sampling point groups indicated by circles B1 to B4 of the blurring target image, B5, B6, B7, The pixel value shown in B8 is obtained. Here, since the reduced image is generated by bilinear sampling (bilinear interpolation texture mapping) in FIG. 2, the information of the sampling points shown in B1 is shown in the surrounding B9, B10, B11, and B12 adjacent thereto. It is the average value of the pixel values. The same applies to the information of the sampling points shown in B2, B3, and B4. However, the reduced image may be generated by point sampling instead of bilinear sampling.

  As shown in FIG. 3, the blurring target image includes information on a first sampling point group indicated by a circle, second sampling point group information indicated by a square, and information on a third sampling point group indicated by a triangle. The fourth sampling point group information indicated by black circles is originally included. Therefore, when a reduced image is generated based on only one sampling point group (circle) as shown in FIG. 2, information originally included in the blurring target image is lost. Therefore, when this reduced image is enlarged and a blurred image is generated, the quality of the game image generated based on this blurred image is reduced, and problems such as flickering of the screen may occur.

  In this case, if a reduced image is generated by bilinear sampling as shown in FIG. 2, the loss of information included in the blurring target image can be prevented to some extent, but there is a limit to this. For example, flickering of the screen is not conspicuous in a still image, but flickering of the screen may occur as the virtual camera moves or the model object moves. In FIG. 2, the reduction ratio is 1/4 (1/2 × 1/2) in area ratio, but the reduction ratio is, for example, 1/16 (1/4 × 1/4) in area ratio. (Times)), even if a reduced image is generated by bilinear sampling, a sampling point group is generated in which the information is not reflected at all in the reduced image.

  Therefore, this embodiment employs a method of generating a plurality of reduced images with different sampling point groups. Specifically, as shown in FIG. 4, a first reduced image is generated based on information (pixel value or average value of pixel values) in the first sampling point group (circle) of the blurring target image. Similarly, based on information (pixel value or average value of pixel values) in the second, third, and fourth sampling point groups (square mark, triangle mark, black circle mark) of the blurring target image, the second, third, A fourth reduced image is generated.

  Then, based on the obtained first to fourth reduced images (first to Jth reduced images in a broad sense), the blurring process and the enlargement process described in FIG. Generate. Then, the generated blurred image, a combined image of the blurred image and the original image, and the like are output as a final game image.

In FIG. 4, four reduced images having different sampling point groups are generated from the blur target image, but a plurality of reduced images having different sampling point groups may be generated. FIG. 4 shows an example in which the reduction ratio is 1/4 (1/2 × 1/2) in terms of area ratio, but the reduction ratio is not limited to this, and is, for example, 1/2 (1/2) ^1/2 × ^{1/2 1/2} times), 1/16 times (1/4 × 1/4 times), or the like. In other words, when the first to Jth reduced images are generated by reducing the blur target image by 1 / L × 1 / L times in area ratio, the relationship of J ≦ L × L (M × N) may be satisfied. .

For example, in FIG. 21A, the reduction magnification is 1/2 ^1/2 × ^{1/2 1/2} times ( ^1/2 times the area ratio) and the enlargement magnification is 2 ^1/2 × 2 ^1/2 times ( An example when the area ratio is 2) is shown. In this case, there are two types of sampling point groups as shown in FIG. Also in FIG. 22 (A), in ^1/2 × 1/5 ^1/2 times the reduction ratio is 1/5 (1/5 in area ratio), the magnification is 5 ^1/2 × 5 ^1/2 times ( An example when the area ratio is 5) is shown. In this case, there are five types of sampling point groups as shown in FIG.

  Now, the first to Jth reduced images having different sampling point groups can be generated by shifting texture coordinates with each other when texture mapping is performed by the bilinear interpolation method. Specifically, the first to fourth reduced images in FIG. 4 can be realized by setting the texture coordinates so as to be shifted from each other as shown in FIG. 5, for example.

  In FIG. 5, the vertical and horizontal lengths of the first to fourth reduced images are 2, and the coordinates (X, Y) of the vertices V1, V2, V3, V4 of the first to fourth reduced images are as follows. (0, 0), (2, 0), (2, 2), (0, 2).

  In the first reduced image, the texture coordinates (U, V) of these vertices V1, V2, V3, V4 are set to (0, 0), (4, 0), (4, 4), (0, 4). Set to. In the second reduced image, the texture coordinates (U, V) of the vertices V1, V2, V3, V4 are set to (-1, -1), (3, -1), (3, 3), (-1, Set to 3). In the third reduced image, the texture coordinates (U, V) of the vertices V1, V2, V3, V4 are (0, -1), (4, -1), (4, 3), (0, 3). Set to. In the fourth reduced image, the texture coordinates (U, V) of the vertices V1, V2, V3, V4 are set to (-1, 0), (3, 0), (3, 4), (-1, 4). Set to. In this way, the image to be blurred is set as a texture, the texture coordinates are shifted (shifted) from each other, texture mapping is performed by bilinear sampling (or point sampling), and a reduced image size polygon (virtual polygon) is drawn. For example, a plurality of reduced images having different sampling point groups can be generated as shown in FIG.

  There are various methods for generating a blurred image based on a plurality of reduced images.

  For example, in FIG. 6A, reduced images DI1 to DI4 (first to Jth reduced images) generated based on the blurring target image IM are combined (α blending), and 1 is applied to the combined reduced image SI. Alternatively, a blur combined reduced image SDI is generated by performing blur processing a plurality of times. The blur combined reduced image SDI is enlarged to generate an IM blurred image DIM. Then, the blurred image DIM or a composite image (α blending image) of the DIM and the original image is output as a final game image.

Further, in FIG. 6B, blurring processing is performed on DI1 to DI4 (first to Jth reduced images) generated based on the blurring target image, and blurred reduced images DDI1 to DDI4 (first to Jth reduced images) are processed. Image). Then, enlargement processing and composition processing of the blurred reduced images DDI1 to DDI4 are performed to generate an IM blurred image DIM. Then, this blurred image DIM or a composite image (α blended image) of the DIM and the original image is output as the final game image. By doing so, the loss of information on the blurred image is minimized. However, a blurred image having a large blur length can be generated with a small processing load.

2.3 Soft Focus In the field of photography, a so-called soft focus technique is known. The soft focus is a so-called flare phenomenon in which a focused portion, line, or outline is sharp and soft light oozes out in the vicinity. An image to which this soft focus effect has been applied is an image with fine details even if it is software. In the field of photography, a soft focus effect is realized by using a method of opening the aperture with a lens having a large aberration, or a lens for soft focus.

  In the present embodiment, the following technique is employed to simulate this soft focus effect. That is, the image is set to a soft focus color tone by video filter processing. Then, the image is blurred using a bilinear filter (bilinear interpolation texture mapping). Then, the blurred image and the original image are synthesized (α blending).

  Specifically, as shown in FIG. 7, a plurality of reduced images DI1 to DI4 (first to Jth reduced images) are generated based on information at a plurality of sampling point groups of the original image IM that is the blurring target image. To do.

  Next, these reduced images DI1 to DI4 are synthesized (α blending) to obtain a synthesized reduced image SI.

  Next, the composite reduced image SI is subjected to video filter processing to obtain a reduced image VSI that has been subjected to video filter processing. Specifically, a luminance correction process such as increasing the luminance of a pixel with high luminance is performed. That is, it increases the brightness of pixels whose brightness is higher than a given value, and weakens the brightness of pixels whose brightness is lower than a given value. Further, a tone correction process such as emphasizing a specific color component of the reduced image is performed. That is, the blue component is emphasized for a moonlit night scene, and the red component is emphasized for a sunset scene. The video filter processing can be variously modified, and both luminance correction and color tone correction may be performed, or one of them may be performed. Alternatively, other correction processing may be further performed.

  Next, one or a plurality of blurring processes are performed on the reduced image VSI that has been subjected to the video filter process to generate a blurred combined reduced image DVSI.

  Next, enlargement processing of the blurred composite reduced image DVSI is performed to return to the original area, and a blurred image DIM is obtained. Then, the blurred image DIM and the original image IM are synthesized (α blending).

  By performing the processing as described above, it is possible to apply a soft focus effect in which an in-focus portion, a line, or an outline is sharp and the periphery thereof is blurred, to the original image. As a result, the variety of generated images can be increased, and the virtual reality of the player can be enhanced.

  8A, 8B, and 8C show examples of conversion tables (look-up table LUT) used in the video filter processing of FIG. This conversion table is stored in the conversion table storage unit. 8A, 8B, and 8C, RIN, GIN, and BIN are R, G, and B components of input pixel values before conversion by video filter processing, and ROUT, GOUT, and BOUT are based on video filter processing. R, G, and B components of the output pixel value after conversion.

  If the conversion tables of FIGS. 8A, 8B, and 8C are used, the luminance of pixels that are greater than or equal to a given value can be increased. In addition, the color of the blue component can be emphasized. This makes it possible to achieve a soft focus effect that is optimal for game scenes illuminated by the moonlight.

  7 to 8C can be realized by using index color / texture mapping using a lookup table (LUT) for index color / texture mapping. Specifically, the information (pixel value) of the composite reduced image is set to the index number of the lookup table (LUT, FIGS. 8A, 8B, and 8C). Then, by using this look-up table, index-color / texture mapping is performed on the reduced-size polygon, so that a composite reduced image subjected to video filter processing can be obtained.

2.4 Conversion of Pixel Value in Blur Process In this embodiment, a plurality of blur processes are performed in order to increase the blur length of a blurred image. Specifically, first, work buffers WBUF1 and WBUF2 used for the blurring process are secured on the VRAM.

  Next, an image to be blurred is drawn on WBUF1. Then, the image drawn on WBUF1 is treated as a texture, and a polygon (sprite) having the same size as the texture, to which this texture is mapped, is drawn on WBUF2. At this time, bilinear interpolation is used with the pixel center corresponding to the middle of four texels. That is, texture mapping is performed by shifting texture coordinates and using a bilinear interpolation method. As a result, an image obtained by blurring the image of WBUF1 by 0.5 pixels is drawn in the work buffer WBUF2.

  Next, the WBUF2 image is treated as a texture, and a polygon (sprite) having the same size as the texture, to which the texture is mapped, is drawn on the WBUF1. At this time, bilinear interpolation is used with the pixel center corresponding to the middle of four texels. That is, texture mapping is performed by shifting texture coordinates and using a bilinear interpolation method. As a result, an image obtained by blurring the image of WBUF2 by 0.5 pixels is drawn in the work buffer WBUF1.

  By performing one round-trip processing of drawing processing from WBUF1 to WBUF2 and drawing processing from WBUF2 to WBUF1 as described above, the image of WBUF1 becomes an image obtained by blurring the blurring target image by one pixel. If the required blur length is N pixels, the blur process may be repeated N times.

  When a blurred image is generated using bilinear interpolation or the like in this way, there is a problem that as the number of blurring processes increases (the blurring length increases), the efficiency of the blurring process decreases. It has been found.

  For example, FIG. 9A shows how the original pixel values are distributed with respect to surrounding pixels when bilinear interpolation blurring is performed four times on the original pixel and only four pixels are blurred. FIG.

  In FIG. 9A, the pixel value before blur processing of the original pixel located at the blur center is 1, and after blur processing is 4900/65536. On the other hand, the pixel value after blurring processing of the pixel indicated by E1, for example, located at the blurring boundary is 70/65536.

  Then, for example, the pixel value (α value) of the original pixel before the blurring process is set to 255, and the result of rounding off fractions less than 1 is as shown in FIG. In practice, the fraction is rounded down every time one (0.5 pixel) blurring process is performed, so the value after 4-pixel blurring is smaller than the value shown in FIG.

  In FIG. 9B, the pixel value after the blurring process of the original pixel located at the blurring center is 255 × 4900/65536 = 19.0658. . On the other hand, the pixel value after the blurring process of the pixel indicated by E1, for example, located at the blurring boundary becomes 255 × 70/65536 = 0.723.

  In the example of FIG. 9B, the value of the outermost peripheral pixel which is a blur boundary is 0 even though the original 1 pixel is expected to be blurred by 4 pixels in the surrounding area. The blur length is 3 pixels. Thus, when the blurring process is repeated, the actual blurring length is shorter than the number of blurring processes, and the blurring efficiency is deteriorated. As the number of blurring processes increases, the blurring efficiency further deteriorates.

  In order to solve such a problem, in this embodiment, for example, table conversion is interposed in the middle of the blurring process, and the conversion process is performed so that the blurring result does not become too small.

  For example, when the first first blurring process (Kth blurring process in a broad sense) is performed on the original pixel in FIG. 10A, the pixel value of the original one pixel oozes out around it. As shown in FIG.

  At this time, in the present embodiment, as shown in FIG. 10C, conversion processing for increasing the pixel value of the blurred image obtained by the first blur processing is performed. For example, in FIG. 10C, a conversion process for increasing a pixel value of a pixel having a pixel value greater than 0 is performed. Then, as shown in FIG. 10D, the next second blurring process (K + 1 blurring process in a broad sense) is performed on the blurred image that has been subjected to the conversion process. Note that the conversion process in FIG. 10C and the blurring process in FIG. 10D may be performed simultaneously.

  Next, in the present embodiment, as shown in FIG. 11A, conversion processing is performed to increase the pixel value of the blurred image obtained by the second blur processing. Then, as shown in FIG. 11B, the next third blurring process is performed on the blurred image that has been subjected to the conversion process.

  In this way, since the pixel value of the blurred image after the blurring process is raised, the situation where the pixel value at the boundary of the blurred image becomes small as shown in FIG. 9B is prevented. it can. As a result, it is possible to obtain a blur length of the size of N pixels by, for example, N round-trip blur processing, and an efficient blur processing can be realized. In particular, the technique of this embodiment is more advantageous than the conventional technique as the number of blurring processes increases.

2.5 Specific Example of Blur Processing Next, a specific example of the blur processing according to the present embodiment will be described.

  As shown in FIG. 12A, the first blurring process is performed by treating the image (reduced image, synthesized reduced image) drawn in the work buffer WBUF1 as a texture and performing bilinear interpolation texture mapping. The blurred image is drawn in the work buffer WBUF2.

  Next, as shown in FIG. 12B, the image drawn in the work buffer WBUF2 is treated as a texture, and texture mapping using the index color & bilinear interpolation method is performed to increase the pixel value. The blurred image that has been subjected to the blurring process is drawn in the work buffer WBUF1.

  Next, as shown in FIG. 12C, an image drawn in the work buffer WBUF1 is treated as a texture, and texture mapping by the bilinear interpolation method is performed, so that the blurred image subjected to the third blurring process is converted into the work buffer. Draw on WBUF2.

  Next, as shown in FIG. 12D, the image drawn in the work buffer WBUF2 is treated as a texture, and texture mapping of the index color & bilinear interpolation method is performed. The blurred image that has been subjected to the blurring process is drawn in the work buffer WBUF1.

  By repeating the above blurring process a predetermined number of times, a blurred image having a desired blur length can be obtained.

  Next, a blur image generation method using bilinear interpolation texture mapping will be described with reference to FIG.

  First, an image (pixel value) in the work buffer WBUF1 is set as a texture. Then, as shown in F1 of FIG. 13, when this texture is mapped to the virtual polygon (virtual object) by the bilinear interpolation method, the texture coordinates given to the vertex of the virtual polygon are, for example, (0.5, 0.5) only. Shift (shift, move) in the lower right direction and draw in the work buffer WBUF2. Then, the color of each pixel of the image of WBUF1 automatically oozes out to the surrounding pixels by bilinear interpolation. As a result, an image obtained by blurring the image of WBUF1 by 0.5 pixels is rendered on WBUF2.

  For example, the coordinates of the vertices VX1, VX2, VX3, and VX4 of the drawing area of the WBUF1 image are (X, Y) = (X0, Y0), (X0, Y1), (X1, Y1), (X1, Y0). Suppose. In this case, the texture coordinates (U, V) given to the vertices VVX1, VVX2, VVX3, VVX4 of the virtual polygon are (X0, Y0), (X0, Y1), (X1, Y1), (X1, Y0), respectively. ), The pixel position matches the texel position without any deviation. Therefore, the image is not blurred. On the other hand, the texture coordinates (U, V) given to the vertices VVX1, VVX2, VVX3, VVX4 of the virtual polygon are (X0 + 0.5, Y0 + 0.5), (X0 + 0.5, Y1 + 0.5), ( If it is set to (X1 + 0.5, Y1 + 0.5), (X1 + 0.5, Y0 + 0.5), the pixel position and the texel position will be shifted. Accordingly, color interpolation is performed by bilinear interpolation, and an image obtained by blurring the image of WBUF1 by 0.5 pixels is rendered on WBUF2.

  Next, an image of WBUF2 is set as a texture. Then, as shown in F2 of FIG. 13, when this texture is mapped to the virtual polygon by the bilinear interpolation method, the texture coordinates given to the vertex of the virtual polygon are, for example, (−0.5, −0.5) in the upper left direction. Shift and draw on WBUF1. As a result, an image obtained by blurring the image of WBUF2 by 0.5 pixels is drawn on WBUF1. As a result, an image obtained by blurring the image drawn on WBUF1 by 1 pixel is overwritten on WBUF1.

  Next, a method for realizing conversion processing using index color / texture mapping will be described.

  In the index color / texture mapping, in order to save the use storage capacity of the texture storage unit, an index number is stored in association with each texel of the texture instead of actual color information (RGB). The index color / texture mapping look-up table LUT stores color information (ROUT, GOUT, BOUT) and α value (αOUT) specified by the index number. In the present embodiment, the index color / texture mapping is used in a form different from the normal one.

  Specifically, as indicated by D1 in FIG. 14, the pixel value (color information, α value, etc.) of the image in the work buffer WBUF2 is set as an index number of the lookup table LUT (it is regarded as an index number). Then, as shown in D2, index color / texture mapping is performed on the virtual polygon (sprite) using the lookup table LUT in which the pixel value of the image of WBUF2 is set as the index number, and the virtual polygon is set to the work buffer WBUF1. To draw.

  In this way, for example, the pixel value of the image of WBUF2 can be converted by the lookup table LUT and rendered on WBUF1. In addition, since the values of all the pixels in WBUF2 can be converted into a single texture mapping and rendered in WBUF1, there is an advantage that the processing efficiency is high.

  In the present embodiment, by performing the index color / texture mapping described with reference to FIG. 14 using the bilinear interpolation method described with reference to FIG. 13, both pixel value conversion processing and blurring processing are performed simultaneously as shown in FIG. Has succeeded in doing. Specifically, the pixel value of the WBUF2 image is set as the index number of the lookup table LUT. Then, using the look-up table LUT, the texture coordinates are shifted to perform index color / texture mapping on the virtual polygon by the bilinear interpolation method, and the virtual polygon is drawn in the work buffer WBUF1. In this way, both pixel value conversion processing and blurring processing can be performed simultaneously with one texture mapping, and the processing can be made more efficient.

3. Processing of this embodiment Next, a detailed processing example of this embodiment will be described with reference to the flowcharts of FIGS.

  FIG. 15 is a flowchart of the initial setting process. First, work buffers WBUF1, WBUF2, and WBUF3 are secured in the VRAM (step S1). Next, various parameters are initialized (step S2). Then, setting processing of a look-up table (LUT for FIGS. 8A, 8B, and 8C) for video filter processing (luminance / color tone correction) is performed (step S3).

  FIG. 16 is a flowchart of processing performed for each frame. First, it is determined whether or not it is a frame (1/60 second) update timing (step S11). If the frame is to be updated, the object (polygon) after geometry processing (after perspective transformation) is drawn in the frame buffer to generate an original image (screen-sized game image) (step S12).

  Next, the original image in the frame buffer is used as a texture, and the polygon is reduced and drawn in the work buffer WBUF2 at an area ratio of 1/16 (1/4 × 1/4) (step S13). As a result, as described in A1 of FIG. 2, the reduction process of the original image that is the blurring target image is realized.

  Next, a look-up table (LUT) for video filter processing is copied to the work buffer WBUF3 (step S21). Then, using the image in the work buffer WBUF2 as a texture, index color / texture mapping using a look-up table (LUT) is performed, and a polygon with the texture mapped in the work buffer WBUF1 is drawn (step S22). Thereby, the video filter processing described in FIGS. 7 to 8C is realized.

  Next, blurring processing (1 to 8 times) is performed (step S24). Specifically, as described with reference to FIGS. 12A to 13, texture mapping is performed on the WBUF1 image by shifting texture coordinates, polygons are drawn on the WBUF2, and texture coordinates are shifted on the WBUF2 image. Then, texture mapping is performed to draw a polygon on WBUF1. Then, this one-way process is repeated 1 to 8 times.

  Next, α blending (about 40%) is drawn while enlarging the image of WBUF1 to a predetermined position of the frame buffer image (step S25). That is, the enlargement process described in A3 in FIG. 2 is performed and the α blending process described in FIG. 7 is performed. Thereby, an image in which the soft focus effect is applied to the original image can be generated. In the process of step S25, the drawing position can be shifted up, down, left, and right according to the parameters set in step S2 of FIG. In this way, it becomes possible to display a blurred image of the moonlight by shifting it to the upper side of the moon, for example, and a more realistic soft focus image can be generated.

  FIGS. 19A and 19B are diagrams showing the state of bilinear sampling when the original image is reduced at a reduction ratio of 1/4 times and 1/16 times (area ratio), respectively. When the reduction ratio is ¼ as shown in FIG. 19A, a reduced image is generated in consideration of the values of all texels by bilinear sampling. Loss can be prevented to some extent. However, when the reduction ratio is 1/16 as shown in FIG. 19B, sampling is performed only from four texels around the sampling point, so that there is a texel that does not reflect information. End up. Therefore, even when there is a change in the value of the texel where the information is not reflected, the change is not reflected in the reduced image.

  FIG. 17 is a flowchart of another processing example of the present embodiment that can solve such problems. FIG. 17 differs from FIG. 16 in the processing of steps S14, S15, and S16.

  That is, in step S14 of FIG. 17, the original image of the frame buffer is used as a texture, and the polygon is reduced and drawn in the work buffer WBUF2 with an area ratio of 1/16. Thereby, the 1st reduction image based on the information in the 1st sampling point group of Drawing 19 (C) can be generated.

  In step S15, the original image in the frame buffer is used as a texture, and the polygon is reduced and drawn in the work buffer WBUF1 with an area ratio of 1/16. At this time, as described with reference to FIG. 5, the texture coordinates (U, V) are shifted to generate a reduced image. Thereby, the 2nd reduction image based on the information in the 2nd sampling point group of Drawing 19 (C) can be generated.

  In step S16, the WBUF1 image (second reduced image) is used as a texture, and a polygon on which the texture is mapped is drawn on WBUF2 (first reduced image) by α blending (50%). As a result, as described with reference to FIG. 6A, a combined reduced image (SI) of the first and second reduced images generated in steps S14 and S15 can be generated. Then, by performing a video filter process, a blurring process, and the like on the combined reduced image by the process after step S21, a soft focus image of the original image can be generated.

  FIG. 18 is also a flowchart of another processing example of this embodiment. FIG. 18 differs from FIG. 16 in the processing of steps S17 and S18.

  That is, in step S17 of FIG. 18, the original image in the frame buffer is used as a texture, and the polygon is reduced and drawn in the work buffer WBUF1 with an area ratio of 1/4. In step S18, a polygon is reduced and drawn in the work buffer WBUF2 with an area ratio of 1/4 using the image in the work buffer WBUF1 as a texture. That is, the 1/4 reduction process is repeated twice. In this way, a 1 / 16-fold reduced image can be generated from the information of all texels using bilinear sampling. However, the method of FIG. 18 has the disadvantages that the processing load is relatively heavy and the required work buffer size is large. However, when the virtual camera or the model object moves, there is an advantage that flickering of the screen is improved as compared with the method of FIG.

4). Hardware Configuration FIG. 20 shows an example of a hardware configuration capable of realizing this embodiment. The main processor 900 operates based on a program stored in a CD 982 (information storage medium), a program downloaded via the communication interface 990, a program stored in the ROM 950, or the like, and includes game processing, image processing, sound processing, and the like. Execute. The coprocessor 902 assists the processing of the main processor 900, and executes matrix operation (vector operation) at high speed. For example, when a matrix calculation process is required for a physical simulation for moving or moving an object, a program operating on the main processor 900 instructs (requests) the process to the coprocessor 902.

  The geometry processor 904 performs geometry processing such as coordinate conversion, perspective conversion, light source calculation, and curved surface generation based on an instruction from a program operating on the main processor 900, and performs matrix calculation at high speed. The data decompression processor 906 performs decoding processing of compressed image data and sound data, and accelerates the decoding processing of the main processor 900. Thereby, a moving image compressed by the MPEG method or the like can be displayed on the opening screen or the game screen.

  The drawing processor 910 executes drawing (rendering) processing of an object composed of primitive surfaces such as polygons and curved surfaces. When drawing an object, the main processor 900 uses the DMA controller 970 to pass the drawing data to the drawing processor 910 and, if necessary, transfers the texture to the texture storage unit 924. Then, the drawing processor 910 draws the object in the frame buffer 922 while performing hidden surface removal using a Z buffer or the like based on the drawing data and texture. The drawing processor 910 also performs α blending (translucent processing), depth cueing, mip mapping, fog processing, bilinear filtering, trilinear filtering, anti-aliasing, shading processing, and the like. When an image for one frame is written in the frame buffer 922, the image is displayed on the display 912.

  The sound processor 930 includes a multi-channel ADPCM sound source and the like, generates game sounds such as BGM, sound effects, and sounds, and outputs them through the speaker 932. Data from the game controller 942 and the memory card 944 is input via the serial interface 940.

  The ROM 950 stores system programs and the like. In the case of an arcade game system, the ROM 950 functions as an information storage medium, and various programs are stored in the ROM 950. A hard disk may be used instead of the ROM 950. The RAM 960 is a work area for various processors. The DMA controller 970 controls DMA transfer between the processor and the memory. The CD drive 980 accesses a CD 982 in which programs, image data, sound data, and the like are stored. The communication interface 990 performs data transfer with the outside via a network (communication line, high-speed serial bus).

  The processing of each unit (each unit) in this embodiment may be realized entirely by hardware, or may be realized by a program stored in an information storage medium or a program distributed via a communication interface. Also good. Alternatively, it may be realized by both hardware and a program.

  When the processing of each part of this embodiment is realized by both hardware and a program, a program for causing the hardware (computer) to function as each part of this embodiment is stored in the information storage medium. More specifically, the program instructs the processors 902, 904, 906, 910, and 930, which are hardware, and passes data if necessary. Each processor 902, 904, 906, 910, 930 realizes the processing of each unit of the present invention based on the instruction and the passed data.

  The present invention is not limited to that described in the above embodiment, and various modifications can be made. For example, terms (frame buffer / work buffer, first to fourth) cited as broad or synonymous terms (drawing area, first to Jth reduced images, conversion table, etc.) in the description or drawings. (Reduced image, lookup table, etc.) can be replaced with terms having a broad meaning or the same meaning in other descriptions in the specification or the drawings.

  Further, the reduction process, the blur process, the enlargement process, the blur process, and the pixel value conversion process of the blur target image are not limited to those described in the present embodiment, and techniques equivalent to these are also included in the scope of the present invention. .

  The present invention can be applied to various games. Further, the present invention is applied to various image generation systems such as a business game system, a home game system, a large attraction system in which a large number of players participate, a simulator, a multimedia terminal, a system board for generating a game image, and a mobile phone. it can.

The example of a functional block diagram of the image generation system of this embodiment. Explanatory drawing of the method of performing a blurring process after reducing the blurring target image. Explanatory drawing of a sampling point group. Explanatory drawing of the method of producing | generating the some reduced image from which a sampling point group differs. Explanatory drawing of the method of producing | generating the some reduced image from which a sampling point group differs. 6A and 6B are explanatory diagrams of a blurred image generation method based on a plurality of reduced images. Explanatory drawing of the production | generation method of a soft focus effect image. 8A, 8B, and 8C are examples of conversion tables for video filter processing. FIGS. 9A and 9B are explanatory diagrams of problems when a plurality of blurring processes are performed. 10A to 10D are explanatory diagrams of pixel value conversion processing at the time of blurring processing. 11A and 11B are explanatory diagrams of pixel value conversion processing during blurring processing. 12A to 12D are explanatory diagrams of specific examples of the blurring process. Explanatory drawing of the blurring process using bilinear interpolation. Explanatory drawing of the conversion process using index color and texture mapping. The flowchart of the specific process of this embodiment. The flowchart of the specific process of this embodiment. The flowchart of the specific process of this embodiment. The flowchart of the specific process of this embodiment. 19A, 19B, and 19C are explanatory diagrams of specific processing of the present embodiment. Hardware configuration example. 21A and 21B are other examples in which the reduction / enlargement magnification is different from that in FIG. 22A and 22B show another example in which the reduction / enlargement magnification is different from that in FIG.

Explanation of symbols

100 processing unit, 110 object space setting unit, 112 movement / motion processing unit,
114 virtual camera control unit, 120 drawing unit, 122, reduction processing unit,
124 blur processing unit, 126 enlargement processing unit, 128 video filter processing unit,
129 α blending unit, 130 sound generation unit, 160 operation unit, 170 storage unit,
171 conversion table storage unit, 172 drawing buffer, 174 texture storage unit,
176 Z buffer, 180 information storage medium, 190 display unit,
192 sound output unit, 194 portable information storage device, 196 communication unit

Claims (14)

A program for generating an image,
A reduction processing unit that reduces the area of the image to be blurred and generates a reduced image;
A blur processing unit that performs blur processing on the reduced image to generate a blurred reduced image;
As an enlargement processing unit that performs processing for enlarging the area of the blurred reduced image,
A program characterized by causing a computer to function. In claim 1,
The reduction processing unit
Generating a first reduced image based on information in the first sampling point group of the blur target image, generating a second reduced image based on information in the second sampling point group of the blur target image; ... A program that generates a Jth reduced image based on information in a Jth sampling point group (J is an integer equal to or greater than 2) of a blurring target image. In claim 2,
The blur processing unit is
Combining the first to Jth reduced images, performing a blurring process on the combined reduced image to generate a blurred combined reduced image,
The enlargement processing unit
A program for performing enlargement processing of the blur combined reduced image. In claim 2,
The blur processing unit is
Blurring the first to Jth reduced images to generate first to Jth reduced images,
The enlargement processing unit
A program for performing enlargement processing and composition processing of the first to Jth blurred reduced images. In any one of Claims 1 thru | or 4,
The reduction processing unit
A program for generating a reduced image by bilinear interpolation texture mapping. In any one of Claims 1 thru | or 5,
For the reduced image, the computer functions as a video filter processing unit that performs a video filter process in which the luminance is higher for a pixel having a given value as a threshold and the luminance is lower for a lower pixel. Let
The blur processing unit is
A program for performing blurring processing on a reduced image subjected to the video filter processing. In claim 6,
The video filter processing unit
A program for performing video filter processing for enhancing a given color component of the reduced image. In any one of Claims 1 thru | or 7,
The blur processing unit is
When the reduced image is subjected to the blurring process a plurality of times, a conversion process for increasing the pixel value of the blurred image obtained in the Kth blurring process among the plurality of the blurring processes is performed. A program characterized by performing the next (K + 1) th blurring process on the blurred image. In claim 8,
Causing a computer to function as a conversion table storage unit that stores a conversion table for the conversion process;
The blur processing unit is
A program for performing the conversion process for increasing a pixel value of a blurred image using the conversion table. In claim 9,
The conversion table is a lookup table for index color / texture mapping,
The blur processing unit is
The pixel value of the blurred image obtained by the Kth blurring process is set as the index number of the lookup table, and index color / texture mapping using the lookup table is performed, so that the pixel value of the blurred image is obtained. A program for performing the conversion process to be increased. In claim 10,
The blur processing unit is
A program characterized by simultaneously performing both the conversion process and the (K + 1) th blurring process by shifting the texture coordinates and performing the index color / texture mapping by a bilinear interpolation method.   A computer-readable information storage medium, wherein the program according to any one of claims 1 to 11 is stored. An image generation system for generating an image,
A reduction processing unit that reduces the area of the image to be blurred and generates a reduced image;
A blur processing unit that performs blur processing on the reduced image to generate a blurred reduced image;
An image generation system comprising: an enlargement processing unit that performs processing for enlarging the area of the blurred reduced image. In claim 13,
The reduction processing unit
Generating a first reduced image based on information in the first sampling point group of the blur target image, generating a second reduced image based on information in the second sampling point group of the blur target image; An image generation system that generates a Jth reduced image based on information in a Jth sampling point group (J is an integer of 2 or more) of a blurring target image.

Images