Patent Publication Number: US-8115780-B2

Title: Image generator

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to European Patent Application No. 06250286.9, filed Jan. 19, 2006, entitled “A METHOD OF GENERATING AN IMAGE”. European Patent Application No. 06250286.9 is assigned to the assignee of the present application and is hereby incorporated by reference into the present disclosure as if fully set forth herein. The present application hereby claims priority under 35 U.S.C. §119(a) to European Patent Application No. 06250286.9. 
     TECHNICAL FIELD 
     The present disclosure is related to methods of generating an image and in particular, but not exclusively, with anti-aliasing an image in a graphics scene generated by a computer. 
     BACKGROUND 
     The field of computer graphics is a fast changing one, in which trade-offs are made between the quality of the images being produced and the processing power of the computing platform being used. The trade-off becomes even more acute when the images need to be displayed in real-time, for example in the video-gaming industry. 
     Rendering is the process of generating an image from a model of a scene, where the model is a description of three dimensional objects. The rendering of an image on a computer screen entails vast amounts of processing. For example, it is necessary to have a so-called “global scene” (also known as the “world scene”) of the images to be displayed, and which will be hereinafter referred to as the scene. Broadly speaking, a scene can be thought of as a snap-shot or picture of the image to be displayed on the screen at any instant in time. As would be expected, a scene will itself comprise many different objects each having their own geometry. For example, a global scene might be the interior of a particular room in a house. The room might have windows, furniture, a TV, etc. Each object, for example TV, table, window will have a different geometry and will need to be created with the correct dimensions and co-ordinates on the screen in relation to the other images. 
     These objects are defined in three dimensions (3D), but will be rendered onto a two-dimensional (2D) computer screen. The technique for rendering a 3D object onto a 2D display involves firstly breaking down the 3D object into polygons defined by primitives. A popular primitive used is a triangle having three vertices. Other primitives can also be used, including points, lines or other polygons. Thus, a 3D image can be transformed into a plurality of, for example, triangles each being defined by a unique set of vertices where each vertex would typically contain information relating to co-ordinates (x, y, z), color, texture and lighting. The data defining the 3D image is continuous vector data. It should be understood that a fairly large storage area is needed to accommodate the vertex information. 
     The creation of an image on a display is performed by a “graphics pipeline”, which takes a geometrical representation of a 3D scene as an input and outputs a 2D image for display on a computer screen. Broadly speaking, the creation of an image on a computer graphics screen can be thought of as consisting of a geometry stage and a rendering stage. In existing systems, the geometry stage is responsible for transformation and lighting, in which the 3D object is converted into a number of polygons defined by a set of suitable primitives. Consider an interactive computer game where the user controls the motion of his player, as the player moves forward or backward the objects in the frame will need to be transformed so that they appear closer to and further away from the user, respectively. 
     In the rendering stage the transformed vertices are placed in a frame buffer in digital form. The frame buffer can in fact comprise a number of buffers, e.g. a color buffer, depth buffer, stencil buffer, accumulation buffer. The frame buffer needs to be continuously managed and updated as the frame (or scene) changes. The rendering stage comprises the process of Rasterization. Rasterization describes the conversion from a vector representation to an x-y coordinate representation. This is the process of taking a two-dimensional image described in a vector format and converting it into pixels, where the pixels are the “dots” that make up the computer display (and correspond to the smallest discrete part of an image that can be displayed). The pixels are drawn and the frame buffer stores lighting, color and intensity information for each pixel that will be enabled. The digital frame data is then converted to an analogue signal to be used to provide the final image for the actual 2D computer display. 
     The problem of “aliasing” in two and three dimensional computer graphics is well known. When an image is rendered, aliasing is a result of the rendering process being a sampling procedure. Continuous vector data, such as the vertex positions of the primitives making up a scene in a three dimensional space, are effectively discretised as they are turned into screen pixels by the rendering process. Smooth polygon edges are drawn onto the display with what are known as “jaggies” because of insufficient pixel resolution. If the 3D images are animated, then the moving edges have “crawlies” as they jump from one pixel to the next, with instantaneous changes of color. 
     An example of this problem is shown in  FIG. 1 , in which is illustrated a grid  100  of 8×8 pixels, representing a portion of a display. The display is rendering a representation of a black shape delimited by continuous line  102 . In the example shown in  FIG. 1 , the color of a pixel is determined by taking a sample of the color at the center point of each pixel. As a result of the sampling, pixels that are fully within the black shape delimited by line  102  are colored black, such as the pixel labelled  104 . Similarly, pixels outside the area of the black shape are colored white, such as the pixel labelled  106 . The pixels at the border of the black shape (i.e. the pixels through which line  102  crosses) are either black or white, depending on the color at the center point of the pixel. For example, the center of the pixel labelled  108  is inside the line  102 , and this pixel is therefore colored black. Conversely, the center of the pixel  110  is just outside the line  102 , and this pixel is therefore colored white. The result of this is that the representation of the shape on the grid of pixels  100  has a jagged edge (hence “jaggies”). 
     A way of minimising this problem is to use anti-aliasing techniques. Anti-aliasing techniques are processes applied as a part of the rendering stage which aim to improve the visual quality of the final displayed image. Anti-aliasing techniques can be divided into two distinct classes: edge anti-aliasing (also called per-primitive anti-aliasing) and full screen anti-aliasing. 
     Edge (or per-primitive) techniques use computations to blend the geometric edges of primitives (i.e. points, lines, triangles, polygons) in order to reduce aliasing effects. Although this method generally requires less computation than other methods, it can also produce lower quality results because it effectively ignores edges introduced by textures or by primitive intersections. 
     Full-screen anti-aliasing works on every fragment, regardless of its location with respect to the primitive (note: a fragment consists of the (X, Y) coordinates of a pixel on the final display surface, plus a collection of other necessary information such as color, relative depth (the Z coordinate), texture coordinates etc.). This can result in some wasted calculations in areas of continuous color but generally provides better overall results. The present application is primarily concerned with full-screen anti-aliasing. 
     In general, full-screen anti-aliasing works by generating more information than is necessary for the final displayed image (in a non anti-aliased system) and then re-sampling this data. This is done by taking several samples of data from the continuous vector image per fragment (i.e. per pixel in the display) which are then combined to give the final result. The samples taken of a fragment may also be called sub-fragments. This formula for combining the samples can be expressed as follows: 
     
       
         
           
             
               
                 
                   
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     Where p is the final pixel color, n is the number of samples per pixel, w i  is a weighting factor (in the range [0, 1]) and c is the sample color for sample i. 
     The precise location within the region defined by the pixel from where these n samples or sub-fragments are taken is determined by the sample pattern being used. Different sample patterns allow a trade off between computation and performance. By using more samples per fragment there is an increase in visual quality but this leads to a much higher computational requirement as each sample has to be processed by the rendering stage. Example known sample patterns are described hereinafter. 
     An illustrative example of full-screen anti-aliasing can be seen with reference to  FIG. 2 . This shows the same grid of pixels  100  as shown in  FIG. 1 , which is rendering the same representation of a black shape delimited by continuous line  102 . However, the color of the pixels through which the line  102  passes are not only either black or white, as was seen in  FIG. 1 . Rather, due to the anti-aliasing, these are levels of grey dependent on the samples taken within the pixel and applied to Equation 1. As a result, the image does not have the jagged edges of  FIG. 1 , and the image as a whole is perceived as being of a higher quality. The pixel sizes of  FIGS. 1 and 2  are, of course, greatly exaggerated compared to a real display. Furthermore, note that  FIGS. 1 and 2  are shown as black and white images merely for illustrative purposes, and that the anti-aliasing techniques also apply for color images. 
     Another distinction that can be made between anti-aliasing techniques is between super-sampling and multisampling. In super-sampling, all fragment data is generated for all samples. This means all fragment data is re-sampled and contributes to the final pixel. In multisampling, sets of samples or sub-fragments may share particular parts of the fragment information. For example, a set of samples may all have the same texture coordinates but different color values. In fact, a multisampling scheme can share everything apart from color, although a penalty is paid in terms of some degradation in quality. 
     A set of six example sample patterns are shown illustrated in  FIG. 3 . The first sample pattern in  FIG. 3  is point sampling  302 . Point sampling corresponds to the non anti-aliased case with one sample per display pixel, whereby the sample is taken from the center of the pixel. This is the type of pattern used to generate the non anti-aliased image shown previously in  FIG. 1 . 
     The second and third sample patterns are denoted 1×2 sample ( 304 ) and 2×1 sample ( 306 ). These sample patterns both use two samples per pixel, wherein the 1×2 sample pattern has the two samples aligned vertically and the 2×1 sample pattern has the two samples aligned horizontally. Both of these two-sample patterns give lower quality results in edges which are either tending towards the horizontal or the vertical (depending on which one is being used). 
     The fourth sample pattern shown in  FIG. 3  is denoted 2×2 sample ( 308 ). The 2×2 sample pattern utilises four samples per pixel in a regular square. This pattern is generally accepted to give good results, although may not provide particularly good resolution in the X and Y directions. The fifth sample pattern, called the Rotated Grid Super Sample pattern ( 310 ) is an attempt to improve on this problem with the 2×2 sample pattern. This pattern again uses four samples per pixel, but the samples are rotated with respect to the center of each pixel when compared to the 2×2 sample pattern. 
     The sixth sample pattern is a 4×4 checker pattern ( 312 ). This pattern uses eight samples per pixel. This type of pattern is typically only used where performance issues are far outweighed by the need for quality (such as for computer aided design (CAD) or design applications). 
     All of the sample patterns shown in  FIG. 3  use a constant down-sampling weight for each sample (i.e. w i =1/n in Equation 1, above). For example, for the 2×2 sample pattern ( 308 ) a weighting value of 0.25 is used. This effectively means a box filter is being used for the down-sampling. 
     The main disadvantage of all the patterns in  FIG. 3  is that, in order to achieve a reasonable level of quality in the results, several samples per pixel are required. All of these samples need to be processed through the graphics pipeline from the rasterizer down. Having to process, for example, four samples per pixel results in a four-fold increase in memory bandwidth requirements, power, etc. Equally, this results in a reduction in performance by a similar factor. 
     In order to address the problem of having to process large numbers of samples per pixel, sample patterns have been proposed that share samples between neighbouring pixels. This means that fewer samples must be processed by the fragment pipeline but a reasonable number of samples still contribute to each final display pixel. 
     An example of two shared sample patterns are shown in  FIG. 4 . Both of these patterns use, on average, two samples per pixel. The “Quincunx” pattern  402  uses a slightly different paradigm to all of the other patterns discussed here in its use of weightings in the down-sampling. Instead of a constant value for each sample, the center value is given a weighting of ½, and each of the four corner samples ⅛. In sampling theory parlance this is a “tent filter”, which is an attempt to use a more accurate model of the ideal low-pass filter: the sinc filter. 
     The “Flipquad” pattern  404  uses a constant weighting, as with the patterns shown in  FIG. 3 . Effectively it is the RGSS pattern  310  except with the samples pushed out to the pixel edges to allow sharing of samples. The pattern alternates between adjacent pixels, which is intended to give better results for horizontal, vertical and 45° edges. It is reputed that the human visual system is more sensitive to quality issues on these kinds of edges. 
     Both of the patterns in  FIG. 4  aim to provide quality on a par with those produced using 2×2 or RGSS patterns ( 308 ,  310 ), whilst requiring a lower number of samples per pixel, and hence less computation. 
     SUMMARY 
     The present disclosure generally provides an image generator. 
     In one embodiment, the present disclosure provides a method of generating an image on a display having a plurality of pixels from a vector description of a scene. The method includes sampling data from the vector description to provide the data samples at locations defined in relation to the pixels from the vector description. The locations include a first and a second location at the edges of the pixels, a third location at the corner of the pixels and a fourth location at the center of the pixels. The method further includes storing the data samples in a buffer and processing the data samples taken for each of the pixels to give an averaged data value for each of the pixels. The method still further includes generating the image on the display by applying the averaged data value to each of the pixels. 
     In another embodiment, the present disclosure provides a system for generating an image on a display. The display includes a plurality of pixels from a vector description of a scene. The system includes a circuit to sample data from the vector description to provide data samples at locations defined in relation to the pixels. The locations include a first location and second location at the edges of the pixels, a third location at the corner of the pixels and a fourth location at the center of the pixels. The system also includes a buffer to store the data samples. The system further includes a processor to process the data samples taken for each of the pixels to give an averaged data value for each of the pixels. The system still further includes a generator to generate the image on the display by applying the averaged data value to each of the pixels. 
     In still another embodiment, the present disclosure provides an image generator including a sampling circuit to sample data from a vector description to provide data samples at locations defined in relation to a plurality of pixels. The locations comprise a first location and second location at the edges of the pixels, a third location at the corner of the pixels and a fourth location at the center of the pixels. The image generator also includes a processor to process the data samples taken for each of the pixels to give an averaged data value for each of the pixels. The image generator still further includes a generator to generate the image on the display by applying the averaged data value to each of the pixels. 
     Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure and its features, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  depicts a conventional image rendered without anti-aliasing; 
         FIG. 2  depicts a conventional image rendered with anti-aliasing; 
         FIG. 3  depicts six conventional sample patterns used for full-screen anti-aliasing; 
         FIG. 4  depicts two conventional shared-sample patterns used for full-screen anti-aliasing; 
         FIG. 5  is a delta sample pattern according to one embodiment of the present disclosure; 
         FIG. 6  is a graphics system using the sample pattern of  FIG. 5  according to one embodiment of the present disclosure; and 
         FIG. 7  shows a rasterizer pattern for the sample pattern of  FIG. 5  according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 5  depicts a shared sample pattern  500  which underlies an embodiment of the present disclosure. The sample pattern  500  is named ST Delta herein, because of the -like pattern of each pixel&#39;s samples. In one embodiment, each pixel of the sample pattern  500  comprises two edge samples (e.g.,  502 ,  504 ) and a corner sample (e.g.,  506 ), which are arranged in an alternating pattern such that the orientation of the samples is a mirror-image of the adjacent pixel, as shown by the alternating patterns of pixels  508 ,  510 ,  512  and  514 . In one embodiment, the edge and corner samples are also combined with a central sample for each pixel. Due to the sample-sharing, the effective cost of each sample is 2¼ (1 for the center sample, % each for the edge samples and ¼ for the corner sample). 
     In one embodiment, the samples can be given different sample weightings. For example, the weighting pattern is 7/16 for the center sample and 3/16 for the other three samples, for an individual display pixel. Other weighting patterns can also be used according to one embodiment of the present disclosure. 
     The sample pattern  500  shown in  FIG. 5  is used to render an image in a graphics system  600  as shown in  FIG. 6  according to one embodiment of the present disclosure. The graphics system  600  takes as input a vector description of a scene  602 , which is provided to a graphics pipeline  604  that will be described in more detail hereinafter. In one embodiment, the graphics pipeline  604  performs (amongst other functions) the anti-aliasing according to the sample pattern  500  shown in  FIG. 5 . The output of the graphics pipeline  604  is an averaged data value for each of the pixels, and this is provided to a display generator  606 , which converts the output of the graphics pipeline  604  into signals for driving a display  608  comprising a plurality of pixels  610 . The result is that the pixels  610  of the display  608  show an anti-aliased representation of the scene  602  according to one embodiment of the present disclosure. 
     The graphics pipeline  604  is now considered in more detail. According to one embodiment of the present disclosure, the graphics pipeline  604  comprises a rasterizer  612 , which operates according to the sample pattern  500  to generate the samples for each fragment. In one embodiment, samples of the scene  602  are taken according to the sample pattern  500  in the rasterizer  612  by implementing a rasterizing scan as shown in the rasterizer pattern  702  shown in  FIG. 7 . The rasterizer pattern  702  is shown illustrating the samples taken for four pixels, wherein the sample numbered P 6  is located at the corner of the four pixels, samples P 1 , P 2 , P 5 , P 7 , P 10 , P 11 , P 16  and P 18  are the edge samples for the four pixels, and samples P 3 , P 4 , P 8  and P 9  are the center samples for the four pixels. The samples P 1 -P 18  listed above are also shown marked on the sample pattern  500  shown in  FIG. 5 , in order to show how the rasterizer pattern  702  corresponds to the sample pattern  500 . In one embodiment, the numbering of the samples shown in the rasterizer pattern  702  defines one possible order in which the rasterizer can take samples for each fragment. The order in which the samples are taken can affect how the samples are written to the frame buffers, as will be discussed later herein. 
     According to one embodiment of the present disclosure, the rasterizer  612  can determine the positions of the samples to be taken relative to the central sample for a fragment using an “offset table”  626 . The offset table  626  stores the offsets from the central sample for a fragment to each of the samples in the rasterizer pattern  702 . In one embodiment, the rasterizer can use these stored offsets to determine the location for each of the samples. Other techniques for determining the positions of the samples can also be implemented, such as on-the-fly calculation of the sample locations. 
     The samples generated by the rasterizer  612  are provided to the fragment pipeline  614 , where they are processed and the relevant parts of the fragment data are placed in buffers. For example, depth information may be placed in a depth buffer. In the case of a super-sampling anti-aliasing technique, all fragment data is generated for all samples, and therefore there will be multiple samples per pixel for the depth information. This is shown in  FIG. 6  as the super-sample depth buffer  622  according to one embodiment of the present disclosure. 
     The samples relating to the color of the fragment taken from the fragment pipeline  614  are stored in a super-sample frame buffer  616 . The super-sample frame buffer  616  stores all of the samples produced by the rasterizer for a given frame. In one embodiment, samples are loaded into the buffer using an addressing scheme controlled by a buffer addressing block  628 , which optimises use of the shared sample pattern of  FIG. 5 , so as to minimise the footprint of the super-sampled buffers. The way in which the samples are stored in the super-sample frame buffer  616  by the buffer addressing block  628  can be seen illustrated in  FIG. 7 . This shows how the rasterizer pattern  702 , which defines the location of the samples and the order in which samples are taken by the rasterizer, determines the structure of the samples stored in the super-sample frame buffer, represented by the compact pattern  704  shown in  FIG. 7  according to one embodiment of the present disclosure. 
     The sample data is entered into the super-sample frame buffer  616  by the buffer addressing block  628  according to the specific patterns shown in  FIG. 7 . These patterns allow the samples to be stored in a compact manner, whilst permitting the samples (and in particular the shared samples) to be easily addressed and rapidly read out for each fragment. The compact pattern  704  shown in  FIG. 7  compactly stores the samples whilst minimising distortion to the original sample pattern. This is because all samples (with one exception) on the same row of the original pattern remain on the same row of the compact pattern. For example, samples P 1  and P 2  are on the same row as each other in the sample pattern  702 , and remain on the same row in the compact pattern  704 . The exception to this is the corner sample P 6 , which is placed on the same row as P 7  and P 18  in order to increase the compactness of the pattern. Sample P 6  could also equally be placed on the same row as P 5  and P 16  in an alternative embodiment. The storing of the samples in the super-sample frame buffer  616  according to the compact pattern  704  is implemented by the buffer addressing block  628  according to one embodiment of the present disclosure. 
     Returning again to  FIG. 6 , once all the samples for a frame have been produced by the rasterizer  612  and entered into the super-sample frame buffer  616 , a trigger  624  is activated by the fragment pipeline  614  according to one embodiment of the present disclosure. This trigger is provided to a re-sampling block  618 . In one embodiment, the re-sampling block  618  implements the Equation 1 shown previously, and processes the samples from the super-sample frame buffer  616  to combine them. 
     The samples for a fragment from the super-sample buffer  616  are read out to the re-sampling block  618  under the control of the buffer addressing block  628 . The re-sampling block comprises a memory  630  for storing the values for the weighting factors to be applied to the samples. The weighting factors from the memory  630  are applied to the samples, and these are summed using an addition unit  632 . The result of the re-sampling block  618  is the weighted average of the samples for a fragment, i.e., the weighted averaged color values for a pixel. The weighted averaged color values for the pixel is stored in a frame buffer  620 . The calculation of the weighted averaged color value is repeated for each of the fragments in the super-sample buffer  616  until all samples have been averaged and written to the frame buffer  620  according to one embodiment of the present disclosure. 
     From the frame buffer the pixel data may be processed by further known parts of the graphics pipeline if required according to one embodiment of the present disclosure. Finally, in one embodiment, the data in the frame buffer  620  is converted by the display generator  606  to an analogue signal to be used to provide the final image for the computer display  608 . 
     It may be advantageous to set forth definitions of certain words and phrases used in this patent document. The term “couple” and its derivatives refer to any direct or indirect communication between two or more elements, whether or not those elements are in physical contact with one another. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like. 
     While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.