TECHNICAL FIELD 
     The present invention relates to an image processing device and method that compresses a vector-based image by removing duplicate information and simplifying the shape(s) of complex edge(s) in vector object(s). 
     BACKGROUND 
     Electronic based images are commonly used today because they are easy to store, retrieve and manipulate when compared to paper based images. Plus, electronic based images are commonly used today because they are easy to distribute when compared to paper based images. With the advent of the Internet this last advantage is an important consideration. If desired, the paper based images can be converted into electronic based images to make them easier to manipulate and distribute. This conversion can be achieved by using a scanner which scans a paper based image and then creates an electronic based image. Of course, an electronic device such as a digital camera (for example) can be used to take a picture and then create an electronic based image. In either case, the electronic based image is created by first generating a raster (bitmap) image and then converting the raster image into a vector-based image (vector graphic image). The procedure used to convert the raster image into the vector-based image is known in the field as a vectorization process. 
     There are many types of vectorization programs available on the market today which can be used to convert a raster image into a vector-based image. Some of the more well-known vectorization programs include (for example): Vector Eye, Adobe Streamline, Silhouette, Synthetik Studio Artist and Macromedia Freehand. How these vectorization programs perform and what parameters they use such as numbers of colors, numbers of shapes, complexity of shapes, etc. . . . , varies greatly and depends on the desired result. However, all of these vectorization programs function to analyze color information within the raster image and then create several larger areas known as vector objects which share the same colors.  FIGS. 1A-1B  (PRIOR ART) are provided to respectively illustrate a raster image and a vector-based image of two automobiles (the vector-based image will be discussed in detail below with respect to the present invention). These vectorization programs all work well to create a suitable vector-based image but they could be improved to better compress the representation of the vector-based image. This need and other needs are satisfied by the image processing device and method of present invention. 
     SUMMARY 
     Today&#39;s computer programs that convert raster images into vector-based images do not optimize/compress the vector representation of the vector-based images. Instead, they simply keep all of the complex edges for the vector objects within the vector-based images. The present invention described herein functions to create a compressed vector-based image by simplifying the shapes of common complex edges which are shared by adjacent vector objects. The compression (lossless compression) of the vector objects is done without affecting the perceived quality of the vector-based image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of the present invention may be obtained by reference to the following detailed description when taken in conjunction with the accompanying drawings wherein: 
         FIGS. 1A-1B  (PRIOR ART) respectively illustrate a raster image and a vector-based image of two automobiles which are used to help explain a problem with the state-of-the-art vectorization programs which is addressed by the present invention; 
         FIG. 2  is a block diagram illustrating the basic components of an image processing device which compresses a vector-based image in accordance with the present invention; 
         FIG. 3  is a flowchart illustrating the basic steps of a method for compressing a vector-based image in accordance with the present invention; 
         FIGS. 4A-4D  is a set of drawings which are used to help explain how a first vector-based image can be compressed by the method shown in  FIG. 3  in accordance with the present invention; 
         FIGS. 5A-5D  is a set of drawings which are used to help explain how a second vector-based image can be compressed by the method shown in  FIG. 3  in accordance with the present invention; 
         FIGS. 6A-6E  is a set of drawings which are used to help explain how a third vector-based image can be compressed by the method shown in  FIG. 3  in accordance with the present invention; 
         FIGS. 7A-7D  is a set of drawings which are used to help explain how a fourth vector-based image can be compressed by the method shown in  FIG. 3  in accordance with the present invention; and 
         FIG. 8  is a flowchart illustrating the basic steps of a method for compressing a vector-based image which is transmitted to a mobile terminal in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIG. 2 , there is illustrated a block diagram of an image processing device  200  which implements a preferred method  300  so it can compress a vector-based image in accordance with the present invention. The image processing device  200  includes a processor  202  which processes instructions stored within a memory  204  to compress a vector-based image  212  (for example) as follows: (1) identify a complex edge  206  that is shared by two adjacent non-transparent vector objects  208  and  210  which are part of the vector-based image  212  (step  302  in  FIG. 3 ); (2) select one vector object  208  (for example) which is going to have an unchanged complex edge  206  when it is used later to form a compressed vector-based image  212 ′ (step  304  in  FIG. 3 ); (3) simplify the complex edge  206  of the other vector object  210  (for example) (where the unchanged vector object  208  and the simplified vector object  210 ′ are shown separated) (step  306  in  FIG. 3 ); and (4) draw the unchanged vector object  208  (and possibly other vector objects) on top of at least a portion of the simplified edge  206 ′ of the simplified vector object  210 ′ to form the compressed vector-based image  212 ′ (step  308  in  FIG. 3 ). How this method  300  can compress a vector-based image is described in more detail below where it is used to compress four different vector-based images. 
     In example #1, the method  300  compresses the vector-based image of a windshield located within the automobile shown on the left side in  FIG. 1B . The vector-based image of the windshield  402  is shown in  FIG. 4A  (note: the vector-based image is shown darker than normal to better help describe the present invention). The three vector objects  404   a ,  404   b  and  404   c  which make-up the vector-based image of the windshield  402  are shown separated from one another in  FIG. 4B . In this example, the method  300  compresses the windshield image  402  by simplifying two complex edges  406   a  and  406   b  where the first complex edge  406   a  is shared between vector objects  404   a  and  404   b  and the second complex edge  406   b  is shared between vector objects  404   b  and  404   c.    
     In particular, the method  300  simplifies the first complex edge  406   a  by performing the following steps: (1) identifying the complex edge  406   a  which is shared by two adjacent vector objects  404   a  and  404   b  (step  302 ); (2) randomly selecting (or intelligently/iteratively selecting) one of the vector objects  404   a  and  404   b  (e.g., vector object  404   b ) to remain unchanged so it can be used later as is to form the compressed vector-based image  402 ′ (in this example however the vector object  404   b  will be subsequently changed as discussed below when another shared complex edge  406   b  is simplified) (step  304 ); and (3) simplifying the first complex edge  406   a  associated with vector object  404   a  by replacing the shape of the complex edge  406   a  with a simpler shape  410   a  which in this case is a straight line but any arbitrary shape can be used so long that it is a simpler shape than the original complex edge  406   a  (see  FIG. 4C ) (step  306 ). The method  300  repeats these steps to simplify the second complex edge  406   b  (associated with vector object  404   b ) by replacing it with a simpler shape  410   b  which in this case is a straight line but again any arbitrary shape can be used so long that it is a simpler shape than the original complex edge  406   b  (see  FIG. 4C ). 
     The method  300  then draws the unchanged vector object  404   c  (with the original complex edge  406   b ) on top of the changed vector object  404   b ′ (with the original complex edge  406   a  and the simplified edge  410   b ) which was drawn on top of the changed vector object  404   a ′ (with the simplified edge  410   a ) to form the compressed vector-image of the windshield  402 ′ (see  FIG. 4D ) (step  308 ). As can be seen, there are no gaps between the vector objects  404   a ′,  404   b ′ and  404   c  which means that the simplified edge  410   a  of the changed vector object  404   a ′ was formed so it will be completely hidden underneath the changed vector object  404   b ′ and the unchanged vector object  404   c . Plus, the simplified edge  410   b  of the changed vector object  404   b ′ was formed so it will be completely hidden underneath the unchanged vector object  402   c . To draw the compressed vector-image of the windshield  402 , the method  300  could use a depth buffer or more specific a vector-graphic description language which uses a depth buffer so it can describe what vector object is to be drawn on top of another vector object. One such language is SVG (Scalable Vector Graphics standardized by W3C) which is an XML-based language that renders vector objects in the same order as they appear in the file. 
     As can be seen, the original non-compressed vector-image of the windshield  402  shown in  FIG. 4A  looks the same as the compressed vector-image of windshield  402 ′ shown in  FIG. 4D . This indicates that the method  300  is indeed an improvement over the state-of-the-art vectorization programs because it reduces the amount of information needed to form the same visual representation of the windshield  402 . Basically, the method  300  reduces the amount of information needed to describe vector objects  404   a  and  404   b  by replacing their shared complex edges  406   a  and  406   b  with simplified shared edges  410   a  and  410   b . This process can be referred to as lossless compression or lossless optimization. 
     In example #2, the method  300  compresses a vector-based image  500  containing two vector objects  502   a  and  502   b  that are defined in accordance with the following SVG file: 
     
       
         
               
             
           
               
                   
               
             
             
               
                 &lt;?xml version=“1.0” encoding=“utf-8”?&gt; 
               
               
                 &lt;svg width=“400” height=“400”&gt; 
               
               
                 &lt;path fill=“#00015F” d=“M0, 0L100, 0L150, 50L100, 100L0, 100z”/&gt; 
               
               
                 &lt;path fill=“#FF0100” d=“M200, 0L100, 0L150, 50L100, 100L200, 100z”/&gt; 
               
               
                 &lt;/svg&gt; 
               
               
                   
               
             
          
         
       
     
     When drawn this SVG file creates the vector-based image  500  which is shown in  FIG. 5A  (where the top left corner is at coordinate x, y=0, 0 and the bottom right corner is at x, y=200,100).  FIG. 5B  shows the two vector objects  502   a  and  502   b  separated. Next, a discussion is provided to explain how the first path in the SVG file is used to draw the vector object  502   a : 
                                                 fill = “#00015F”   Fill the shape with this color.           d=   Start the path here.           M0, 0   Move to 0, 0, meaning start to draw from               this coordinate (e.g., put the “pen”               here)           L100, 0   Line to (absolute) 100, 0, meaning draw               a line to coordinate x, y = 100, 0 (from               previous point).           L150, 50   Line to (absolute) 150, 50, meaning draw               a line to coordinate x, y = 150, 50 (from               previous point).           L100, 100   Line to (absolute) 100, 100,               meaning draw a line to coordinate               x, y = 100, 100 (from previous point).           L0, 100   Line to (absolute) 0, 100, meaning draw               a line to coordinate x, y = 0, 100 (from               previous point).           z   Close the shape, which is the same as               drawing a line from the last point to the               end point.                        
Note: SVG enables one to describe paths in several ways, e.g. using “l” or “L” which means “line to” in both cases but in the first case it is relevant (from previous drawing point) and in the second case it is definite (to a fixed coordinate). Plus, one can use “C” which means to “curve to” the next coordinate, using a Bezier curve.
 
     Thus, when method  300  compares the two paths within the SVG file it sees that the part “L100, 0L150, 50L100, 100” is the same in both paths. This is how method  300  can identify a common complex edge  504  which is shared by two vector objects  502   a  and  502   b  (step  302 ). Note: in SVG the order in which the paths are drawn might be reversed meaning it is possible to travel the same path but in different directions, this means the method  300  should also compare the reversed paths in the SVG file to discover the common edges. In either case, the method  300  compares the description of the paths and decides if they are actually the same which indicates a common edge between adjacent vector objects. 
     The method  300  uses this knowledge to simplify the vector image  500  by replacing the complex edge  504  associated with one of the vector objects (e.g., vector object  502   b ) with a simplified edge  506  (step  306 ). The vector object  502   b  can be simplified in the SVG file as follows: 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                   
                 &lt;?xml version=“1.0” encoding=“utf-8”?&gt; 
               
               
                   
                 &lt;svg width=“400” height=“400”&gt; 
               
               
                   
                 &lt;path fill=“#FF0100”d=“M200, 0L100, 0L100, 100L200, 100z”/&gt; 
               
               
                   
                 &lt;path fill=“#00015F” d=“M0, 0L100, 0L150, 50L100, 100L0, 100z”/&gt; 
               
               
                   
                 &lt;/svg&gt; 
               
               
                   
               
             
          
         
       
     
     As shown in  FIG. 5C , the method  300  has simplified the two lines in the complex edge  504  associated with vector object  502   b  by replacing them with one vertical line  506  from x, y=100,0 to x, y=100,100. In addition, the method  300  has changed the drawing order of the two vector objects  502   a  and  502   b ′ such that the unchanged vector object  502   a  is now drawn on top of the changed vector object  502   b ′ in order to form the compressed vector image  500 ′ (step  308 ) (see  FIG. 5D ). As a result, the method  300  reduced the amount of information which was needed to form the compressed vector image  500 ′. As can be seen, the compressed vector image  500 ′ has the same visual representation as the non-compressed vector image  500  (compare  FIGS. 5A and 5D ). In this example, the method  300  enabled a gain of 7 characters (L150,50) out of 198, which is approximately a 3.5% gain in size. In fact, the more complex the shared edge, then the more the method  300  can gain by simplifying that shared edge with a theoretical maximum gain approaching 50%. This is desirable because the method  300  by simplifying a shared edge in effect reduces the amount of information needed to describe the associated vector object. 
     In example #3, the method  300  compresses the vector-based image  600  shown in  FIG. 6A . The three vector objects  602   a ,  602   b  and  602   c  which make-up the vector-based image  600  are shown separated from one another in  FIG. 6B . As can be seen, the vector objects  602   a  and  602   b  share a complex edge  604   a  and vector objects  602   b  and  602   c  share a complex edge  604   b . Assume, the method  300  simplified the two complex edges  604   a  and  604   b  and created two simplified edges  604   a ′ and  604   b ′ which are part of the simplified vector image  600 ′ shown in  FIG. 6C . If this happened, the simplified edge  604   a ′ would be too small because there would be a space  608  between the simplified vector objects  602   a ′ and  602   b ′. Of course, the method  300  would not do this however the defective simplified edge  604   a ′ was created to illustrate a point that a simplified edge needs to be completely hidden underneath one or more vector objects. 
     In practice, the method  300  would simplify the two complex edges  604   a  and  604   b  and possibly create simplified edges  604   a ″ and  604   b ′ respectively associated with changed vector objects  602   a ″ and  602   b ′ to form the simplified vector image  600 ″ shown in  FIG. 6D . Now, it can be seen that the simplified edge  604   a ″ associated with changed vector object  602   a ″ is completely hidden under the simplified vector object  602   b ′ (compare  FIGS. 6C and 6D ). This all works fine. However, the method  300  could also have logic that knows when one can draw another vector object in this case vector object  602   c  on top of the other two vector objects  602   a  and  602   b . Then, the method  300  can use that information to simplify the simplified edge  604   a ″ even further so as to create the simplified edge  604   a ′″ shown in  FIG. 6E . This particular simplified edge  604   a ′″ is a bit of a construction but it helps illustrate a point that if desired one could simplify an edge so it would be hidden under multiple vector objects. In either case, the visual appearances of the simplified vector images  600 ″ and  600 ′″ are the same as the visual appearance of the non-simplified vector image  600  (compare  FIGS. 6A ,  6 D and  6 E). But, the simplified vector objects  602   a ″,  602   a ′″ and  602   b ′ require less data to form them when compared to the data needed to form the unchanged vector objects  602   a  and  602   b  which are associated with the non-simplified vector image  600 . 
     In example #4, the method  300  compresses the vector-based image  700  shown in  FIG. 7A . The three vector objects  702   a ,  702   b  and  702   c  which make-up the vector-based image  700  are shown separated from one another in  FIG. 7B . As can be seen, the two vector objects  702   a  and  702   b  share a complex edge  704   a  and the two vector objects  702   b  and  702   c  share a complex edge  704   b . The method  300  could simplify these two complex edges  704   a  and  704   b  by creating two simplified edges  704   a ′ and  704   b ′ which are respectively associated with the changed vector objects  702   a ′ and  702   b ′ (see  FIG. 7C ). Then, the method  300  could draw the unchanged vector object  702   c  on top of the changed vector object  702   b ′ which was drawn on top of the changed vector object  702   a ′ to form a simplified vector image  700 ′ (see  FIG. 7D ). As can be seen, the simplified edge  704   a ′ of the changed vector object  702   a ′ is completely hidden under two different vector objects  702   b ′ and  702   c . And, the simplified edge  704   b ′ of the changed vector object  702   b  is completely hidden under one vector object  702   c . After this simplification, the visual appearance of the simplified vector image  700 ′ remains the same as the visual appearance of the non-simplified vector image  700  (compare  FIGS. 7A and 7D ). 
     From the foregoing, it should be appreciated that a basic idea of method  300  is that a complex edge shared between two vector objects is simplified to have one complex shape and one simplified edge. The simplified edge can be created by using lines, curves or any other shapes that are simpler than the original complex edge. Here simpler means that it can be defined using less information. The method  300  also draws the unchanged vector object which has the complex edge on top of the changed vector object which has the simplified edge. The drawing order can be controlled by using a depth buffer which specifies what vector object is to be drawn on top of another. This drawing order concept is also known as the “painters model”. Simply described it means that what is painted last is what is seen. If for example, a picture is painted on the screen and then a red circle is painted on top of it, then one will not see the part of the picture underneath the red circle. Because of this drawing order, the method  300  works well with oblique (non-transparent) adjacent vector objects but it does not work with “see-through” vector objects. Lastly, the method  300  as described above effectively provides for a more compact representation of a vector-based image than was output by a vectorization program. However, the method  300  could also be used as part of the vectorization program itself meaning that the enhanced vectorization program would immediately create and output the compressed vector-based image. 
     In one application, the present invention can be used to create smaller files to be sent to a mobile terminal (e.g., mobile phone, PDA, laptop computer) which satisfies an important goal of the mobile community. In this case, the enhanced method  300 ′ would have the following steps: (1) identify a complex edge that is shared by two adjacent vector objects which are part of a vector-based image (step  302  in  FIG. 8 ); (2) select one of the vector objects (e.g., first vector object) which will have an unchanged complex edge when it is later used to form a compressed vector-based image (step  304  in  FIG. 8 ); (3) simplify the complex edge of the other vector object (e.g., second vector object) (step  306  in  FIG. 8 ); (4) use a scalable vector graphics language (e.g., SVG, SVG Basic (SVGB), SVG Tiny (SVGT)) to prepare a file which indicates that the simplified edge of the changed vector object (e.g., simplified second vector object) is to be drawn so as to be completely hidden underneath the unchanged vector object (e.g., unchanged first vector object) or is to be drawn so as to be completely hidden underneath the unchanged vector object (e.g., unchanged first vector object) and at least one more additional vector object (step  308  in  FIG. 8 ); and (5) transmit the file to the mobile terminal which then draws/forms the compressed vector-based image (step  310  in  FIG. 8 ). Note: SVGT is a format which was included in the 3GPP release 5 and 6 for Personal Shopping System (PSS) and Multimedia Messaging Service (MMS) (see 3GPP PSS Release 5 &amp; 6 (3GPP TS 26.234 v5.7.0 &amp; 3GPP TS 26.234 v.6.7.0) and 3GPP MMS Release 5 &amp; 6 (3GPP TS 26.140 v5.2.0 &amp; 3GPP TS 26.140 v.6.3.0)). 
     Although one embodiment of the present invention has been illustrated in the accompanying Drawings and described in the foregoing Detailed Description, it should be understood that the invention is not limited to the disclosed embodiment, but is also capable of numerous rearrangements, modifications and substitutions without departing from the spirit of the invention as set forth and defined by the following claims.
Today's computer programs that convert raster images into vector-based images do not optimize/compress the vector representation of the vector-based images. Instead, they simply keep all of the complex edges for the vector objects within the vector-based images. The present invention described herein functions to create a compressed vector-based image by simplifying the shapes of common complex edges which are shared by adjacent vector objects. The compression (lossless compression) of the vector objects is done without affecting the perceived quality of the vector-based image.