Abstract:
A method for resizing an input digital image having input pixel values to produce an output digital image having output pixel values, wherein the output digital image has a lower resolution and wherein single pixel width details in the input digital image are preserved, the method being performed by a processor and comprising: determining an output pixel value for the output digital image by interpolating within a corresponding neighborhood of image pixels in the input digital image; detecting whether the input digital image contains a fine detail within the corresponding neighborhood of image pixels; adjusting the output pixel value when a fine detail is detected; and repeating steps a)-c) for a plurality of output pixels.

Description:
FIELD OF THE INVENTION 
     This invention pertains to the field of image resizing, and more particularly to a method for image down-sampling wherein single pixel width details in the input digital image are preserved. 
     BACKGROUND OF THE INVENTION 
     Image resizing is an operation that is used in many different digital image processing chains. Often digital output devices such as printers and softcopy displays require digital images of a particular image resolution. When it is desired to display digital images having a different resolution, it is necessary to use an image resizing operation to produce an image of the desired resolution. For example, a particular printer may be designed to print 600 dpi images. To print a 200 dpi input image, it would be necessary to resize the image by up-sampling the image by a factor of 3×. In other cases, it may be necessary to down-sample the image when the input image has a higher resolution than the printer requires. 
     Another application where image resizing can be required is when images need to be stored in a limited amount of memory, or sent across a communication link having a limited amount of bandwidth. In this ease, high resolution images are down-sampled to create smaller images having a lower resolution. 
       FIG. 1  shows an example of a system  100  for printing images where image resizing is typically applied. The system  100  has two components: a host device  101 , and a printer  102 , connected by a communication link  110 . The host device  101  could, for example, be a personal computer connected to the printer using a USB cable. Alternately, the host device  101  could be some other device such as a digital camera that can communicate directly with the printer  102  using a wireless connection, such as a wireless connection using the well-known Bluetooth protocol. 
     The host device  101  performs host device processing  106  to process an input digital image  104  to produce a processed digital image  108 . For the example where the host device  101  is a personal computer, the host device processing  106  would typically occur in a printer driver. Conventionally, printer drivers will receive the input digital image  104  from a host application using standard operating system interfaces. Generally, the printer driver would receive input digital images  104  having a specific image resolution (e.g., 600 dpi). The input digital image  104  may be a photographic image, or alternately may be some other form of digital image, such as documents containing text and graphics. 
     The host device processing  106  can perform a variety of different image processing operations depending on the requirements of the particular printer  102 . Examples of such image processing operations would include color correction, sharpening, halftoning and resizing. Typically, the printer  102  may require that the host device  101  supply digital image data of a particular resolution. The required resolution may correspond to the native resolution of the printer  102 , or may correspond to a resolution that will enable the printer to receive the image data at a sufficient speed to support the printer&#39;s optimum throughput. For example, if the printer is specified to print a certain number of pages per minute in a draft printing mode, the image data must be supplied to the printer at that rate. In this case, it may be necessary to limit the resolution of the image data being sent to the printer in order to achieve the required speed, particularly when using low bandwidth communication links such as wireless links. Therefore, it will often be necessary for the host device processing  106  to down-sample the image using a resizing operation. 
     The printer  102  receives the processed digital image  108  from the host device  101  via the communication link  110 . The printer  102  applies printer processing  112  to produce a print-ready digital image  114 . The printer processing could include a wide variety of image processing operations including color correction, resizing, sharpening, halftoning, and swath generation. The print-ready digital image  114  is then passed to a printer engine  116  to produce the output printed image  118 . 
     Consider the case where the host device  101  receives an input digital image  104  having a 600 dpi resolution, but the printer  102  requires that the host device  101  supply a processed digital image  108  having a 300 dpi resolution. In this case, the host device processing must apply a resizing operation that applies a 2× down-sampling of the image data. The down-sampling operation can sometimes result in a loss of image quality. This is particularly true when the input digital image  104  contains fine details, such as single pixel width lines. 
       FIG. 2A  shows an example of a grayscale input digital image  120  having a pair of crossed single pixel width lines. The input digital image  120  has a background comprised of white pixels  122  having an 8-bit code value of 255. The single pixel width lines are shown as black pixels  124  having an 8-bit code value of 0. 
     One common algorithm that is used in many resizing operations is cubic interpolation. With this method, interpolated pixels are determined by performing a weighted average of pixel values for a neighborhood of input pixels. Some resizing algorithms employ a bicubic interpolation algorithm which considers a 2-dimensional neighborhood of pixels. In other implementations a 1-dimensional cubic interpolation algorithm is first applied to the image rows to resize the digital image in one direction, and then subsequently applied to the image columns to resize the digital image in the other direction. 
       FIG. 2B  illustrates the use of a cubic interpolation algorithm to apply a 2× down-sampling operation to one row of image pixels  130  from the input digital image  120  shown in  FIG. 2A . To compute a particular output pixel value  136 B, the cubic interpolation algorithm computes a weighted summation of the input pixels values for a pixel neighborhood  131 . A set of weighting factors  132  is used to weight the pixel values in the pixel neighborhood  131  to produce weighted pixel values  134 . The weighted pixel values  134  are then added together to produce the corresponding output pixel value  136 B. In  FIGS. 2C-2E  the same cubic interpolation algorithm is applied to produce output pixel values  136 C,  136 D and  136 E using corresponding pixel neighborhoods  131 . 
       FIG. 2F  illustrates a down-sampled digital image  140  determined by applying the cubic interpolation algorithm just described to each row and then to each column of the input digital image  120 . It can be seen that the pixel values for down-sampled line pixels  141  are no longer black, but rather correspond to light gray pixel values. This represents a significant loss of image quality relative to the input digital image  120 , because the visibility of the single pixel width details will be substantially reduced. 
     Consider the case where this approach is used by the host device processing  106  in the system  100  of  FIG. 1  to produce a 300 dpi processed digital image  108 , which is passed to the printer  102  via the communication link  110 . If the printer engine  116  has a native resolution of 600 dpi, the printer processing  112  must then apply a resizing algorithm to perform a 2× up-sampling operation. One common resizing algorithm that is used for up-sampling operations is pixel-replication (also known as nearest-neighbor interpolation).  FIG. 2G  shows a pixel-replicated digital image  142  produced by applying pixel replication to the down-sampled digital image  140 . Comparing the pixel-replicated digital image  142  to the input digital image  120  reveals that the pixel-replicated line pixels  143  have a much lower density than the original black line in the input digital image  120 . Thus the printed image  118  will suffer from significant image quality degradation due to the round-trip 2× down-sampling/2× up-sampling operations. 
     A number of resizing algorithms have been developed to try to address the loss of image quality that is associated with down-sampling images having fine details. 
     U.S. Pat. No. 5,995,682 to Pawlicki et al., entitled “Method for resizing of a digital image,” discloses a method in which interpolation weights are determined based on local image derivative values, thereby producing images having improved continuity and sharpness characteristics. 
     U.S. Pat. No. 6,816,166 to Shimizu et al., entitled “Image conversion method, image processing apparatus, and image display apparatus,” discloses a method in which a linear interpolation algorithm and a nonlinear interpolation algorithm are applied to an input digital image. Weighting values are determined as a function of the image content for the input digital image, and are used to combine the two interpolated images. 
     U.S. Pat. No. 6,832,009 to Shezaf et al., entitled “Method and apparatus for improved image interpolation,” discloses a method for up-sampling images where an interpolation formula is changed depending on local image gradient values. 
     U.S. Pat. No. 7,046,390 to Atkins, entitled “System and method for scaling and enhancing color text images,” discloses a method for up-sampling text images wherein super-pixels are selected for the output image by applying a template matching algorithm to local image neighborhoods to identify characteristic image patterns. 
     U.S. Pat. No. 7,149,369 to Atkins, entitled “Method and system image scaling,” discloses an image resizing algorithm wherein different interpolation filters are used depending on edge characteristics determined for a local pixel neighborhood. 
     U.S. Pat. No. 7,200,278 to Long et al., entitled “4×4 pixel-based edge detection and edge enhancement without line buffer overhead,” discloses a method for up-sampling images wherein a rotated interpolation filter is used when diagonal edges are detected. 
     U.S. Patent Application Publication No. 2007/0172152 to Altunbasak et al., entitled “Method and apparatus for adjusting the resolution of a digital image,” discloses an image resizing method where interpolation filters are updated based on iteratively applying a classifier to the image pixels. 
     SUMMARY OF THE INVENTION 
     The present invention represents a method for resizing an input digital image having input pixel values to produce an output digital image having output pixel values, wherein the output digital image has a lower resolution and wherein single pixel width details in the input digital image are preserved, the method being performed by a processor and comprising: 
     a) determining an output pixel value for the output digital image by interpolating within a corresponding neighborhood of image pixels in the input digital image; 
     b) detecting whether the input digital image contains a fine detail within the corresponding neighborhood of image pixels; 
     c) adjusting the output pixel value when a fine detail is detected; and 
     d) repeating steps a)-c) for a plurality of output pixels. 
     This invention has the advantage that the appearance of fine details is preserved in down-sampled images. 
     It has the additional advantage that down-sampled images can be transmitted to a printer across a communication link having a low bandwidth without suffering a significant loss of fine details in prints created from the down-sampled image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a prior art system for printing images; 
         FIG. 2A  shows an example input digital image; 
         FIGS. 2B ,  2 C,  2 D and  2 E illustrate computing interpolated pixel values using a prior art cubic interpolation algorithm; 
         FIG. 2F  illustrates a down-sampled image created using a prior art cubic interpolation algorithm; 
         FIG. 2G  illustrates a pixel-replicated image created from the down-sampled image of  FIG. 2F ; 
         FIG. 3  is high-level diagram showing the components of a system for determining a resized image according to an embodiment of the present invention; 
         FIG. 4  is a flow chart illustrating the method of the present invention; 
         FIG. 5  illustrates a cubic interpolation algorithm that can be used in accordance with the present invention; 
         FIG. 6A  illustrates a down-sampled digital image created using one embodiment of the present invention; 
         FIG. 6B  illustrates a pixel-replicated image created from the down-sampled image of  FIG. 6A ; 
         FIG. 7A  illustrates a down-sampled digital image created using an alternate embodiment of the present invention; 
         FIG. 7B  illustrates a pixel-replicated image created from the down-sampled image of  FIG. 7A ; 
         FIG. 8A  illustrates a down-sampled digital image created using an alternate embodiment of the present invention; and 
         FIG. 8B  illustrates a pixel-replicated image created from the down-sampled image of  FIG. 8A . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, some embodiments of the present invention will be described in terms that would ordinarily be implemented as software programs. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. Because image manipulation algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the method in accordance with the present invention. Other aspects of such algorithms and systems, together with hardware and software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein may be selected from such systems, algorithms, components, and elements known in the art. Given the system as described according to the invention in the following, software not specifically shown, suggested, or described herein that is useful for implementation of the invention is conventional and within the ordinary skill in such arts. 
     The invention is inclusive of combinations of the embodiments described herein. References to “a particular embodiment” and the like refer to features that are present in at least one embodiment of the invention. Separate references to “an embodiment” or “particular embodiments” or the like do not necessarily refer to the same embodiment or embodiments; however, such embodiments are not mutually exclusive, unless so indicated or as are readily apparent to one of skill in the art. The use of singular or plural in referring to the “method” or “methods” and the like is not limiting. It should be noted that, unless otherwise explicitly noted or required by context, the word “or” is used in this disclosure in a non-exclusive sense. 
     The phrase, “digital image file”, as used herein, refers to any digital image file, such as a digital still image or a digital video file. 
       FIG. 3  is a high-level diagram showing the components of a system for resizing an image according to an embodiment of the present invention. The system includes a data processing system  310 , a peripheral system  320 , a user interface system  330 , and a data storage system  340 . The peripheral system  320 , the user interface system  330  and the data storage system  340  are communicatively connected to the data processing system  310 . 
     The data processing system  310  includes one or more data processing devices that implement the processes of the various embodiments of the present invention, including the example processes described herein. The phrases “data processing device” or “data processor” are intended to include any data processing device, such as a central processing unit (“CPU”), a desktop computer, a laptop computer, a mainframe computer, a personal digital assistant, a Blackberry™, a digital camera, cellular phone, or any other device for processing data, managing data, or handling data, whether implemented with electrical, magnetic, optical, biological components, or otherwise. 
     The data storage system  340  includes one or more processor-accessible memories configured to store information, including the information needed to execute the processes of the various embodiments of the present invention, including the example processes described herein. The data storage system  340  may be a distributed processor-accessible memory system including multiple processor-accessible memories communicatively connected to the data processing system  310  via a plurality of computers or devices. On the other hand, the data storage system  340  need not be a distributed processor-accessible memory system and, consequently, may include one or more processor-accessible memories located within a single data processor or device. 
     The phrase “processor-accessible memory” is intended to include any processor-accessible data storage device, whether volatile or nonvolatile, electronic, magnetic, optical, or otherwise, including but not limited to, registers, floppy disks, hard disks, Compact Discs, DVDs, flash memories, ROMs, and RAMs. 
     The phrase “communicatively connected” is intended to include any type of connection, whether wired or wireless, between devices, data processors, or programs in which data may be communicated. The phrase “communicatively connected” is intended to include a connection between devices or programs within a single data processor, a connection between devices or programs located in different data processors, and a connection between devices not located in data processors at all. In this regard, although the data storage system  340  is shown separately from the data processing system  310 , one skilled in the art will appreciate that the data storage system  340  may be stored completely or partially within the data processing system  310 . Further in this regard, although the peripheral system  320  and the user interface system  330  are shown separately from the data processing system  310 , one skilled in the art will appreciate that one or both of such systems may be stored completely or partially within the data processing system  310 . 
     The peripheral system  320  may include one or more devices configured to provide digital content records to the data processing system  310 . For example, the peripheral system  320  may include digital still cameras, digital video cameras, cellular phones, or other data processors. The data processing system  310 , upon receipt of digital content records from a device in the peripheral system  320 , may store such digital content records in the data storage system  340 . 
     The user interface system  330  may include a mouse, a keyboard, another computer, or any device or combination of devices from which data is input to the data processing system  310 . In this regard, although the peripheral system  320  is shown separately from the user interface system  330 , the peripheral system  320  may be included as part of the user interface system  330 . 
     The user interface system  330  also may include a display device, a processor-accessible memory, or any device or combination of devices to which data is output by the data processing system  310 . In this regard, if the user interface system  330  includes a processor-accessible memory, such memory may be part of the data storage system  340  even though the user interface system  330  and the data storage system  340  are shown separately in  FIG. 3 . 
     The present invention will now be described with reference to  FIG. 4 . An interpolation step  402  is used to determine an output pixel value  404  from a corresponding pixel neighborhood of an input digital image  400 . The interpolation step  402  can use any interpolation method known to those skilled in the art. In a preferred embodiment of the present invention, the interpolation step  402  utilizes the well-known one-dimensional cubic interpolation algorithm to first down-sample the rows of the input digital image  400 . The cubic interpolation algorithm is then applied a second time to down-sample the image columns. 
       FIG. 5  illustrates how a cubic interpolation algorithm can be used to process a row of input image pixels  500 . To compute an output pixel value  508  (D), a corresponding pixel neighborhood  502  from the row of input image pixels  500  is identified. In this example, the pixel neighborhood  502  comprises 4 pixels having pixel values P i . A set of weighting factors  504  (W i ) is then used to scale the pixel values to determine weighted pixel values  506  (d i =W i ×P i ). Finally, the weighted pixel values  506  are summed to determine the output pixel value  508 . This can be represented in equation form as: 
                   D   =       ∑     i   =   1     4     ⁢       W   i     ⁢       P   i     .                 (   1   )               
The exact values of the weights will vary depending on the down-sampling factor. For a 2× down-sampling factor, weighting factors of W 1 =W 4 =0.17 and W 2 =W 3 =0.33 can be used.
 
     Returning now to a discussion of  FIG. 4 , in alternate embodiments of the present invention other interpolation algorithms can be used by the interpolation step  402 . Examples of well known interpolation algorithms would include bicubic and bilinear interpolation algorithms. 
     Next, a detail detection step  406  is used to analyze the input digital image to determine a detail status  408 , which is an indication of whether the corresponding pixel neighborhood in the input digital image  400  contains fine details. The detail detection step  406  can use any method known to one skilled in the art to detect image details. 
     In a preferred embodiment of the present invention the fine details are single pixel width details, and the single pixel width details are detected by determining whether an input pixel value differs by more than a specified threshold from the input pixel values of adjacent input pixels within the neighborhood of input pixels. With respect to the pixel neighborhood  502  of  FIG. 5 , one means for detecting single pixel width details is to use the following test: 
                   DetailStatus   =     {           TRUE   ;             (       (       (       P   ⁢           ⁢   1     -     P   ⁢           ⁢   2       )     &gt;   T     )     ⁢           ⁢   AND   ⁢           ⁢     (       (       P   ⁢           ⁢   3     -     P   ⁢           ⁢   2       )     &gt;   T     )       )     ⁢           ⁢   OR   ⁢           ⁢     (       (       (       P   ⁢           ⁢   2     -     P   ⁢           ⁢   3       )     &gt;   T     )     ⁢           ⁢   AND   ⁢           ⁢     (       (       P   ⁢           ⁢   4     -     P   ⁢           ⁢   3       )     &gt;   T     )       )                 FALSE   ;         Otherwise                   (   2   )               
where T is a threshold value, AND is a logical “and” operation, OR is a logical “or” operation and DetailStatus is a variable representing the detail status  408 . It can be seen that the effect of this test is to identify cases where either the P2 or the P3 input pixel value is darker than both of its immediate neighbors by more than the threshold value. In this way, pixel neighborhoods containing single pixel width dark vertical details (e.g., vertical lines) on a light background are identified.
 
     In an alternate embodiment of the present invention, an absolute value operation can be applied to the pixel differences in Eq. (2) before they are compared to the threshold value: 
                   DetailStatus   =     {           TRUE   ;             (       (              P   ⁢           ⁢   1     -     P   ⁢           ⁢   2            &gt;   T     )     ⁢           ⁢   AND   ⁢           ⁢     (              P   ⁢           ⁢   3     -     P   ⁢           ⁢   2            &gt;   T     )       )     ⁢           ⁢   OR   ⁢           ⁢     (       (              P   ⁢           ⁢   2     -     P   ⁢           ⁢   3            &gt;   T     )     ⁢           ⁢   AND   ⁢           ⁢     (              P   ⁢           ⁢   4     -     P   ⁢           ⁢   3            &gt;   T     )       )                 FALSE   ;         Otherwise                   (   3   )               
In this way, the detail detection step  406  can detect both dark details on a light background and light details on a dark background.
 
     The test shown in Eq. (2) can be repeated for a vertically oriented pixel neighborhood to identify single pixel width horizontal details (e.g., horizontal lines). In one embodiment of the present invention, the detail status  408  is a Boolean value which is set to TRUE if either a vertical or horizontal single pixel width detail is detected, and is set to FALSE otherwise. In other embodiments, it is desirable to distinguish between vertical and horizontal details. In this case, the value of the detail status  408  can be an integer value which is given different values depending on the type of fine detail detected. For example: a value of “0” can indicate that no fine details were detected; a value of “1” can indicate that a vertical detail was detected; a value of “2” can indicate that a horizontal detail was detected; and a value of “3” can indicate that both vertical and horizontal details were detected (e.g., due to a diagonal line or an isolated dot). 
     In alternate embodiments of the present invention, other types of detail detection algorithms can be used by the detail detection step  406 . Examples of detail detection algorithms that are well-known in the art would include Canny edge detectors and gradient-based edge detectors. 
     Next, an adjust output pixel value step  410  is used to adjust the output pixel value  404  responsive to the detail status  408  producing an adjusted output pixel value  412 . In one embodiment of the present invention, the adjusted output pixel value  412  is determined by adding or subtracting a specified incremental value to/from the output pixel value  404  for pixels where the detail status  408  indicates that the input digital image  400  contains a fine detail. This can be represented in equation form by: 
                     D   A     =     {           D   ;           DetailStatus   =   FALSE                 D   -     Δ   ⁢           ⁢   D       ;           DetailStatus   =   TRUE                     (   4   )               
where D A  is the adjusted output pixel value  412  and ΔD is the incremental value.
 
     The incremental value ΔD used for a particular implementation can be empirically determined. The optimal value will generally be a function of the interpolation method and the resolution difference between the input digital image and the output digital image. When the resized image is adapted to be printed on a digital printer, the optimal value of the incremental value ΔD may also be a function of the characteristics of the digital printer. For the case where cubic interpolation is used to reduce the resolution of the output digital image to half that of the input digital image, incremental values in the range of 65 to 150 have been found to produce good results for images to be printed on an inkjet printer. 
     As discussed earlier, in one embodiment of the present invention the interpolation process is implemented by two successive one-dimensional interpolation steps. In this case, the adjust output pixel value step  410  can be applied once after both of the horizontal and vertical interpolation steps have been applied.  FIG. 6A  illustrates a down-sampled digital image  600  determined by using this approach to apply the method of the present invention to the input digital image  120  ( FIG. 2A ). In this case, an incremental value of ΔD=100 was subtracted from the output pixel values  404  for each of the detail pixels  602  within the bold outline region. 
     In an alternate embodiment of the present invention, the adjust output pixel value step  410  can be applied after each of the successive interpolation steps. In this case, it may be desirable to use different incremental values ΔD following the horizontal and vertical interpolation steps. For example, if a vertical detail is detected the output pixel value  404  can be adjusted by an incremental value of ΔD V  following the horizontal interpolation step, and if a horizontal detail is detected the output pixel value  404  can be adjusted by an incremental value of ΔD H  following the vertical interpolation step. 
     In yet another embodiment of the present invention, the adjust output pixel value step  410  is applied only after the first interpolation step. In this case, it may be desirable to used different incremental values ΔD depending on whether horizontal or vertical details are detected.  FIG. 7A  illustrates a down-sampled digital image  700  determined by using this approach to apply the method of the present invention to the input digital image  120  ( FIG. 2A ). In this case, an incremental value of ΔD H =150 was subtracted from the output pixel values  404  following the first interpolation step when horizontal details were detected and a value of ΔD V =65 was subtracted from the output pixel values  404  following the first interpolation step when vertical details were detected. The output pixels that were affected by the adjust output pixels value step  410  are detail pixels  702  shown within the bold outline region. One reason that a larger incremental value is used for horizontal details is that the subsequent vertical interpolation step will result in and additional loss of contrast for the horizontal details, whereas the contrast of the vertical details will remain largely unchanged. 
     For the case where the detail detection step  406  detects both light and dark details (e.g., using Eq. (3)), the incremental value ΔD would be subtracted for dark details (assuming that 0 is dark and 255 is light), and would be added for light details. 
     After the incremental value ΔD is added or subtracted from the output pixel value  404 , the adjusted output pixel value  412  should be checked to make sure it is within a specified valid range (e.g., 0 to 255 for 8-bit pixel values). For cases where the adjusted output pixel value  412  exceeds this range, it should be constrained accordingly. 
     In an alternate embodiment of the present invention, the adjust output pixel value step  410  can adjust the output pixel value  404  by setting it equal to an input pixel value of the detected fine detail. For example, if a single pixel width detail having a particular code value (e.g., 0 for a black line) is detected, the adjusted output pixel value  412  can be set to be equal to that same value in order to preserve the density of the fine detail.  FIG. 8A  illustrates a down-sampled digital image  800  determined by using this approach to apply the method of the present invention to the input digital image  120  ( FIG. 2A ). In this case, the output pixel values  404  for each of the detail pixels  802  within the bold outline region were set to be equal to the corresponding input pixel values in the input digital image  120 . 
     In a preferred embodiment of the present invention, the input digital image is a color digital image having a plurality of color channels (e.g., RGB or CMYK). In one implementation, each of the color channels is processed independently. In this manner, fine details may exist and be adjusted in one color channel, but no adjustment may be applied to the corresponding pixels in the other color channels. 
     In an alternate embodiment, fine details are detected by analyzing the color digital image, and then the output pixel values for all of the color channels are adjusted if a fine detail is detected in the color digital image. In one arrangement, a fine detail is detected in the color digital image by analyzing each of the color channels independently. If any one of the color channels is found to contain a fine detail, the color digital image is said to have a fine detail at that location. In another arrangement, a plurality of the color channels of the color digital image can be processed together to detect fine details. For example, a luminance image can be determined by computing a weighted combination of the color channels of the color digital image; fine details in the color digital image can then be detected by analyzing the resulting luminance image. 
     In one embodiment of the present invention, the image resizing process is applied as part of a system for printing images, such as that shown in  FIG. 1 . For example, the image resizing operation can be applied in the host device processing  106  to produce a lower resolution processed digital image  108  for transmission to the printer across the communication link  110  for printing on a printer  102 , such as an inkjet printer. Transmitting a lower resolution image across the communication link  110  has the advantage that a smaller data bandwidth is required. This can be particularly significant when communication links  110  having a limited bandwidth are used (e.g., wireless communication links). 
     Often, it may be desirable for the printer processing  112  to apply a second resizing process to resize the output digital image to a printer resolution before it is printed. The second resizing process can use any resizing method known to one skilled in the art. In one embodiment of the present invention, this second resizing process uses a nearest neighbor interpolation algorithm.  FIGS. 6B ,  7 B and  8 B show pixel-replicated digital images corresponding to the down-sampled digital images shown in  FIGS. 6A ,  7 A and  8 A, respectively. In this case the pixel-replicated digital images have a resolution  2 × the resolution of the down-sampled digital images. It can be seen that the improved density of the fine details has been preserved through the second resizing process. 
     A computer program product can include one or more storage medium, for example; magnetic storage media such as magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as optical disk, optical tape, or machine readable bar code; solid-state electronic storage devices such as random access memory (RAM), or read-only memory (ROM); or any other physical device or media employed to store a computer program having instructions for controlling one or more computers to practice the method according to the present invention. 
     The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. 
     PARTS LIST 
     
         
           100  System 
           101  Host device 
           102  Printer 
           104  Input digital image 
           106  Host device processing 
           108  Processed digital image 
           110  Communication link 
           112  Printer processing 
           114  Print-ready digital image 
           116  Printer engine 
           118  Printed image 
           120  Input digital image 
           122  White pixel 
           124  Black pixel 
           130  Row of input image pixels 
           131  Pixel neighborhood 
           132  Weighting factors 
           134  Weighted pixel values 
           136 B Output pixel value 
           136 C Output pixel value 
           136 D Output pixel value 
           136 E Output pixel value 
           140  Down-sampled digital image 
           141  Down-sampled line pixels 
           142  Pixel-replicated digital image 
           143  Pixel-replicated line pixels 
           310  Data processing system 
           320  Peripheral system 
           330  User interface system 
           340  Data storage system 
           400  Input digital image 
           402  Interpolation step 
           404  Output pixel value 
           406  Detail detection step 
           408  Detail status 
           410  Adjust output pixel value step 
           412  Adjusted output pixel value 
           500  Row of input image pixels 
           502  Pixel neighborhood 
           504  Weighting factors 
           506  Weighted pixel values 
           508  Output pixel value 
           600  Down-sampled digital image 
           602  Detail pixels 
           604  Pixel-replicated digital image 
           700  Down-sampled digital image 
           702  Detail pixels 
           704  Pixel-replicated digital image 
           800  Down-sampled digital image 
           802  Detail pixels 
           804  Pixel-replicated digital image