Abstract:
A method for compressing digital image data to improve the efficiency of serial data transmission is disclosed. More specifically, the present invention accomplishes image compression by performing the most complex portions of a standard compression technique on a subset of the originally provided data utilizing a modified two-dimensional discrete cosine transform. The invention includes a fast JPEG compressor using a Haar transform with a conditional transform.

Description:
RELATED CASES 
     Cross reference is made to the following application incorporated by reference herein for its teaching: U.S. patent application Ser. No. 09/119,023 entitled “Improved Method Of Compressing JPEG Files” to Ricardo L. de Queiroz. 
    
    
     BACKGROUND OF THE INVENTION AND MATERIAL DISCLOSURE STATEMENT 
     The transmission of electronic data via facsimile machines and similar devices has become quite common. Efforts to transmit significantly larger volumes of this data within a substantially shortened period of time are constantly being made. This is true not only to allow for data to be sent from one location to another at faster speeds and thereby causing less inconvenience to the user, but to enable more complex data to be transmitted between the same locations without drastically increasing the required transmission time. For example the facsimile transmission time for a detailed halftoned image will be many times more than that of a simple sheet of black text on a white page when using the same fax machine. By the same token, fax transmission of a color image will require an even greater amount of time than its greatly detailed halftoned counterpart. 
     Without any form of data reduction, transmission of color image data files via facsimile would require extensive resources—very fast modems and/or large buffers—and would still take a great deal of time, thereby causing such transmission to become very expensive and therefore, impractical. Instead, the transmission of color image data via fax is typically accomplished using some form of data compression prior to transmission. 
     The JPEG (Joint Photographic Experts Group) standard provides a well known method of compressing electronic data. JPEG uses the discrete cosine transform (DCT) to map space data into spatial frequency domain data. Simply put, the first step in JPEG compression is to transform an 8×8 block of pixels into a set of 8×8 coefficients using the DCT. The DCT with the lowest frequency is referred to as the DC coefficient (DCC), and the remaining coefficients are AC coefficients (ACCs). The DCC and ACCs are quantized—divided by an integer referred to as the “step size” and rounded to the nearest whole number. The losses that occur during JPEG compression typically occur during the quantization step. The magnitude of this loss is obviously dependent upon the step size selected and the resulting amount of round-off required to perform quantization. 
     Next, the quantized coefficients are arranged in a one dimensional vector by following a selected path (i.e. zigzag) through the 8×8 block of quantized coefficients. The DCC is typically the first value in the vector. Ordinary JPEG compression typically includes replacing the quantized DCC with the difference of its actual value minus the DCC of the previous block, to provide a differential DCC. Finally, the vector is encoded into a bit stream through a sequence of Run Length Counting (RLC) operations, combined with Variable Length Codes (VLC) to produce a compressed data stream. 
     Fax transmission of color image data is often accomplished by scanning the image at the sending fax to generate digital color image data, subjecting this digital color image data to JPEG compression and then transmitting the compressed digital color image data over telephone lines to the receiving fax. Since color image data is so complex, high compression ratios must usually be applied in order to complete the transmission within an acceptable time frame. High compression ratios lead to more data loss, which typically occurs at the higher end of the frequency range. Further, the imaging devices typically included with fax machines in the lower end of the market usually include thermal ink-jet printers and would likely use error diffusion halftoning techniques. The halftoning that occurs when using a thermal ink jet printer results in an additional loss of high frequency data. Thus, much of the detail in the original image that is preserved and transmitted will never actually be viewed by the ultimate user. 
     The “sending” portion of fax transmission includes scanning the original image, generation of a corresponding digital image, and any one of a number of data reduction techniques, most notably some form of data compression. Once these steps are completed, the compressed data is transmitted serially to the receiving fax in a bit stream. The length of the bit stream used to describe the image is inversely proportional to the amount of compression that has been applied. Thus, if the compression ratio is large the length of the bit stream used to describe the image will be very short, resulting in a substantial reduction in the transmission time for the data stream. 
     With this in mind, successful fax transmission requires a proper correspondence between the compression ratio being applied to the image and the clock speed of CPU of the sending fax. In other words, if the compression ratio is smaller than necessary for a given CPU speed the data will have to wait to be transmitted, and an appropriately sized buffer will be required. On the other hand, if the compression ratio is high relative to the CPU speed the modem will become idle waiting for the CPU to complete image processing and transmit more data. Since modems are typically configured to detect a large lapse in data transmission as the end of transmission, this large gap typically causes them to disconnect. Thus, it is advantageous to continue the stream of data from the sending fax to the receiving fax, and eliminate gaps in the data stream. One way to do this is obviously to implement a faster JPEG compressor which can keep the data moving through the modem even if a high compression ratio is used. However, this solution results in significant cost increases and is often impractical. Thus, it is advantageous to provide a continuous stream of data during transmission of a color facsimile by implementing a faster data compressor without having to resort to the purchase of more expensive equipment. 
     The following disclosures may be relevant to aspects of the present invention: 
     U.S. Pat. No. 5,737,450 to Hajjahmad et al. issued Apr. 7, 1998 discloses a method and apparatus for applying an image filter to an image signal where image data terms, corresponding to the image signal, are converted by means of an overlapping operation and a scaled forward orthogonal transformation to form frequency coefficient matrices, the image filter is converted by means of a descaled orthogonal transformation to form a descaled frequency filter matrix, and the frequency coefficient matrices are multiplied by the descaled frequency filter matrix to form filtered coefficient matrices for conversion into a filtered image signal by means of an inverse orthogonal transformation process. 
     U.S. Pat. No. 5,699,170 to Yokose et al. issued Dec. 16, 1997, discloses an image communication system wherein transmission of an image between an image transmission apparatus and an image reception apparatus which include image output sections having different performances can be performed without making an inquiry for the performance prior to transmission is disclosed. An image is inputted by an image input section and sent to a hierarchization section in the image transmission apparatus. The hierarchization section converts the inputted image into hierarchic communication data and transmits hierarchized data to a selection section of the image reception apparatus. The selection section extracts only necessary data from the hierarchic communication data transmitted thereto in accordance with the performance of an image output section of the image reception section and then sends the necessary data to the image output section after, if necessary, they are converted into image data. The image output section visualize the image data transmitted thereto from the selection section. 
     U.S. Pat. No. 5,642,438 to Babkin issued Jun. 24, 1997 discloses image compression implementing a fast two-dimensional discrete cosine transform. More specifically, Babkin discloses a method and apparatus for the realization of two-dimensional discrete cosine transform (DCT) for an 8×8 image fragment with three levels of approximation of DCT coefficients. “JPEG: Still Image Compression Standard”, New York, N.Y., Van Nostrand Reinhold, 1993 by W. B. Pennebaker and J. L. Mitchell. 
     All of the references cited herein are incorporated by reference in their entirety for their teachings. 
     Accordingly, although known apparatus and processes are suitable for their intended purposes, a need remains for image compression implementing a two-dimensional discrete cosine transform using a fast JPEG compressor based on a modified two-dimensional discrete cosine transform to compress digital image data thereby improving the efficiency of serial data transmission. 
     SUMMARY OF THE INVENTION 
     The present invention relates to a method of improving the speed and efficiency of electronic data compression. The method comprises obtaining input image data which includes discrete values that represent light intensity in an image. Then applying a first transform to the input image data to produce a first transform result. This is followed by comparing the first transform result to a threshold. Finally, a either second transform is applied to the first transform result, or the substitution of a zero value for the first transform result is used to generate approximation data. 
     In accordance with another aspect of the invention there is provided a method of transmitting a facsimile of an original image from a sending location to a receiving location. The method comprises acquiring the original image and generating digital image data therefrom, wherein the digital image data includes pixel values which represent the light intensity of the original image. Then applying a first transform to the input image data to produce a first transform result. This is followed by comparing the first transform result to a threshold. Then in generating approximation data which provides an estimated value of image light intensity, either a second transform is applied to the first transform result, or a zero value is substituted for the first transform result. Finally the output image data is derived from the approximation data. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Other features and advantages of the present invention will become apparent as the following description proceeds and upon reference to the drawings, in which: 
     FIG. 1 is a generalized block diagram illustrating general aspects of a facsimile machine that may be used to practice the present invention. 
     FIG. 2 contains a schematic illustration of the steps used to carry out a JPEG compression scheme. 
     FIG. 3 is a detailed illustration of an example of a labeling configuration of an 8×8 block of pixels. 
     FIG. 4 contains a detailed illustration of the labeling configuration of DCT coefficients obtained by application of a discrete cosine transformation to the 8×8 block of pixels illustrated in FIG.  3 . 
     FIG. 5 illustrates one example of the manner in which the DCT coefficients of FIG. 4 may be labeled after quantization. 
     FIG. 6 depicts a “zig zag” pattern, one embodiment of the manner in which quantized pixels may be selected for placement into a one dimensional vector. 
     FIG. 7 contains a schematic illustration of one way the present invention may be implemented in a JPEG compression technique. 
     FIG. 8 shows a quantization table and derived threshold table. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     The present invention is directed to a method and apparatus for compressing complex digital image data to enhance the efficiency of data transmission. 
     Referring now to the drawings where the showings are for the purpose of describing an embodiment of the invention and not for limiting same, FIG. 1 is a block diagram showing structure of an embodiment of a facsimile (fax) apparatus  10  according to the present invention. Fax  10  includes a CPU  12  for executing controlling processes and facsimile transmission control procedures, a RAM  14  for controlling programs and a display console  16  with various buttons and/or switches for controlling the facsimile apparatus and LCDs or LED&#39;s for reviewing the status of system operation. A scanner  20  is also included for acquiring an original image and generating image data therefrom. Image processing unit  22  is included to perform encoding and decoding (compression and decompression) processes between an image signal and transmitted codes. Significantly for purposes of this invention, fax  10  includes or interfaces with a modem  24 , which is a modulating and demodulating device that transmits and receives picture information over telephone lines to a compatible receiving device  26 , such as another facsimile machine, a printer, computer terminal or similar apparatus. 
     As stated above, image processing unit  22  is used to compress and decompress image signals and transmitted codes. One common method of compressing and decompressing image signals is through use of the JPEG (Joint Photographic Experts Group) standard described in detail with reference to FIG.  2 . An original image is scanned by fax  10  to generate a corresponding digital image. The digital image is separated into 8×8 blocks  102  of picture elements  120  or “pixels” which indicate the intensity of the light that is measured at discrete intervals throughout the surface of the page. For example, a spot that is covered with black ink will not reflect any light. The value of the pixel  120  will typically be 0 at that location. On the other hand, a spot that is completely uncovered by ink will reflect the color of the page on which the image resides. Assuming the sheet paper on which the image has been placed is white, the measured light intensity of the pixel  120  would be 1 at that spot. Gray areas, such as those which represent color or black and white halftoned areas of the image would register a light intensity somewhere between 0 and 1. The values of the pixels  120  in block  102  are transformed through DCT into a set  106  of 8×8 coefficients as indicated in step  104 . The DCT coefficient with the lowest frequency is referred to as the DC coefficient (DCC), and the remaining coefficients are AC coefficients (ACCs). The DCC and ACCs are quantized—each coefficient is divided by a predetermined whole number referred to as the “step size” at step  108  and then a selected pattern (usually a “zigzag”) is followed through the 8×8 block of quantized coefficients  110  as indicated in step  112  to place the coefficients in a desired order in a one dimensional vector  114 . The quantized DCC is typically the first value of the vector  114 , and is represented differentially as the actual DCC value minus the DCC of the previous block as shown in block  116 . Vector  114  is encoded into a bit stream through a sequence of Run Length Counting (RLC) operations which count the number of zero ACCs that reside in the path before a non-zero ACC. These RLC operations are combined with Variable Length Codes (VLC) as indicated in block  118  which encode a symbol that includes a combination of the number of zeros preceding a non-zero ACC and the ACC amplitude. This encoding produces a compressed data stream which can be transmitted to receiving device  26  over communication lines. 
     FIG. 3 contains a detailed illustration of an 8×8 block of pixels  120  and the labeling configuration that will be used throughout the description of the present invention. It should be noted here that pixels  120  and pixel blocks  102  can be labeled in numerous other ways and it is not intended to imply that either JPEG compression or the present invention are limited to the ordering scheme shown here. Similarly, FIGS. 4 and 5 contain detailed illustrations of the unquantized and quantized DCT coefficients respectively that correspond to the 8×8 block  102  of pixels  120  illustrated in FIG.  3 . Again, neither standard JPEG compression or the present invention are limited to these embodiments. 
     FIG. 6 contains a detailed illustration of one pattern  112  in which the quantized DCT coefficients may be selected for placement into one dimensional vector  114 . As those skilled in the at will recognize, the illustration shown in FIG. 6 merely shows one of many possible zigzag coefficient selection patterns  112  that may be followed in order to practice the present invention. 
     Referring now to FIG. 7, generally speaking the present invention includes performing a portion of the JPEG compression method on a reduced set of data, without producing a substantial loss in the quality of the output image. The invention takes advantage of the fact that a 4-point DCT can be performed more quickly than an 8-point DCT can. As is well known in the art, JPEG achieves compression because most DCT coefficients in a block after quantization are zero. The present invention saves time by not computing zero coefficients. It is also known that a certain amount of high frequency loss can be tolerated particularly with color separation data or in a fax environment. The invention identifies and examines high band pass information for activity. Low activity blocks below a threshold as identified by the invention need not have a DCT coefficient calculated. Instead a zero value is substituted. This also allows the invention to save computation time. 
     In FIG. 7 as with the standard JPEG method described above, the scanned image is separated into blocks  701  of pixels  120  which indicate the intensity of the light at the various locations of the image. As before, an 8×8 block of pixels has shown to be very successful when used with the present invention. However, other pixel block dimensions are possible and the invention is not limited to this embodiment. Those skilled in the art will recognize that a smaller or larger block size might be chosen when it is desired to preserve more or less image detail. In fact it should be noted that while the horizontal and vertical dimensions are identical in the embodiment of pixel block  701  described here, this is not a requirement for practicing the present invention. For example, a non-square block might be chosen if the is image was generated for a device possessing asymmetric resolutions in the vertical and horizontal directions. 
     Once a pixel block  701  having the appropriate size and dimensions is chosen, block  701  must be segmented into sub-blocks  700  as indicated in FIG.  7 . In a preferred embodiment, an 8×8 block of pixels is used to practice the invention, segmenting pixel block  701  into 4 sub-blocks  700 , where each sub-block  700  is an array having four pixels  120  in the horizontal direction and four pixels  120  in the vertical direction. Those of ordinary skill in the art will recognize that if the user of the invention wishes to sacrifice some image reproduction accuracy in order to save costs, other array sizes might be used. If the user wishes to obtain higher image reproduction accuracy in some areas of the image, but requires less accuracy in other areas, a fine grid could be applied to some areas with a larger grid applied to other areas. Again, it is intended to embrace all such alternatives, and the invention is not limited to the examples provided here. 
     With continued reference to FIG. 7, each of the above described sub-blocks  700  shown in block  701  are labeled in block  702 . Each illustrated sub-block  700  is now represented as B 00 , B 01 , B 10 , and B 11  in block  702 . In a preferred embodiment a Haar transform  703  is performed upon the data in block  702 . A Haar transform is a technique well understood in the art as performing a matrix sum and difference operation. The Haar transform provides quadrant results A 00 , A 01 , A 10 , A 11  in the 8×8 quadrant blocks  704 ,  705 ,  706 , and  707 , respectively. Using the 4×4 Haar transform operation here separates out low pass band and high pass band components for subsequent operation. 
     In quadrant block  704  the A 00  result provides the low pass band information. Because low pass band information is essential for successful reconstruction of an image, the A 00  result is subsequently transformed by a 4-point DCT operation  708 . The A 01 , A 10 , A 11  quadrant results constitute higher frequency or high pass band information. Therefore, for quadrant results A 01 , A 10 , A 11  as found in quadrant blocks  709 ,  710 , and  711  respectively, a comparison is made to a threshold value T 01 , T 10 , T 11  respectively. The comparison of the quadrant results to a threshold is provided for in decision blocks  709 ,  710 , and  711 . Threshold table  802  as found in FIG.  8  and described below, is utilized for supplying the threshold values used in the decision blocks  709 ,  710 , and  711 . If a given high pass band quadrant result is found to be above a particular threshold value, it is deemed as having enough information to warrant 4-point DCT transformation at DCT-4 block  712 ,  713  and  714  respectively. However, as will often happen in a preferred embodiment, when a high pass quadrant result is found to be below the threshold value, that result is small enough to discard. In that event a zero is substituted as shown in FIG. 7 by the “Set to zero” blocks  715 ,  716  and  717 . This provides the advantage of saving the 4-point DCT computational time which in a preferred embodiment constitutes picture detail information which is of little or even no consequence for a given targeted receiving device  26 . 
     The 4-point DCT must be scaled so that when C 00 , C 01 , C 10 , and C 11  are combined in approximation block  718 , the coefficients are compatible with the coefficients which would be obtained from an 8-point DCT. Therefore, the 4-point DCT is defined as:          DCT4        (   U   )       ≡       1   2          D   4            UD   4   T     .                              
     Where D 4  is a 4×4 matrix with the following entries:          d   ij     =       k   i        cos                   (         i        (       2      j     +   1     )       8        π     )                              
     where k 0 =½, and 
     
       
           k   i&gt;0 =1 /sqrt (2). 
       
     
     With the completion of the 4-point DCT and the assembly of C 00 , C 01 , C 10  and C 11  into approximation block  718 , quantization of the data is now performed at block  108  utilizing a quantization table. 
     Provided in FIG. 8 is a preferred embodiment quantization table  800 . As will be well understood by those skilled in the art, this table  800  is but one of many possibilities. It is provided as an example, and as an aid for the explanation of the origin of a preferred embodiment threshold table  802 . The values found in threshold table  802  may be developed in many ways. However, for a preferred embodiment threshold table  802  is derived from quantization table  800 . To do this an average of the values by quadrant in the quantization table  800  is made to arrive at the values found in the threshold table  802 . Because there is no comparison made for A 00 , an “X” is shown as the value for that quadrant. The values shown “44”, “44”, and “108” are the thresholds for comparison to A 01 , A 10 , and A 11  respectively, and as stated above are averages from quantization table  800 . 
     Returning to FIG. 7, the remainder of the data processing as described above, is conventional as typified in a JPEG system. As indicated in step  112  a “zigzag” is followed through the 8×8 block of quantized coefficients resulting from step  108  to place the coefficients in a desired order in a one dimensional vector  114 . The quantized DCC is typically the first value of the vector  114 , and is represented differentially as the actual DCC value minus the DCC of the previous block as performed in block  116 . Vector  114  is encoded into a bit stream through a sequence of Run Length Counting (RLC) operations which count the number of zero ACCs that reside in the path before a non-zero ACC. These RLC operations are combined with Variable Length Codes (VLC) as indicated in block  118  which encode a symbol that includes a combination of the number of zeros preceding a non-zero ACC and the ACC amplitude. This encoding produces a compressed data stream which can be transmitted to receiving device  26  over communication lines. 
     To reiterate the basic steps involved in the invention using nomenclature designations in accord with FIG.  7 : 
     For a given 8×8-pixel block B. 
     The 4-point DCT is defined as          DCT4        (   U   )       ≡       1   2          D   4            UD   4   T     .                              
      And D 4  is a 4×4 matrix with the following entries          d   ij     =       k   i        cos                   (         i        (       2      j     +   1     )       8        π     )                              
      where k 0 =½ and k i&gt;0 =1/sqrt(2) 
     Divide B into 4 sub-blocks of 4×4 pixels each. As        B   =       (           B   00           B   01               B   10           B   11           )     .                            
     Create new blocks of samples (Haar transform) as: 
     
       
         
           A 
           00 
           =B 
           00 
           +B 
           01 
           +B 
           10 
           +B 
           11 
         
       
     
     
       
         
           A 
           01 
           =B 
           00 
           −B 
           01 
           +B 
           10 
           −B 
           11 
         
       
     
     
       
         
           A 
           10 
           =B 
           00 
           +B 
           01 
           −B 
           10 
           −B 
           11 
         
       
     
     
       
         
           A 
           11 
           =B 
           00 
           −B 
           01 
           −B 
           10 
           +B 
           11 
         
       
     
     And create yet another sub-block C 00 =DCT4(A 00 ) 
     For given thresholds T 01 , T 10 , T 11 : 
     If any element in A 01  is greater than T 01  then make C 01 =DCT4(A 01 ), otherwise make C 01 =0. 
     If any element in A 10  is greater than T 10  then make C 10 =DCT4(A 10 ), otherwise make C 10 =0. 
     If any element in A 11  is greater than T 11  then make C 11 =DCT4(A 11 ), otherwise make C 11 =0. 
     Compose the matrix with the transformed samples as        C   =     (           C   00           C   01               C   10           C   11           )                            
     A preferred embodiment as described above utilizing Haar transformation and 4-point DCT is exemplary for the greater processing speed it realizes by reducing the total processing complexity. Counting addition&#39;s, multiplication&#39;s, compares (to zero), shifts, etc. as one operation (op), the total complexity for the 8×8 2D DCT is 768 ops assuming separable fast algorithms. Using the same algorithm for the 4×4 2D DCT the complexity is 112 ops. The total complexity for the proposed transform is 336+112n ops, where n is the number of active high-frequency bands (0,1,2,3), i.e. bands above the threshold. For a given image, the average complexity per block is 336+112 {overscore (n)} , where  {overscore (n)}  is the average value of n. Compared to the full DCT, the relative complexity is C({overscore (n)})=0.4375+0.1458{overscore (n)}, therefore: 
     
       
         0.437 ≦C ( {overscore (n)} )≦0.875 
       
     
     In other words, in the best case the complexity is less than half of that of the DCT and in the worst case it is at least 12.5% less that the DCT. 
     While the application of a preferred embodiment to a JPEG compression framework will yield faster processing, it is not identical to the implementation used in the JPEG standard and thus there is some small loss of detail information. This loss in a color facsimile system is of small consequence in budget situations and the compression ratio achieved is very similar to the one achieved by using a regular DCT. However, there are actually two options on the decompression side. First, if the decoder is a standard JPEG decoder, it may not know how to use the proposed transform. The image in that situation is still decompressed but of course using the regular JPEG 8-point DCT. For high compression ratios there is virtually no loss of quality. As the compression ratio decreases, small ringing (jagged) artifacts may appear near sharp edges. These artifacts are light in intensity and may or may not be noticeable in a printing environment. In a cost benefit analysis this has been determined to be an acceptable trade-off for the increased processing speed. In the preferred alternative, it is conveyed to the receiver information that such a transform, as described in the present invention has been used. A smart de-compressor is therein provided which is will then use the inverse transform of the present invention (inverse 4×4 DCTs and inverse Haar). The image quality and compression ratios in this case are then about the same as those obtained by using a regular DCT-JPEG. 
     It is, therefore, apparent that there has been provided in accordance with the present invention, a method and apparatus for fast compression of JPEG files. While this invention has been described in conjunction with a specific embodiment thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.