JP2008076438A - Method for estimating coverage of ink/toner in printing, data processing system, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method, a device, and a program for calculating the estimated value of coverage amount of toner required to print printing data regulated in the form of a compression data stream. <P>SOLUTION: The estimated value is obtained by reduced processing to be performed by acquiring coverage data from the partially decompressed data stream and obtaining the estimated value of the coverage by the use of the acquired coverage data. Therefore, the data stream need not be fully decompressed. For example, the coverage data is the number of color pixels to at least one density level. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to the field of printing, and more particularly to estimating the amount of marking material, such as toner or ink, required to print a data stream.

  In the field of printers and printing, it is useful to estimate the amount of marking material such as toner and ink required for printing a print job. For example, this estimate can be used to estimate the cost of a job, to control the supply of printing material during printing, or to track usage so that the user can be provided with an estimate of when the supply of marking material will run out, Or it can be used for any combination thereof. In addition, some printers have limitations regarding the amount of marking material that can be used for printing due to, for example, fuser or ink drying problems. For such printers, ink / toner usage estimates can be used to determine whether a printer can print a particular job.

  There are several alternative methods in the art that are used to estimate ink / toner usage. Specifically, in one method used, to determine the number of colored (set) pels / pixels of output required by the print job, and then output that number of colored pels / pixels. Calculate how much ink / toner is needed for each. In fact, there are several examples according to the prior art where such an estimation method was used.

  For example, Xerox Corp. US Pat. No. 5,636,032 discloses a method for estimating ink / toner usage information by counting the number of pixels printed in a print job. The ink / toner usage information is then used to provide details of the number of pages on which the ink / toner supply continues. In this solution, the number of pixels is obtained during printing by monitoring the activation signal sequence of the optical raster output scanner or thermal inkjet cartridge during the printing process.

  For example, US Patent No. 5,802,240 to Lexmark International Inc. discloses a method for estimating toner usage information by counting the number of pels for each page of a print job. The toner usage information is used to compare the amount of toner used by various print jobs. In this solution, the pel count is still obtained during the printing of the job, but in this case it is obtained by counting the output signal with the upper bit set.

  For example, Hewlett-Packard Co., US Pat. No. 6,456.802, also discloses a method for counting the number of pixels in a print job to obtain toner usage information. The toner usage information is used to track the remaining amount of toner in the toner cartridge. In this solution, the number of pixels is obtained by counting each pixel printed during printing.

  In each of these methods, the number of pels / pixels is obtained during the actual printing process, so these methods are for applications that require prior (pre-printing) knowledge of ink / toner usage. Note that is not suitable. For example, such prior knowledge is useful when determining whether a printer can print a print job.

However, for example, US Pat. No. 5,937,225 of IBM Corp. (IBM Corp.) also discloses a method for obtaining ink / toner usage information by counting the number of colored pixels in a print job. However, in this solution, the number of pixels is obtained by converting the printer specific data stream to the number of pixels. Therefore, this method can be performed either before printing or during printing. Although this method allows obtaining prior knowledge about ink / toner usage, this method also requires that the data stream be first rasterized. Rasterization requires a large amount of computation and is therefore undesirable if the job is not printed. Note that one problem with pre-estimating the amount of marking material required for a print job is that the print job typically contains compressed data. For example, well-known compression methods include ITU-TSS T.264 for bi-level data. 6 Group 4, LZW (Lempel-Ziv-Welch) for bi-level or multi-bit data, JPEG for continuous tone data, and the like. It is impossible to obtain the pel / pixel count number from such compressed data, and a large amount of calculation is required for the decompression process. However, thawing is necessary for printing, and thus the estimation of the amount of marking material used will be delayed until after the printing process after such thawing is completed.
U.S. Pat.No. 5,636,032 U.S. Pat.No. 5,802,240 U.S. Patent No. 6,456.802 U.S. Pat.No. 5,937,225 "JPEG Still Image DataCompression Standard" by Pennebaker and Mitchell, Van Nostrand Reinhold, 1993

  An object of the present invention is to provide a method for estimating the amount of marking material, such as toner or ink, required for a print job, which can be performed before the print job is printed and further compressed To provide a method that does not require full decompression of the data.

  With regard to this problem, the present inventors have realized a method capable of obtaining the number of colored pels of compressed print data from a partially decompressed version of print data in a print job. Such partial thawing requires a relatively small amount of computation compared to full thawing, so that an estimate of the marking material can be obtained earlier in the printing process than the prior art. Become.

  Thus, according to a first aspect, the present invention comprises the steps of receiving a compressed data stream containing print data, performing partial decompression of the data stream, and partially decompressed. Determining the coverage data of the print data using the determined data stream and estimating the amount of marking material required to print the print data using the determined coverage data And a method comprising:

  According to a second aspect, the present invention provides a data processing system including at least one processor and an interface and memory accessible from the at least one processor. In this data processing system, the interface receives a compressed data stream containing print data, and the at least one processor includes a sub-process that performs partial decompression of the data stream and the partially decompressed data stream. A sub-process for determining coverage data of the print data using the determined data stream and a sub-process for estimating the amount of marking material required to print the print data using the determined coverage data And process.

  According to a third aspect, the invention comprises the steps of receiving a compressed data stream containing print data, performing partial decompression of the data stream, and the partially decompressed data Determining the coverage data of the print data using a stream and estimating the amount of marking material required to print the print data using the determined coverage data. Provide a program to be executed.

  For example, the coverage data may be the number of colored pixels / pels of the print data, and the marking material estimation is performed using the average intensity value for each colored pixel / pel.

  Alternatively, for example, if the device used to print the print data supports multiple pixel densities, the number of colored pixels can be counted for each multiple density level, The estimation can be performed using different density levels for different pixels. Optionally, the counting of colored pixels at each such density level also takes into account other printing effects depending on the printing device. For example, for two colored pixels of a given density, the printing device can require less marking material when the pixels are adjacent than when the pixels are not adjacent. . In this case, the density level of the colored pixel is determined based on the density level of the pixel and at least one adjacent pixel.

  For example, the print data can include a single color plane (monochrome) or multiple color planes. Preferably, when the print data includes a plurality of color planes, the number of colored pixels is obtained for each of the plurality of color planes.

  Optionally, print data including a plurality of color planes may further include a plurality of highlight color planes. Optionally, the number of colored pixels can also be determined for a plurality of highlight color planes.

  Optionally, an estimate of the amount of marking material can be used to determine the cost of printing the print data.

  Optionally, an estimate of the amount of marking material can be used to determine whether a given printing device can print the print data.

  For example, if the data stream is a Group 4 compressed data stream, the data stream is partially decompressed in the form of a run format, and the coverage data is extracted from the run format from colored pixels. It can be obtained in the form of a number. Furthermore, this technique can be used for any compression algorithm that uses an intermediate run format.

  For example, if the data stream is an LZW (Lempel-Ziv-Welch) compressed data stream, the data stream is partially decompressed by at least partially decompressing the data stream's string table. The The string table includes a plurality of entries, each containing an associated string. The coverage data is then obtained by determining the number of colored pixels for each string in the string table and using the determined number of colored pixels to determine the number of colored pixels in the data stream. Optionally, the number of colored pixels in the string includes the number of pixels for each of the plurality of densities.

  For example, if the data stream is a JPEG compressed data stream, the data stream is partially decompressed by obtaining the DC coefficient of the pixel block from the compressed data stream, and the coverage data is the DC coefficient. To obtain the average density level of the pixel block.

  Thus far, some of the objects of the present invention have been described, but other objects should become apparent from the following description read in conjunction with the accompanying drawings.

  FIG. 1 shows a schematic block diagram of a printing system to which a preferred embodiment of the present invention is advantageously applied. The figure shows a print server 100 and a printing device 110. The print server 100 includes a network interface 101 for accessing the network 120, a processor 102 capable of accessing the volatile memory 103 and the nonvolatile memory 104, and a printer interface for transmitting a print request to the printing apparatus 110. 105. The printing apparatus 110 includes a host interface 111 for receiving a print job from the print server 100, a processor 112 capable of accessing the volatile memory 113 and the non-volatile memory 114, and a print job for printing the print job. A head 115 and a toner reservoir 116 used by the print head 115 during printing are provided.

  For example, in the case of a conventional print job, the print server 100 receives a compressed data stream including print data for printing via the network 120 and the network interface 101. The processor 102 maintains a data stream in the volatile memory 103 and implements a sub-process that includes decompressing and obtaining print data and rasterizing the print data and sending it to a printer. While performing this sub-process, the processor 102 can access font information stored in the non-volatile memory 104, for example. Next, the rasterized print data is transmitted to the printing apparatus 110 via the printer interface 105 as a print job for printing. The printing apparatus 110 receives a print job via the host interface 111. The processor 112 then maintains the print job in volatile memory 113 and provides the job in an appropriate format for printing by the print head 115. During this process, the processor 112 accesses information such as the paper size in the nonvolatile memory 114. Next, the print head 115 uses the toner from the toner reservoir 116 to print the print data.

  It should be noted that many alternatives for the printing system can be used, for example, the print server 100 and the printing device 110 can be incorporated into a single unit. Further, for example, the printing device 110 can include a network interface instead of or in addition to the host interface 111 to receive print jobs over the network. Similarly, the print server 100 can include a host interface instead of or in addition to the network interface 101 to receive print jobs via the host interface. Further, for example, either device can include one or more additional processors.

  According to a preferred embodiment of the present invention, print server 102 implements the method shown in FIG. At step 201, a compressed data stream that includes print data for printing on a target device is received via the network interface 101. Next, at step 202, the data stream is partially decompressed. In step 203, coverage data for the print data is determined from the decompressed data stream. Coverage data includes the number of colored pixels for one or more density levels supported by the target device and / or provided in the form of print data. At step 204, an estimate of toner required to print the print data on the target device is calculated by taking the sum of the result of multiplying the number of each pixel by the toner value. Here, the toner value represents the amount of toner required to print a single pixel at a density related to the number of pixels. After the estimated value is calculated, it is determined in step 205 whether this value meets a predetermined criterion (criteria). For example, the estimated value may be the amount of toner and the reference may represent the maximum amount of toner used in the print job. Alternatively, for example, the estimate may be the cost of the toner required and the criteria may be the amount that can be used to print the print job. If the predetermined standard is not satisfied, the print job processing is terminated. On the other hand, if the criteria are met, step 206 completes the decompression of the data stream to obtain print data, and step 207 rasterizes the print data and sends it to the target device for printing. The

  The amount of toner needed to print a pixel depends on the total number of pixels printed in a given print area, and therefore the toner value used is the total number of pixels and the area in which the pixels are printed. Note that it depends on size.

  The number of density levels from which the number of pixels is obtained depends on the toner values available on the printing device used to print the data stream and the accuracy of the required estimate.

  For example, for a given printer, a single toner value that represents the average amount of toner used to print a pixel can be used. In this case, the estimated value is calculated as the number of colored pixels multiplied by the average toner value. However, this calculation is a relatively simple calculation, but the estimate generated is relatively inaccurate. Instead, if multiple toner values are available, eg, each toner value represents the average amount of toner used to print a page with different colored pixel coverage, it is relatively accurate. An estimate is obtained. For example, in some devices, a first toner value is specified with 5% coverage (ie, 5% of the pixels on the page are colored), a second toner value is specified with 10% coverage, and so on. The In this case, the coverage of the pixels on the page is calculated and an estimated value is determined using an appropriate toner value. Further, if the determined coverage is between two coverage amounts where the toner value is available, a weighted average of the two toner values can be calculated.

  Alternatively, for example, consider a printer that can print at 5 density levels, ie, 20%, 40%, 60%, 80%, and 100%, with known toner values for each density level. In the case of a relatively rough estimation, a single count of colored pixels is determined and multiplied by a toner value representing the average density, for example a 60% density toner value or an average of five toner values. In the case of more accurate estimation, the number of colored pixel counts up to a maximum of five of the five density levels is obtained and multiplied by an appropriate density toner value.

  However, the amount of toner required to print a pixel may depend on which neighboring pixels and how many neighboring pixels are printed simultaneously. For example, if a 3 × 3 block of pixels is printed at 100% density, the center pixel requires less toner than the outer pixels. Furthermore, the outer pixels require less toner than single pixels that are printed at 100% density but do not have adjacent colored pixels. Thus, by taking into account such effects, a relatively accurate estimate can be obtained, but this increases the more complex computational requirements.

  Thus, one skilled in the art can use various techniques to obtain estimates of various accuracy, and the technique chosen is the accuracy of the estimation and its calculation required for the application for which the estimate is used. It will be understood that this is determined by the balance between the available computing resources.

  Next, a preferred embodiment for determining coverage data including the number of pixels is described in three well-known compression schemes, namely ITU-TSS T.2 for bi-level data. 6 Group 4, LZW (Lempel-Ziv-Welch) for bi-level or multi-bit data, and JPEG for continuous tone data are described. However, those skilled in the art will appreciate that the present invention is equally applicable to other compression schemes such as JBIG2, RLE (Run Length Encoding), Packbits, JPEG2000, and the like.

  ITU-TSS T6 Group 4 (hereinafter referred to as Group 4) is an example of a bi-level compression algorithm. The bi-level compression algorithm is used for bi-level data where each pixel can take “0” or “1”. Therefore, it is only possible to determine the number of pixels at a single density level, and thus calculating the coverage data of a bi-level image (two-level image), eg counting the number of “1” pixels. It is only possible to compare with the image size.

  The Group 4 algorithm can be processed as a two-stage method. In the first stage, print data such as an image bitmap is converted into a run format, and in the second stage, the run format is compressed into the final Group4 format. In the run format, a pixel string that is all 0s or all 1s can be encoded and stored as a run length or a run end. Thawing is the reverse of this process.

  For example, FIG. 3 shows a simple image bitmap 301 and a hexadecimal run-end format 302 of that bitmap. The image bitmap 301 shows a 4 × 16 bilevel image including a 2 × 8 black region in the lower right corner. The run-end format 302 is composed of a 2-byte short numeric string in which each row represents one line of the image bitmap 301. For a given line, the first 2 byte short represents the length of the image line in bytes. Subsequent 2-byte shorts alternately represent the end positions of consecutive numeric strings “1” and “0”, respectively. The end of the line is indicated by repetition of the end position. For example, the fourth line of the run-end format bitmap image has a length “00 10” (310) indicating that it is 16 and “00 00” indicating that the line does not start with “1”. ”(311), the end position of the first“ 1 ”number sequence, and the next“ 0 ”indicated by“ 00 08 ”(312) indicating that the line starts with eight“ 0 ”s. ”, The end position of the next“ 1 ”numeric string indicated by“ 00 10 ”(313) indicating 16 and the position before the end of the line is instructed. Including "00 10" (314).

  According to a preferred embodiment of the present invention, in order to determine the number of pixels for a Group 4 compressed image, the compressed image is partially decompressed to obtain the details of the bit string (run). From this detail, the length of each “1” bit string can be obtained and added to obtain the number of colored pixels. This method is faster to compute than fully decompressing the image and calculating the number of pixels from the bitmap. After the number of colored pixels is obtained, the toner / ink estimate is calculated.

  It should be noted that other bi-level compression algorithms can also be partially decompressed to determine bit string details. It should also be noted that the run-end format shown in FIG. 3 is an example. One skilled in the art will appreciate that many different run formats are possible.

  LZW (Lempel-Ziv-Welch) compression is a multi-level compression algorithm. Therefore, pixels with various densities can be included. LZW compression includes generating a string table and a codeword stream from the compressed print data. Each entry in the string table contains a string from the print data and an associated codeword. The codeword stream includes a series of codewords that define compressed print data. Thus, during compression, byte strings are effectively replaced by codewords each representing a string of one or more bytes. For example, during compression, the first occurrence of a byte string is copied into a string table and associated with a codeword. Then, subsequent occurrences of the same byte string are replaced by a reference to the code word. In addition, the new byte string can be replaced by following the reference to the previous codeword (prefix) with an additional byte. As a result, a new codeword and a new entry are generated in the string table. When the data is decompressed, the string table is restored and references to codewords are replaced with the byte strings they represent.

  There are many variations on the LZW algorithm, which can be modified according to the shape of the data being compressed. Next, an example of such an LZW algorithm will be described with reference to FIG. FIG. 4 shows an example of print data including hexadecimal image data 401. This image data 401 is represented by a string table 402 and a code stream 403 when compressed. The image data 401 represents an 8 × 4 image including a black single density 2 × 4 region in the lower right corner indicated by “ff” in the image data.

  During LZW compression, the string table is initialized with all possible single character values. In many applications, initialization will result in 256 entries for each possible single byte value, but for simplicity, in this example, the possible value pairs are “00” and “ff”. Limit to. Thus, the first two entries of string table 402 contain these characters and are associated with codewords 00 and 01, respectively. In this example, those characters are just two entries in the string table 402 at this stage. Note that in many applications, the string table is then followed by one or more reserved entries, but no reserved entries are used in this example.

  The image data 401 is then processed by reading the first character “00” and using it as a search string to search the string table. This character is found in code word 00. Therefore, the next (second) character from the image data is added to the search string to obtain “00 00” and the string table is searched again. This time it is not found in the table, so it is added to the table as codeword 02, codeword 00 for the last string found is added to codestream 403, and the last added to the previous search string A new search string is generated that includes the character and the next (third) character in the image data. In this case, the new search string is “00 00” (second and third characters of the image data), which is found in code word 02 in the table. Therefore, the next (fourth) character from the image data is appended to the search string to obtain “00 00 00” and the string table is searched again. This time the string is not found in the table, so it is added to the table as codeword 03, codeword 02 of the last string found is added to code stream 403, and the last character added to the previous search string A new search string is generated that includes (the fourth character of the image data) and the next (fifth) character in the image data. The process then continues to add a new byte from the image data to the search string until the search string is not found in the string table. When no longer found, the search string is added to the string table, the codeword of the most recently found string is added to the code stream, the last byte added to the search string and the next byte from the image data A new search using is started. When this process for image data 401 is complete, a string table 402 and a code stream 403 are generated from which the code stream 403 and, optionally, an initialized form of the string table, codeword 00, are generated. And an LZW compressed data stream containing enough information to restore a string table containing only entries for 01.

  Decompressing the LZW compressed data stream includes restoring the string table and restoring the print data using the restored string table and code stream. These two tasks can be performed in parallel. However, according to a preferred embodiment of the present invention, partial decompression, including string table restoration, is performed to determine coverage data that includes the number of colored pixels. After the string table is generated, the coverage estimate associates the number of colored pixels and optionally the number of colored pixels for each of the plurality of density levels with each codeword in the string table and The number is used to calculate the number of colored pixels for the code stream.

  In order to recreate the string table, the string table is first initialized with all possible single character values, but this information is received in the form of a compressed data stream. Or it is already known to the decompression unit. The first codeword is read from the code stream and looked up in the string table. Each subsequent codeword is then read and processed in sequence. The action taken depends on whether the codeword exists in the string table. If the codeword exists in the string table, a new entry is added to the string table using the next free codeword. The string value is composed of a value obtained by adding the first character of the code word to be processed to the value of the previously processed code word. If the codeword does not exist in the string table, the codeword is added to the string table, and the string value is the value of the previously processed codeword plus the first character of the previously processed codeword. Consists of appended values.

  For example, referring to FIG. 4, the string table is generated by first initializing the string table 402 to include the characters “00” and “ff”. Thus, the first two entries of string table 402 contain these characters and are associated with codewords 00 and 01, respectively. The code stream 403 is then processed by reading and processing each codeword in turn. Since the first codeword 00 exists in the string table, no action is taken. The next codeword 02 does not exist in the string table. Therefore, the code word 02 is given by adding the first character (“00”) of the code word (00) processed last time to the value (“00”) of the code word (00) processed last time. Added to the string table as “00 00”. This process continues to construct each codeword 03, 04, and 05 that is not already in the string table from the code stream into the string table, and the second 05 codeword is read. Will continue until. In this case, this codeword already exists in the string table. Thus, using the next free codeword, ie 06, the first character (05) of the codeword (05) that is processed to the value of the previously processed codeword (05) (“00 00 00 00 00 00”) A new codeword having a value with “00” added, ie “00 00 00 00 00 00” is added to the string table. The remaining codewords are then processed as described above, depending on whether they are already in the string table, and the complete string table 402 is constructed.

  According to a preferred embodiment of the present invention, the coverage data is obtained by associating the number of colored pixels for each density level or levels with each codeword in the reconstructed string table. The number of density levels for which the number of colored pixels is determined depends on the number of density levels specified in the print data and / or the number of density levels supported by the target device. In the example of FIG. 4, the print data includes a single density level in which each “ff” is one pixel. Thus, the number of colored pixels in the print data can be calculated by adding the number of colored pixels in each codeword in the code stream without having to restore the print data. For example, in table 402 of FIG. 4, codewords 01, 07, and 11 include 1 pixel, and codewords 08, 09, and 12 include 2, 3, and 4 pixels, respectively. The code stream 403 includes three 01 code words totaling three pixels, one 08 code word including two pixels, and one 09 code word including three pixels. Therefore, the total number of pixels is required to be 8 without fully decompressing the compressed data stream.

  Note that in table 402, each added codeword can be represented by other codewords and additional characters. For example, the code word 05 can be represented by adding an additional character “00” to the code word 04. Similarly, the number of colored pixels of a certain code word is the sum of the number of colored pixels of an additional character and the number of colored pixels of another code word.

  Further, in other examples, the image data 401 can include additional values, each of which indicates the various levels of pixel density or color required for a gray scale image or color image, for example. Note that you can. In this case, the number of pixels can be modified to take into account pixels of various densities.

  The JPEG algorithm is based on a two-stage compression and decompression process. In the case of compression, in the first stage, each 8 × 8 block of data is compressed using a Discrete Cosine Transformation (DCT) that outputs 64 DCT coefficients, each representing a unique frequency. Is done. In the second stage, each DCT coefficient is scaled using quantized values and entropy coding. In the case of decompression, 64 DCT coefficients are obtained by canceling entropy coding and performing scale conversion with the inverse of the quantized value in the first stage. In the second stage, each 8 × 8 block of data is reconstructed from the DCT coefficients using an inverse discrete cosine transformation (IDCT). However, it should be noted that this algorithm is “lossy” and therefore the recovered 8 × 8 block of data may not exactly match its original data. This algorithm is described in detail in "JPEGStill Image Data Compression Standard" by Pennebaker and Mitchell, Van Nostrand Reinhold, 1993.

  During compression, after the DCT coefficients for each 8 × 8 block of the image are calculated, the coefficients are quantized (divided by some integer) to reduce their size. Also, this quantization often reduces some coefficients to zero. Next, the quantized coefficients are entropy encoded. Two entropy schemes called Huffman and arithmetic are known, both of which use the same data structure. In this structure, each non-zero quantized DCT coefficient has a 4-bit zero-run field representing the number of DCT coefficients quantized to zero immediately preceding this coefficient and the number of bits required to store the DCT coefficient. A length field indicating DCT coefficient and a DCT coefficient.

  As an example of this structure, an example in the case of eight quantized DCT coefficients 501 is shown in FIG. In this example, DCT1, DCT3, and DCT8 have values of 5, 8, and 3, respectively, and all other coefficients are zero. Entropy encoding 502 of these values is shown in binary representation, with each row containing DCT coefficients as shown by the dashed lines. For example, the entropy encoding of DCT8 is a 4-bit value (503) set to 4 (ie, DCT4, 5, 6, and 7), which is the number of zero quantized DCT coefficients immediately preceding this coefficient, and DCT8 A 4-bit value (504) set to 2 which is the number of bits used to store the data and a 2-bit value (505) which is the value of DCT8 are included.

  One of the 64 DCT coefficients representing the 8 × 8 block is called a DC coefficient and represents the average density of the 8 × 8 block. The other 63 coefficients are called AC coefficients and define a frequency term that can be used to calculate the actual pixels of the 8x8 block.

  In the preferred embodiment of the present invention, the coverage data is determined by taking its DC coefficient for each 8 × 8 block of JPEG print data and using it as the average density of 8 × 8 blocks. The coverage estimate for the entire print data is then calculated by calculating the coverage estimate for each block based on the determined average density and adding the results.

  In order to obtain DC coefficients, the JPEG data stream is partially decompressed. This partial decompression is done by obtaining quantized DC coefficients from entropy coding and dequantizing them. For example, the DC coefficient is the first coefficient of each block, and after the DC coefficient is detected, the next block is detected from entropy coding. The next block is detected by repeatedly reading the length field of each DCT coefficient and skipping to the next non-zero coefficient until the next block is detected.

  Optionally, one or more AC coefficients may be considered. The DC coefficient provides a good approximation, but the toner usage of the block varies with the 8 × 8 block pattern. For example, if the block contains a small rectangle in the center and the rest is blank, the DC estimate will be slightly below actual usage. Thus, AC coefficients can be used to detect toner distribution within a block and thereby modify the estimate.

  Note that an image can include one or more color planes. Accordingly, the preferred embodiment of the present invention is applicable to any kind of image, from a black and white image with 1 bit per spot to a full color image with 32 bits (or more).

  In the preferred embodiment, the present invention has been described with respect to three very well-known compression algorithms, but it will be appreciated by those skilled in the art that several other compression algorithms, such as JBIG2, are suitable for the same approach. It will be clear.

  The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In the preferred embodiment, the present invention is implemented in software. Software includes, but is not limited to, firmware, resident software, microcode, and the like.

  Furthermore, the present invention may take the form of a computer usable computer program that provides program code for use by or in conjunction with a computer or any instruction execution system. For the purposes of this specification, a computer-usable or computer-readable medium contains, stores, communicates, propagates a program used by or used with an instruction execution system, apparatus, or device. Or any device capable of transmitting.

  The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of computer readable media include semiconductor or solid state memory, magnetic tape, removable computer diskette, RAM (Random Access Memory), ROM (Read Only Memory), hard magnetic disk, optical disk and the like. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read / write (CD-RW), and DVD.

  A data processing system suitable for storing and / or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory element is a local memory used during the actual execution of the program code, a mass storage device, and at least a program program to reduce the number of times code must be fetched from the mass storage device during execution. A cache memory may be included that provides temporary storage of a portion of code.

  Input / output or I / O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through an intermediate I / O controller.

  Network adapters can also be coupled to the system so that the data processing system can be coupled to other data processing systems or remote printers or storage devices via an intermediate private or public network. it can. Modems, cable modems, and Ethernet cards are just one example of the types of network adapters currently available.

  In summary, the present invention provides a method, apparatus, and program for calculating an estimate of the amount of toner coverage required to print print data defined in the form of a compressed data stream. The estimate is obtained by a reduced process by obtaining coverage data from the partially decompressed data stream and using it to determine a coverage estimate. Therefore, it is not necessary to perform a full decompression of the data stream. For example, the coverage data is the number of colored pixels for at least one density level.

1 is a schematic configuration diagram illustrating a printing system to which a preferred embodiment of the present invention is advantageously applied. Figure 5 is a flow diagram for determining an estimate of marking material from a compressed data stream. FIG. 4 shows the contents of an exemplary data stream used for partial decompression of a Group 4 compressed data stream. FIG. 2 shows the contents of an exemplary data stream used for partial decompression of an LZW (Lempel-Ziv Welch) compressed data stream. FIG. 3 shows the contents of an exemplary data stream used for partial decompression of a JPEG compressed data stream.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Print server 101 Network interface 102 Processor 103 Volatile memory 104 Non-volatile memory 105 Printer interface 110 Printing apparatus 111 Host interface 112 Processor 113 Volatile memory 114 Non-volatile memory 115 Print head 116 Toner reservoir 120 Network 301 Image Bitmap 302 Image Bitmap Hexadecimal Run End Format 401 Hexadecimal Image Data 402 String Table 403 Code Stream 501 DCT Coefficient 502 Entropy Coding

Claims (18)

Receiving a compressed data stream containing print data;
Performing partial decompression of the data stream;
Determining the print data coverage data using the partially decompressed data stream;
Using the determined coverage data to estimate the amount of marking material required to print the print data.   The method of claim 1, wherein the coverage data includes a number of pixels for each of a plurality of density levels.   The method of claim 2, further comprising determining a density level of a pixel based on the density level of the pixel and at least one adjacent pixel.   The method of claim 1, wherein the coverage data is determined for a plurality of color planes.   The method of claim 1, further comprising calculating a cost for printing the print data using the estimate of the amount of marking material.   The method of claim 1, further comprising determining whether a given printing device is capable of printing the print data using the estimate of the amount of marking material. The compressed data stream is a Group 4 compressed data stream;
The step of performing partial decompression comprises obtaining a run format of the data stream;
Determining the coverage data comprises determining the number of colored pixels from the run format;
The method of claim 1. The compressed data stream is an LZW (Lempel-Ziv Welch) compressed data stream;
Said step of performing partial decompression,
Generating at least in part a string table from the data stream, the string table comprising a plurality of entries each having an associated string;
The step of determining coverage data comprises:
Determining the number of colored pixels for each string in the string table;
And determining the number of colored pixels of the print data using the determined number of colored pixels. The compressed data stream is an entropy encoded JPEG compressed data stream;
The step of performing partial decompression comprises obtaining DC coefficients of pixel blocks from the compressed data stream;
Determining the coverage data comprises determining an average density level of the pixel block using the DC coefficient;
The method of claim 1. At least one processor;
A data processing system comprising an interface and a memory accessible from the at least one processor,
The interface receives a compressed data stream containing print data;
The at least one processor comprises:
A sub-process that performs partial decompression of the data stream;
A sub-process for determining coverage data of the print data using the partially decompressed data stream;
And a sub-process for estimating an amount of marking material required to print the print data using the determined coverage data.   The data processing system of claim 10, wherein the coverage data includes a number of pixels for each of a plurality of density levels. The at least one processor comprises:
The data processing system of claim 11, further comprising a sub-process that determines a density level of a pixel based on the density level of the pixel and at least one adjacent pixel. The at least one processor comprises:
The data processing system of claim 10, further comprising a sub-process that uses the estimate of the amount of marking material to calculate a cost for printing the data stream. The at least one processor comprises:
The data processing system of claim 10, further comprising a sub-process that uses the estimate of the amount of marking material to determine whether a given printing device is capable of printing the data stream. Receiving a compressed data stream containing print data;
Performing partial decompression of the data stream;
Determining the print data coverage data using the partially decompressed data stream;
Using the obtained coverage data to estimate the amount of marking material required to print the print data. The coverage data includes the number of pixels for each of a plurality of density levels;
The program according to claim 15, further causing the computer to execute a step of determining a density level of a pixel based on the density level of the pixel and at least one adjacent pixel.   The program according to claim 15, further causing the computer to perform a step of calculating a cost for printing the print data using the estimate of the amount of marking material.   The program of claim 15, further causing the computer to perform a step of determining whether a given printing device is capable of printing the print data using the estimate of the amount of marking material.

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