JP2007265055A - Resolution conversion processing method of binary image - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain an image having high image quality in a short time when converting resolution of a binary line drawing image or the like such as a character image. <P>SOLUTION: A ratio of a main scan pixel number of the image before and after the resolution conversion is calculated, an interpolation bit wherein the main scan pixel number of the image after the resolution conversion is increased as compared to the main scan pixel number of the image before the conversion, or a thinning bit wherein the main scan pixel number of the image after the resolution conversion is reduced is calculated on the basis of the thinning ratio, the interpolation bit is set as the same kind of pixel as a left adjacent pixel, and the thinning bit is thinned. A longitudinal line slim mode line width pixel number that is a pixel number of integer-obtained by truncating a decimal place of a longitudinal line number in the image after the resolution conversion and a longitudinal line thick mode line width pixel number that is a pixel number of integer-obtained by rounding the decimal place are calculated on the basis of the calculated ratio of the main scan pixel number of the images before and the after the resolution conversion, and the image outputted by resolution conversion processing is uniformly processed to a line width mode designated by designation according to designation of a longitudinal line slim mode line width or a longitudinal line thick mode line width. A sub-scan direction is similarly processed. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a resolution conversion processing method for a binary image, and in particular, a binary image for converting a binary line drawing image such as a character image from a low resolution image to a high resolution image and from a high resolution image to a low resolution image. This relates to a resolution conversion processing method.

  As an example of a conventional binary image resolution conversion technique, Non-Patent Document 1 focuses on the periodicity of image data and uses discrete Fourier transform and discrete inverse Fourier transform, which are frequently used in signal processing, to obtain image data. A method is described in which resolution conversion is performed by converting the period. Specifically, a binary image is converted into a multi-valued image, a discrete Fourier transform process is performed, a surrounding image is cut out from the spectrum image obtained by the discrete Fourier transform, and a value normalization process is performed. Perform discrete Fourier transform processing. Next, the multi-value image data obtained by the inverse discrete Fourier transform process is binarized to create a binary image after resolution conversion. The binarization processing includes fixed value binarization, error diffusion, dithering, and the like.

  On the other hand, in the binary image resolution conversion method described in Patent Document 1, a resolution conversion table is created based on the resolution before and after the linear density conversion of the binary image, and the post-conversion image is obtained from the binary data in each dot of the pre-conversion image. The score is calculated by the ratio of black in each dot. Then, a resolution-converted image is obtained by performing black / white determination after conversion from the calculated black point of each dot and the set determination threshold.

"Gravitational rearrangement of halftone dots for moire-free color proofing" Image Electronics Society of Japan, Vol. 13, No. 5 (2005) JP 2001-358937 A

  However, the method described in Non-Patent Document 1 does not consider the characteristics (smoothing) of the outline of the line drawing when converting the multi-valued image obtained by inverse Fourier transform into a binary image. There has been a problem that the image is deteriorated due to collapse or the like. Further, since the two-dimensional discrete Fourier transform and the inverse two-dimensional discrete Fourier transform are performed, there is a problem that the amount of calculation of the image processing is large and the processing takes a long time.

  On the other hand, in the method described in Patent Document 1, the proportion of black pixels is converted into points by weighting the image pixels before conversion with the resolution conversion ratio, and the converted points are converted from the set black and white determination threshold value. Since the subsequent pixels are determined, random pixels are added to the horizontal and vertical lines of the character, which causes a problem of image deterioration.

  Therefore, the present invention has been made in view of the problems in the above conventional binary image resolution conversion processing method. In converting the resolution of a binary line drawing image or the like, a high-quality image can be obtained in a short time. An object of the present invention is to provide a binary image resolution conversion method that can be obtained.

  In order to achieve the above object, the present invention is a resolution conversion processing method for a binary image, and calculates a ratio between the number of main scanning pixels of an image before resolution conversion and the number of main scanning pixels of an image after resolution conversion. Based on the calculated ratio, an interpolation bit representing a pixel in which the number of main scanning pixels of the image after resolution conversion is larger than the number of main scanning pixels in the image before resolution conversion or a thinning bit representing a decreasing pixel is calculated. The interpolation bit is the same type as the pixel on the left, and the thinning bit is subjected to thinning processing.

  According to the present invention, the resolution conversion process based on the pixel position information of the image before and after the resolution conversion can be performed in the main scanning direction, so that a high-quality resolution converted image can be obtained. is there.

  In the binary image resolution conversion processing method, a main scanning interpolation line image table for determining a bit interpolation operation of the main scanning resolution conversion image is created and corresponds to the pixel bits of the main scanning interpolation line image table. When the main scanning interpolation line image table is an interpolation bit, the main scanning image of the image before resolution conversion is the same type of pixel as the adjacent pixel on the left, and in the case of a thinning bit, thinning processing can be performed. . Since the resolution conversion process can be performed using a main scanning interpolation line image table created in advance, the resolution conversion process can be performed at high speed.

  The present invention is also a resolution conversion processing method for a binary image, which calculates a ratio between the number of sub-scanning lines of an image before resolution conversion and the number of sub-scanning lines of an image after resolution conversion. Based on the ratio, an interpolation bit representing a line in which the number of sub-scanning lines of the image after resolution conversion is greater than the number of sub-scanning lines in the image before resolution conversion or a thinning bit representing a decreasing line is calculated, and the interpolation bits Is the same type of pixel as the pixel immediately above, and the thinning bit is subjected to thinning processing.

  According to the present invention, it is possible to perform resolution conversion processing based on pixel position information of images before and after resolution conversion in the sub-scanning direction, so that a high-quality resolution-converted image can be obtained.

  In the binary image resolution conversion processing method, a sub-scan interpolation line image table for determining a line interpolation operation of the sub-scan resolution conversion image is created and corresponds to the pixel bits of the sub-scan interpolation line image table. When the sub-scanning interpolation line image table is an interpolation bit, the sub-scanned image of the pre-resolution conversion image is the same type of pixel as the pixel immediately above, and when it is a thinning-out bit, thinning processing can be performed. Since the resolution conversion process can be performed using the sub-scan interpolation line image table created in advance, the resolution conversion process can be performed at high speed.

  The present invention also relates to a resolution conversion processing method for binary images, and a binary image resolution conversion processing method for the main scanning direction and a binary image for the sub-scanning direction, which are independent of each other and have no correlation. And a resolution conversion processing method. As a result, the two-dimensional image processing can be decomposed into two one-dimensional signal processing, and the resolution conversion processing can be performed at a higher speed.

  Furthermore, the present invention is the resolution conversion processing method for binary images in the main scanning direction, based on the ratio between the calculated number of main scanning pixels of the image before resolution conversion and the number of main scanning pixels of the image after resolution conversion. , The number of vertical lines in the image after resolution conversion, the number of vertical line thinning mode line width pixels that are rounded down to the nearest whole number, and the number of pixels that are rounded up to the nearest whole number. The mode line width pixel count is calculated, and the image output by the resolution conversion processing is uniformly processed to the specified line width mode by specifying the vertical line thin mode line width or the vertical line thick mode line width. It is characterized by doing. Thereby, the line width in the image after resolution conversion can be processed uniformly in the main scanning direction.

  In the resolution conversion processing method of the binary image in the main scanning direction, a vertical line width correction table is created based on the calculated vertical line thinning mode line width pixel number and vertical line thickening mode line width pixel number, and the vertical line width correction table is generated. By specifying the line width mode width or the vertical line width mode width, it is possible to uniformly process the line width mode specified using the vertical line width correction table. Since the resolution conversion process can be performed using the vertical line width correction table created in advance, the resolution conversion process can be performed at high speed.

  In the resolution conversion processing method for binary images in the sub-scanning direction, the present invention is based on the ratio between the calculated number of sub-scanning lines of the image before resolution conversion and the number of sub-scanning lines of the image after resolution conversion. , The horizontal line thinning mode line width, which is the number of lines in the image after resolution conversion, rounded down to the nearest whole number, and the horizontal line thick mode line width, which is the number of lines rounded up to the nearest whole number The number of lines is calculated, and the image output by the resolution conversion processing is uniformly processed into a designated line width mode by designating the horizontal line thin mode line width or the horizontal line thick mode line width. To do. Thereby, the line width in the image after resolution conversion can be processed uniformly in the sub-scanning direction.

  In the resolution conversion processing method of the binary image in the sub-scanning direction, a horizontal line width correction table based on the calculated horizontal line thinning mode line width line number and horizontal line thickening mode line width line number is created, and the horizontal line thinning mode line is generated. By designating the width or the horizontal line thick mode line width, uniform processing can be performed in the line width mode specified using the horizontal line width correction table. Since the resolution conversion process can be performed using the horizontal line width correction table created in advance, the resolution conversion process can be performed at high speed.

  Further, the present invention is a resolution conversion processing method for a binary image, wherein the line width in the main scanning direction and the line width in the sub-scanning direction are independent from each other and have no correlation. It is characterized by combining uniform processing. As a result, the two-dimensional image processing can be decomposed into two one-dimensional signal processing, and resolution conversion can be processed at a higher speed.

  As described above, according to the binary image resolution conversion method of the present invention, it is possible to obtain a high-quality image in a short time when converting the resolution of a binary line drawing image or the like.

  Next, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 shows the overall processing flow of a binary image resolution conversion processing method according to the present invention. In this method, when converting the resolution of the binary image, the pixel position of the image before the resolution conversion and the image after the conversion are not changed. Therefore, the image after the resolution conversion is based on the pixel position information of the image before the resolution conversion. A main scanning and sub-scanning interpolation line image table representing the pixel position is created (step S1), and resolution conversion is independently performed in each of the main and sub-scanning directions based on the main and sub-scanning interpolation line image tables. Processing is performed (step S2), and a main-scanning and sub-scanning line-width correction table for correcting a variation in line width that occurs in the range of one pixel in the resolution-converted image is created (step S3). ), The line width of the image after resolution conversion is corrected based on the line width correction table (step S4). In the embodiment of the present invention, there are a first embodiment for converting resolution of a binary image from low resolution to high resolution and a second embodiment for converting resolution from high resolution to low resolution.

  First, the flow of a process according to the first embodiment of the present invention will be described with reference to FIG.

  Based on the dimension information of the image before resolution conversion and the resolution of the image after resolution conversion, the dimension information of the image after resolution conversion is obtained (step S100), an interpolation line image table is created (step S110), and resolution conversion is performed. This is performed (step S120). Thereafter, a line width correction table is created (step S130), and a line width correction process (step S140) is performed.

  The interpolation line image table creation process (step S110) includes a main scanning interpolation line image table creation process (step S111) and a sub-scanning interpolation line image table creation process (step S112). The correction table creation process (step S130) includes a vertical line width correction table creation process (step S131) and a horizontal line width correction table creation process (step S132).

  Next, the overall operation of the first exemplary embodiment of the present invention will be described in detail with reference to FIGS.

  First, in step S100 of FIG. 2 for obtaining the dimension information of the image after resolution conversion, the dimension information of the image before resolution conversion is set to the number of scanning pixels W, the number of sub-scanning lines L, and the resolution R, and the image after resolution conversion. Is the number of main scanning pixels W ′, the number of sub-scanning lines L ′, and the resolution R ′, the resolution from the W, L, R of the image before the resolution conversion and the R ′ of the image after the resolution conversion. The converted image information W and L ′ are obtained by the following method.

W ′ = W × R ′ ÷ R (rounded down) ・ ・ ・ Equation (1)
L ′ = L × R ′ ÷ R (rounded down) ・ ・ ・ Equation (2)
Next, the interpolation line image table creation step S110 will be described with reference to FIG.

  In the main scanning interpolation line image table creation step S111, first, a line image buffer (table) having the same number of bits as the number of main scanning pixels of the image after resolution conversion is secured. The bits of the line image table have a one-to-one correspondence with the main scanning pixels of the image after resolution conversion.

  Next, by raster scanning, the pixel position c (integer) in the main scanning direction of the image before resolution conversion when the position of the leftmost pixel in the main scanning direction is set to 0, and the main position in the main scanning direction of the image after resolution conversion. A scanning direction pixel position c ′ (integer) is set. The pixel position represents the position of the pixel in the main scanning direction of the binary image, and is represented by 0 to W-1 (in the case of c) or 0 to W'-1 (in the case of c '). Further, c is sequentially increased from 0 to W−1, and the corresponding c ′ value is obtained by the following equation (3).

c ′ = c × W ′ ÷ W (rounded down to the nearest decimal point) (3)
Corresponding to the value of c ′ obtained by Expression (3), the c′-th bit value of the line image table is set to the value 1. In addition, a bit value that is not supported is set to 0.

  Here, a bit having a value of 1 in the interpolation line image table is defined as a pixel corresponding to a pixel of the image before resolution conversion in the line image of the image after resolution conversion, and is defined as a pixel bit. A bit having a value of 0 is defined as an interpolation bit because it associates a pixel to be interpolated (a pixel added by resolution processing) in the line image of the image after resolution conversion.

  The example of FIG. 3 shows an image line image before resolution conversion and an image after resolution conversion when converting an image before resolution conversion of 454 dpi with 15 pixels of main scanning into an image after resolution conversion of 602 dpi. It represents the relationship between an image line image and an interpolation line image table.

  In this example, the number of main scanning pixels of the image after resolution conversion according to equation (1) is 15 × 602 ÷ 454 = 19 (truncated after the decimal point), so this value is applied to equation (3) to convert the resolution. When the pixel position c in the main scanning direction of the previous image is sequentially increased from 0 to 14, the pixel position c in the main scanning direction of the image after resolution conversion calculated from c ′ = c × 19 ÷ 15 (truncated after the decimal point). The values are obtained as 0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17. These bits are pixel bits having a value of 1, and the remaining 4, 9, 14, and 18th bits are interpolation bits having a value of 0.

  In the sub-scanning interpolation line image table creation step S112, as in the main-scanning interpolation line image table creation step S111, the sub-scanning line number L ′ of the image before resolution conversion and the sub-scanning line number L ′ of the image after resolution conversion. Based on the above, a sub-scanning interpolation line image table is created. The pixel bit of the sub-scan interpolation line image table is a line representing the sub-scan line image of the image after resolution conversion corresponding to the sub-scan line image of the image before resolution conversion, and is interpolated in the sub-scan interpolation line image table. A bit is a line representing a sub-scanning line image added to an image after resolution conversion by resolution conversion processing.

  Next, the resolution conversion step S120 in FIG. 2 will be described with reference to FIG. FIG. 4 illustrates the operation in the case of converting the resolution of the 454 dpi resolution-converted image of 15 pixels in the main scanning into the image after 602 dpi resolution conversion, as in FIG. 3.

  Each bit of the main scanning interpolation line image table has a one-to-one correspondence with the main scanning pixel of the image after resolution conversion, and the line image table pixel bit (one value bit) has a pair with the pixel of the image before resolution conversion. It corresponds with one. Specifically, the image line image before the resolution conversion is raster scanned from the left end to the right end, and the pixels after the resolution conversion corresponding to the pixel bits of the main scanning interpolation line image table are converted from the pixels before the resolution conversion. The pixel of the image after resolution conversion corresponding to the interpolation bit (bit of 0 value) of the main scanning interpolation line image table is written to the left pixel (bit of 1 value) as it is. Write the same pixel value as the next bit.

  Further, the resolution conversion process will be described in detail with reference to the flowchart of the resolution conversion process in FIG.

  First, the sub-scanning line position N of the image before resolution conversion and the bit position M of the sub-scanning interpolation line image table are set to 0th (steps S11 and S12). Next, the Mth bit of the sub-scanning interpolation line image table is determined (step S13). When the Mth bit is a pixel bit (bit value is 1), the Nth row image line image is read from the image before resolution conversion, and at the same time, 1 is added to the sub-scanning line position N of the image before resolution conversion ( Indicates the line of the image before the next resolution conversion) (step S14). Next, by performing the main scanning resolution conversion based on the main scanning interpolation line image table, the image of the image after the Mth row resolution conversion corresponding to the line image of the image before the Nth row resolution conversion is converted. A line image is obtained (step S15).

  Also, when the M-th bit of the sub-scanning interpolation line image table is an interpolation bit (bit value is 0), the main scanning resolution is not converted, and the resolution conversion of the (M−1) th row obtained by processing on the previous line is performed. The line image of the image is copied to the image line image after the M-th row resolution conversion to obtain an image line image after the M-th row resolution conversion (step S16).

  After the process of step S15 or step S16, the line image of the image after the Mth line resolution conversion is written into the image after the resolution conversion, and at the same time, 1 is added to the bit number M of the sub-scanning interpolation line image table (next Indicates the line of the image after resolution conversion (step S17).

  Finally, it is determined whether or not all of the image line images after resolution conversion have been written (step S18). If all of the image line images after resolution conversion have been written, the resolution conversion processing is terminated. If there is a remainder, the above processing is repeated until all the image line images after resolution conversion are written.

  In the resolution conversion process based on the interpolation line image table, the main scanning resolution conversion process and the sub-scanning resolution conversion process are independent of each other. Accordingly, the resolution-converted image obtained by performing the sub-scanning resolution conversion process after the main-scanning resolution conversion process is the same as the image after resolution conversion obtained by performing the main-scanning resolution conversion process after the sub-scanning resolution conversion process. For this reason, the resolution conversion processing of the image is performed separately in the main and sub-scanning directions, so that the two-dimensional image processing is decomposed into two one-dimensional signal processing, thereby greatly reducing the amount of calculation of the image processing. However, high-speed processing becomes possible.

  In addition, in order to maintain the image quality of the image before the resolution conversion at the maximum after the resolution conversion, the size of the image after the resolution conversion, the density of the black pixels, the position information of each pixel, etc. Although it is necessary to be as different as possible from the image, there is a problem that resolution conversion processing results in an image (line width variation) after resolution conversion of two different line widths having a difference of one pixel.

  For example, in a case where resolution conversion is performed from an image before 454 dpi resolution conversion of 10000 pixels in the main scanning direction to an image after 602 dpi resolution conversion, the image after conversion to 602 dpi resolution results in 13200 pixels. In addition, if an image before resolution conversion of 454 dpi has 1000 black pixels that are independent from each other among 10000 pixels, in order to maintain the black pixel density of the image quality for the image after 602 dpi resolution conversion, Since 1320 pixels out of 13200 pixels must be black pixels, after the resolution conversion is performed on one independent black point of the image before the 454 dpi resolution conversion, the average is 1.32 black pixels. However, in an actual binary image, there are two black dots, one black pixel (matched to an integer by rounding down the decimal point) and two black pixels (matched to an integer by rounding up the decimal point). There exist types (the difference in the number of black pixels is 1). Further, since the ratio of the two types of black dots is the ratio of the number of pixels of the image before resolution conversion and the number of pixels of the image after resolution conversion, the number of black dots of one black pixel is 68 in the whole. %, The number of black dots of two black pixels must be 32% of the total.

  From the above, in the case of resolution conversion processing in which the resolution of the image before resolution conversion and the resolution of the image after resolution conversion are not an integral multiple, the line width of the same image before resolution conversion is a difference of one pixel. It is inevitable that there will be an image after resolution conversion of two different line widths (line width variation problem).

  Next, a description will be given of the creation of a line width correction table (step S130 in FIG. 1) for reducing the problem relating to the variation in line width in the case of the resolution conversion process between resolution images that do not become integer multiples as described above.

The line width correction table is a table representing the image line width (number of pixels) after two types of resolution conversion by resolution conversion processing with respect to the line width (number of pixels) of the image before resolution conversion. For example, when converting from 454 dpi to 602 dpi, if the image before resolution conversion is one line width, the number of line width lines of the image after resolution conversion is theoretically 1 × 602 ÷ 454 = 1 by resolution conversion. .32
become.

  Accordingly, the image after resolution conversion has a 1-pixel line width (68% probability) and a 2-pixel line width (32% probability). Among these, the line width of one pixel is defined as a narrow line width, and the line width of two pixels is defined as a thick line width. A table for obtaining the number of thin line width pixels and the number of thick line width pixels of the image after resolution conversion for each pixel number line width of the image before resolution conversion is a line width correction table.

In the creation of the vertical line width correction table (step S131 in FIG. 2), the number of vertical line width pixels of the image before resolution conversion is n (integer) and corresponds to the vertical line width of the image before resolution conversion. When the number of vertical line thin line width pixels of the image after resolution conversion is defined as m0 (integer) and the number of thick line width pixels as m1 (integer), m0 and m1 are determined by the following equations.
m0 = n × W ′ ÷ W (matched to an integer by rounding down the decimal point) (4)
m1 = n × W ′ ÷ W (matched to an integer by rounding up the decimal point) Expression (5)
Note that W ′ is the number of pixels in the main scanning direction of the image after resolution conversion, and W is the number of pixels in the main scanning direction of the image before resolution conversion.

  Here, by increasing n in order from 1 to W (the total number of pixels of the vertical line width pixels that can exist in the image before resolution conversion), vertical line lines are obtained by obtaining m0 and m1 corresponding to n. A width correction table can be created. FIG. 6 is an example of a vertical line width correction table in the case where a 454 dpi image having a line width of 15 pixels and a vertical line created by Expression (4) and Expression (5) is converted into a 602 dpi image.

In the creation of the horizontal line width correction table (step S132 in FIG. 2), as in the creation of the vertical line correction table, the number of lines L in the sub-scanning direction of the image before resolution conversion and the number of lines L in the sub-scanning direction of the image after resolution conversion. Create using '. The horizontal line width line number of the image before resolution conversion is s (integer), the horizontal line thin line width line number of the image after resolution conversion corresponding to the horizontal line width of the image before resolution conversion is p0 (integer), and the thick line width If the number of lines is defined as p1 (integer), p0 and p1 are determined by the following equations.
p0 = s × L ′ ÷ L (matched to an integer by rounding down the decimal point) (6)
p1 = s × L ′ ÷ L (matched to an integer by rounding up the decimal point) Expression (7)
Note that L ′ is the number of sub-scanning lines of the image after resolution conversion, and L is the number of sub-scanning lines of the image before resolution conversion.

  Here, s is increased in order from 1 to L (the number of all horizontal line width lines in which an image before resolution conversion can exist), and a horizontal line width correction table is created by obtaining p0 and p1 for s. be able to.

  The line width correction table represents the line width that the line width existing in the image before resolution conversion changes in the image after resolution conversion by resolution conversion processing.

  Finally, the line width correction process (step S140 in FIG. 2) will be described with reference to FIG. The line width correction process is a process for equalizing two types of line widths in the image after resolution conversion to one type of line width by resolution conversion processing. Here, uniform line width correction processing mode 1 is defined for all line widths of images after resolution conversion, and uniform line width correction processing mode 2 is defined for all thin line widths.

  As shown in FIG. 7, the vertical line width of the image after resolution conversion is added to the number of pixel bits which are image pixels before resolution conversion in the vertical line width pixels by referring to the interpolation line image table of FIG. The number of interpolated bits can be identified. In the case of processing mode 1 in which the line width correction processing is made uniform to a thick line width, if the line width of the image after resolution conversion is a thick line width, the correction processing is not performed and the image after resolution conversion is performed. When the line width is a thin line width, one pixel at the right end of the image line width after resolution conversion is changed to a black pixel to make it a thick line width. In the case of the processing mode 2 in which the line width correction processing is uniformized to the fine line width, if the vertical line width of the image after the resolution conversion is a thin line width, the correction processing is not performed and the resolution conversion is performed. When the vertical line width of the image is a thick line width, one pixel at the right end of the image vertical line after resolution conversion is changed to a white pixel to make a thin line width vertical line.

  Similar to the vertical line width correction process, the horizontal line width correction process is performed on the horizontal line width of the image after the resolution conversion by using the horizontal line width correction table. However, the process of changing one pixel by the line width correction process is performed on the lowermost line of the horizontal line of the image after resolution conversion.

  Next, the flow of processing for the second embodiment of the present invention (when a binary image is converted from high resolution to low resolution) will be described with reference to FIG.

  Based on the dimension information of the image before resolution conversion and the resolution of the image after resolution conversion, the dimension information of the image after resolution conversion is obtained (step S200), an interpolation line image table is created (step S210), and resolution conversion is performed. This is performed (step S220). Thereafter, a line width correction table is created (step S230), and a line width correction process (step S240) is performed.

  Here, the interpolation line image table creation process (step S210) includes a main scanning interpolation line image table creation process (step S211) and a sub-scanning interpolation line image table creation process (step S212). The correction table creation process (step S230) includes a vertical line width correction table creation process (step S231) and a horizontal line width correction table creation process (step S232).

  Next, the overall operation of the present embodiment will be described in detail with reference to FIGS.

  First, in step S200 of FIG. 8 for obtaining the dimension information of the image after resolution conversion, the dimension information of the image before resolution conversion is set to the number of pixels W in the main scanning direction, the number of sub-scanning lines L, and the resolution R, and after the resolution conversion. Is the number of main scanning pixels W ′, the number of sub-scanning lines L ′, and the resolution R ′, the W, L, R of the image before resolution conversion and the R ′ of the image after resolution conversion. The image information W ′ and L ′ after the resolution conversion are obtained as follows, similarly to the expressions (1) and (2) of the first embodiment described above.

W ′ = W × R ′ ÷ R (rounded down) ・ ・ ・ Equation (1)
L ′ = L × R ′ ÷ R (rounded down) ・ ・ ・ Equation (2)

  Next, the interpolation line image table creation step S210 will be described with reference to FIG.

  In the main scanning interpolation line image table creation step S211, first, a line image buffer (table) having the same number of bits as the number of main scanning pixels of the image before resolution conversion is secured. The bits of the line image table have a one-to-one correspondence with the main scanning pixels of the image before resolution conversion.

Next, by raster scanning, the pixel position c (integer) in the main scanning direction of the image before resolution conversion, which sets the position of the leftmost pixel in the main scanning direction to 0, and the main scanning direction in the main scanning direction of the image after resolution conversion A pixel position c ′ (integer) is set. The pixel position represents the position of the pixel in the main scanning direction of the binary image, and is represented by 0 to W-1 (in the case of c) or 0 to W′-1 (in the case of c ′). Further, c ′ is sequentially increased from 0 to W′−1, and the value of c corresponding to the following equation (8) is obtained.
c = c ′ × W ÷ W ′ (rounded down to the nearest decimal point) (8)

  The c-th bit value of the line image table is set to a value 1 corresponding to the value of c obtained by Expression (8). In addition, a bit value that is not supported is set to 0.

  Here, a bit having a value of 1 in the interpolation line image table is defined as a pixel corresponding to the pixel of the image after resolution conversion in the line image of the image before resolution conversion, and is defined as a pixel bit. Bits having a value of 0 are defined as thinning bits because they correspond to thinning pixels (pixels reduced by resolution processing) in the line image of the image before resolution conversion.

  The example of FIG. 9 shows an image line image before resolution conversion and an image line after resolution conversion when converting an image before 602 dpi resolution conversion of 20 pixels in the main scanning into an image after 454 dpi resolution conversion. It represents the relationship between the image and the interpolation line image table.

  In this example, the number of main scanning pixels of the image after resolution conversion according to equation (1) is 20 × 454 ÷ 602 = 15 (truncated after the decimal point), so this value is applied to equation (8) to convert the resolution. When the pixel position c ′ in the main scanning direction of the subsequent image is sequentially increased from 0 to 14, pixels in the main scanning direction of the image before resolution conversion calculated from c = c ′ × 20 ÷ 15 (rounded down to the nearest decimal place) The position c value is 0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18. These bits are pixel bits having a value of 1, and the remaining third, seventh, eleventh, fifteenth and nineteenth bits are thinning bits having a value of zero.

  In the sub scanning interpolation line image table creation step S212, as in the main scanning interpolation line image table creation step S211, the sub scanning line number L of the image before resolution conversion and the sub scanning line number L of the image after resolution conversion. A sub-scan interpolation line image table is created based on 'and. The pixel bit of the sub-scanning interpolation line image table is a line representing the sub-scanning line image of the image before resolution conversion corresponding to the sub-scanning line image of the image after resolution conversion, and the sub-scanning interpolation line image table is thinned out. A bit is a line representing a sub-scanning line image that is thinned out to an image before resolution conversion by resolution conversion processing.

  Next, the resolution conversion step S220 for performing the resolution conversion process from the high resolution to the low resolution in FIG. 8 will be described. FIG. 10 illustrates the operation in the case of converting the resolution of an image before 602 dpi resolution conversion of 20 pixels in the main scanning into an image after 454 dpi resolution conversion, as in FIG. 9.

  Each bit of the main scanning interpolation line image table has a one-to-one correspondence with the image main scanning pixel before resolution conversion, and a pixel bit (1 value bit) of the line image table corresponds to a pixel of the image after resolution conversion. It corresponds with one. Specifically, the image line image before the resolution conversion is raster scanned from the left end to the right end, and the pixels after the resolution conversion corresponding to the pixel bits of the main scanning interpolation line image table are converted from the pixels before the resolution conversion. The pixel of the image before resolution conversion corresponding to the thinning bit (bit of 0 value) corresponding to the thinning bit (bit of 0 value) in the main scanning interpolation line image table is thinned out as a thinning pixel. Processing (not written in the image after resolution conversion) is performed.

  Further, the resolution conversion process will be described in detail using the flowchart of the resolution conversion process of FIG.

  First, the sub-scan line position N of the image before resolution conversion and the bit position M of the sub-scan interpolation line image table are set to 0th (steps S21 and S22). Next, the image line image before resolution conversion of the Nth row is read (step S23). Further, the Mth bit of the sub-scanning interpolation line image table is determined (step S24). If the M-th bit is a pixel bit (bit value is 1), the read N-th line image before resolution conversion is subjected to the above-described resolution conversion in the main scanning direction based on the main scanning interpolation line image table. Thus, an image line image after the M-th row resolution conversion corresponding to the image line image before the N-th row resolution conversion is obtained (step S25).

  Further, the line image of the image after the Mth line resolution conversion is written into the image after the resolution conversion, and at the same time, 1 is added to the position M of the sub-scanning interpolation line image table (refers to the line of the image after the next resolution conversion). (Step S26). Further, when the Mth bit of the sub-scanning interpolation line image table is an interpolation bit (bit value is 0), the main scanning resolution is not converted.

  Finally, it is determined whether or not all of the image line images before the resolution conversion have been read (step S27). If all of the image line images before the resolution conversion have been read, the resolution conversion process ends. If there is a remainder, 1 is added to the sub-scan line position N of the image before resolution conversion (refers to the line of the image before the next resolution conversion) (step S28), and before the resolution conversion by the above processing Repeat until all the image line images are read out.

  In the resolution conversion process based on the interpolation line image table of the second embodiment, the main scanning resolution conversion process and the sub-scanning resolution conversion process are independent of each other, as in the resolution conversion process of the first embodiment. Since the image processing that is a dimension can be decomposed into two one-dimensional signal processing, the amount of calculation of the image processing is greatly reduced, and high-speed processing becomes possible.

  Further, similarly to the resolution conversion process of the first embodiment, there is a problem that the resolution conversion process results in an image (line width variation) after resolution conversion of two different line widths having a difference of one pixel.

  For example, in a case where resolution conversion is performed from an image before 602 dpi resolution conversion of 10,000 pixels in the main scanning direction to an image after 454 dpi resolution conversion, the image after 454 dpi resolution conversion has 7541 pixels. Also, if there are 1000 black dots of 2 black pixels that are independent of each other among 10000 pixels, the image before 602 dpi resolution conversion maintains the black pixel density of the image quality after resolution conversion to the image after 454 dpi resolution conversion. In order to achieve this, 1508 pixels out of 7541 pixels must be black pixels, so an average of 1.5 pixels for two independent black dots in the image before 602 dpi resolution conversion. In the actual binary image, there are two types of black pixels, one black pixel (matching to an integer by rounding down the decimal point) and two black pixels (matching to an integer by rounding up the decimal point). (The difference in the number of black pixels is 1). Further, since the ratio of the two types of black dots is the ratio of the number of pixels of the image before resolution conversion and the image after resolution conversion, the number of black dots for one black pixel is 50% of the total, The number of black dots of black pixels must be 50% of the total.

  From the above, in the case of resolution conversion processing in which the resolution of the image before resolution conversion and the resolution of the image after resolution conversion are not an integral multiple, the line width of the same image before resolution conversion is one pixel. It is inevitable that the images after resolution conversion of two different line widths having a difference (line width variation problem) will be unavoidable.

  Next, in the case of resolution conversion processing between resolution images that do not become integer multiples as described above, a description will be given of the creation of a line width correction table (step S230 in FIG. 8) for reducing the problem of line width variation.

The line width correction table is a table representing the image line width (number of pixels) after two types of resolution conversion by resolution conversion processing with respect to the line width (number of pixels) of the image before resolution conversion. For example, when converting from 602 dpi to 454 dpi, if the image before resolution conversion has a two-line line width, the number of line width lines in the image after resolution conversion is theoretically 2 × 454 ÷ 602 by resolution conversion. = 1.50
become.

  Therefore, the image after resolution conversion has one pixel line width (probability 50%) and two pixel line widths (probability 50%). Among these, the line width of one pixel is defined as a narrow line width, and the line width of two pixels is defined as a thick line width. A table for obtaining the number of thin line width pixels and the number of thick line width pixels of the image after resolution conversion for each pixel number line width of the image before resolution conversion is a line width correction table.

In the creation of the vertical line width correction table (step S231 in FIG. 8), the number of line width pixels of the image before resolution conversion is n (integer), and the resolution conversion corresponding to the vertical line width of the image before resolution conversion. If the number of vertical line thin line width pixels in the subsequent image is defined as m0 (integer) and the number of thick line width pixels is defined as m1 (integer), m0 and m1 are expressed by the formula (1) as in the first embodiment described above. 4) and equation (5).
m0 = n × W ′ ÷ W (matched to an integer by rounding down the decimal point) (4)
m1 = n × W ′ ÷ W (matched to an integer by rounding up the decimal point) Expression (5)
Note that W ′ is the number of pixels in the main scanning direction of the image after resolution conversion, and W is the number of pixels in the main scanning direction of the image before resolution conversion.

  Here, by increasing n from 1 to W (the total number of pixels of the vertical line width that can exist in the image before resolution conversion) in order, the vertical line is obtained by obtaining m0 and m1 corresponding to n. A width correction table can be created.

  FIG. 12 is an example of a vertical line width correction table in the case of converting a 602 dpi image having a line width of 20 pixels and a vertical line created to 454 dpi image by the above formulas (4) and (5).

In the creation of the horizontal line width correction table (step S232 in FIG. 8), similarly to the creation of the vertical line width correction table, the number of sub-scanning lines L of the image before resolution conversion and the number of sub-scanning lines of the image after resolution conversion. Created using L '. The number of horizontal line width lines of the image before resolution conversion is s (integer), the number of horizontal line thin line width lines of the image after resolution conversion corresponding to the horizontal line width of the image before resolution conversion is p0 (integer), thick If the number of line width lines is defined as p1 (integer), p0 and p1 are determined by equations (6) and (7) as in the first embodiment described above.
p0 = s × L ′ ÷ L (matched to an integer by rounding down the decimal point) (6)
p1 = s × L ′ ÷ L (matched to an integer by rounding up the decimal point) Expression (7)
Note that L ′ is the number of sub-scanning lines of the image after resolution conversion, and L is the number of sub-scanning lines of the image before resolution conversion.

  Here, s is increased in order from 2 to L (the number of all horizontal line width lines in which an image before resolution conversion can exist), and a horizontal line width correction table is created by obtaining p0 and p1 for s. it can.

  The line width correction table represents line widths in which all line widths in which an image before resolution conversion can exist have changed in the image after resolution conversion by resolution conversion processing.

  Finally, the line width correction process (step S240 in FIG. 8) will be described with reference to FIG.

  The line width correction process is a process for equalizing two types of line widths in the image after resolution conversion to one type of line width by resolution conversion processing. Here, a line width correction processing mode 1 in which all the line widths of the image after resolution conversion are made uniform to a thick line width and a line width correction processing mode 2 in which all the line widths are made to be thin line width are defined.

  As shown in FIG. 13, the horizontal line width of the image before resolution conversion is referred to the interpolation line image table of FIG. 9, and the number of pixel bits which are image pixels after resolution conversion in the vertical line width pixels is thinned out. Numbers can be identified. In the case of processing mode 1 in which the line width correction processing is made uniform to a thick line width, if the line width of the image after resolution conversion is a thick line width, the correction processing is not performed and the image after resolution conversion is performed. When the line width is a thin line width, one pixel at the right end of the image line width after resolution conversion is changed to a black pixel to make it a thick line width. On the other hand, in the case of the processing mode 2 in which the line width correction processing is uniformized to the thin line width, if the vertical line width of the image after resolution conversion is the thin line width, the correction processing is not performed and the resolution is not performed. When the line width of the image after conversion is a thick line width, one pixel at the right end of the image line width after resolution conversion is changed to a white pixel to make a vertical line with a thin line width.

  Similar to the vertical line width correction process, the horizontal line width correction process performs a line width correction process on the horizontal line width of the image after resolution conversion by using a horizontal line width correction table. However, the process of changing one pixel by the line width correction process is performed on the lowermost line of the horizontal line of the image after resolution conversion.

It is a flowchart which shows the characteristic of the resolution conversion processing method of the binary image concerning this invention. It is a flowchart for demonstrating the whole flow at the time of performing resolution conversion from a low resolution image to a high resolution image by the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the interpolation line image table used when performing the resolution conversion process from a low image degree image to a high resolution image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the resolution conversion process from the low linear density image of the main scanning direction to the high linear density image in the resolution conversion processing method of the binary image concerning this invention. It is a flowchart which shows the operation | movement of the resolution conversion process from the low image degree image to a high resolution image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing which shows the line | wire width correction table used when the resolution conversion process is carried out from the low linear density image to the high linear density image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the line | wire width correction process of the resolution conversion process from the low linear density image to the high linear density image in the resolution conversion processing method of the binary image concerning this invention. It is a flowchart for demonstrating the whole flow when performing resolution conversion from a high resolution image to a low resolution image by the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the interpolation line image table used when performing the resolution conversion process from a high image degree image to a low resolution image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the resolution conversion process from the high linear density image of a main scanning direction to the low linear density image in the resolution conversion processing method of the binary image concerning this invention. It is a flowchart which shows the operation | movement of the resolution conversion process from the high image degree image to the low resolution image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the interpolation line image table used when performing the resolution conversion process from a high resolution image degree to a low resolution image in the resolution conversion processing method of the binary image concerning this invention. It is explanatory drawing of the line | wire width correction process of the resolution conversion process from the high linear density image to the low linear density image in the resolution conversion processing method of the binary image concerning this invention.

Claims (10)

Calculate the ratio between the number of main scanning pixels of the image before resolution conversion and the number of main scanning pixels of the image after resolution conversion,
Based on the calculated ratio, an interpolation bit representing a pixel in which the number of main scanning pixels of the image after resolution conversion increases from the number of main scanning pixels in the image before resolution conversion, or a thinning bit representing a decreasing pixel is calculated,
The binary image resolution conversion processing method is characterized in that the interpolation bit is the same type of pixel as the pixel on the left and the thinning bit is thinned.   A main scanning interpolation line image table for determining a bit interpolation operation of the resolution conversion image of the main scanning is created, and the main scanning image of the pre-resolution conversion image corresponding to the pixel bit of the main scanning interpolation line image table is 2. The binary image according to claim 1, wherein when the main scanning interpolation line image table is an interpolation bit, a pixel of the same type as the pixel on the left is used, and when it is a thinning bit, a thinning process is performed. Resolution conversion processing method. Calculate the ratio between the number of sub-scanning lines of the image before resolution conversion and the number of sub-scanning lines of the image after resolution conversion,
Based on the calculated ratio, an interpolation bit representing a line in which the number of sub-scanning lines of the image after resolution conversion is increased from the number of sub-scanning lines in the image before resolution conversion or a thinning bit representing a decreasing line is calculated,
A binary image resolution conversion processing method, wherein the interpolation bit is the same type of pixel as the pixel immediately above, and the thinning bit is thinned.   A sub-scanning interpolation line image table for determining a line interpolation operation of the resolution-converted image of the sub-scanning is created, and the sub-scanning image of the pre-resolution conversion image corresponding to the pixel bit of the sub-scanning interpolation line image table is 4. The binary image according to claim 3, wherein when the sub-scanning interpolation line image table is an interpolation bit, the pixel is the same type as the pixel immediately above, and when the sub-scanning interpolation line image table is a decimation bit, a decimation process is performed. Resolution conversion processing method.   The binary image resolution conversion processing method according to claim 1 or 2 that is independent of each other and has no correlation, and the binary image resolution conversion processing method according to claim 3 or 4 are combined. A binary image resolution conversion processing method. Based on the ratio between the calculated number of main scanning pixels of the image before resolution conversion and the number of main scanning pixels of the image after resolution conversion, the number of vertical lines in the image after resolution conversion is rounded down to an integer. Calculate the number of vertical line thin mode line width pixels, which is the number of pixels, and the number of vertical line thick mode line width pixels, which is the number of pixels rounded up to the nearest whole number,
2. The image output by the resolution conversion processing is uniformly processed into a designated line width mode by designating the vertical line thin mode line width or the vertical line thick mode line width. Or a binary image resolution conversion processing method described in 2; Create a vertical line width correction table based on the calculated vertical line thin mode line width pixel number and vertical line thick mode line width pixel number,
7. The processing according to claim 6, wherein the processing is uniformly performed in the line width mode specified by using the vertical line width correction table by designating the vertical line thin mode line width or the vertical line thick mode line width. A resolution conversion processing method of the binary image described. Based on the ratio between the calculated sub-scan line number of the image before resolution conversion and the sub-scan line number of the image after resolution conversion, the number of lines in the image after resolution conversion is converted to an integer by rounding off the decimal part. Calculate the number of horizontal line thin mode line width lines and the number of horizontal line thick mode line width lines that are rounded up to the nearest whole number.
3. The image output by the resolution conversion processing is uniformly processed into a designated line width mode by designating the horizontal line thin mode line width or the horizontal line thick mode line width. 2. A resolution conversion processing method for a binary image described in 1. Create a horizontal line width correction table based on the calculated horizontal line thin mode line width line number and horizontal line thick mode line width line number,
9. The uniform processing according to claim 8, wherein the processing is uniformly performed in the line width mode specified by using the horizontal line width correction table by designating the horizontal line thin mode line width or the horizontal line thick mode line width. Value image resolution conversion processing method.   The binary image resolution conversion processing method according to claim 6 or 7, which is independent of each other and has no correlation, and the binary image resolution conversion processing method according to claim 8 or 9, are combined. A binary image resolution conversion processing method.

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