JP2010109441A - Image processing apparatus, image processing method, image processing program, and storage medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing apparatus for easily performing even pseudo-halftone processing with many output gradations fast and also performing systematized pseudo-halftone processing without depending upon the number of output gradations. <P>SOLUTION: The image processing apparatus which performs pseudo-halftone processing by error diffusion processing on multivalued image data comprising a plurality of density components performs quantization by the plurality of density components, determines a correction value for a quantization output value by comparing the sum of a negative sign of a quantization error of each density component and a quantization error of the each density component with a threshold, and spreads quantization errors after correcting the quantization output value with the correction value. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an image processing method and an image processing apparatus, and in particular, an image for quantizing input data into binary or multi-value data while storing a difference between input image density and output image density by an error diffusion method or the like. The present invention relates to a processing method and an image processing apparatus.

  Conventionally, pseudo-halftone processing is used to represent input multilevel data with multilevels having a level lower than that of binary or input multilevel data. As an example, an error diffusion method is known. This error diffusion method is published in “An Adaptive Algorithm for Spatial Gray Scale” in society for Information Display 1975 Symposium Digest of Technol, 75. In this method, the pixel of interest is P, the density of the pixel is v, and the densities of pixels P0, P1, P2, and P3 existing around P before binarization are v0, v1, v2, and v3, respectively. The threshold for binarization is set to T, and the weighting factors W0, W1, W2, and W3 obtained empirically are used to weight the binarization error E at the point of interest P, and the surrounding pixels P0, P1, P2, Allocate to each of P3. That is, this method is a method of making the average density of the output image equal to the density of the input image in a macro manner. At this time, if the output binary data is o, errors E0, E1, E2, and E3 with respect to the surrounding pixels P0, P1, P2, and P3 can be obtained by the following formula.

If v ≧ T, o = 1, E = v−Vmax
If v <T, o = 0, E = v-Vmin
(However, Vmax: maximum density, Vmin: minimum density)
E0 = E × W0;
E1 = E × W1;
E2 = E × W2;
E3 = E × W3;
(Examples of weighting factors: W0 = 7/16, W1 = 1/16, W2 = 5/16, W3 = 3/16)
By the way, in a color ink jet printer or the like, when outputting multi-value image data using inks such as cyan (C), magenta (M), yellow (Y), and black (K), an error diffusion method or the like is independently used for each color. Used to perform pseudo halftone processing. For this reason, when one color is seen, even if it looks high in quality, if two or more colors overlap, a good image quality may not always be obtained.

  In order to improve this problem, in Japanese Patent Application Laid-Open Nos. 8-279920 and 11-10918, an error diffusion method is used by combining two or more colors, so that even when two or more colors overlap, An excellent pseudo halftone processing method is disclosed.

  Japanese Patent Laid-Open No. 9-139841 discloses a method in which the same effect as described above can be obtained by correcting the output value by summing up the input values after performing pseudo-middle gradation processing on two or more colors independently. It is disclosed.

  In particular, it is effective to perform image processing so that the dots of the cyan (C) component and the magenta (M) component do not overlap each other in order to reduce the graininess in the middle density region of the color image. The following method is used.

  FIG. 9 is an explanatory view for explaining a conventional color image processing method.

  In FIG. 9, the image data of a color image is expressed by multi-value data in which each pixel and each density component (YMCK) are 8 bits (gradation value is 0 to 255). The density value Ct of the cyan (C) component and the density value Mt of the magenta (M) component of an arbitrary pixel of interest in the image data are given by the following equations, where C and M are the density values of the original image: It is represented by Here, Ce and Me are values obtained by error diffusion with respect to the target pixel of the C component and the M component, respectively.

Ct = C + Ce
Mt = M + Me
In a conventional color image processing method, the following four types of image processing control are performed according to the density of the C component and M component of the target pixel.

  1. When (Ct + Mt) is equal to or less than the threshold Th1, that is, when it belongs to the region 1 shown in FIG. 9, dot recording is not performed.

  2. If (Ct + Mt) exceeds the threshold value Th1, and (Ct + Mt) is not more than another threshold value Th2 and Ct> Mt, that is, if it belongs to the region 2 in FIG. Make a record.

  3. If (Ct + Mt) exceeds the threshold value Th1, and (Ct + Mt) is equal to or less than another threshold value Th2 and Ct ≦ Mt, that is, belongs to the region 3 in FIG. Make a record.

4). When (Ct + Mt) exceeds another threshold value Th2, that is, when it belongs to the region 4 in FIG. 9, dot recording is performed using C ink and M ink. It is assumed that Th1 <Th2.
JP-A-8-279920 Japanese Patent Laid-Open No. 11-10918 Japanese Patent Laid-Open No. 9-139841

  However, the above-described conventional image processing method has a problem in that as the number of gradations increases when quantization is performed, the determination formula becomes more complicated and the processing time becomes longer. For example, the following equation shows an algorithm when cyan and magenta are quantized to three values by a conventional image processing method.

Ct = C + Ce
Mt = M + Me
Cout = 0
Mout = 0
if (Ct + Mt> Threshold1)
if (Ct + Mt <Threshold2)
if (Ct> Mt)
Cout = 1
else
Mout = 1
else
if (Ct + Mt <Threshold3)
if (Ct> Mt + Const1)
Cout = 2
else
if (Mt <Ct + Const1)
Mout = 2
else
Cout = 1
Mout = 1
else
if (Ct + Mt <Threshold4)
if (Ct> Mt)
Cout = 2
Mout = 1
else
Cout = 1
Mout = 2
else
Cout = 2
Mout = 2
As described above, even when cyan and magenta are quantized to ternary values, the processing becomes complicated, and when quantizing to more gradations, the processing becomes more complicated.

  The present invention has been made in view of the above problems, and can be processed at high speed and easily even in pseudo halftone processing (halftone) with a larger number of output gradations, and does not depend on the number of output gradations. An object of the present invention is to provide an image processing method and apparatus capable of performing unified pseudo-halftone processing.

  In order to achieve the above object, an image processing apparatus of the present invention is an image processing apparatus comprising error diffusion processing means for performing pseudo halftone processing on multi-value image data composed of a plurality of density components, wherein the plurality of density Perform quantization for each component, determine the correction value based on the comparison result of the quantization error negative sign of each density component, the number of quantization error negative signs near the pixel of interest, random numbers, and the quantization error of each density component, After the quantization output value is corrected, the quantization error is diffused.

  As described above, according to the first aspect, since the correction area is determined by matching the negative sign of the quantization error and comparing the sum of the quantization errors with the threshold value, the correction area can be easily determined. The correction value can be easily generated. Further, by determining the priority order of the color planes to be corrected based on the quantization error negative number of neighboring pixels, efficient dot distribution can be achieved with a small memory capacity. Furthermore, when the quantization error negative signs of neighboring pixels are the same, the connection of the same color dots can be eliminated by determining the priority order with a random number. Since these processes do not depend on the number of quantization levels, they can be easily expanded to N-value error diffusion. Furthermore, as described above, expansion to three or more colors is also possible.

  Further, according to the second aspect, even if the threshold value is sequentially changed for each color plane, the correction area is determined by matching the negative sign of the quantization error and comparing some bits of the quantization error. The correction area can be easily determined, and correction value generation is facilitated.

  Further, according to the third aspect, since the correction area determination method is switched according to the input density value, it is possible to easily obtain the optimum overlap between colors with little graininess.

  Next, details of the present invention will be described in accordance with the description of the embodiments.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram illustrating the configuration of an image processing apparatus according to the first embodiment of the present invention. Here, for convenience, it is assumed that processing is performed on two color planes of cyan and magenta.

  In FIG. 1, reference numerals 1 and 2 denote adders, which add correction values from the diffusion filters 3 and 4 to pixel values C and M of the input image, respectively. Numerals 3 and 4 denote diffusion filters, which are obtained by multiplying the quantization error of the quantization error diffusion target pixel by the corresponding diffusion coefficient to obtain a sum, and adders 1 and 2 as the correction values are pixels of the next input image. Add to each value. Reference numerals 5 and 6 denote subtractors, which generate quantization errors by subtracting the quantized representative values output from the inverse quantizers 17 and 18 from the pixel values of the input image corrected by the diffusion filters 3 and 4. Are respectively input to the diffusion filters 3 and 4. Reference numerals 7 and 8 denote subtracters, which subtract the quantized representative values output from the inverse quantizers 13 and 14 from the pixel values of the input image corrected by the diffusion filters 3 and 4 before the quantization code correction. Quantization errors are generated and input to the correction area determination unit 19, respectively. Reference numerals 9 and 10 denote adders, which respectively add the threshold value Th generated by the threshold value generation unit 24 to the pixel value of the corrected input image. Reference numerals 11 and 12 denote quantizers that quantize the input pixel values to which the threshold value Th is added in a predetermined quantization step. Reference numerals 13 and 14 denote inverse quantizers that convert the output codes of the quantizers 11 and 12 into quantized representative values, respectively. Reference numerals 15 and 16 denote adders that correct the output code by adding the correction values output from the correction value generator 20. Reference numerals 17 and 18 denote inverse quantizers that convert the corrected output codes into quantized representative values, respectively. Reference numeral 19 denotes a correction area determination unit, which determines whether the correction area is inside or outside from the negative sign of the quantization error before correction and the threshold value Th. If the correction area is determined, the vicinity output from the negative number counting units 21 and 22 The negative numbers of the pixels are compared, and the determination result is input to the correction value generation unit 20. A correction value generation unit 20 generates a correction value from the output codes of the quantizers 11 and 12, the determination result of the correction region determination unit 19, and the random number from the random number generation unit 23, and the adder 15 , 16. Reference numerals 21 and 22 denote negative number counting units, which count the number of negative signs of quantization errors in the vicinity of the target pixel (that is, the number of pixels in which the quantization error is negative). A random number generator 23 generates a 1-bit random number by using a known random number generation method such as an M-sequence random number using LFSR (Linear Feedback Shift Register). Reference numeral 24 denotes a threshold value generator, which varies the threshold value based on the number of negative signs of neighboring pixels output from the negative number counters 21 and 22, for example.

  First, the quantization process will be specifically described. Hereinafter, specific numerical values when the present invention is applied to ternary error diffusion will be described.

  The threshold value Th is added to the input pixel data corrected by the peripheral quantization error by the adders 9 and 10, respectively. Here, if the quantization characteristics of the quantizers 11 and 12 are linear and the input pixel value is x, the output code c and the quantized representative value r are as follows.

When x <64, c = 0, r = 0
When 64 ≦ x <192, c = 1, r = 128
When 192 ≦ x, c = 2, r = 255
That is, 64 is added to the input pixel as the threshold Th, and if the carry and MSB are extracted, the output code c is obtained, and if the lower 7 bits are padded with 0, the quantized representative value r is obtained. However, since the quantized representative value r = 256 generated by the above method when the output code c = 2 exceeds the range of the input pixel data, the quantized representative value when the carry is 1 is set to 1 for all bits. (Ie 255). As a result, the quantizers 11 and 12 and the inverse quantizers 13, 14, 17, and 18 are simplified. It should be noted that in order to eliminate “creaking” and “texture”, even when the threshold value is changed, it can be dealt with by similarly adding the threshold value Th.

  Next, specific correction processing will be described in detail with reference to the flowchart of FIG. FIG. 2 is a flowchart for explaining a correction process according to the first embodiment of the present invention. In the following description, it is assumed that the color plane to be corrected is two colors C and M, the quantization error before correction is Cer and Mer, and the numbers of negative quantization error of neighboring pixels are Css and Mss.

  In step S1, quantization errors Cer and Mer are obtained from the pixel values Ct and Mt of the input image corrected by the diffusion filters 3 and 4. In step S2, it is determined whether or not the negative signs of the quantization errors Cer and Mer match. If the negative signs do not match, it is determined that the output code is not corrected, the correction value is set to 0, and the process proceeds to step S23. In step S23, after the determined correction value is added to the output code, a quantization error is obtained, the quantization error is diffused to surrounding pixels, and the process proceeds to step S24. In step S24, it is determined whether or not all pixels have been processed. If all pixels have been processed, the process ends. If all pixels have not been processed, the next pixel is processed. Return to S1.

  On the other hand, if it is determined in step S2 that the negative signs of the quantization errors Cer and Mer match, the process proceeds to step S3. In step S3, it is determined whether the sum of the quantization errors Cer and Mer exceeds the threshold value Th, or whether the sum of the quantization errors Cer and Mer is less than a value Th−Qs obtained by subtracting the quantization step width Qs from the threshold value Th. If the above condition is not satisfied, it is determined that the output code is not corrected, the correction value is set to 0, and the process proceeds to step S23. If the above condition is satisfied, the process proceeds to step S4. In step S4, the numbers Css and Mss of quantization error negative signs of neighboring pixels are obtained for each color plane, and the process proceeds to step S5. In step S4, it is determined whether or not the numbers Css and Mss of the quantization error signs of the neighboring pixels are equal. If Css ≠ Mss, the process proceeds to step S6, and if Css = Mss, the process proceeds to step S7. In step S6, the quantization error negative numbers Css and Mss of the neighboring pixels are compared. If Css> Mss, the process proceeds to step S8, and if Css ≦ Mss, the process proceeds to step S9. In step S7, a 1-bit random number is generated. If the random number is 0, the process proceeds to step S8. If the random number is 1, the process proceeds to step S9. In step S8, the negative sign of the quantization error Cer is detected. If the negative sign is 1, the process proceeds to step S12, and if the negative sign is 0, the process proceeds to step S14. Similarly, in step S9, the negative sign of the quantization error Cer is detected. If the negative sign is 1, the process proceeds to step S10, and if the negative sign is 0, the process proceeds to step S16. Through the above series of operations, the priority order of the color plane to be corrected and the negative sign of the correction value are determined.

  In steps S10, S11, S12, and S13, since the quantization output is decremented by -1 in steps S18 and S19, it is checked whether the quantization output value before correction is the minimum value 0 or not. If the quantized output value of a color plane having a high correction priority is the minimum value, the correction target is moved to the next priority color plane without correcting the color plane. If the quantized output values of all the color planes are the minimum value, the process proceeds to step S23 with no correction.

  On the other hand, in steps S14, S15, S16, and S17, since the quantization output is incremented by 1 in steps S21 and S22, it is checked whether the input pixel value is 0 or the quantization output value before correction is the maximum value. If the input pixel value of a color plane having a high correction priority is 0 or the quantized output value before correction is the maximum value, the color plane is corrected to the next priority color plane without correction. Move the subject. If the input pixel values of all color planes are 0 or the quantized output value before correction is the maximum value, the process proceeds to step S23 with no correction. Here, the reason for checking whether the input pixel value is 0 is to prevent generation of corrected dots in a portion where dots should not be generated.

  When the correction process in steps S18, S19, S20, S21, and S22 ends, the process proceeds to step S23. In step S23, after the determined correction value is added to the output code, a quantization error is obtained, the quantization error is diffused to surrounding pixels, and the process proceeds to step S24. In step S24, it is determined whether or not all pixels have been processed. If all pixels have been processed, the process ends. If all pixels have not been processed, the next pixel is processed. Return to S1.

  Next, a specific correction processing determination method will be described in detail using the flowchart of FIG.

  In steps S2 and S3, a determination is made of an area that does not require correction. For example, the negative sign of the quantization error does not match in areas that do not require correction, such as areas 2 and 3 in FIG. Accordingly, by examining the coincidence of the quantization error negative sign before correction, it is possible to determine a portion that does not require correction. Further, when both of the quantization error negative signs are positive, dots are added to a region where the sum of the quantization errors exceeds the threshold Th, and therefore no correction is required when the sum of the quantization errors is equal to or less than the threshold Th. On the other hand, when the quantization error negative signs are both negative, the dots in the region where the sum of the quantization errors is less than the threshold value -Th are deleted. Therefore, if the sum of the quantization errors is greater than or equal to the threshold value -Th, correction is unnecessary. Become. In step S3, the absolute value of the sum of the quantization errors is used because the processing is commonly performed regardless of the quantization error negative sign. However, when processing is performed for each quantization error negative sign, steps S3, S5, S6, and so on are used. By performing S7 for each quantization error sign, steps S8 and S9 become unnecessary.

  Next, the priority order of the color planes to be corrected will be described.

  In error diffusion, when a dot is generated, the negative sign of the quantization error is inverted. Accordingly, in a visually important highlight portion, no dot is generated in a region where pixels where the negative sign of the quantization error is 0 are continuous. That is, the presence or absence of dots in the vicinity of the predetermined range can be determined by checking the number of quantization error negative signs in the predetermined range. In particular, since error quantization diffuses the quantization error two-dimensionally, the quantization error negative sign also diffuses two-dimensionally, and the presence / absence of dots in the range exceeding the predetermined range can be determined. Further, since the quantization error negative sign is 1 bit, a wide range of dot detection can be performed with a very small memory capacity. Therefore, it is possible to determine a color plane with a small number of dots by obtaining and comparing the number of quantization error negative signs of neighboring pixels for each color plane. In other words, the priority order of the smaller quantization error negative number of the neighboring pixels is increased. When the quantization error negative signs of neighboring pixels are the same, it is determined that both color planes are the same, and the priority is determined by a random number.

  Similarly, in the shadow portion, no void (a blank portion where no dot is formed) is not generated in a region where pixels where the negative sign of the quantization error is 1 are continuous. That is, the presence or absence of a void near the predetermined range can be determined by examining the number of quantization error negative signs in the predetermined range. In particular, in error diffusion, the quantization error is two-dimensionally diffused, so that the quantization error negative sign is also two-dimensionally diffused, and the presence / absence of voids in a range exceeding the predetermined range can be determined. Therefore, a color plane with a small number of voids can be determined by obtaining and comparing the number of quantization error negative signs of neighboring pixels for each color plane. That is, the priority order of the one with the larger quantization error negative number of the neighboring pixels is increased. When the quantization error negative signs of neighboring pixels are the same, it is determined that both color planes are the same, and the priority is determined by a random number.

  Next, the correction value will be described.

  As described above, when both of the quantization error negative signs are positive, the quantization output of any color plane is incremented by 1 in order to add a dot. At this time, as described above, the quantized output of the portion where the input pixel value is 0 is not corrected. Therefore, when the input pixel value of a color plane having a high correction priority is 0, the correction target is moved to the color plane having the next priority. Further, even when the quantized output value before correction is the maximum value, if the value is incremented by 1, the range of the quantized output value is exceeded, so correction is not performed. Also in this case, the correction target is moved to the next priority color plane. When it is determined that all the color planes to be corrected are not corrected, no correction is performed even in an area where correction is required.

  Similarly, when both of the quantization error negative signs are negative, the quantization output of any color plane is decreased by −1 in order to delete the dot. At this time, if the quantized output value before correction is the minimum value (usually 0), since it exceeds the range of the quantized output value by -1, correction is not performed. In this case, the correction target is moved to the next priority color plane. When it is determined that all the color planes to be corrected are not corrected, no correction is performed even in an area where correction is required.

  In order to perform correction processing on three or more color planes, steps S3 and S6 are expanded. For example, in the case of three colors, the correction determination for all three colors and the correction determination for any two colors are performed, the correction values are tabulated for each plane, the absolute value of the correction values is limited to 1 or less, and then the correction is performed. Do. Priorities among the three colors are determined by comparing the quantization error negative numbers of neighboring pixels. If all three colors match, the random number is determined. However, in this case, the random number is 2 bits. However, random numbers that are not assigned to the color plane are also generated.In this case, generation of random numbers is repeated until the number assigned to the color plane is obtained, or a ternary counter is separately provided and assigned to the color plane. If no random number is generated, the value of the ternary counter may be used.

  As described above, according to the first embodiment of the present invention, the correction region is compared by matching the negative sign of the quantization error and comparing the sum of the quantization errors with a threshold value or a value obtained by subtracting the quantization step width from the threshold value. Since the determination is performed, the correction area can be easily determined, and at the same time, the negative sign of the correction value is determined. Further, by determining the priority order of the color planes to be corrected based on the quantization error negative number of neighboring pixels, efficient dot distribution can be achieved with a small memory capacity. Furthermore, when the quantization error negative signs of neighboring pixels are the same, the connection of the same color dots can be eliminated by determining the priority order with a random number. Since these processes do not depend on the number of quantization levels, they can be easily expanded to N-value error diffusion. Furthermore, as described above, expansion to three or more colors is also possible.

  FIG. 3 is a block diagram illustrating the configuration of an image processing apparatus according to the second embodiment of the present invention. In the figure, 25 and 26 are threshold generation units, 27 is a correction area determination unit, and 28 and 29 are negative number counting units. Only the parts different from the first embodiment will be described below.

  In the present embodiment, the threshold value generators 25 and 26 can control the overlap between colors even if the threshold value is sequentially changed for each color plane.

  The threshold value control units 25 and 26 change the threshold value in order to eliminate “arrival” (dot generation delay) and “texture” that are causes of image quality degradation due to error diffusion. Ordinarily, “creeping” appears prominently at the edge portion adjacent to the input value near the quantized representative value. Therefore, a method is known in which dot generation is accelerated (delayed) by changing the threshold value in a direction approaching the quantized representative value according to the difference from the quantized representative value closest to the input value. In the present embodiment, since there are input values of a plurality of colors, the effect differs depending on which input value is used.

  For example, when the threshold value is changed by the sum C + M of the pixel values of the input image, “arrival” with respect to the total dots of the two planes is reduced, but “occurring only in a certain color plane” is It is difficult to eliminate “getting together”. In other words, even if the input value of a certain color plane is very close to the quantized representative value, if the input value of the other color plane is sufficiently far from the quantized representative value, the amount of variation in the threshold will be reduced. The effect will fade.

  In addition, if the threshold value is changed using the input value with the smaller distance from the quantized representative value, the “rushing” of a specific color plane can be effectively eliminated, but for other color planes There may be overcompensation.

  Therefore, the quantization error negative number in the vicinity of the target pixel is counted for each color plane, and the threshold value is changed based on the count value, thereby eliminating the above-mentioned “choke up” and “texture”. For example, as described above, no dot is generated in a region where pixels where the negative sign of the quantization error is 0 are continuous. In other words, such a region has a possibility of “crushing”, so the threshold value is lowered and dot generation is accelerated. Similarly, no void (blank) is generated in a region where pixels where the negative sign of the quantization error is 1 are continuous. In other words, since such a region may have a “cracking” of voids (blanks), the threshold value is increased to delay dot generation. Since the density of output dots varies depending on the input density value, it is desirable to change the size (shape) of the window for detecting a quantization error negative sign depending on the input density value.

  FIG. 4 shows an example of changing the window for detecting the negative sign of the quantization error at each concentration. From the highlight to the intermediate density, the range narrows as shown in (A) → (B) → (C) → (D). On the contrary, from the intermediate density to the shadow portion, (D) → The range is expanded as (C) → (B) → (A). In the case of multilevel error diffusion, the range narrows from the representative value of low density toward the median value of the quantization step, and the range is expanded from the median value of the quantization step to the representative value of the next density. . In this way, control is performed so that the fluctuation of the window for detecting the negative sign is repeated at each quantization step.

  If such threshold control is performed for each color plane, it is difficult to use the threshold for correction area determination. Therefore, in this embodiment, the correction area determination is performed only by the quantization error without using the threshold value.

  FIG. 5 is a flowchart for explaining a correction method of the image processing apparatus according to the second embodiment of the present invention. In the figure, parts that perform the same operations as in FIG. Hereinafter, only different portions from FIG. 2 will be described.

  If it is determined in step S2 that the negative signs of the quantization errors Cer and Mer match, the correction area is determined in step S25.

  In step S25, predetermined bits of the quantization errors Cer and Mer are extracted, and correction regions (regions classified by priority of color planes to be corrected and negative signs of correction values) are determined based on the extracted bits. For example, when each quantization step width of the quantization errors Cer and Mer is divided into four regions by 2 bits extracted from the quantization errors Cer and Mer (see FIG. 6A), the extracted bits are 01 or 10 Is the region 0, the Cer side of the extracted bit is 01 and the Mer side is 00, the region 1 is the extracted bit, the Cer side of the extracted bit is 00 and the Mer side is 01, the region 2 is the Cer of the extracted bit If the side is 10 and the Mer side is 11, the region 3 is set. If the Cer side of the extracted bit is 11 and the Mer side is 10, the region 4 is set. Further, when each quantization step width of the quantization errors Cer and Mer is divided into 8 regions by 3 bits extracted from the quantization errors Cer and Mer (see FIG. 6B), the upper 2 bits of the extracted bits Is the region 0, the Cer side of the extracted bit is 011 or the Cer side of the extracted bit is 010 and the Mer side is 001, is the region 1 and whether the Mer side of the extracted bit is 011? If the extracted bit Cer side is 001 and the Mer side is 010, the region 2; if the extracted bit Cer side is 100 or the extracted bit is the Cer side 101 and the Mer side is 110, the region 3; the extracted bit Mer side is If the extracted bit is 110 on the Cer side and 101 on the Mer side, it is determined as the region 4.

  In general, in the area where the MSB of the extracted bits is 0, bits other than the MSB are added, and there is no correction unless the MSB of the addition result is 1 and all the bits other than the MSB are 1. If the MSB as a result of the addition is 1 or all the bits other than the MSB are 1, and if the extracted bits match, the area 0 is set, and comparison is made with all the bits other than the MSB of the extracted bits. If the side is large, the region 1 is determined. If the Mer side is large, the region 2 is determined. Similarly, when the MSB of the extracted bit is 1, the bits other than the MSB are added. When the MSB as the result of addition is 1, no correction is performed. When the MSB of the result of addition is 0, the extracted is performed. If the bits match, the area is set to 0, and all the bits other than the MSB of the extracted bits are compared, and if the Cer side is large, the area 4 is determined, and if the Mer side is large, the area 3 is determined.

  If it is determined in step S25 that there is no correction, the process proceeds to step S20. If it is determined as area 0, the process proceeds to step S7. If it is determined as area 1, the process proceeds to step S16. The process proceeds to step S14. If it is determined that the region 3 is determined, the process proceeds to step S12. If it is determined that the region 4 is determined, the process proceeds to step S10.

  As described above, according to the second embodiment of the present invention, even if the threshold value is sequentially changed for each color plane, the correction region is obtained by matching the negative sign of the quantization error and comparing some bits of the quantization error. Therefore, the correction area can be easily determined, and the negative sign of the correction value is determined at the same time.

  FIG. 7 is a block diagram illustrating the configuration of an image processing apparatus according to the third embodiment of the present invention. In the figure, reference numeral 30 denotes a correction area determination unit. Hereinafter, only different parts from the first and second embodiments will be described.

  In the present embodiment, the optimum overlap between colors can be controlled by switching the correction area determination method of the first and second embodiments according to the input density value. Therefore, not only the threshold value of each color plane but also the input density value of each color plane is input to the correction area determination unit 30. A specific determination method will be described with reference to the flowchart of FIG.

  FIG. 7 is a flowchart for explaining a correction method of the image processing apparatus according to the third embodiment of the present invention. In the figure, parts that perform the same operations as in FIGS. 2 and 5 are given the same reference numerals. Hereinafter, only different portions from FIGS. 2 and 5 will be described.

In FIG.
In step S26, the sum of the quantization errors Cer and Mer exceeds the average of the two thresholds Th ′ = (Th_m + Th_c) / 2, or the sum of the quantization errors Cer and Mer is quantized from the average Th ′ of the two thresholds. It is determined whether or not the value Th−Qs is less than the value Th−Qs obtained by subtracting the step width Qs. If the above condition is not satisfied, it is determined that the output code is not corrected. If yes, go to Step S27. In step S27, it is determined whether or not the absolute value of the sum of the input density values C and M is less than a predetermined value α. If the absolute value of the sum is less than the predetermined value α, the process proceeds to step S4, where the absolute value of the sum is If it is greater than or equal to the predetermined value α, the process proceeds to step S25. That is, when the input density values of two colors are close, the area is determined by the negative sign of the input error of neighboring pixels, and when the difference between the input density values of the two colors is large, the area is divided by the quantization error of the two colors. Do it. This is because when the input densities of the two colors are close (when a light blue image is input), the dots may appear to be separated for each color. Dispersion between color planes is better when the area is divided. On the other hand, when the difference between the input density values of the two colors is large (when a magenta image is input), the other dot is inadvertently applied to the highlight portion of the low-density color plane. Sometimes. As described above, when there is an optimum region determination method depending on the input density, it is better to select the optimum region determination method according to the input density. Even if such switching is performed, feedback is applied in error diffusion, so the macro density does not change.

  As described above, according to the third embodiment of the present invention, the optimum overlap between colors can be easily obtained by switching the correction area determination method according to the input density value.

[Other Embodiments]
A storage medium storing software program codes for realizing the functions of the above-described embodiments is supplied to a system or apparatus, and a computer (or CPU or MPU) of the system or apparatus stores program codes stored in the storage medium. The object of the present invention is also achieved by executing the reading.

  In this case, the program code itself read from the storage medium realizes the novel function of the present invention, and the storage medium storing the program code constitutes the present invention.

  As a storage medium for supplying the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

  In addition to executing the program code read out by the computer, the functions of the above-described embodiments are realized, and an OS running on the computer is part of the actual processing based on an instruction of the program code. Alternatively, the functions of the above-described embodiment can be realized by performing all of them and performing the processing.

  Further, after the program code read from the storage medium is written to a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. The CPU of the board or the function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments can also be realized by the processing.

  The present invention also applies to a case where the program is distributed to a requester via a communication line such as personal computer communication from a storage medium in which a program code of software that realizes the functions of the above-described embodiments is recorded. Applicable.

FIG. 1 is a block diagram illustrating a configuration of an exemplary image processing apparatus according to the first embodiment. FIG. 2 is a flowchart for explaining the correction processing according to the first embodiment. FIG. 3 is a block diagram illustrating a configuration of an exemplary image processing apparatus according to the second embodiment. FIG. 4 is an example of changing the window for detecting the negative sign of the quantization error at each concentration. FIG. 5 is a flowchart for explaining the correction processing according to the second embodiment. FIG. 6 is a diagram illustrating a determination region for correction processing according to the second embodiment. FIG. 7 is a block diagram illustrating a configuration of an exemplary image processing apparatus according to the third embodiment. FIG. 8 is a flowchart for explaining the correction processing according to the third embodiment. FIG. 9 is a diagram for explaining a conventional correction process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Adder 2 Adder 3 Spread filter 4 Spread filter 5 Subtractor 6 Subtractor 7 Subtractor 8 Subtractor 9 Adder 10 Adder 11 Quantizer 12 Quantizer 13 Inverse quantizer 14 Inverse quantizer 15 Addition Device 16 Adder 17 Inverse quantizer 18 Inverse quantizer 19 Correction area determination unit 20 Correction value generation unit 21 Negative sign count unit 22 Negative sign count unit 23 Random number generation unit 24 Threshold generation unit 25 Threshold generation unit 26 Threshold Generation unit 27 Correction region determination unit 28 Negative sign count unit 29 Negative sign count unit 30 Correction region determination unit

Claims (28)

Means for inputting image data of multiple colors;
A means for weighting a quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization means for quantizing the input image data to which the quantization error is added;
First quantization error calculation means for calculating a plurality of quantization errors generated by the quantization processing of the quantization means;
The correction of the quantization output is determined by comparing the negative sign of the calculated plurality of quantization errors and the sum of the plurality of quantization errors with a quantization threshold value or a value obtained by subtracting the quantization step width from the quantization threshold value. A determination means;
Means for detecting the number of negative quantization error of neighboring pixels for each of the plurality of colors;
Determining means for determining a correction region based on the number of negative quantization errors of the neighboring pixels;
Means for determining priority and correction value of the color to be corrected for each correction region;
Means for correcting the quantized output by the correction value;
Second quantization error calculation means for calculating a quantization error from the corrected quantization output,
An image processing apparatus, wherein error diffusion processing is performed using an error generated by the second quantization error calculating means.   The image processing apparatus according to claim 1, wherein the quantization output is corrected when negative signs of quantization errors of two or more colors coincide with each other.   The correction value of the quantization output is +1 or 0 when the negative sign of the matched quantization error is positive, and is -1 or 0 when the negative sign of the matched quantization error is negative. The image processing apparatus according to claim 2.   The correction value of the quantization output is a value in a color plane in which the negative sign of the quantization error matches when the sum of the quantization errors before correction exceeds the threshold when the negative sign of the matched quantization error is positive. , The priority of correction is determined in ascending order of the number of negative signs of quantization error, and if the negative sign of the matched quantization error is negative, the sum of quantization errors before correction is set to the quantization step width Qs from the threshold Th. 4. The correction priority order is determined in descending order of the number of negative signs of quantization error in a color plane in which the negative signs of quantization error coincide with each other when less than the subtracted value Th-Qs. An image processing apparatus according to 1.   5. The image processing apparatus according to claim 4, wherein when the negative numbers of the quantization errors are equal, priority is determined by a random number. Means for determining whether the input density is 0;
Means for determining whether the quantized output value is a maximum value;
If the negative sign of the matched quantization error is positive, it is determined whether the input density value is not 0 and the quantized output value is not the maximum value from the higher priority, and the above condition is satisfied first. The image processing apparatus according to claim 3, wherein the color correction value is +1.   The image processing apparatus according to claim 6, wherein when there is no color satisfying the condition, correction is not performed. Means for determining whether the quantized output value is a minimum value;
When the negative sign of the matched quantization error is negative, it is determined whether the quantized output value is not the minimum value from the higher priority order, and the correction value of the color satisfying the above condition is first set to -1. The image processing apparatus according to claim 3.   The image processing apparatus according to claim 8, wherein no correction is performed when there is no color satisfying the condition.   The image processing apparatus according to claim 1, wherein the plurality of colors of image data are cyan and magenta.   The image processing apparatus according to claim 1, wherein the plurality of colors of image data are cyan, magenta, and yellow. Means for inputting image data of multiple colors;
A means for weighting a quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization means for quantizing the input image data to which the quantization error is added;
First quantization error calculation means for calculating a plurality of quantization errors generated by the quantization processing of the quantization means;
Bit extraction means for extracting predetermined bits from the plurality of quantization errors;
A region determination unit that determines a correction region of the quantization output by comparing the calculated negative sign of the plurality of quantization errors and the bit string extracted by the bit extraction unit;
Means for determining priority and correction value of the color to be corrected for each correction region;
Means for correcting the quantized output by the correction value;
Second quantization error calculation means for calculating a quantization error from the corrected quantization output,
An image processing apparatus, wherein error diffusion processing is performed using an error generated by the second quantization error calculating means.   The image processing apparatus according to claim 12, wherein the quantization output is corrected when negative signs of quantization errors of two or more colors coincide with each other.   The correction value of the quantization output is +1 or 0 when the negative sign of the matched quantization error is positive, and is -1 or 0 when the negative sign of the matched quantization error is negative. The image processing apparatus according to claim 13.   13. The image processing apparatus according to claim 12, wherein the bit extraction unit extracts bits that divide the quantized representative values into powers of 2.   In the region where the MSB of the extracted bit string is 0, a bit string other than the MSB is added, and the correction value is 0 unless the MSB of the result of addition is 1 or all the bits other than the MSB are 1, and the addition If the resulting MSB is 1 or all the bits other than the MSB are 1 and the extracted bit strings match, the area 0 is set, and comparison is made with all the bits other than the MSB of the extracted bit string. The image processing apparatus according to claim 15, wherein the image processing apparatus corrects by determining as a region 1 if the color side is large, and as a region 2 if the second color side is large.   In the region where the MSB of the extracted bits is 1, a bit string other than the MSB is added. When the MSB of the result of addition is 1, the correction value is 0, and when the MSB of the result of addition is 0, If the extracted bit strings match, the area 0 is set, and all bits other than the MSB of the extracted bits are compared. If the first color side is large, the area 4 is determined, and if the second color side is large, the area 3 is determined and corrected. The image processing apparatus according to claim 15.   18. The image processing apparatus according to claim 16, wherein when the area is determined to be 0, the priority order is determined by a random number. Means for determining whether the input density is 0;
Means for determining whether the quantized output value is a maximum value;
When it is determined that the region 1 is determined, it is determined whether the input density value is not 0 and the quantized output value is not the maximum value in the order of the first color and the second color, and the above condition is satisfied first. If the color correction value is set to +1 and the area 2 is determined, it is determined whether the input density value is not 0 and the quantized output value is not the maximum value in the order of the second color and the first color. The image processing apparatus according to claim 16, wherein a correction value of a color that first satisfies the above condition is set to +1. Means for determining whether the quantized output value is a minimum value;
When it is determined as the region 3, it is determined whether the quantized output value is not the minimum value in the order of the first color and the second color, and the correction value of the color satisfying the above condition is first set to -1. If it is determined that the region 4 is determined, it is determined whether the quantized output value is not the minimum value in the order of the second color and the first color, and the correction value of the color satisfying the above condition is first set to -1. The image processing apparatus according to claim 17.   21. The image processing apparatus according to claim 19, wherein correction is not performed when there is no color that satisfies the above condition.   The image processing apparatus according to claim 12, wherein the plurality of colors of image data are cyan and magenta. Means for inputting image data of multiple colors;
A means for weighting a quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization means for quantizing the input image data to which the quantization error is added;
First quantization error calculation means for calculating a plurality of quantization errors generated by the quantization processing of the quantization means;
Threshold averaging means for obtaining an average value of quantization thresholds in the plurality of quantization means;
By comparing the negative sign of the calculated plurality of quantization errors and the sum of the plurality of quantization errors with an average value of the quantization threshold or a value obtained by subtracting the quantization step width from the average value of the quantization threshold. A determination means for determining correction of the quantized output;
A quantization error negative number detection means for detecting the number of quantization error negative signs of neighboring pixels for each of the plurality of colors;
First correction region determination means for determining a correction region based on the calculated negative number of quantization errors and the number of quantization error negative signs of the neighboring pixels;
Bit extraction means for extracting predetermined bits from the plurality of quantization errors;
A second correction area determination means for determining a correction area of the quantization output by comparing the calculated negative sign of the plurality of quantization errors and the bit string extracted by the bit extraction means;
Means for determining priority and correction value of the color to be corrected for each correction region;
A quantized output correcting means for correcting the quantized output by the correction value;
Difference comparison means for comparing the absolute value of the difference between the image data of the plurality of colors with a predetermined value;
Second quantization error calculating means for calculating a quantization error from the corrected quantization output;
Means for performing error diffusion processing using an error from the second quantization error calculation means,
An image processing apparatus characterized by selecting and correcting the area determination results by the first and second correction area determination means according to the determination result of the difference comparison means. Inputting multiple color image data;
Weighting the quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization steps for quantizing the input image data to which the quantization error is added;
A first quantization error calculation step for calculating a plurality of quantization errors generated by the quantization process of the quantization step;
The correction of the quantization output is determined by comparing the negative sign of the calculated plurality of quantization errors and the sum of the plurality of quantization errors with a quantization threshold value or a value obtained by subtracting the quantization step width from the quantization threshold value. A determination step;
Detecting a quantization error negative number of neighboring pixels for each of the plurality of colors;
A determination step of determining a correction region based on the number of negative quantization errors of the neighboring pixels;
Determining the priority order and correction value of the color to be corrected for each correction region;
Correcting the quantized output by the correction value;
A second quantization error calculation step of calculating a quantization error from the corrected quantization output,
An image processing method, wherein error diffusion processing is performed using an error in the second quantization error calculation step. Inputting multiple color image data;
Weighting the quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization steps for quantizing the input image data to which the quantization error is added;
A first quantization error calculation step for calculating a plurality of quantization errors generated by the quantization process of the quantization step;
A bit extraction step for extracting a predetermined bit from the plurality of quantization errors;
A region determination step of determining a correction region of the quantization output by comparing the calculated negative sign of the plurality of quantization errors and the bit string extracted by the bit extraction step;
Determining the priority order and correction value of the color to be corrected for each correction region;
Correcting the quantized output by the correction value;
A second quantization error calculation step of calculating a quantization error from the corrected quantization output,
An image processing method, wherein error diffusion processing is performed using an error in the second quantization error calculation step. Inputting multiple color image data;
Weighting the quantization error in the vicinity of the target pixel for each of the plurality of colors, and adding to the input image data;
A plurality of quantization steps for quantizing the input image data to which the quantization error is added;
A first quantization error calculation step for calculating a plurality of quantization errors generated by the quantization process of the quantization step;
A threshold averaging step for obtaining an average value of quantization thresholds in the plurality of quantization steps;
By comparing the negative sign of the calculated plurality of quantization errors and the sum of the plurality of quantization errors with an average value of the quantization threshold or a value obtained by subtracting the quantization step width from the average value of the quantization threshold. A determination step of determining correction of the quantized output;
A quantization error negative number detection step for detecting the number of quantization error negative signs of neighboring pixels for each of the plurality of colors;
A first correction area determination step of determining a correction area based on the calculated negative number of quantization errors and the number of quantization error negative signs of the neighboring pixels;
A bit extraction step for extracting a predetermined bit from the plurality of quantization errors;
A second correction region determination step of determining a correction region of the quantization output by comparing the calculated negative sign of the plurality of quantization errors and the bit string extracted by the bit extraction step;
Determining the priority order and correction value of the color to be corrected for each correction region;
A quantization output correction step of correcting the quantization output by the correction value;
A difference comparison step of comparing the absolute value of the difference between the image data of the plurality of colors with a predetermined value;
A second quantization error calculation step for calculating a quantization error from the corrected quantization output;
Performing error diffusion processing using the error in the second quantization error calculation step,
An image processing method comprising: selecting and correcting the region determination results in the first and second correction region determination steps according to the determination result in the difference comparison step.   An image processing program for causing a computer to execute the image processing method according to any one of claims 24 to 26.   A storage medium storing a computer-readable program, wherein the image processing program according to claim 27 is stored.

Images