JP7621135B2 - Image Processing Device - Google Patents

Description

The present invention relates to an image processing device that performs halftone processing on image data.

Conventionally, inkjet printing devices have been proposed that perform printing processes by ejecting ink from an inkjet head onto a print medium transported along a transport path.

In such inkjet printing devices, air currents are generated when the print medium is transported directly below the inkjet head.

In addition, when ink is continuously ejected from the nozzles of an inkjet head, the ink droplets are ejected accompanied by their own air currents, which act like a wall blocking the air current caused by the transport of the print medium described above. The air current generated by the transport of the print medium may then collide with the wall of the own air current, causing complex turbulence in the air current. This turbulence may cause the landing position of the ink ejected from the inkjet head to deviate from the desired position, which may result in density unevenness known as wind ripples.

As a method for correcting this density unevenness, Patent Document 1 proposes a method in which, when the image data is high resolution and the density is within a predetermined range, the resolution of the image data is converted to a low resolution depending on the degree of self-generated air currents, making the wind ripple unevenness less noticeable.

JP 2016-013645 A

However, the method described in Patent Document 1 cannot be applied when the image data is originally low resolution. In addition, the method described in Patent Document 1 has problems such as reduced graininess due to the larger ink droplet size in areas converted to low resolution, and tone jumps occurring in boundary areas where the image data resolution is switched to low resolution. Tone jumps are a phenomenon in which the gradation of an area that is originally smooth changes abruptly, resulting in the appearance of striped patterns.

In view of the above circumstances, the present invention aims to provide an image processing device that can suppress wind ripple unevenness even in low-resolution image data without causing a decrease in graininess or tone jumps.

The image processing device of the present invention includes an image data receiving unit that receives image data, and a halftone processing unit that performs halftone processing on the image data to convert it to at least three values and generate multi-value data, and the halftone processing unit performs halftone processing so that the zero value is always included, except when all pixels constituting the image data are converted to maximum values.

According to the image processing device of the present invention, when image data is subjected to halftone processing to convert it to at least three values and generate multi-value data, the halftone processing is always performed to include zero values, except in cases where all pixels constituting the image data are converted to maximum values, thereby suppressing wind ripple unevenness. This makes it possible to suppress wind ripple unevenness even in low-resolution image data without reducing granularity or causing tone jumps.

FIG. 1 is a block diagram showing a configuration of an inkjet printing apparatus using a first and second embodiment of an image processing apparatus according to the present invention; FIG. 1 is a block diagram showing a configuration of an image processing unit according to a first embodiment of the image processing device of the present invention. A diagram to explain the concept of existing halftone processing FIG. 1 is a diagram for explaining the concept of halftone processing in the first embodiment of the image processing device of the present invention; 1 is a flowchart for explaining an outline of calculation processing for halftone processing in the first embodiment of the image processing apparatus of the present invention. FIG. 1 is a diagram for explaining specific calculation processing of halftone processing in the first embodiment of the image processing device of the present invention; A flowchart for explaining specific calculation processing of halftone processing in the first embodiment of the image processing device of the present invention. FIG. 11 is a block diagram showing the configuration of an image processing unit according to a second embodiment of the image processing device of the present invention. FIG. 1 is a diagram for explaining binary error diffusion processing by the Floyd-Steinberg method; FIG. 1 is a diagram for explaining a binary error diffusion process equivalent to the Floyd-Steinberg method. FIG. 13 is a diagram for explaining a multi-value error diffusion process in the halftone processing unit of the second embodiment; 11 is a flowchart for explaining the flow of calculation processing in halftone processing according to the second embodiment. FIG. 13 is a block diagram showing a configuration of a modified example of an image processing unit according to the second embodiment. FIG. 13 is a diagram for explaining an example of density conversion processing for adjusting white pixel density; FIG. 1 is a diagram for explaining a method for adjusting the white pixel density in binary error diffusion processing; A diagram showing the results of applying existing multi-level error diffusion processing to a monochrome gradation image. FIG. 13 is a diagram showing the result of performing halftone processing according to the second embodiment on a monochrome gradation image.

The following describes in detail an inkjet printing device 1 using a first embodiment of the image processing device of the present invention with reference to the drawings. Figure 1 is a schematic diagram of the inkjet printing device 1 of this embodiment.

The inkjet printing device 1 performs printing processing by ejecting ink onto a sheet-like printing medium such as paper or film based on image data output from a computer or image data output from a document reading device. As shown in FIG. 1, the inkjet printing device 1 includes an image processing unit 10, a head drive control unit 20, an inkjet head unit 30, a transport unit 40, and a control unit 50.

The image processing unit 10 receives image data output from a computer or a document reading device, and performs various processes on the image data. In this embodiment, the image processing unit 10 corresponds to one embodiment of the image processing device of the present invention. The image processing unit 10 includes a CPU (Central Processing Unit) and a semiconductor memory. The CPU and semiconductor memory of the image processing unit 10 may be shared with the control unit 50 described below, or may be provided separately.

The image processing unit 10 executes an image processing program stored in advance in a storage medium such as a semiconductor memory or a hard disk, and operates electrical circuits to perform the processing of each unit described below.

As shown in FIG. 2, the image processing unit 10 includes an image data receiving unit 11, a color conversion unit 12, and a halftone processing unit 13.

The image data receiving unit 11 receives image data in RGB format output from a computer or document reading device, and outputs it to the color conversion unit 12.

The color conversion unit 12 converts the image data in RGB format into image data in CMYK format and outputs it to the halftone processing unit 13.

The halftone processing unit 13 performs halftone processing on the C, M, Y, and K image data output from the color conversion unit 12, converting the data into at least three values to generate multi-value data. The multi-value data in this embodiment is data that specifies the number of ink drops ejected from one nozzle of the inkjet head to form one dot of the print image, and is also called ink drop data.

The halftone processing unit 13 of the first embodiment performs halftone processing using a dithering method, which suppresses the wind ripple unevenness described above. The halftone processing unit 13 of this embodiment performs halftone processing so that zero values are always included, except when all pixels constituting the image data are converted to their maximum values (maximum drop number), and the specific processing method will be described in detail later.

In addition to the above-mentioned processing, the image processing unit 10 also performs various other well-known image processing such as gamma correction processing and edge enhancement processing.

The head drive control unit 20 drives the inkjet head unit 30 based on the ink drop data for each color generated by the halftone processing unit 13 to eject ink from each nozzle of the inkjet head for each color.

The inkjet head unit 30 is equipped with multiple inkjet heads that eject ink of each color, C, M, Y, and K. As described above, each inkjet head is controlled by the head drive control unit 20 based on the ink drop data for each color to eject ink onto the print medium and form a print image on the print medium.

The transport unit 40 has a transport mechanism that transports the print medium to the inkjet head unit 30.

The control unit 50 includes a CPU, a semiconductor memory, and the like, and controls the entire inkjet printing device 1. The control unit 50 executes a control program prestored in a storage medium such as a semiconductor memory or a hard disk, and controls the operation of each part of the inkjet printing device 1 by operating an electric circuit.

Before describing the halftone process of the inkjet printing device 1 of this embodiment, we will next describe existing halftone processes with reference to the conceptual diagram of FIG. 3. Note that we will describe a case where the maximum number of drops of ink ejected per print dot is three drops. The existing halftone process described here is also a halftone process that uses a dither method, and is a halftone process that uses a 4x4 dither matrix.

Figures 3 (1) to (49) show the results of applying existing halftone processing to a specific 4 x 4 pixel in image data, and show the results of halftone processing when the print density varies from minimum density (zero) to maximum density. As the number in parentheses increases, the print density increases by one step. Each square in Figures 3 (1) to (49) represents one pixel.

Figures 3 (1) to (17) show the results of halftone processing when the print density changes from zero to 17 levels. As shown in Figures 3 (1) to (17), when the print density changes from zero to 17 levels, each time the print density increases by one level, data "1" indicating the number of ink drops "1" is added one by one according to the distribution of each dither threshold value in the dither matrix. And when the print density is at 17 levels, all pixels become data "1".

Next, as shown in Figure 3 (18) to (33), for print density levels 18 to 33, all pixels start out with data "1," and for each step of increase in print density, data "2," indicating the number of ink drops "2," is added one at a time according to the distribution of each dither threshold value in the dither matrix. Then, when the print density is at 33 steps, all pixels become data "2."

Next, as shown in Figure 3 (34) to (49), for print density levels 34 to 49, all pixels start out with data "2," and for each step of print density increase, data "3" indicating the number of ink drops "3" is added one by one according to the distribution of each dither threshold value in the dither matrix. Then, when the print density is at 49 levels, all pixels have data "3." In other words, all pixels are at the maximum value (maximum number of drops).

As mentioned above, in the case of existing halftone processing, when the print density reaches level 17, all pixels have data other than 0, and when the print density reaches level 18 and above, data of "2" or "3" is added sequentially. In other words, when the print density reaches level 17 and above, there are no zero values between pixels, so the print dot density is always high, making it easy for the self-current caused by the ink ejection mentioned above to occur, and wind ripples to easily occur.

Next, the concept of halftone processing in the inkjet printing device 1 of this embodiment will be explained with reference to FIG. 4. Note that here too, the explanation will be given for the case where the maximum number of drops of ink ejected per print dot is three drops. The halftone processing of this embodiment explained here also uses a 4x4 dither matrix.

Figures 4 (1) to (37) show the results of applying halftone processing of this embodiment to a specific 4x4 pixel in image data, and show the results of halftone processing when the print density changes from the minimum density (zero) to the maximum density. As the number in parentheses increases, the print density increases by one step. Note that, for ease of explanation, it is assumed here that all 4x4 pixels have the same density.

In the halftone process of this embodiment, first, the number of ink drops "1" is sequentially added in accordance with the increase in print density, but the addition of "1" data is stopped when a zero value remains. In other words, the addition of "1" data is stopped when the number of zero-value pixels reaches a predetermined number of pixels.

Next, as the print density increases, data "2" is added sequentially, but is arranged according to the same arrangement rules as when data "1" was arranged. Then, the addition of data "2" is stopped when zero values remain. Furthermore, as the print density increases, data "3" is added sequentially, but is arranged according to the same arrangement rules as when data "2" was arranged. Then, zero values remain until the print density reaches its maximum density, at which point all pixels become their maximum value (data "3"). A more detailed explanation will be given below with reference to Figure 4.

Figures 4 (1) to (9) show the halftone process results when the print density changes from zero to ninth level. As shown in Figures 4 (1) to (9), when the print density changes from zero to ninth level, data "1" indicating the number of ink drops "1" is added one by one according to the distribution of each dither threshold value of the dither matrix for each increase in density level. Then, as described above, the addition of "1" data is stopped while leaving zero values, but here the addition of "1" data is stopped when the print density reaches the ninth level. The print density at which the addition of "1" data is stopped is not limited to nine levels, but is set to a level at which wind ripples can be suppressed.

Next, as shown in Figure 4 (10) to (21), for print density levels 10 to 21, data "1" or "0" is replaced with data "2."

First, as shown in Figure 4 (10) to (17), for print density levels 10 to 17, data "1" is replaced with data "2" using the same arrangement rule as the data "0" arrangement rule when the print density changes from zero to nine levels. This makes it possible to replace data "1" with data "2" while maintaining the number of zero values in the nine levels.

As a result, it is possible to maintain the effect of suppressing wind ripples while also smoothing the print density gradation, thereby suppressing tone jumps.

Next, as shown in Figure 4 (18) to (21), for print densities from 18 to 21, data "0" is replaced with data "2" according to the distribution of each dither threshold value in the dither matrix. In the halftone process of this embodiment, the addition of data "2" is stopped while leaving zero values as described above, but here the addition of data "2" is stopped when the print density reaches 21. The print density at which the addition of data "2" is stopped is not limited to 21, but is set to a level at which wind ripples can be suppressed.

The number of zero values at the time when the addition of the data "2" is stopped can be made smaller than the number of zero values at the time when the addition of the data "1" described above is stopped. This is because the size of the ink droplets increases as the number of ink drops increases, and therefore the gaps between the ink dots on the printing surface are small or overlap, so that even if the landing position is shifted due to the influence of air currents, wind ripples are less likely to occur.

In this way, by making the number of zero values at the time when the addition of data "2" is stopped less than the number of zero values at the time when the addition of data "1" is stopped, in other words, by performing halftone processing so that the proportion of zero values contained in the entire multi-value data decreases as the multi-value data becomes larger, the print density gradation can be increased and the image quality of the printed image can be improved.

Next, as shown in Figure 4 (22) to (37), for print density levels 22 to 37, data "2" or "0" is replaced with data "3."

First, as shown in Figure 4 (22) to (33), when the print density is from 22 to 33 levels, the data "2" is replaced with data "3" using the same arrangement rule as the arrangement rule for data "2" when the print density changes from, for example, 10 to 21 levels, while maintaining the data "2" in 21 levels, as shown in Figure 4 (22) to (33). This makes it possible to replace data "2" with data "3" while maintaining the number of zero values in 21 levels.

As a result, it is possible to maintain the effect of suppressing wind ripples while also smoothing the print density gradation, thereby suppressing tone jumps.

Next, as shown in Figure 4 (34) to (37), for print density levels 34 to 37, data "0" is replaced with data "3" according to the distribution of each dither threshold value of the dither matrix. Then, zero values remain until the print density reaches its maximum density, at which point all pixels become the maximum value (data "3").

As shown in Figures 4 (1) to (37), the halftone processing unit 13 of this embodiment performs halftone processing so that it always includes zero values, except when all pixels that make up the image data are converted to maximum values (as in Figure 4 (37)). This makes it possible to achieve a lower print dot density than with existing halftone processing, making it less likely for the self-generated airflow caused by the ink ejection described above to occur, and making it possible to suppress wind ripple unevenness. In addition, wind ripple unevenness can be suppressed even with low-resolution image data, without reducing granularity or incurring tone jumps.

As shown in Fig. 4 (1) to (9), when converting data "0" to data "1", it is preferable to make the pixels of data "0" adjacent to the pixels of data "1" and to make sure that the pixels of data "1" do not overlap by more than two pixels in the vertical and horizontal directions. However, the arrangement of data "0" and data "1" depends on the density of each pixel of the image data. Therefore, for example, in halftone processing, after converting each pixel of the image data to data "0" and "1", it is possible to detect the part where data "1" overlaps by more than two pixels in the vertical or horizontal directions and to further convert the data of "1" in the detected part to data of "0", so that the pixels of data "1" do not overlap by more than two pixels in the vertical and horizontal directions. This makes it possible to maintain a low print dot density without depending on the image data.

Next, the outline of the calculation process for the halftone process of this embodiment will be explained with reference to the flowchart shown in FIG.

First, the halftone processing unit 13 sets the y coordinate of the pixel that constitutes the image data to y = 0 (S10) and sets the x coordinate to x = 0 (S12). Note that the initial values of the x and y coordinates of the pixel that constitutes the image data are zero.

Then, the density data data[x][y] of the pixel at the x-coordinate and y-coordinate position set in S10 and S12 and the dither threshold dth of the dither matrix Dth corresponding to the x-coordinate and y-coordinate position are obtained (S14). The position of the dither threshold dth in the dither matrix Dth can be found, for example, by calculating the remainder when the x-coordinate of data[x][y] is divided by the size of the dither matrix Dth in the x-direction, and the remainder when the y-coordinate is divided by the size of the dither matrix Dth in the y-direction. The dither matrix Dth and the dither threshold dth will be described in detail later.

Then, the halftone processing unit 13 performs a calculation process using the density data data[x][y] obtained in S14 and the dither threshold dth to calculate multi-value data (ink drop data) corresponding to the density data data[x][y] (S16). The specific calculation process will be described in detail later.

Next, the halftone processing unit 13 increments the x coordinate by 1 (S18), and if the x value is smaller than the size of the image data in the x direction (x_size) (S20, YES), repeats the processes of S14 to S18.

Then, in S20, if x≧x_size (S20, NO), the halftone processing unit 13 increments the y coordinate by 1 (S22), and if the y value is smaller than the size of the image data in the y direction (y_size) (S24, YES), it repeats the processes of S12 to S22.

Then, in S24, the halftone processing unit 13 ends the process when y≧y_size (S24, NO). This makes it possible to obtain multi-value data (ink drop data) for all pixels that make up the image data.

Next, the calculation process of S16 in FIG. 5 in the halftone process of this embodiment will be specifically described. Here, the density data of each pixel in the image data is set to 0 to 255, and as shown in FIG. 4, it is converted into multi-value data of 0 to 3 by applying halftone process.

First, the density threshold Th and dither matrix Dth used in the halftone process of this embodiment will be described. In this embodiment, as shown in FIG. 6, the density range of 0 to 255 is divided into three ranges. Note that the horizontal axis of FIG. 6 is density data, and the vertical axis is print density. The boundary values of the divisions are density thresholds Th[0], Th[1], Th[2], and Th[3]. Th[0] = 0, and Th[3] = 255. Th[1] is a value that determines the print density range in which data "1" is added by halftone process, and is a value that determines the print density range from 0 to 9 levels shown in FIG. 4. In other words, it is the print density at which the addition of data "1" stops. For example, Th[1] = 30 is set. If Th[1] is increased, the density of "1" data will increase, i.e. the print dot density will increase and wind ripple unevenness will occur. If Th[1] is decreased, wind ripple unevenness will not occur, but there will be fewer small dots and the graininess will increase, so Th[1] should be set to a value that strikes a good balance between wind ripple unevenness and graininess through experimentation, etc.

Th[2] is a value that determines the print density range in which data "2" is added by halftone processing, and is a value that determines the print density range from 10 levels to 21 levels shown in Figure 4. In other words, it is the print density at which the addition of data "2" stops. For example, Th[2] = 110 is set. As with Th[1], Th[2] is also set to a value that strikes a good balance between wind ripple unevenness and graininess through experiments, etc.

In this embodiment, Th[0] to Th[1] are set as concentration range index m=0, Th[1] to Th[2] are set as concentration range index m=1, and Th[2] to Th[3] are set as concentration range index m=2. The concentration range index m=0 to 2 will be used later in the explanation of the specific calculation process.

Next, in this embodiment, halftone processing is performed using three dither matrices Dth[1], Dth[2], and Dth[3] in accordance with the three density ranges of the image data.
The dither matrix Dth[1] is a dither matrix that converts image data in the first density range 0 to 105 into data of "0" or "1" by halftone processing. The dither matrix Dth[1] is obtained by normalizing the dither matrix made of a blue noise mask with density values in the first density range 0 to 105. For example, when one threshold value of the dither matrix made of a blue noise mask is dth and one corresponding threshold value of the dither matrix Dth[1] is dth[1], it is calculated by the following formula. Note that, although a dither matrix made of a blue noise mask is used in this embodiment, a halftone dot type dither matrix may also be used.
dth[1]=(first normalized value×dth)/256

The first normalization value in the above formula is a value that controls the speed at which the dots become denser. For example, if the first normalization value is 256, and dth is 10 and 100, then dth[1] will be 10 and 100, but if the first normalization value is 128, then dth[1] will be 5 and 50, which is a smaller threshold value and makes it easier to print. In other words, this means that the smaller the first normalization value, the faster the dots become denser. In this embodiment, 105 is set as the first normalization value.

The dither matrix Dth[2] is a dither matrix that converts image data in the second density range of 30 to 135 into data of "2" by halftone processing. The dither matrix Dth[2] is obtained by normalizing the dither matrix made of a blue noise mask with density values in the second density range of 30 to 135. For example, when one threshold value of the dither matrix made of a blue noise mask is dth and one corresponding threshold value of the dither matrix Dth[2] is dth[2], it is calculated by the following formula. The meaning of the second normalization value is the same as that of the first normalization value, and in this embodiment, 105 is set as the second normalization value.
dth[2]=(second normalized value×dth)/256

The dither matrix Dth[3] is a dither matrix that converts image data in the third density range of 105 to 255 into data of "3" by halftone processing. The dither matrix Dth[3] is obtained by normalizing the dither matrix made of a blue noise mask with density values in the second density range of 105 to 255. For example, when one threshold value of the dither matrix made of a blue noise mask is dth and one corresponding threshold value of the dither matrix Dth[3] is dth[3], it is calculated by the following formula. The meaning of the third normalization value is the same as that of the first normalization value, and in this embodiment, the third normalization value=255-Th[2]. By using such a value, it is possible to obtain solid data of an ink drop of "3" when the density data is 255.
dth[3]=(third normalized value×dth)/256

Figure 6 also shows the area converted to ink drop data of "0" and "1" (0,1 drop area), the area converted to ink drop data of "0", "1" and "2" (0,1,2 drop area), the area converted to ink drop data of "0" and "2" (0,2 drop area), the area converted to ink drop data of "0", "2" and "3" (0,2,3 drop area), and the area converted to ink drop data of "0" and "3" (0,3 drop area).

Next, halftone processing using the above-mentioned density thresholds Th[0] to Th[3] and dither matrices Dth[1] to [3] will be explained with reference to the flowchart shown in FIG. 7.

First, the halftone processor 13 acquires density data of a specific pixel that constitutes the image data, and determines whether the density data data belongs to one of the density range indices m = 0 to 2 described above (S30). Specifically, it determines m that satisfies Th[m] ≦ data < Th[m + 1].

Next, the halftone processing unit 13 calculates the above-mentioned dth[1] to dth[3] (S32).

If the value of m determined in S13 is zero (S34, YES), the density data of that pixel is compared with the dither threshold dth[1] of the dither matrix Dth[1] at the position corresponding to that pixel, and if data>dth[1] (S36, YES), the multi-value data (ink drop data) of that pixel is set to "1" (S38).

On the other hand, if data≦dth[1] (S36, NO), the multi-value data (ink drop data) for that pixel is set to “0” (S40).

If the value of m determined in S30 is not zero (S34, NO), the halftone processing unit 13 compares the value (data-Th[m]) obtained by subtracting Th[m] from the density data of that pixel with the dither threshold value dth[m+1] of the dither matrix Dth[m+1] at the position corresponding to that pixel (S42). Note that data-Th[m] is a calculation that uses the density data of the pixel as input data based on Th[m].

Then, if the input data is greater than dth[m+1] (S42, YES), the halftone processing unit 13 sets the multi-value data (ink drop data) for that pixel to m+1 (S44).

Specifically, when m is 1, for example, the halftone processing unit 13 compares data-Th[1] with dth[2]. Then, when data-Th[1] is greater than dth[2], the multi-value data (ink drop data) for that pixel is set to 2. As a result, the data "2" shown in (10) to (21) of Figure 4 is arranged.

On the other hand, if data-Th[m]≦dth[m+1] is satisfied in S42 (S42, NO), the dither threshold dth[m] of the dither matrix Dth[m] at the position corresponding to the pixel is compared with Th[m]-Th[m-1] (S46).

Here, Th[m]-Th[m-1] is Th[1]-Th[0] when m=1, for example, and indicates the concentration range from when the addition of data "1" starts to when it stops, as shown in Figures 4 (1) to (9). And, as shown in Figures 4 (10) to (17), data "2" needs to be placed in the same location as data "1", which is achieved by placing data "2" based on the conditions in S42.

However, if the condition in S42 is not met, it is necessary to maintain the concentration at time Th[1]. Therefore, if data "2" was not placed in S42, data "1" must be placed in the location where data "1" was placed at time Th[1].

Therefore, if the data "2" was not placed in S42, in the comparison formula of S46, if the dither threshold dth[1] for the data "1" is smaller than the density at which the addition of the data "1" was stopped, then the data "1" is placed (S48). This is because if the dither threshold dth[1] is smaller than Th[1]-Th"0", it indicates that the data "1" has already been placed at that position.

On the other hand, if the comparison formula in S46 is dth[m]≧Th[m]-Th[m-1], the multi-value data (ink drop data) for the pixel is set to 0 (S50).

As a result, when the density data of a pixel in the image data is in the density range of m=1, it can be converted to data of "0", "1", or "2". In other words, it is possible to smooth out the change in print density while still mixing in data of "0".

In the above explanation, the case of m=1 was explained, but even when m=2, the density data in the density range of m=2 can be converted to data of "0", "2", or "3" by performing the processing based on the comparison formulas of S42 and S46. In other words, it is possible to smooth the change in print density while still mixing in data of "0".

Next, an inkjet printing device 2 using a second embodiment of the image processing device of the present invention will be described in detail. The general configuration of the inkjet printing device 2 of the second embodiment is similar to that of the inkjet printing device 1 of the first embodiment shown in FIG. 1, but the configuration of the image processing unit 10 is different from that of the first embodiment.

The halftone processing unit 13 of the first embodiment performs halftone processing using the dither method as described above, while the image processing unit 10 of the second embodiment performs halftone processing using the multi-value error diffusion method. In the case of halftone processing using the dither method as in the first embodiment, memory capacity is required to store the dither matrix. For example, when a 256 x 256 dither matrix is used, 64 kByte of memory capacity is required. In contrast, since the multi-value error diffusion process does not need to store the dither matrix, the memory capacity can be reduced, and the calculation process can be realized by simple hardware, so that the entire hardware can be reduced. The memory capacity required for the multi-value error diffusion process will be described in detail later.

Figure 8 is a block diagram showing the configuration of the image processing unit 10 of the second embodiment. As with the first embodiment, the halftone processing unit 13 of the second embodiment also performs halftone processing so that zero values are always included, except when all pixels constituting the image data are converted to the maximum value (maximum drop number). In order to perform such halftone processing, the image processing unit 10 of the second embodiment is equipped with a binary error diffusion processing unit 14. The halftone processing unit 13 of the second embodiment then performs multi-value error diffusion processing based on the processing result of the binary error diffusion processing unit 14, thereby performing halftone processing that always includes zero values, as described above.

The binary error diffusion processing unit 14 performs binary error diffusion processing on each of the C, M, Y, and K image data before the halftone processing by the halftone processing unit 13. As the binary error diffusion processing, for example, the binary error diffusion processing by the Floyd-Steinberg method using the diffusion coefficient as shown in FIG. 9 is known, but in this embodiment, the binary error diffusion processing is performed using a method equivalent to this. The Floyd-Steinberg method is a method of dispersing an error generated in a pixel of interest by multiplying it by a diffusion coefficient to surrounding pixels as shown in FIG. 9, but in this embodiment, a method of multiplying the error of the surrounding pixels by a diffusion coefficient and distributing it to the pixel of interest as shown in FIG. 10 is used. In this method, the error err_bin distributed to the pixel of interest is expressed by the following formula. In the following formula, err_mem0[ ] is a memory that stores the error of the line immediately before the pixel of interest, err_pre is the error of the pixel immediately before the pixel of interest, and err_mem1[ ] is a memory that stores the error used in the line next to the pixel of interest.
err_bin=err_mem0[x-1]*1/16+err_mem0[x]*3/16+err_mem0[x+1]*5/16+err_pre*7/16

If the density data of the pixel of interest is D, then the pixel data Derr after the error is distributed is expressed by the following formula.
Derr=D+err_bin

If Derr>127, it is determined to be black and its quantization value is set to 255. On the other hand, if Derr≦127, it is determined to be white and its quantization value is set to 0.

Then, calculate err = Derr - quantized value, set err_mem1[x] = err, and set err_pre = err.

By performing binary error diffusion processing using the calculation process described above, the hardware only requires a total of two lines of memory, err_mem0[ ] and err_mem1[ ]. Furthermore, by shifting the timing of saving the error, the error of the pixel of interest can be written back into the used area of err_mem0[ ] after the error has been read out, so essentially only one line of memory is required. Note that the error distributed to the pixels on the first line (y = 0) can be set in advance.

Then, the binary error diffusion processing unit 14 performs binary error diffusion processing on the C, M, Y, and K image data, respectively, and adds a white pixel flag to pixels that are determined to be white as a result, i.e., pixels whose quantization value is 0.

When performing halftone processing using multi-value error diffusion processing, the halftone processing unit 13 of the second embodiment always sets pixels determined to be white by the binary error diffusion processing unit 14 to a zero value. The multi-value error diffusion processing in the halftone processing unit 13 of this embodiment is described below.

The halftone processing unit 13 quantizes the density data of each pixel using a quantization threshold, similar to the binary error diffusion process, and calculates the error from the quantization value and the density data.

Specifically, if the quantization value is q[i], the quantization threshold is q_th[i], and the density data of each pixel is D, and the maximum drop number is maxdrop = 3, the values can be set as shown in Figure 11, for example.

Then, similarly to the binary error diffusion process, if the error distributed from the surrounding pixels to the pixel of interest is err_mlt, the value of i that determines which of the quantization thresholds q_th[1] to q_th[3] shown in Fig. 11 to use is calculated by the following formula: Note that INT() is a function that rounds down the digits after the decimal point and returns the maximum integer that does not exceed the original number.
i=INT((D+err_mlt)*maxdrop/255)

For example, when D+err_mlt=150 as shown in FIG. 11, i is calculated to be 1 according to the following formula.
i=INT(150*3/255)=INT(1.76)=1

At this time, the quantization threshold used is q_th[1] = 127, and D+err_mlt = 150 is quantized to q[1] = 85 or q[2] = 170. However, since D+err_mlt = 150 is greater than q_th[1] = 127, the quantization value becomes q[2] = 170. The multi-value data (ink drop data) becomes "2", which corresponds to q[2] = 170.

More specifically, first, the above-mentioned Derr is calculated, and then the value of i is calculated. Then, if i>maxdrop, i is set to maxdrop. Then, if i=maxdrop, the quantization value becomes q[maxdrop], and the multi-value data becomes maxdrop.

If i=maxdrop is not true, the pixel is checked to see if it has a white pixel flag attached, and if it has, the quantization value of that pixel is calculated as zero, and the multi-value data is also set to zero. As mentioned above, for pixels that have a white pixel flag attached, the quantization value will be zero, but because the error between the input density and the quantization value of zero is compensated for by surrounding pixels, the density will not decrease due to the characteristics of error diffusion.

On the other hand, if the white pixel flag is not added, and Derr>q_th[i], the quantization value is calculated as q[i+1], and the multi-value data becomes i+1. If Derr≦q_th[i], the quantization value is calculated as q[i], and the multi-value data becomes i.

Next, the flow of calculation processing for the halftone processing of the second embodiment described above will be explained with reference to the flowchart shown in FIG.

First, the halftone processing unit 13 sets the y coordinate of the pixel that constitutes the image data to y = 0 (S90), and sets the x coordinate to x = 0 (S92). Note that the initial values of the x and y coordinates of the pixel that constitutes the image data are zero.

Then, binary error diffusion processing is applied to the density data of the pixel at the x-coordinate and y-coordinate positions set in S90 and S92 (S94). If the pixel is determined to be white, a white pixel flag is added to that pixel (S96).

Next, multi-value error diffusion processing is applied to the density data of the pixel at the x and y coordinate positions set in S90 and S92, and the multi-value data for that pixel is calculated (S98). During the multi-value error diffusion processing, the white pixel flag added in S96, as described above, is referenced.

Next, the halftone processing unit 13 increments the x coordinate by 1 (S100), and if the x value is smaller than the size of the image data in the x direction (x_size) (S102, YES), repeats the processes of S94 to S100.

Then, in S102, if x≧x_size (S102, NO), the halftone processing unit 13 increments the y coordinate by 1 (S104), and if the y value is smaller than the size of the image data in the y direction (y_size) (S106, YES), repeats the processes of S92 to S104.

Then, in S106, the halftone processing unit 13 ends the process when y≧y_size (S106, NO). This makes it possible to obtain multi-value data (ink drop data) for all pixels that make up the image data.

Next, a modified example of the second embodiment described above will be described. In the second embodiment, a white pixel flag is added to pixels determined to be white by performing binary error diffusion processing, and the multi-value data for these pixels is forcibly set to zero. However, for example, in bright density areas, the density of white pixels is originally high, so when the above-mentioned processing is performed, the quantization value of the multi-value error diffusion processing for most pixels becomes zero. In such a state, during the multi-value error diffusion processing, errors of pixels whose quantization value becomes zero accumulate, and the quantization value suddenly becomes large in the printable area. For example, in bright density areas, a mixture of 0 drop and 1 drop is desired, but a mixture of 0 drop and 3 drop is obtained, which deteriorates the graininess of the printed image.

Therefore, in a modified example of the second embodiment, the density of white pixels is prevented from becoming too high after binary error diffusion processing. FIG. 13 is a diagram showing the configuration of a modified example of the image processing unit 10 of the second embodiment. As shown in FIG. 13, the modified example of the image processing unit 10 of the second embodiment further includes a white pixel density adjustment unit 15.

The white pixel density adjustment unit 15 performs density conversion processing as a white pixel density adjustment process on each of the input C, M, Y, and K image data. Specifically, as shown in FIG. 14, the white pixel density adjustment unit 15 performs density conversion processing on each image data such that the density of pixels determined to be white becomes sparser the brighter or darker the input density of the image data, and the density of pixels determined to be white becomes maximum at intermediate densities.

In the graph shown in Figure 14, at intermediate densities, the output density is controlled so that the white pixel density is sufficient to reduce wind ripple unevenness, and in areas brighter than the intermediate density, the brighter the area, the darker the output density relative to the input density (the white pixel density is sparse), and in areas darker than the intermediate density, the darker the area, the darker the output density relative to the input density (the white pixel density is sparse). The density conversion process can be realized using a lookup table or a function such as a predetermined linear equation.

Then, the binary error diffusion processing unit 14 performs the binary error diffusion processing on each image data that has been subjected to density conversion processing by the white pixel density adjustment unit 15. The rest of the configuration of the modified example of the second embodiment is the same as that of the second embodiment.

According to a modified version of the second embodiment, the density of pixels determined to be white is made sparser in areas with brighter input density of image data, thereby preventing deterioration of graininess.

In addition, the method for adjusting the density of white pixels after binary error diffusion processing is not limited to the above method, and the density of pixels determined to be white may be adjusted in the binary error diffusion processing unit 14.

Specifically, the quantization value of a pixel determined to be white is set to a negative value, and a quantization threshold is set based on this negative value and the quantization value of a pixel determined to be black, and binary error diffusion processing is performed. Specifically, as shown in FIG. 15, the quantization value of a pixel determined to be white is set to a negative value white_dens (for example, -20), and a quantization threshold (137) is calculated based on this negative value white_dens and the quantization value black_dens (255) of a pixel determined to be black, and binary error diffusion processing is performed. As described above, if the quantization value is set to a negative value (-20), even if the input density is 0, for example, the error will be input density (0) - quantization value (-20) = 20, so a positive error is distributed to the surrounding pixels. In other words, it acts to increase the density of the surrounding pixels, which makes the density of white pixels sparse.

By applying this type of binary error diffusion processing to areas of the image data that are brighter than the mid-tone, the density of white pixels after the binary error diffusion processing can be made sparse. This makes it possible to prevent the deterioration of graininess even in bright areas, just like the modified example of the second embodiment.

Figures 16A to 16D are diagrams showing the results of applying existing multi-value error diffusion processing to a monochrome gradation image. Figures 16A to 16D are diagrams showing a portion of a gradation image that has been applied with multi-value error diffusion processing, with the density decreasing from A to D. Figure 16A includes a portion that has been converted to maximum density. If only existing multi-value error diffusion processing is applied, wind ripple unevenness is likely to occur in the density range R.

Figures 17A to 17D are diagrams showing the results of applying the halftone process of the second embodiment to a monochrome gradation image similar to that of Figures 16A to 16D. In Figures 17A to 17D, the image has been converted so that all white pixels (zero value) are included, except for the parts converted to maximum density. In particular, it can be seen that the solid parts of Figures 17B and 17C contain white pixels. This makes it possible to suppress the effects of wind ripple unevenness in the density range R.

The following supplementary notes are further disclosed regarding the image processing device of the present invention.
(Additional Note)

In the image processing device of the present invention, in the process of converting image data into multi-value data including n values (n is an integer equal to or greater than 0) and n+1 values according to the density of the image data, the halftone processing unit stops increasing the number of n+1 value pixels when the number of zero-value pixels in the multi-value data reaches a predetermined number, and then can place the n+2 value in the same position as the n+1 value according to the increase in the density of the image data.

In addition, in the image processing device of the present invention, the halftone processing unit can perform halftone processing by placing pixels with a value of zero adjacent to pixels with a value of one, so that there are no more than two consecutive pixels with a value of one.

In addition, in the image processing device of the present invention, the halftone processing unit can perform halftone processing so that the proportion of zero values contained in the entire multi-value data decreases as the multi-value data corresponding to each pixel becomes larger.

The image processing device of the present invention can also include a binary error diffusion processing section that performs binary error diffusion processing on image data before halftone processing by the halftone processing section, and when performing halftone processing, the halftone processing section can set the quantized values and multi-value data of pixels determined to be white by the binary error diffusion processing section to zero values.

In addition, the image processing device of the present invention can be provided with a white pixel density adjustment section that applies white pixel density adjustment processing to image data to adjust the density of pixels determined to be white by the binary error diffusion processing before the binary error diffusion processing by the binary error diffusion processing section, and the binary error diffusion processing section can apply binary error diffusion processing to the image data that has been subjected to the white pixel density adjustment processing.

In addition, in the image processing device of the present invention, the binary error diffusion processing unit can perform binary error diffusion processing by taking the quantization value of a pixel determined to be white as a negative value, and setting a binary threshold based on the negative value and the quantization value of a pixel determined to be black.

In addition, in the image processing device of the present invention, the density of pixels determined to be white can be made sparser as the density of the image data becomes brighter.

REFERENCE SIGNS LIST 1 Inkjet printing device 10 Image processing section 11 Image data receiving section 12 Color conversion section 13 Halftone processing section 14 Binary error diffusion processing section 15 White pixel density adjustment section 20 Head drive control section 30 Inkjet head section 40 Conveyance section 50 Control section

Claims (8)

an image data receiving unit that receives image data;
a halftone processing unit that performs halftone processing on the image data to convert it into at least three values and generate multi-value data that represents the print density of the image data ;
An image processing device in which the halftone processing unit generates the multi-value data that always includes zero values through the halftone processing , except when all pixels that make up the image data are converted to maximum values. The image processing device according to claim 1, wherein, in the process of converting the image data into multi-value data including n values (n is an integer equal to or greater than 0) and n+1 values according to the density of the image data, the halftone processing unit stops increasing the number of pixels of the n+1 value when a predetermined number of pixels in the multi-value data have a zero value, and then places the n+2 value in the same position as the n+1 value according to the increase in the density of the image data. The image processing device according to claim 2, wherein the halftone processing unit performs the halftone processing such that a pixel with a value of 1 is adjacent to a pixel with a value of zero, and two or more pixels with a value of 1 are not consecutive. An image processing device according to any one of claims 1 to 3, wherein the halftone processing unit performs the halftone processing so that the proportion of zero values contained in the entire multi-value data decreases as the multi-value data corresponding to each pixel becomes larger. a binary error diffusion processing unit that performs binary error diffusion processing on the image data before the halftone processing by the halftone processing unit;
2. The image processing apparatus according to claim 1, wherein said halftone processing section sets the quantized value and the multi-value data of a pixel determined to be white by said binary error diffusion processing section to a zero value when performing said halftone processing. a white pixel density adjustment unit that performs a white pixel density adjustment process on the image data to adjust a density of pixels determined to be white by the binary error diffusion process before the binary error diffusion process is performed by the binary error diffusion processing unit,
6. The image processing apparatus according to claim 5, wherein the binary error diffusion processing section performs binary error diffusion processing on the image data that has been subjected to the white pixel density adjustment processing. The image processing device according to claim 5, wherein the binary error diffusion processing unit performs binary error diffusion processing by setting the quantization value of a pixel determined to be white as a negative value, and setting a quantization threshold based on the negative value and the quantization value of a pixel determined to be black. 8. The image processing apparatus according to claim 6, wherein the density of pixels determined to be white is made sparser as the density of the image data becomes brighter.