JPH08317206A - Image processor - Google Patents

Abstract

PURPOSE: To fast produce a pseudo halftone image which never generates a pseudo boundary regardless of the allocated error by generating a random number based on the binarization error that is caused in an already binarized peripheral pixel. CONSTITUTION: The weighted error sum allocated to a marked pixel is read out of a RAM 3 and added to the input density of the marked pixel that is read out of an image memory 2, so that the correction density is calculated. A random number generation means 8 generates a random number based on the binarization error that is caused in an already binarized pixel peripheral to the marked one. The random number outputted from the means 8 is added to the standard threshold value, and the threshold value of the marked pixel is decided. The calculated correction density is compared with the threshold value and an output signal of the marked pixel is decided. Then the binarization error caused in the marked pixel is calculated and then multiplied by the coefficient value corresponding to the coefficient matrix that is read out of a ROM 4 based on the positional relation between the marked pixel and its peripheral one. This multiplied binarization error is allocated to every peripheral pixel.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus,
More specifically, for example, an image processing apparatus in a digital electrophotographic copying machine, a thermal transfer type or an ink jet type printer having a function of binarizing a halftone image to create pseudo halftone image data. It is about.

[0002]

2. Description of the Related Art Conventionally, as a means for binarizing a halftone image to create a pseudo halftone image, a systematic dither method for binarizing the image according to a threshold matrix (dither matrix) table, etc. The various dither methods are widely used. However, these conventional methods have a contradiction that the matrix table must be large in order to improve the gradation reproducibility, and the matrix table must be small in order to obtain high resolution. It was difficult to achieve both reproducibility and high resolution.

In addition to the above, there is an error diffusion method as a method for achieving both gradation reproducibility and high resolution, which is given a relatively good evaluation among various conventional methods. The error diffusion method distributes the error between the actual density and the binarized value, which occurs when binarizing the halftone pixel, to the surrounding pixels, corrects the density, and This is a process of making a determination based on a predetermined threshold value and determining one of binary values.

In the following description, it is assumed that the input density of each pixel takes an integer value of 0 to 255 and the output density is 0 (non-printing output) or 255 (printing output). In the uniform density area A where the input density is the same throughout, the distribution of pixels that take each output signal is the same throughout the binarization process. For example, consider a uniform density region B having an input density of 128. In the region B, the ratio of pixels having an output signal of 0 and pixels having an output signal of 255 is approximately 50% in all regions.

[0005]

However, even in a region in which two types of output signals are evenly distributed over the entire area in this way, in the binarization process by the error diffusion method, the binarization distributed from the peripheral pixels is performed. The placement pattern of the two output signals may change due to the influence of the error.

For example, in the uniform density region B,
A pattern B01 in which pixels having an output density of 0 (represented by white pixels) and pixels having an output density of 255 (represented by black pixels) appear alternately as shown in FIG. 4A, and as shown in FIG. There is a pattern B02 in which pixels having an output density of 0 and pixels having an output density of 255 are randomly arranged. Therefore, in the conventional error diffusion method, even though the area has a uniform input density,
There is a problem that a pseudo boundary is generated where the pattern described above changes.

As a method of improving this problem, Japanese Patent Application No. 6-244287 proposes a method of determining a threshold value by adding a random number generated by a random number generating means to a standard threshold value by the same applicant. . According to this method, since the arrangement of the recording pixels has an appropriate degree of randomness, a pseudo boundary does not occur regardless of the distributed error. However, in this method, since it is necessary to generate a random number for each pixel by the random number generating means, there is a problem that the amount of calculation increases and the processing speed becomes slow.

The present invention has been made in order to solve the above-mentioned problems, and a more preferable pseudo-halftone image in which a pseudo boundary does not occur regardless of an error to be distributed can be obtained at high speed. An object of the present invention is to provide an image processing device that can be created.

[0009]

In order to achieve this object, in the image processing apparatus according to the first aspect of the present invention, attention is paid based on the binarization error distributed from the already binarized peripheral pixel to the pixel of interest. An input density correction unit that corrects the input density of a pixel to obtain a corrected density, a random number generation unit that generates a random number based on a binarization error that has already occurred in a binarized peripheral pixel, and the random number is standard. A threshold value determining unit that adds the threshold value to obtain a threshold value for the pixel of interest, an output signal determining unit that compares the correction density and the threshold value to determine an output signal, and the output signal and the correction density It is provided with a binarization error calculation means for calculating the generated binarization error and a binarization error distribution means for distributing the binarization error to peripheral pixels.

According to a second aspect of the present invention, there is provided the image processing apparatus according to the first aspect, wherein the random number generating means generates a part of the binarization error value generated in the immediately preceding pixel as a random number. It was done.

According to a third aspect of the present invention, in the image processing apparatus according to the first or second aspect, the random number has a positive threshold value obtained by the threshold value determining means, and
The input density is smaller than the maximum value of the input density.

[0012]

In the image processing apparatus having the above structure, the input density correction means inputs the pixel of interest based on the binarization error distributed from the already binarized peripheral pixel to the pixel of interest. The density is corrected to obtain the corrected density, and the random number generating means generates a random number. Further, the threshold value determining means adds the random number to the standard threshold value to obtain the threshold value for the target pixel, and the output signal determining means compares the corrected density with the threshold value to determine the output signal. From the output signal and the corrected density, the binarization error calculation means calculates the binarization error generated in the target pixel. The binarization error is distributed to the peripheral pixels by the binarization error distribution means.

In the image processing apparatus according to the second aspect, in the image processing apparatus according to the first aspect, a part of the binarization error value generated in the immediately preceding pixel is generated as random numbers by the random number generation means.

According to a third aspect of the present invention, in the image processing apparatus according to the first or second aspect, the threshold value obtained by the threshold value determining means is a positive value and is smaller than the maximum value of the input density. Set to a random value.

[0015]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings.

The image processing apparatus of this embodiment is applied to a printer, and FIG. 1 is a block diagram showing an image processing circuit for performing error diffusion processing in this embodiment. The image processing circuit 1 is configured as a computer,
The input data is subjected to required arithmetic processing. The image processing circuit 1 is an original image storage device (image memory) that stores a part of the original image information input by a digital electric signal.
2, RAM3 for storing each calculation result, ROM4,
It is composed of a CPU 5 that performs various image processes, an output image storage device (image memory) 6 that stores the signal after the error diffusion process, a random number generation device 8 and the like.

The ROM 4 stores an image processing program, a weighting coefficient matrix for binarization error distribution of peripheral pixels, a standard threshold value T0, and the like.
Original image data (halftone image data) input from an external device such as a host computer is stored in the original image storage device 2. The original image data may be input pixel by pixel, line by line, or sent for one screen. For the digital signal of the stored halftone image information, CP
U5 uses the image processing program in ROM4 to perform error diffusion processing.

Next, the procedure for determining the output signal in the error diffusion processing in this embodiment will be described with reference to FIGS. 2 and 3. FIG. 2 is a flowchart showing the processing of the error diffusion method in this embodiment.

First, the input density I of the pixel of interest is fetched from the image memory 2 (step S1), and the standard threshold value T0 is read from the ROM 4 (S2). Next, the weighting error sum E distributed to the pixel of interest is read out from the RAM 3, and the image memory 2
The corrected density I'is obtained by adding to the input density I of the pixel of interest read from (S3).

Further, the random number generating means 8 generates a random number R using the binarization error generated in the already binarized pixels around the pixel of interest (S4). The random number in this embodiment uses the lower bit of the binarization error e ′ generated in the immediately preceding pixel as a random number in accordance with Expression 1, and is −32 to 31.
Shall take values in the range.

[0021]

[Equation 1]

Although the values of -32 to 31 can be increased or decreased, the threshold value T is set to a value range such that it does not become a negative value or become larger than the maximum value of the input density I. Threshold T
, Takes a negative value, print pixels may occur even in a uniform area where the value of the input density I is 0. If the threshold value is larger than the maximum value of the input density I, the value of the input density I This is because a non-recording pixel may occur even in a uniform area of 255.

The method of generating the random number R by using the binarization error generated in the already binarized pixel around the pixel of interest is not limited to the method defined by the equation (1). For example, the RA corresponding to the pixel directly below the pixel of interest
The lower bit of the numerical value stored at the time of binarization of the pixel of interest may be used for the error storage buffer in M3.

Next, the random number R output from the random number generating means 8 is added to the standard threshold value T0 to determine the threshold value T for the target pixel (S5). Then, the corrected density I ′ calculated above is compared with the threshold value T (S6) to determine the output signal O of the target pixel. When I ′ ≧ T, the output signal O is set to 255 indicating recording output (S7), and when I ′ <T, the output signal O is set to 0 indicating non-recording output (S8). The output signal O of the pixel of interest determined above is stored in the output image storage device 6 (S9).

Thereafter, the binarization error e generated in the target pixel
Is calculated according to equation 2 (S10).

[0026]

[Equation 2]

Next, the weighting coefficient matrix previously stored in the ROM 4 is read (S11). In this embodiment, the matrix shown in Expression 3 is used as the weighting coefficient matrix.

[0028]

(Equation 3)

The binarization error e of the pixel of interest is multiplied by the corresponding coefficient value of the coefficient matrix read from the ROM 4 in accordance with the positional relationship between the pixel of interest and the peripheral pixels, and the binarization error distribution shown in FIG. As shown in the figure for explaining the method, it is distributed to each peripheral pixel and added to the buffer for storing the binarization error in the RAM 3.

Although the above processing is repeated for each pixel, the processing order of pixels is such that the leftmost pixel of the uppermost line is processed horizontally to the right, and the same processing is sequentially moved downward line by line. This is the order in which the lower right pixel is processed at the end.

Then, the above-described processing is executed for all the pixels, so that pseudo-halftone image data is created.

In the above embodiment, step S3 corresponds to the processing as the input density correcting means, step S5 corresponds to the processing as the threshold value determining means, and steps S6 to S6.
8 corresponds to the processing as the output signal determining means, step S10 corresponds to the processing as the binarization error calculating means, and step S12 corresponds to the processing as the binarization error distributing means.

The present invention is not limited to the above embodiment, but various modifications can be added. For example,
Although the image processing apparatus of this embodiment is applied to a printer, it is applicable to an apparatus that outputs a pseudo halftone image such as a CRT display.

[0034]

As is apparent from the above description, according to the image processing apparatus of the first aspect, a more preferable pseudo halftone in which a pseudo boundary does not occur regardless of the error to be distributed. An image can be created at high speed.

According to the image processing device of the second aspect,
Random numbers can be generated faster.

According to the image processing device of claim 3,
This is due to the occurrence of print pixels even in a uniform area where the input density value is 0, which is caused when the threshold value takes a negative value, and when the threshold value is larger than the maximum value of the input density.
Even in a uniform area where the input density value is the maximum value, non-printing pixels cannot occur, and a good pseudo halftone image can be created.

[Brief description of drawings]

FIG. 1 is a block diagram illustrating an image processing circuit according to an embodiment.

FIG. 2 is a flowchart showing the binarization process.

FIG. 3 is a diagram illustrating a method of distributing a binarization error.

FIG. 4 is an explanatory diagram showing a difference in pixel distribution pattern after binarization processing by an error diffusion method in a uniform uniform density region.

[Explanation of symbols]

 1 Image Processing Device 2 Original Image Storage Device (Image Memory) 3 RAM 4 ROM 5 CPU 6 Output Image Storage Device (Image Memory) 8 Random Number Generating Means

Claims (3)

[Claims] 1. An image processing apparatus for binarizing a halftone image to create pseudo halftone image data, wherein based on a binarization error distributed from an already binarized peripheral pixel to a target pixel, An input density correction unit that corrects the input density of the pixel of interest to obtain a corrected density, a random number generation unit that generates a random number based on the binarization error that has already occurred in the binarized peripheral pixels, and the random number generation unit The random number generated by the means is added to the standard threshold value to compare the threshold density determined by the input density correction means with the threshold density determined by the threshold density determination means for determining the threshold value for the pixel of interest. Output signal determining means for determining a binary output signal indicating recording or non-recording, the output signal determined by the output signal determining means, and the correction obtained by the input density correcting means. And binarization error calculating means for calculating the binarization error generated in the pixel of interest, and the binarization error for distributing the binarization error obtained by the binarization error calculating means to the surrounding pixels. An image processing apparatus comprising: an error distribution unit. 2. The image processing apparatus according to claim 1, wherein the random number generating means generates a part of the value of the binarization error generated in the immediately preceding pixel as a random number. 3. The random number is a value in which the threshold value obtained by the threshold value determining means is a positive value and is a value smaller than the maximum value of the input density.
Alternatively, the image processing device according to 2.