JPH03196769A - Picture processor - Google Patents

Abstract

PURPOSE:To reduce a stripe pattern in a medium density part in an original by calculating an average density based on an original picture signal inputted by a calculation means and allowing a binarizing means to binarize and output the original picture signal based on the calculated mean density CONSTITUTION:An error corrected signal 310 is inputted to a comparator 10, where the signal is compared with a mean density signal 320 calculated by a mean density computing element 12. When the signal 310 is larger than the signal 320, '1', is outputted and when smaller, '0' is outputted as a binary signal 400. The outputted signal 400 is inputted to an output device 6 and inputted to a prescribed picture element position in a binary memory 11. Moreover, the computing element 12 multiplies a weight coefficient signal 300 for mean density calculation with the binary signal preserved in the memory 11 and obtains total sum form the results of the calculation to calculate the signal 320. Then the signal 310 is inputted to the computing element 13 together with the signal 320 outputted from the computing element 12. A difference between both the signals 310, 320 is calculated in the computing element 13 and the result is outputted as a difference signal 330.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an image processing apparatus, for example, an image processing apparatus such as a digital printer and a digital facsimile.

[Prior Art] Recently, as a binarization method for reproducing halftones in this type of device, a method called error diffusion method, which disperses errors generated in the binarization process to surrounding pixels, has been attracting attention. .

In addition, as a similar method, the pixel of interest is binarized using the average density calculated from the dot placement in the already processed area around the pixel of interest as a threshold, and the binarization error that occurs at this time is distributed to the surrounding pixels. There is an average concentration preservation method. However, if the binarization error is larger than a predetermined threshold, the error diffusion process is stopped (adaptive error correction). This average concentration preservation method is based on the patent application No. 1-31 filed by the applicant on February 10, 1999.
No. 404 (Image Processing Device), resolution,
This method is superior to the error diffusion method in terms of gradation.

[Problems to be Solved by the Invention] However, the conventional example described above has a drawback in that a unique striped pattern occurs in the medium-density portion of the original, which significantly deteriorates the quality of the image.

The present invention has been made in view of the above-mentioned drawbacks of the conventional example, and its purpose is to provide an image processing device that can reproduce high-quality images by suppressing the occurrence of striped patterns when reproducing the middle portion of a document. The point is to provide the following.

[Means for Solving the Problems] In order to solve the above-mentioned problems and achieve the objectives, an image processing device according to the present invention is an image processing device that performs binarization using an average density preservation method, and includes: an input means for inputting an original image signal of a pixel of interest; a calculation means for calculating an average density based on the input original image signal; and an input means for calculating an average density based on the calculated average density; It is characterized by comprising a binarization means for converting into a value, and an output means for outputting a binary signal binarized by the binarization means.

[Operation] According to this configuration, the input means inputs the original image signal of the pixel of interest, the calculation means calculates the average density based on the input original image signal, and the binarization means calculates the average density based on the input original image signal. The input original image signal is binarized based on the input original image signal, and the output means outputs the binarized binary signal by the binarization means.

[Embodiments] Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

FIG. 1 is a block diagram showing the configuration of an image processing apparatus according to this embodiment. In the same figure, 1 is a solid-state image sensor (CCD)
2 shows an input device having a photoelectric conversion element such as and a drive system for scanning them, and 2 is an input device A for converting pixel input data obtained from the input device 1, that is, an analog signal into a digital signal.
/D converter (A/D) is shown. In A and /D2, for example, the data of each pixel is converted into 8-bit digital data. Therefore, pixel input data is quantized into data having 256 levels of gradation.

3 indicates a correction circuit, which applies corrections such as shading correction to correct uneven sensitivity of the CCD sensor and uneven illuminance caused by the illumination light source to the digital signal (density of the target pixel) output from the A/D 2. give This corrected digital signal (corrected signal) is indicated by 100.

Reference numeral 4 denotes a weighting coefficient setting circuit for calculating the average density, in which a weighting coefficient for calculating the average density is set according to the position signal of the pixel of interest, indicated by 200, output from the correction circuit 3, and this coefficient is set by 300. Output as the weighting coefficient signal shown. 5 indicates a binarization circuit, which binarizes the corrected signal 100 outputted from the correction circuit 3 using the weighting coefficient signal 300 outputted from the weighting coefficient setting circuit 4, and converts this binarized signal into 400 bits. Output as a binary signal shown by .

Reference numeral 6 indicates an output device constituted by a laser beam printer, an inkjet printer, etc., which converts the binary signal 400 outputted from the binarization circuit 5 into dot on/off.
Image formation according to off.

FIG. 2 is a block diagram showing the configuration of the weighting coefficient setting circuit 4 of this embodiment, and FIG. 3 is a diagram explaining the weighting coefficient setting method of this embodiment. As shown in FIG. 2, the weighting coefficient setting circuit 4 of this embodiment is composed of a ROM 7, and the ROM 7 generates a weighting coefficient signal 300 suitable for the position signal 200 of the pixel of interest output from the correction circuit 3. Output.

With this ROM 7, results similar to the detailed processing described below can be obtained.

First, when the X coordinate signal 210 (not shown) and the Y coordinate signal 220 (not shown) of the pixel of interest shown in the position signal 200 are each divided by 3'', the remainder is a signal 230 (not shown).

signal 240 (not shown). The value of signal 230 “
3" is added, signal 240 is subtracted, and the remainder when the result is divided by °3" becomes signal 250 (not shown). When the signal 250 is "o", the weighting coefficient αKL, when the signal 250 is °°1", the weighting coefficient β1, and when the signal 250 is 2°, the weighting coefficient γ is '
L is output as a weighting coefficient signal 300 (not shown). When such processing is performed, αKL, β,
As shown in FIG.
) appear in this order. Here, the weighting coefficient setting circuit 4
Since it is ROMized like OM7, αKL can be written at high speed.
.

β1 or γ8L is obtained.

FIGS. 4A to 4C are diagrams showing an example of a coefficient matrix according to the position of the pixel of interest in this embodiment. 4A, 4B, and 4C show coefficient matrices when the weighting coefficients αKL and β are γIIL, respectively.

* in each matrix corresponds to the position (i, j) of the pixel of interest. Each weighting coefficient α1. When using a coefficient matrix of βKL, γ1, each coefficient α1. β1. γ1
Since the total sum is "256", weighting around the pixel of interest is performed using the value of coefficient ÷ 256.

FIG. 5 is a block diagram showing the configuration of the binarization circuit 5 of this embodiment. In the figure, 8 is an adder, 9 is an error buffer memory, 10.14 is a comparator, and 11 is a binary memory. Reference numeral 12 indicates an average density computing unit, 13 indicates a computing unit, and 15 indicates a weighting circuit.

In the operation of the binarization circuit 5, the corrected signal 100 (image density of interest) inputted from the correction circuit 3 is processed by the adder 8 to receive the error EIJ (distributed to the pixel of interest) stored in the error buffer memory 9. As a result, an error-corrected signal indicated by 310 is output.

The error corrected signal 310 is then input to the comparator 10,
Here, it is compared with an average density signal 320 indicated by 320 calculated by the average density calculator 12. Then, if the error corrected signal 310 is larger than the average density signal 320, °
If it is smaller than 1°, “o” is output as a binary signal 400. The binary signal 400 outputted here is input to the output device 6 and is manually input to a predetermined pixel position in the binary memory 11.

In addition, the average concentration calculator 12 adds a weighting coefficient signal 300 for average concentration calculation to the binary signal stored in the binary memory 11.
The average density signal 320 is calculated by multiplying and summing , and calculating the sum from each calculation result.

The error corrected signal 310 is then output to the average concentration calculator 1.
The computing unit 13 together with the average concentration signal 320 output from the
is input. Here, the difference (ΔEIJ) between both signals 310 and 320 is calculated and output as a difference signal indicated by 330. This difference signal 330 is input to the comparator 14. Here, if the value of the difference signal 330 is larger than the threshold for stopping error diffusion "126°" (in the case of 8-bit data), the value of "°O" (stopping error diffusion),
If it is smaller, the value is output as is as a signal indicated by 340. This signal 340 is input to the weighting circuit 15, where it is weighted (+) to δ and then added to the error at a predetermined pixel position in the error buffer memory 9.

FIG. 6 is a diagram showing an example of weighting coefficients during error diffusion in this embodiment. In the figure, * corresponds to the pixel position of interest (i, j). Note that the weighting is based on the signal 34 input to the circuit 15.
Needless to say, error diffusion is not performed when the value of 0 is °'0''. By repeating the above operations, binarization is performed using the mean density conservation method.

As explained above, according to this embodiment, since the weighting coefficient for calculating the average density is changed according to the position of the pixel of interest,
It is possible to reduce the occurrence of unique striped patterns in medium-density areas of a document, and to output high-quality images.

Next, a modification of the above-described embodiment will be described.

FIG. 7 is a block diagram showing a partially modified configuration of the weight mask setting circuit 4. As shown in FIG. In the figure, 16 indicates a ROM,
Here, 0 or more and 2 or less (o, i.

2) --like random numbers are stored. The ROM 16 generates the random number when the position signal 200 (any other signal may be used as long as the input of the pixel of interest can be detected) is input. Random number signal (4
10) is input to the ROM 17, which will be described later. 17 is ROM
Here, when the random number signal 410 is °°O'', the weighting coefficient is α, when the random number signal 410 is °゛1'', the weighting coefficient is β, and when the random number signal 410 is °2°゛, the weighting coefficient is γ
is output as a weighting coefficient signal 300.

In the above configuration, by changing the weighting coefficient for calculating the average density using random numbers depending on the position of the pixel of interest, it is possible to reduce the occurrence of unique striped patterns in the medium density part of the document, as in the above-mentioned embodiment. Now, in the embodiment and modification described above, ROM7.17
Although three types of weighting coefficient distributions (coefficient matrices) were used, the present invention is not limited to these.
A multi-level distribution of weighting coefficients (coefficient matrix) may be used.

[Effects of the Invention] As explained above (according to the present invention, it is possible to reduce the occurrence of unique striped patterns in the medium density portion of a document, reduce the occurrence of unique striped patterns, and output high-quality images. becomes.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of the image processing device of this embodiment, FIG. 2 is a block diagram showing the configuration of the weighting coefficient setting circuit 4 of this embodiment, and FIG. 3 is a weighting coefficient setting method of this embodiment. FIGS. 4A to 4C are diagrams showing an example of a coefficient matrix according to the pixel position of interest in this embodiment, and FIG. 5 is a block diagram showing the configuration of the binarization circuit 5 of this embodiment. , FIG. 6 is a diagram showing an example of weighting coefficients during error diffusion in this embodiment, and FIG. 7 is a block diagram showing a partially modified configuration of the weight mask setting circuit 4. In the figure, 1... input device, 2... A/D, 3... correction circuit, 4... weighting factor setting circuit, 5... binarization circuit, 6... output device, 7,16.17...ROM,
8...Adder, 9...Error buffer memory, 10
... Comparator, 11... Binary memory, 12... Average concentration calculator, 13... Arithmetic unit, 14... Comparator, 1
5... Weighting is circuit, 17... RAM, 100...
- Corrected signal, 200... Position signal, 300... Weighting coefficient signal, 310... Error corrected signal, 320...
・Average density signal, 330...Difference signal, 340...
Signal, 400...binary signal, 410...random number signal.

Claims (1)

[Scope of Claims] (1) An image processing device that performs binarization using an average density preservation method, which comprises: an input means for inputting an original image signal of a pixel of interest; and an input means for inputting an original image signal of a pixel of interest; a calculation means for calculating an average density; a binarization means for binarizing the input original image signal based on the calculated average density; and a binarization means for binarizing the input original image signal based on the calculated average density; An image processing device comprising: output means for outputting a signal. (2) The image processing apparatus according to claim 1, wherein the original image signal includes a position signal indicating the position of the pixel of interest, and the calculation means calculates the average density based on the position signal. (3) The calculating means includes a setting means for setting a weighting coefficient for calculating the average concentration based on the position signal;
4. A multiplier for obtaining an average density by multiplying the set weighting coefficient by a binary signal of a pixel around the pixel of interest, which has already been binarized by the binarizing means. The image processing device according to item 2. (4) The image processing apparatus according to claim 1, wherein the setting means includes a storage means in which the weighting coefficient corresponding to the position of the pixel of interest is stored in advance.