JPH03186064A - Picture processor - Google Patents

Abstract

PURPOSE:To improve the quality of a picture by setting a weighting coefficient for average density calculation corresponding to the density of an input picture element. CONSTITUTION:Read picture data are successively converted to digital data by an A/D converter 2 and signals 100, which are corrected by shading correction, etc., are simultaneously outputted to a weighting coefficient setting circuit 4 and a binarizing processing circuit 5. In the weighting coefficient setting circuit 4, the weighting coefficient for average density calculation is set by the corrected signal 100 and a weighting coefficient signal 200 is outputted. In the binarizing circuit 5, a binarizing processing is executed to the corrected signal 100 by an average density value calculated by the weighting coefficient signal 200. Namely, according to the variably set weighting coefficient for average density calculation corresponding to the density of the input picture element data, the average density value is calculated and the binarizing processing is executed. Thus, the quality of the picture can be improved.

Description

Detailed Description of the Invention [Field of Industrial Application] The present invention relates to an image processing device, and in particular, binarizes a pixel of interest based on the average density value of a predetermined area, and converts the generated binarization error to surrounding pixels. The present invention relates to an image processing device that can diffuse images into images and store the density of images.

[Prior Art] In recent years, in image processing devices such as digital printers and facsimile machines, as a binarization method for reproducing halftones, an error method that disperses the error generated in the binarization process to surrounding pixels has been used. A method called diffusion method is attracting attention.

[Problem to be solved by the invention] Also. A similar method called the average concentration conservation method has been proposed. This average density preservation method binarizes the pixel of interest by thresholding the average density calculated from the arrangement of dots in the already processed area around the pixel of interest, and subtracts the binarization error that occurs at this time. This is a method of preserving density by dispersing it into pixels. However, if the binarization error is larger than a predetermined threshold, error diffusion processing is stopped (adaptive error correction).
is configured to do so.

However, in the above average density preservation method, when calculating the average density of a predetermined area, a predetermined weighting coefficient is used. In areas where the density is low or high, the dots are close together and connected in a linear manner, resulting in a significant deterioration in the quality of the image.

The present invention was made to solve the above problems, and
It is an object of the present invention to provide an image processing device that can improve the quality of an image by variably setting a weighting coefficient for calculating an average density according to an input pixel density.

[Means for Solving the Problems] In order to achieve the above object, an image processing device of the present invention has the following configuration. That is, the image processing device is capable of binarizing a pixel of interest based on the average density value of a predetermined area, diffusing the generated binarization error to surrounding pixels, and storing the density of the image, and which is capable of storing the density of the image. a setting means for variably setting a weighting coefficient for calculating the average density according to the density of the image, and a binarizing means for calculating the average density value according to the weighting coefficient set by the setting means and performing binarization processing. have

[Operation] In the above configuration, depending on the density of input pixel data,
It operates to calculate an average density value according to a variably set weighting coefficient for calculating the average density, and to perform binarization processing.

[Embodiment J] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a schematic block diagram showing the configuration of an image processing apparatus in this embodiment.

First, image data read by an input device 1 including a photoelectric conversion element such as a CCD and a drive system for scanning it is sequentially sent to an A/D converter 2. The converter 2 converts the analog data of each pixel into, for example, 8-bit digital data, which is then quantized into data having 256 levels of gradation.

Next, in the correction circuit 3, corrections such as shading correction are performed by digital calculation processing in order to correct unevenness in sensitivity of the CCD sensor and unevenness in illuminance due to the illumination light source. Then, the corrected signal (corrected signal) 100 is simultaneously output to the weighting coefficient setting circuit 4 and the binarization processing circuit 5. This weighting coefficient setting circuit 4 sets a weighting coefficient for calculating the average concentration using the corrected signal lOO output from the correction circuit 3,
A weighting coefficient signal 200 is output.

On the other hand, the binarization circuit 5 converts the corrected signal (8-bit multivalued image data) 100 output from the correction circuit 3 into a 1-bit binary binary signal using the average density value calculated by the weighting coefficient signal 200. Binarization processing to 300 (quantization processing)
and outputs a binary signal 300. An output device 6 configured with a laser beam printer, an inkjet printer, etc. receives a binary signal 300 from the binarization circuit 5.
Image formation is performed by turning on and off the dots.

FIG. 2 is a block diagram showing details of the weighting coefficient setting circuit 4 for calculating the average density described above.

In the figure, a corrected signal 10 output from the correction circuit 3
0 is input to the ROM 7, and the corresponding value is the weighting coefficient signal 2.
Output as 00. This weighting factor α. , β■ are shown in FIGS. 3(a) and 3(b). However, "*" corresponds to the pixel position of interest (I, J). And Table 1 below
is an example showing the relationship between input and output.

Table 1 Here, two types of weighting coefficients are used by the ROM 7 as described above, but it is also possible to use multi-level weighting coefficients. At that time, the area where the image density is low.

In the high part, near the density 128, it is preferable to use a somewhat large weight mask as shown in FIG. 3(b).

FIG. 4 is a block diagram showing the configuration of the binarization circuit 5 described above.

In the figure, a corrected signal 10 input from the correction circuit 3
0 (targeted image density) is added by the adder 8 to the binarization errors E and J (sum of errors allocated to the target pixel) stored in the error buffer memory 9, and as a result, the error-corrected A signal 210 is output.

Then, the error-corrected signal 210 is input to the comparator 1o, and the average concentration signal 220 calculated by the average concentration calculator 12
compared to Here, if the error corrected signal 210 is larger than the average density signal 220, it is "l", and if it is smaller, it is "O".
is output as a binary signal 300. Further, this binary signal 300 is input to the output device 6 and is also input to a predetermined pixel position in the binary memory 11.

Next, the average density calculator 12 weights the weighting coefficient signal 200 from the weighting coefficient setting circuit 4 described above with respect to the binary memory 11 5, and calculates the average density signal 220. This average density is the sum of binary data multiplied by a weighting coefficient (α1 or βIIL).

This average density signal 220 is input to the above-mentioned comparator 10 as well as to the arithmetic unit 13.

The arithmetic unit 13 also includes the above-mentioned error corrected signal 210.
is entered. Here, the difference between these two signals is calculated and output as signal 230 (ΔEiJ).

Next, this signal 230 is input to the comparator 14 and compared with a threshold value (for example, "126") for stopping error diffusion. As a result, if it is larger than the threshold value, “O” (stop error diffusion), and if it is equal or smaller, the value remains as it is on the signal 240.
is output as This signal 240 is then input to the weighting circuit 15, where it is weighted (γKl) and then added to the error at a predetermined pixel position in the error buffer memory 9. FIG. 5 shows an example of the weighting coefficient (γ□). Note that "*j corresponds to the pixel position of interest (I, J).

By repeating the above operations, binarization processing using the average density preservation method is performed.

In the above configuration, by using the weighting coefficient for average density calculation that is adapted to the input image density, it is possible to reduce the occurrence of unique striped patterns in the medium density portion of the document.

[Other Embodiments] Next, other embodiments according to the present invention will be described below with reference to the drawings.

In this embodiment, a case will be described in which a part of the weighting factor setting circuit 4 described above is changed.

A signal 100 output from the correction circuit 3 is input to the ROM 7. This ROM 7 outputs a signal 200 according to the relationship shown in Table 2 below.

Moreover, FIGS. 6(a) and (b) show the weighting coefficients δ□.

This shows an example of EKL.

Table 2 With the above configuration, it is possible to prevent the phenomenon of dots being placed close to each other, which occurred in areas with low and high document density, as in the case of the previous embodiment. , it is possible to improve the quality of images.

[Effects of the Invention] As explained above, according to the present invention, by setting the weighting coefficient for calculating the average density adapted to the input pixel density,
It is possible to reduce the unique striped pattern that occurs in the medium-density areas of images, and also to prevent the phenomenon of dots being placed close to each other that occurs in areas of low image density, improving the image quality. It is possible to improve the

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of the image processing device in this embodiment, FIG. 2 is a block diagram showing the configuration of the weighting coefficient setting circuit 4, and FIGS. 3(a) and (b) are the blocks in this embodiment. 4 is a block diagram showing the detailed configuration of the binarization circuit 5, FIG. 5 is a diagram showing the weighting coefficients for error diffusion calculation, and FIG.
) and (b) are diagrams showing weighting coefficients in other embodiments. In the figure, 1... input device, 2... A/D converter, 3...
...Correction circuit, 4...Weighting coefficient setting circuit, 5...2
Value converting circuit 6... Output device.

Claims (1)

[Claims] An image processing device capable of binarizing a pixel of interest based on the average density value of a predetermined area, diffusing the generated binarization error to surrounding pixels, and preserving the density of the image. , a setting means for variably setting a weighting coefficient for calculating an average density according to the density of input pixel data, and a binary system for calculating an average density value according to the weighting coefficient set by the setting means and performing a binarization process. An image processing device characterized by comprising: converting means.