JPH07298054A - Image threshold processing unit - Google Patents

Abstract

PURPOSE:To provide image threshold processing unit in which density of a pseudo gradation image is changed without density conversion of an original image. CONSTITUTION:The processing unit is made up of an image sensor reading an image, an A/D converter converting an image analog signal from the sensor into a digital signal, and an error spread processing section thresholding the digital signal, and the error spread processing section is made up of a subtractor 11a subtracting a WH signal (b) from an original image correction signal (a), a subtractor 11b subtracting a BL signal (c) from the signal (a), a comparator 12 comparing the signal (a) and a threshold level (d) to provide an output of binary data, a selector 13 outputting selectively either of output signals e, f of the subtractors 11a, 11b depending on the output of the comparator 12, an error memory 14 storing an output of the selector 13, and an adder 15 adding data from the error memory 14 to an original image signal.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a binarized image processing apparatus used in facsimiles, copiers and the like, and more particularly to a binarized image processing apparatus using an error diffusion method.

[0002]

2. Description of the Related Art Conventionally, when a continuous image signal is converted into a discrete image (hereinafter referred to as an original image) signal and then converted into a binary digital image (hereinafter referred to as a binary image) signal, error diffusion is performed. There is a method of obtaining a pseudo gradation by the method. This method is described by Floid and Steinberg in 1976 as "An Adaptive Algorithm for Spatial Grayscale".
(Proceeding of the SID Vol. 17/2 Second
This is proposed by a paper called Quater 1976) and is characterized by preserving the image density by dispersing the error with respect to the original image generated by the binarization process to surrounding pixels.

By the way, since this error diffusion method expresses a pseudo gradation image by changing the image data, even if the threshold value for binarization is changed,
The brightness of the output image does not change. Therefore, conventionally, in order to change the brightness of the binary image by the error diffusion method, the density conversion of the original image is performed before the error diffusion process. (For example, S
ANYO Facsimile Controller LC8214
1. This method is used in Kawasaki Steel FAX image processing ASSP (LVP) and the like. )

Next, a conventional image processing apparatus that performs density conversion of an original image before such error diffusion processing will be described. FIG. 10A is a block diagram showing a configuration of a conventional binarized image processing apparatus that does not perform density conversion processing. In FIG. 10A, 101 is an image sensor such as a CCD that functions as an image capturing means, and 10 is an image sensor.
Reference numeral 2 denotes an A / D converter that converts a signal obtained from the image sensor into an n-bit digital signal, and 103 denotes a known error diffusion processing unit that converts the n-bit digital signal into a binary digital signal. .

In the image processing apparatus having the above configuration, when the brightness of the output image is changed, a general method shown in FIG.
It has a configuration as shown in 0 (B). In FIG. 10B, 104 is a memory used for density conversion. In the memory 104, conversion output is obtained using the input image data as an address. Therefore, by preparing the data shown in FIG. 11 in the memory 104, the image data can be converted into arbitrary data. For example, if the density of the original image is X, the memory is accessed using X as an address, and the corresponding data Y is obtained.

[0006]

However, in the conventional image processing apparatus configured to perform the density conversion having the above-described structure, a conversion means such as a memory must be added, and further, different density conversion characteristics are obtained. Must be equipped with a conversion means for each conversion characteristic, which requires an increase in circuit scale and the number of parts.

The present invention has been made to solve the above problems in the conventional image processing apparatus capable of changing the density. The invention according to claim 1 adds a special circuit and parts. Without converting the density of the original image,
It is an object of the present invention to provide a binarized image processing device capable of changing the density of a pseudo gradation image, and the invention according to claim 2 changes the density of the pseudo gradation image according to the density of the original image. It is an object of the present invention to provide a binarized image processing device that can be changed.

[0008]

In order to solve the above problems, the invention according to claim 1 is an image in which an original image obtained by digitizing a subject image by an A / D converter is binarized by error diffusion means. In the processing device, the error diffusion means sets a first reference level and a second reference level that can be respectively changed, and calculates first error data using the pixel data of interest and the first reference level. Means, means for calculating second error data using the pixel data of interest and the second reference level, and means for selecting the first or second error data according to the binarization mode of the pixel data of interest. It is composed of.

According to a second aspect of the present invention, the means for setting the first and second reference levels uses the output of an A / D converter for digitizing a subject image as the first and second reference levels. It is configured to change the level.

[0010]

[Operation] In a conventional binarized image processing apparatus using the error diffusion method, 0 and the dynamic range R of the image are adopted as the reference value used for the error calculation with the original image. In the invention described in 1, the first reference level and the second reference level that can be varied are used as the reference values, and the brightness of the output binary image is changed to the original image by changing these reference levels. It is possible to change without changing the density of. Further, in the invention according to claim 2, since the first reference level and the second reference level are configured to be changed by the output of the A / D converter, the brightness of the output binary image is changed. , And can be changed according to the density of the original image.

As described above, according to the present invention, since the brightness of the binary image obtained by the image processing can be changed, the density conversion of the output image can be easily performed without the density conversion of the original image. . Next, this point will be specifically described in detail. When performing the error diffusion processing on the n-bit digital signal input from the A / D converter that digitizes the subject image, the white reference level WH and the black reference level BL used for calculating the error data are set in advance, or Control from outside. The error data obtained by the calculation is the difference between a signal obtained by adding the error data generated in the peripheral pixels to the image signal (hereinafter referred to as the corrected image signal) and the binary level. For example, when the binarization result is white and WH is the white reference level R (R: image dynamic range) in the conventional error diffusion method, the error data E generated is E = corrected image signal −R. Since E is negative, if it is later added to another image signal, the corrected image signal becomes small. That is, the error data E at this time works so as to darken the image.

Now, when WH is made smaller than R, the resulting error data becomes smaller than E. In other words, the degree to which the image is darkened is reduced, so the image becomes brighter. The pseudo gradation level of the output image at this time is expressed by the following equation. Pseudo gradation level = R · input level / WH Here, the pseudo gradation level is a gradation represented by an area change of binary data of white and black. For example, when R = 256 and WH = 200, the input level 200 is output as the entire surface 256 of the pseudo gradation level 256. This indicates that an image having the same brightness as the binary image obtained by processing the image having the density of 256 by the conventional error diffusion method can be obtained.

Similarly, the pseudo gradation level when BL is changed is expressed by the following equation. Pseudo gradation level = R. (Input level-BL) / (RB)
L)

[0014]

【Example】

(First Embodiment) Next, an embodiment will be described. Figure 1
1 is a schematic block configuration diagram showing a first embodiment of a binarized image processing device according to the present invention. In FIG. 1, 1 is an image sensor for capturing an image, 2 is an A / D converter for quantizing an image signal output from the image sensor 1 into an n-bit digital signal, 3 is an A / D converter 2 An error diffusion processing unit that binarizes the output digital signal.

In the binarized image processing apparatus configured as described above, the subject image is captured by the image sensor 1, converted into an n-bit digital signal by the A / D converter 2, and the error diffusion processing section In 3, the binary data is output. Here, the white reference level WH and the black reference level BL signal are used by the error diffusion processing unit 3 when calculating an error with the original image.

Next, the error diffusion processing section 3 will be described with reference to FIG.
Will be described in detail. In FIG. 2, 11a is a subtracter for subtracting the signal b (WH signal) from the signal a,
1b is a subtractor for subtracting the signal c (BL signal) from the signal a. 12 compares the signal a with the signal d (threshold value), and a>
The comparator outputs "L" when d, and outputs "H" otherwise. Reference numeral 13 denotes a selector which outputs the output signal e of the subtractor 11a when the output signal g of the comparator 12 is g = “L” and outputs the output signal f of the subtractor 11b when g = “H”.
Is output as the output signal h. Reference numeral 14 is an error memory for storing an error amount from the white or black reference level calculated by the subtractor 11a or 11b, and 15 is the original image signal and the error memory 1
4 is an adder that adds the data of 4.

Next, the operation of the error diffusion unit configured as described above will be described. The digital signal output from the A / D converter 2 is added with the error data calculated in the past output from the error memory 14 by the adder 15, and becomes a signal a. That is, the signal a becomes a corrected image signal for the original image signal, and the signal a is binarized. WH and B set in the subtractors 11a and 11b
Subtraction with the L signal, ab, and ac are performed, and either of these two results becomes error data generated during binarization. In the comparator 12, the signal a is compared with a preset threshold value d, for example, 1/2 of the dynamic range R of the image, and the result is output as binary data g. The selector 13 operates to switch the error data output to the error memory 14 depending on whether the binary data g is white (g = “L”) or black (g = “H”). Ie 2
If the result of binarization is white, the error data is the difference (ab) between the corrected image signal and the WH signal, and if the result of binarization is black, the error data is the corrected image signal and the BL signal. The difference (a-c) is the output.

Here, the relationship between the values of WH and BL and the density of the output image (pseudo gradation output) will be described with reference to FIGS. In the conventional error diffusion method, WH = R,
BL = 0, and as indicated by the straight line 25 in FIGS. 3, 4, and 5, the input density is directly output as a pseudo gradation.

Now, a case where the WH value is changed will be described with reference to FIG. For example, WH = WH
When 1 (WH1 <R), the subtraction result in the subtractor 11a becomes smaller than when WH = R, whatever the value of the signal a. That is, the error data for blackening the other pixels acts so as to be small, and the output image is converted as shown by the straight line 21 as a whole and becomes bright. Further, since the error data is 0 in the range where the signal a is WH1 ≦ a ≦ R, the binary data are all white in this range as indicated by the straight line 22.

Similarly, even when BL = BL1 (BL1> 0), the error in the direction of whitening other pixels is small, so that the output image is transformed as a straight line 23 as shown in FIG. , Becomes dark overall. In the range where the signal a is 0 ≦ a ≦ BL1, the binary data are all black as shown by the straight line 24. Further, if WH and BL are changed at the same time, the output image shows that the signal a is 0 <a <, as shown by the curve 26 in FIG.
When [R · BL1 / (R-WH1 + BL1)] is dark,
The signal a is [R · BL1 / (R-WH1 + BL1)] <a
<When R, it becomes bright. As described above, the brightness of the binary image obtained by the error diffusion process can be changed without performing the density conversion of the original image.

Incidentally, in the first embodiment shown in FIG.
The image sensor 1 and the A / D converter 2 are portions for inputting a multi-gradation image, which may be independent of the present binary image processing device. Alternatively, the multi-tone image may be stored in a memory or the like, and the pixel information may be input from the memory. The range of WH and BL is shown as WH <R, BL> 0 in this embodiment, but if BL <R, WH> 0, WH> BL,
It can be any value.

(Second Embodiment) Next, a second embodiment of the present invention will be described. FIG. 6 is a schematic block configuration diagram showing the second embodiment, and the image sensor 1, the A / D converter 2, and the error diffusion processing unit 3 have the same configuration as the first embodiment. Reference numeral 4 is a block that outputs preset WH and BL values. In this embodiment, (BL = BL2, WH =
R), (BL = 0, WH = WH2), (BL = 0, WH
7 shows the pseudo gradation output in the case of switching and outputting three types of WH and BL value pairs (= R).
In block 4, to brighten the output image,
By selecting WH = WH2 and BL = 0, the pseudo gradation output shown by the curve 31 is obtained, and in order to darken the output image, by selecting WH = R and BL = BL2, the curve 3
The pseudo gradation output shown by 2 is obtained. Further, when it is desired to faithfully represent the input density by the pseudo gradation, the output shown by the straight line 33 is obtained by setting WH = R and BL = 0. Where WH
By varying 2 and BL2 from the outside, an output image having an arbitrary density can be obtained.

(Third Embodiment) Next, a third embodiment of the present invention will be described. FIG. 8 is a schematic block configuration diagram showing the third embodiment, and the image sensor 1, the A / D converter 2, and the error diffusion processing unit 3 have the same configuration as the first embodiment. Reference numeral 5 is a block for changing and outputting WH and BL according to the density of the original image. The control of WH and BL will be described with reference to FIG. In this embodiment, in order to simplify the explanation, the input signal is shown as an example divided into three regions A, B, and C. In the graph shown in FIG. 9, when the input original image signal is in the range of the area A, WH = W1 and BL = B1 are processed. As a result, the pseudo gradation of the output image is converted as shown by the straight line 41. Similarly, in region B, WH = W
2, BL = B2, and in the area C, WH = W3, BL = B
By outputting as 3, the straight lines 42 and 43 are respectively output.
The converted pseudo gradation is output as shown by.

As described above, by preparing some WH and BL corresponding to the output of the A / D converter 2 and changing the values according to the density of the original image, the brightness of the output image can be freely changed. can do. For example, the straight line 4 in FIG.
When 1, 42 and 43 are approximated to the γ correction curve, the same effect as that obtained by subjecting the original image to the γ correction can be obtained. BL and WH may be changed by a corrected image signal which is the sum of the original image signal and the error data.

[0025]

As described above on the basis of the embodiments,
According to the first aspect of the present invention, it is possible to provide a binarized image processing apparatus suitable for downsizing, which can change the brightness of an output image from the error diffusion means without performing density conversion of the original image. it can. According to the second aspect of the invention, the brightness of the output image from the error diffusion means can be changed according to the density of the original image without converting the density of the original image. An image processing apparatus can be provided.

[Brief description of drawings]

FIG. 1 is a schematic block configuration diagram showing a first embodiment of a binarized image processing device according to the present invention.

FIG. 2 is a circuit configuration diagram showing a detailed configuration example of an error diffusion processing unit in the first embodiment shown in FIG.

FIG. 3 is a diagram showing pseudo gradation output when a white reference level is changed in the first embodiment.

FIG. 4 is a diagram showing pseudo gradation output when the black reference level is changed in the first embodiment.

FIG. 5 is a diagram showing pseudo gradation output when the white reference level and the black reference level are changed in the first embodiment.

FIG. 6 is a schematic block configuration diagram showing a second embodiment.

FIG. 7 is a diagram showing pseudo gradation output when a white reference level or a black reference level is changed in the second embodiment.

FIG. 8 is a schematic block configuration diagram showing a third embodiment.

FIG. 9 is a diagram showing pseudo gradation output when the white reference level and the black reference level are changed in each region of the input signal in the third embodiment.

FIG. 10 is a schematic block diagram showing a configuration example of a conventional image processing device when density conversion of an original image is not performed and when density conversion is performed.

11 is a diagram showing an example of density conversion in the conventional image processing apparatus shown in FIG.

[Explanation of symbols]

 1 image sensor 2 A / D converter 3 error diffusion processing unit 4 block for outputting set WH, BL 5 block for outputting WH, BL according to original image density 11a, 11b subtractor 12 comparator 13 selector 14 error Memory 15 adder

Continuation of front page (51) Int.Cl. ⁶ Identification number Office reference number FI technical display location H04N 1/40 101 E

Claims (2)

[Claims] 1. An image processing apparatus for binarizing an original image obtained by digitizing a subject image by an A / D converter by an error diffusion means, wherein the error diffusion means has a variable first reference level and a variable first reference level. Means for setting the second reference level, and first using the pixel data of interest and the first reference level
Means for calculating the error data of the
Means for calculating the second error data using the reference level of, and the first or second depending on the binarization mode of the pixel data of interest.
And a unit for selecting the error data of 1. 2. The means for setting the first and second reference levels is configured to change the first and second reference levels using the output of an A / D converter that digitizes a subject image. The binarized image processing device according to claim 1, wherein