JPH03136467A - Picture processor - Google Patents

Abstract

PURPOSE:To enable high-quality binary reproducing without a regular pattern by providing an average value correcting means to correct an average value based on a dummy random number. CONSTITUTION:A binary processing part 200 executes a processing while integrating an average density arithmetic processing part 200a to calculate average density as a binary threshold value and an error dispersion processing part 200b to execute an error correction processing. The binary processing part 200 is further equipped with a random number generation part 200c and the regular pattern of a binary result is prevented by the random number from the random number generation part 200c. Thus, in respect to a picture with a uniform density level over a wide area like a CG picture, the high-quality binary reproducing without the regular pattern is realized.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an image processing apparatus such as a facsimile machine or a digital copying machine, and particularly to a pseudo halftone processing apparatus.

[Prior Art] Conventionally, as this type of pseudo halftone processing method, ■Error diffusion method and ■Average density approximation method (Japanese Patent Application Laid-Open No. 1043693/1983)
is known.

The former binarizes the multi-value pixel data of the pixel of interest using a fixed threshold, and distributes and adds the error between the binarization level and the multi-value pixel data of the pixel of interest to pixels near the pixel of interest at a predetermined distribution rate. This is a binarization method. The latter uses binary data that has already been binarized near the pixel of interest, and when the pixel of interest is binarized into black or white, the weighting of both is determined by the neighborhood average value, and the average value of the pixel of interest is calculated from the average value. This is a method of selecting the one closer to the level and binarizing the pixel of interest.

The former requires two-dimensional calculations on multivalued data, and has the disadvantage that it cannot be made into hardware at low cost due to the large amount of processing. texture occurs, and the number of gradations that can be reproduced is actually extremely inferior to the former.

[Problems to be Solved by the Invention] Therefore, an average density preservation method is proposed in which a single average density is determined, the image is binarized using this value as a threshold value, and a binarization error correction process is added. A new binarization method is being considered.

However, when binarizing an image that has a uniform density level over a wide area, such as a CG image, the image is binarized into a very regular pattern with black or white aligned especially at low nodes or high nodes. , there are drawbacks that make it look very unnatural.

The present invention eliminates the drawbacks of the conventional art and provides an image processing device that realizes high-quality binarized reproduction without regular patterns for images having uniform density levels over a wide area, such as CG images. do.

[Means for Solving the Problem] In order to solve this problem, the image processing device of the present invention has the following features:
An image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, comprising: a random number generating means that generates pseudo-random numbers; and an average value correction means for correcting the average value.

Also, an image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, and which diffuses errors caused by the binarization to surrounding pixels. A random number generating means generates a pseudo-random number, and an error correcting means corrects the error to be diffused based on the pseudo-random number.

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

Example of Configuration of Image Processing Apparatus> FIG. 1 is a block diagram showing the configuration of an image processing apparatus of this embodiment.

Multivalued image data 100a, such as image data read from a document, input from the image data input section 100 is binarized by the binarization processing section 200 as pseudo halftone processing,
The image data output unit 300 outputs it as a display screen, printed matter, or the like. Here, as will be described in detail below, the binarization processing unit 200 includes an average density calculation processing unit 200a that calculates an average density as a binarization threshold, and an error diffusion processing unit 200b that performs error correction processing. Perform integrated processing. Hereinafter, in this embodiment, this processing method will be referred to as the average density preservation method.

In this embodiment, the binarization processing section 200 further includes a random number generation section 2.
00c, and the random number from the random number generator 200c prevents regular patterns in the binarization result.

Principle of average concentration preservation method〉 About the principle of average concentration preservation method of this method
This will be explained with reference to Figure C.

This method uses input multi-value data f(i, j
) (0 to 255), a plurality of binary data B (i, j) located in the vicinity and already binarized as shown in Fig. 2B, and a plurality of binary data B (i, j) as shown in Fig. 2C A weighted average value m (i, j) is calculated using a weight mask R (x, y) prepared in advance as shown in FIG.

j) as a threshold value, and the average value m(
This is a method of correcting adjacent input multi-value data to be binarized using the difference value between input image data f (i, j) and input image data f (i, j) and preserving the density.

In the example shown in FIGS. 2A to 2C, f(i, j)+
When E (i, j) > m (i, j), set B (i, j) l f (i, j) + E (i, j) 5 m (i,
j), then B (i, j)-0 and E+(i, j+
1) ・-[f(i,j)+E(i,j)-m(i,
j)] −■ Earthquake Ex (i day, j) However, E (i, j)・E+ (i, J) +Ei
(i, j)...■ Now, the above principle will be explained with reference to FIG.

The weight mask used is 12 in the vicinity of the pixel of interest as shown in the figure.
The total sum is set to 255 for each pixel. Therefore, m(i, j) can be used as is for binarization as a threshold value between 0 and 255. Now, the threshold value m (i, j) for binarizing the multi-value data 20 at the pixel position of interest in FIG.
(i, i) = (37x O+27X 1)+
(16x O+27x O+37x 1+27x O
+15x l)+ (6xO+16xO+27xO+
15xO+ 5x 1) = 84 Therefore, B (i, j) is binarized with O, the error generated is 2O-84 = -64, and E + (i.

j+t)=Ez(i+1.j)=-32.

Due to this error diffusion, f(i+1.j) changes from 25 to 2
5-32=-7, f (i, j+1) is 3o to 3O
It is corrected to -32=-2.

Principle of binarization processing in this embodiment> In this embodiment, when performing the above processing on an output multi-value image such as CG (computer graphics), uniform and fixed multi-value data is converted into two In order to solve the problem that when converted into a binary image, a binary image having an unnatural texture that goes regularly orthogonally is generated, the dither signal P is generated according to the output from the pseudo-random number generator during the binarization according to the above formula 0. , 4(i, j).

In other words, the aforementioned equations ■ and ■ are f (i, j) + E (i, j) ”Ps (i
, when l > m (i, j), B (i, j
) =1f (i, j) +E (i, j) +P
When N (i, j)6m(i, j), B (i,
j) ・0...■ E+ (i, j+1) = 1/2 (f (i, j
) +E (i, j) +PN (i, l -m
(i, j) )=Ez (i+1. j) However, E (i, j)・E+ (1, J) ”E2
(i, j) ...■.

The pseudo-random number generator used in this example is a known M-sequence code (S, W, Golomb, “Shift-Regis
ter 5equences”Ho1den-Day
, Inc., San Francisco, 1967)
When processing an A4 document at 400dpi using
The cycle is set to 225-1, and a density level width of ±δ (δ is assumed to be 2 to 4 levels) is provided corresponding to 0 and 1 of the 1-bit output.

<Example of configuration of binarization processing unit> An example of the hardware configuration of the binarization processing unit of this embodiment will be explained in detail using FIG. 4.

First, the binary data converted into a binary data by the comparator 10 is manually inputted to a D type flip-flop (hereinafter referred to as D-F/F) 31 and a line memory 2, and sequentially converted into 10 D4/F3 data.
By shifting to a to 3j and line memory 1, each output terminal corresponds to 12 2 of the weight mask area shown in FIG.
Output value data at the same time.

If the average density calculating unit 8 inputting the same output is implemented in a ROM that calculates and stores the average density in advance according to the formula 0 using a weight mask, the average density m (i, j) will be calculated based on the table conversion method based on the output. is obtained. The output is inputted to one input terminal of the comparator 10 and used as a threshold value, and the subtracter 9 performs subtraction according to equation (2). Its output is
It is input to the error ROM 12, and within the ROM it is divided into two parts, E and E2, as shown in equation (2), and E+(i, j) is input to the error memory 14, where it is divided into approximately one line until the next line is binarized. The corresponding amount of delay is held. On the other hand, E+(t + 1,
J) is already held delayed by one line from the error memory 14 and is used together with the output E+(i+1, J) to correct the manual data f(i+1.j) using the adder 13.

Now, the pseudo-random number generation section 19 surrounded by a broken line has 25 1-bit input/output D-F/Fl7-t to 17-zs and three exclusive ORs (EXOR) 18-1+1s-z
, 1 s-8 and a multiplexer 16. The 25 D-F/Fs are prevented from having their outputs all set to O by a preset circuit (not shown).

Therefore, the M-sequence code with one period T=2''-1 is D −
F/Fl is obtained at the 7-25 output terminal. The multiplexer 16 selects a preset value of ±δ (in this embodiment, ±2 to 4) depending on the state of 0 and 1 of the same 1-bit pseudo-random number.
is converted into a several-bit value, and added to f (i+1°j) together with the correction data in the adder 13 described above.

Note that this random number generation unit adds an average value of 0 to the input image without generating periodicity when binarizing an A4 document.
Even when viewed as an average density, the image is not disturbed, and in particular, when a CG image that is uniform and has the same level in a plane is binarized, a binary image with disordered regularity is obtained. The data corrected with the random numbers is input to the subtracter 9 and the comparator 10 with the timing adjusted by DF/Fil, and the next pixel is binarized.

The above processing is repeated for each pixel in synchronization with a pixel clock applied to all F/Fs (not shown).

Other configuration examples of the binarization processing unit> In the embodiment described above, a dither signal PN of ±δ based on random numbers was given to the input image data.
Even if j) is moved to the right-hand side, the same effect can be obtained by adding it to the threshold value m('i, j) so that equation (2) holds true.

FIG. 5 shows an embodiment in which PN is added to the threshold chain side, and the output dither signal PN of the pseudorandom number generator 19 similar to the embodiment described above is converted to the average concentration m(i, j) by the adder 15. added to. This embodiment has the advantage of reducing the burden on the former adder 13.

Regarding the value of ±δ> Generally, the amplitude δ of the dither signal is made to decrease according to the value of the input data f, that is, when f is small, so that the disturbance of dots due to relative random numbers can be avoided regardless of the density value. can be constant.

<2.4 Other side of the generator> In this embodiment, when T=2"-1, the synchronization is set to be large compared to the amount of data when processing an A4 document. For example, when T=2"
-1 to reduce the hardware scale and reduce the image size to 2.
During value conversion, other data such as the absolute address of the pixel of interest or the image multilevel data itself is used to convert D-F/F17.
By presetting , it becomes possible to lengthen the period T in a pseudo manner.

[Effects of the Invention] According to the present invention, it is possible to provide an image processing device that realizes high-quality binarized reproduction without regular patterns for images having uniform density levels over a wide area, such as CG images.

In other words, despite the small amount of processing equipment, the ED
It is possible to provide a pseudo halftone processing device that can achieve higher image quality than the conventional method. Furthermore, by adding simple pseudo-random numbers, a binary image with irregularities can be obtained with respect to the CG image.

[Brief explanation of the drawing]

FIG. 1 is a block diagram showing the configuration of the image processing device of this embodiment. FIGS. 2A to 2C are diagrams explaining the average error preservation method. FIG. 3 is a diagram explaining the average density calculation. 5 is a diagram showing the hardware configuration of the binarization processing unit of this embodiment, and FIG. 5 is a diagram showing the hardware configuration of the binarization processing unit on the other side. In the figure, 100...image data input section, 200...2
Value conversion processing unit, 200a... Average concentration calculation processing unit, 20
0b...error diffusion processing section, 200c...random number generation section, 300...image data output section. -51( Figure 3

Claims (2)

[Claims] (1) An image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, comprising: random number generation means for generating pseudorandom numbers; An image processing apparatus comprising: average value correction means for correcting the average value based on random numbers. (2) An image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, and which converts errors caused by the binarization into peripheral pixels. An image processing apparatus comprising: an error diffusion means for diffusing; a random number generation means for generating a pseudo-random number; and an error correction means for correcting the error to be diffused based on the pseudo-random number.