JPS62164371A - Estimation method for halftone picture of multivalued picture - Google Patents

Abstract

PURPOSE:To satisfactorily estimate an original halftone picture from a multivalued picture (multivalued dither picture, for instance) by selecting an aperture satisfying the prescribed conditions among plural types of apertures by moving in units of picture elements and estimating a picture based on the mean value of multivalued levels in the aperture. CONSTITUTION:Plural types of apertures are set in the multivalued picture composed of multivalued levels. Among them the aperture satisfying the prescribed conditions is selected by moving at every picture element, and the halftone picture is estimated according to the mean value of the multivalued levels in the aperture. Namely, in accordance with a threshold matrix the original halftone picture is converted into a white, grey and black ternary picture (multivalued dither picture), and conditions that there is no change in a picture element level are decided as inequalities show. Where the central positions of the apertures of the multivalued dither picture are 2 and 2, a=7, b=12, c=13 and d=24 hold, and the optimum aperture is C. Since the sum of the multivalued picture element levels at an initial position is c=12 and the gain of the opening C is 2, the halftone picture estimated value comes to 13X2=26.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention provides a method for estimating a halftone image of a multi-valued image, which can effectively estimate an original halftone image from a multi-valued image displayed in a pseudo-halftone manner. Regarding.

[Prior Art] Many of the output devices 1 currently in practical use, such as display devices and printing devices, have insufficient gradation.

A multi-value density pattern method (multi-value luminance pattern method), a multi-value dither method, etc. are known as methods for using such an output 92t and expressing halftones in a pseudo manner at a multi-value level.

In the case of 3-gradation expression, the multilevel density pattern method uses a threshold matrix composed of the first and second matrices to create multiple portions corresponding to one pixel of the document, as shown in Figure 16 (b). This is a method of recording with multivalued dots, and the multivalued dither method uses a similar threshold matrix to divide a portion of the original into one pixel, as shown in Figure 16 (a).
This is a method of recording with one multilevel dot. As shown in the figures, multivalued output data is obtained.

This output data represents a halftone image in pseudo-multivalued form.

Here, the white area indicates the white level, the black area indicates the black level, and the diagonally lined area indicates the gray level.

Conversion to a multivalued dither image using the threshold matrix (b) is performed under the following conditions.

That is, if the pixel level of the original halftone image (a) is both smaller than the corresponding levels of the first and second matrices, the pixel level is converted to the black level.

Original intermediate ml! If the pixel level of the i image (a) is both higher than the corresponding levels of the first and second matrices, the pixel level is converted to a white level.

If the pixel level of the original halftone image (A) is greater than the level of the corresponding first matrix and smaller than the second matrix, the pixel level is converted to a gray level.

By performing such conversion processing, a multivalued image (ternary image) as shown in FIG. 16 is obtained.

[Problems to be Solved by the Invention] By the way, if the original halftone image (corresponding to the input data in FIG. 16) can be created from such a multivalued pseudo halftone image, various problems can be solved. Since data processing can be performed, image conversion can also be performed with a degree of freedom, which is convenient.

In the case of a multilevel density pattern image, once the quality of the pattern level is known, it can be immediately restored to a halftone image. However, the resolution is low compared to the amount of information.
A multi-value dithered image has a higher resolution than a multi-value density pattern image in terms of the amount of information, but it is difficult to restore it to a one-dimensional halftone image. Therefore, it has not been possible to perform various image conversions using only multivalued dithered images.

The present invention solves these conventional drawbacks, and provides a halftone image of a multivalued image that can satisfactorily estimate the original halftone image from a multivalued image (for example, a multivalued dithered image). This paper proposes an estimation method.

[Means for Solving the Problems] In order to solve the above-mentioned problems, in this invention, several 81 types of apertures are set in a multivalued image consisting of multivalued levels, and apertures of 81 types are set among these multiple types of apertures. The present invention is characterized in that an aperture that satisfies a predetermined condition is selected while moving pixel by pixel, and a halftone image is estimated based on the average value of the multilevel levels within the aperture.

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

First, as one of the systematic multilevel dithering methods, an example will be described in which a 4×4 Bayer matrix with three gradations is used as a threshold matrix.

FIG. 1 is a diagram showing an example of a multivalued dither image for explaining the present invention. (B) is the original halftone image converted to digital data, (B) is the two-sided 4x4 Bayer ternary dither value matrix consisting of the first and second matrices, and (C) is the threshold value matrix ( This is a ternary dithered image of the original image (a) which was converted into a ternary image (multi-valued dithered image) of white, gray and black by (b).

Note that the multivalued dithered image shown in FIG. 1(C) is illustrated with the white level set to 2, the gray level set to 1, and the black level set to O. The same applies to the subsequent explanation.

The Bayer threshold matrix has a two-sided dither pattern in which dots are dispersed, as shown in Figure (b).

FIG. 2 is a diagram showing an example of a plurality of types of openings (unit areas) used in the present invention. A has a size of 2 rows x 2 columns, B has a size of 2 rows x 4 columns, C has a size of 4 rows x 2 columns, and D
1 and 2 respectively indicate openings having a size of 4 rows and 4 columns.

The black circles shown in each aperture A to D are the centers of movement when moving on the dithered image in FIG. 1(C).

Therefore, while keeping the apertures shown in Figure 2 fixed, they were moved on the multi-value dithered image shown in Figure 1 (C), the multi-value pixel levels in the apertures were summed, and the total value was multiplied by the gain. If the value is an estimated value of the intermediate 31 images, estimated halftone images as shown in FIGS. 3(a) to (d) are obtained. The gain is a coefficient that corrects the influence of the size of each aperture, and when the aperture is used as a reference, the gains of apertures A, B, C, and D are 4.2 and 2.1, respectively.

Figure 3 (A) is based on Figure 2 A, (B) is based on Figure 2 B, (C) is based on Figure 2 C, and (D) is the middle of the opening based on Figure 2. Shows a tone image.

Next, a method for obtaining the estimated halftone image shown in FIG. 3(d) will be described.

Now, as shown in FIG. 4, the aperture defined in FIG. 2]).

In this case, it is desirable that each pixel contained within the aperture be completely contained as shown in the figure.

That is, it is preferable to prevent a certain pixel from being partially missing.

Next, the pixel levels of the portion surrounded by this aperture are totaled and the resulting value is used as the estimated value of the halftone image.

In this case, it is 24. Therefore, the 1st row and 1st column (i,
The estimated value of H is 24.

Next, the aperture is moved to the right by one pixel (in this case, one column), and the multi-value pixel levels within the aperture at (1, 2) are summed up to 25 in the same manner as described above. Such calculation processing is performed sequentially for all columns in the same column.

Then, when the first row is finished, move the aperture by one row to the next row (second row) and perform the halftone density estimation operation in the same way as above, starting from the center at [3, 2]. Execute.

Multi-value dithering is performed on the unit area up to the last row and last column! r! By sequentially moving and executing each pixel of image j, a 1ft halftone image constant value is obtained, and the halftone image estimation operation is completed.

FIG. 3(d) is a diagram showing the estimated halftone image obtained in this manner.

Next, a method for obtaining an estimated halftone image using the aperture B shown in FIG. 3(b) will be described.

When aperture B is selected, the movement center of aperture B needs to be aligned with the center of the largest aperture, so the movement start position of aperture B is as shown in 5th Uf4. The multilevel pixel level in this state is 12, and the area is
In order to match gJD, it is necessary to double the total value in aperture B, so the pixel level in aperture B is 12X2.
-24. In this case, the gain of the aperture B is said to be 2.

Similarly, when finding the gain of each aperture in Figure 2B, A
is 4, and C is 2.

If such a calculation is executed every time the aperture B is moved by one pixel, a halftone image shown in FIG. 3(b) can be obtained. 3(a) and 3(c) can be considered in the same way, so the explanation thereof will be omitted.

This method also makes it possible to estimate a halftone image well.

Of course, with such a method, a multi-valued dithered image (
Since the halftone image (d) is estimated from the figure (c)), it does not completely match the halftone image created from the original halftone image, as shown in (d).

However, except where the original halftone image sharply changes, a halftone image that is quite similar to the original halftone image can be obtained. Particularly, when there is no change in intensity within a unit area, the estimated halftone image value completely matches the halftone image value from the original halftone image.

By the way, human vision has a high ability to discriminate pixel level gradations in low spatial frequency regions (regions with few pixel level changes), and low pixel level gradations in high spatial frequency regions (regions with many pixel level changes). It has the characteristic of only having the ability to discriminate.

Therefore, if a large aperture is used in the low spatial frequency region to express high gradations, and a small aperture is used in the high spatial frequency region to reproduce an image with high resolution, the halftone image estimate shown in Figure 3 It is possible to perform an even better estimation of a halftone image.

In the present invention, a halftone image is created taking into consideration the human ability to discriminate pixel-level gradations.

The method of this invention will be specifically explained below.

The example shown below takes as an example the case of the pixel in the first row and first column of the multilevel dither image shown in 1UgJ (c), and explains how to select the aperture among the multiple types of apertures shown in FIG. I will explain what to choose.

Here, the condition that the total value of the multivalued pixel levels in each of the apertures A to D is selected as a to d, respectively, and that there is no change in one pixel level is determined as follows.

12a-bl≦1 (1) 12a-cl
≦1 (2) 12b-dl ≦1
(3) 12 cmdl ≦1
(4) If each of these conditions is satisfied, O1 is not satisfied, and the apertures to be used are determined as shown in FIG.

The main river in the figure indicates ○ or X.

For example, if formulas (1) and (2) are not satisfied,
Aperture A is selected without checking whether it satisfies formulas (3) and (4), and if formula (1) is satisfied but formula (2) is not satisfied, aperture B is Although equation (1) is not satisfied.

When formula (2) is satisfied, aperture C is selected. (
When all of formulas 1) to (4) are satisfied.

An aperture is selected.

Under the above conditions, when finding the optimal aperture when the center position of each aperture in the multivalued dither image shown in FIG. 1(c) is [2, 2], a=7, b=12, c =13, d=24.

First, conditional expressions (1) and (2) are determined.

12a-b l=114-12 l=2, which does not satisfy equation (1).

12a-cl=114-131=1 Then, formula (2) is satisfied.

Therefore, the optimum aperture is C according to FIG.

The value of the pixel in the first row and first column of the halftone image when C is selected as the aperture is estimated. The total value of multi-value pixel levels at the initial position when aperture C is selected is C=
13. Since the gain of the aperture C is 2, the halftone image estimated value is 13X2=26.

FIG. 7(A) is a diagram showing the estimated halftone image obtained in this manner. Incidentally, FIG. 7(b) shows which apertures are used for each halftone image estimation.

To explain the case of the first row as an example, (t, i) of the halftone estimated image is C1 (1, 2) is D, (1, 3) is C3 (1, 4) is C1 (1, 5 ) is A, (1,6) is B
, (1,7) is B.

This also applies to the estimated halftone image shown in FIG. 7(a).

In areas with little pixel level change, a large aperture is used to estimate the halftone image, and in areas with many pixel level changes, a small aperture is used to estimate the halftone image, so it is in line with human visual characteristics. It has become.

Therefore, the estimated halftone image is extremely close to the original halftone image shown in FIG. 1(a).

By the way, in the above description, we have explained the case where a halftone image is estimated from a single multi-image image. , a new multivalued image can be obtained.

FIG. 8 is a flowchart showing a case where tone conversion (gradation processing) is performed on the estimated halftone image.

The flow shown in the figure performs tone conversion on the halftone image estimated by the present invention, and performs tone conversion on the converted halftone image.
A new multivalued image is obtained using the IIl value matrix.

As the gradation conversion characteristic, the one shown in FIG. 9 can be considered.

In the figure, fl and f2 are tone conversion characteristic curves, the horizontal axis of which is the input, and the vertical axis of which is the output.The numbers shown in the figure are the density levels.

Figure 10 (A) is a halftone image obtained by converting the gradation of Fig. 7 (A) using the fl characteristic shown in Fig. 9, (B) is a halftone image obtained by converting the gradation level using the f2 characteristic shown in Fig. 9, and (H ) is a ternary image obtained by converting the image shown in (a) into a ternary value using a ternary threshold matrix consisting of the first and second matrices shown in FIG. This is a ternary image obtained by converting the image shown in (b) into a ternary value. As is clear from (c) and (d), the ternary images differ greatly due to the difference in tone conversion characteristics.

FIG. 11 is a flowchart showing a case where an estimated halftone image is filtered. The flow shown in FIG. 6 is a flowchart showing a case where an estimated halftone image is filtered. is used to obtain a new multivalued image.

Examples of filter characteristics are shown in FIG. 12. (A) is a bypass convolution filter, and (B) is a low-pass convolution filter.

The estimated halftone image shown in FIG. 7(a) is shown in FIG. 12(a).
By applying the filters having the characteristics shown in (a) and (b), bypass and low-pass halftone images as shown in FIGS. 13 (a) and (b), respectively, are obtained.

When these halftone images are ternarized using the first and second dither matrices shown in FIG. A multivalued image (ternary dither image) as shown in FIG.

FIG. 14 is a flowchart showing the case of enlarging/reducing an estimated halftone image.

The flow shown in the figure is to enlarge/reduce the halftone image estimated by JI, and create a new multi-f1 image using a threshold matrix for the enlarged/reduced halftone image.
), for example, an interpolation method is used as a method of enlarging/reducing.

FIG. 15(A) shows the halftone image shown in FIG. 7(A) using the Nearest Neighborhood method (Nearest Neighborhood method).
(b) is a halftone image enlarged by a factor of 1.25 using the borhood method, and (b) is a halftone image reduced by a factor of <0.75.

When these halftone images are multivalued dithered using the first and second dither matrices shown in FIG. A dithered image is obtained.

Note that when estimating a halftone image from the multivalued image described above, the multivalued image is preferably a multivalued dithered image or a multivalued density pattern image, and particularly preferably a multivalued dithered image.

When using a multi-value dithered image, a multi-value dithered image using systematic multi-value dithering is preferable to random dithering or conditional dithering.In this systematic multi-value dithering image, the size of the unit area is It is preferable that the size of the multi-level dither threshold matrix is equal to the size of the multi-value dither threshold matrix so as to include one threshold value each.

When using a multi-value density pattern image, it is preferable that the size of the unit area is equal to the size of the density pattern matrix of the multi-value density pattern image.

In the above description, a halftone image is obtained by scanning one pixel at a time, but the present invention is not limited to this, and two or more pixels may be scanned at a time.

In addition, in the above explanation, four types of openings are illustrated as examples of multiple types of openings, but the opening types are not limited to those, and the size of the openings is not limited to the exemplified ones. can be used.

[Effect of the Invention 1 As explained above, according to the present invention, a plurality of types of apertures are set, and an aperture that satisfies a predetermined condition is selected from among these several types of apertures while moving pixel by pixel. Since the halftone image is estimated based on the average value of the multivalue levels within the aperture, an image close to the original halftone image can be obtained with relatively low IPIrrL.

Further, by using this halftone image, various image processing such as one-tone conversion, enlargement/reduction, etc. can be performed.

[Brief explanation of drawings]

Fig. 1 is an explanatory diagram when obtaining a multi-level dither image from an original halftone image, Fig. 2 is a diagram showing multiple types of apertures, and Fig. 3
The figures show examples of estimated halftone images obtained, Figures 4 to 7.
8 is a flowchart showing gradation conversion, FIG. 9 is a diagram showing gradation conversion characteristics, FIG. 10 is a diagram showing multi-value processing by gradation conversion, and FIG. Figure 11 is a flowchart showing filtering, Figure 12 is a diagram showing filter characteristics, Figure 13 is a diagram showing multi-value processing by filtering, Figure 14 is a flowchart showing enlargement/reduction, and Figure 15 is enlargement/reduction. FIG. 16 is a diagram showing a conventional multi-value quantization method. Figure 7 (A) (B) Aperture selection example Figure 8 Figure 6 Figure 9 Input 32 (A) (C) Multi-value dither image Figure 10 (B) (D) Multi-value dither image Figure 11 Figure 12 Fillwork Figure 14 Figure 16 1st matrix diagram output data

Claims (12)

[Claims] (1) Set multiple types of apertures in a multilevel image consisting of multiple levels, select an aperture that satisfies a predetermined condition from among these multiple types while moving pixel by pixel, and A method for estimating a halftone image of a multi-valued image, the method comprising estimating a halftone image based on an average value of multi-value levels. (2) The method for estimating a halftone image of a multivalued image according to claim 1, wherein the multivalued image is a multivalued dithered image. (3) The method for estimating a halftone image of a multivalued image according to claim 2, wherein the multivalued dithered image is a systematic multivalued dithered image. (4) The method for estimating a halftone image of a multivalued image according to claim 3, wherein the systematic multivalued dithered image is a dot-distributed multivalued dithered image. (5) Claim 4, wherein the dot-dispersed multi-value dither image is a Bayer multi-value dither image.
A method for estimating a halftone image of a multivalued image as described in Section 2. (6) The size of the aperture with the largest area among the plurality of types of apertures is made equal to the size of the threshold matrix of the systematic multilevel dither image. Halftone image estimation method for multilevel images. (7) Using a digital multilevel image as the multilevel image, setting multiple types of scanning apertures, finding the average value of the multilevel levels within the scanning aperture for each scanning aperture, and calculating the average value of the multilevel levels. A unique aperture is selected for each pixel of the halftone image to be estimated by performing prescribed calculation processing on A method for estimating a halftone image of a multivalued image according to claim 1. (8) The method for estimating a halftone image of a multivalued image according to claim 7, wherein the multivalued image is a multivalued dithered image. (9) The method for estimating a halftone image of a digital multivalued image according to claim 8, wherein the multivalued dithered image is a systematic multivalued dithered image. (10) Claim 9, wherein the systematic multi-value dither image is a dot-dispersed multi-value dither image.
A method for estimating a halftone image of a multivalued image as described in Section 2. (11) The method for estimating a halftone image of a multivalued image according to claim 10, wherein the dot-dispersed multivalued dithered image is a Bayer type multivalued dithered image. (12) The size of the aperture with the largest area among the plurality of types of apertures is made equal to the size of the threshold matrix of the systematic multilevel dither image. 12. The method for estimating a halftone image of a multivalued image according to item 11.