JP3453939B2 - Image processing device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus capable of discriminating a pseudo halftone image area by an error diffusion method or the like.

[0002]

2. Description of the Related Art Binary image data input to a facsimile device, a printer device, or the like includes various types of binary image data such as "character image", "halftone image by dither method", and "halftone image by error diffusion method". There is data based on the valuation method.
The type of binary image in which data with different attributes are mixed in the same page is determined for each region or pixel, and the resolution conversion method and the gradation number conversion method are adaptively applied according to the determined image type. The method of switching to is conventionally known.

For example, in Japanese Unexamined Patent Publication No. 2-9268, the periodicity of a pattern in a fixed block unit and the number of black pixels in a fixed block of a binary image received by a facsimile apparatus are measured, and black pixels between adjacent blocks are measured. “Character area”, “Dither area”,
There has been proposed a method of classifying into four areas of an “error diffusion area” and an “undetermined area”, and switching an interpolation method for resolution conversion according to the classification result.

Further, in Japanese Patent Laid-Open No. 4-361475, an input binary image is discriminated into a "character region" and a "region other than a character" by using the connectivity and periodicity of pixels, and this discrimination is performed. There has been proposed a method of selecting a multi-value restoration method for binary images according to the result.

[0005]

However, in the conventional method exemplified in the above publication, when the "dither area" is discriminated from the input binary image, the two-dimensional size equal to or larger than the dither matrix size is used. It is necessary to perform pattern matching. For example, as shown in FIG. 3, in the case of a dither image having a period of 17 gradations of 4 pixels in the main scanning direction and 4 pixels in the sub scanning direction, two-dimensional pattern matching of 4 × 4 pixels is required. In FIG. 3, assuming that the upper right block is A and the upper left block is B, in the two-dimensional pattern matching, the block A and the block B are compared as shown in FIG. 4, and a comparison result schematically shown by C is obtained. . In the comparison result C, the portion indicated by “white” indicates the pixels that match between the blocks A and B, and the portion indicated by “black” indicates the pixels that do not match between the blocks A and B. There is. In this case, 15 pixels match each other, and if the number of matched pixels is larger than a preset threshold value, it is determined to be a “dither area”. In such a two-dimensional pattern matching, when a binarized pseudo halftone image is processed using a dither matrix having a larger number of gradations, the matching size also increases accordingly. For example, 8 pixels in the main scanning direction and 8 pixels in the sub scanning direction.
If the dither image has a cycle of 65 gradations of pixels, 8 ×
Pattern matching of 8 pixels is required, and for a dither image having a cycle of 257 gradations of 16 pixels in the main scanning direction and 16 pixels in the sub scanning direction, pattern matching of 16 × 16 pixels is required. As described above, in the discrimination method using the conventional two-dimensional pattern matching, the scale of image processing increases with an increase in the number of gradations of dither. In the case of processing by hardware, there is a problem that the circuit scale of hardware becomes large.

Further, in the conventional method for discriminating the "error diffusion area", the number of inversions and continuity of "black" and "white" within a certain area is used. However, as shown in FIG. In the highlight part of (that is, the part with high brightness),
Since there is no difference in the number of inversions and continuity of “black” and “white” between the “error diffusion area” (FIG. (A)) and the “dither area” (FIG. (B)), both In order to make a reliable distinction, it is ultimately necessary to rely on the discrimination of the “dither area” using the above-mentioned two-dimensional pattern matching or the like.

Further, in the facsimile machine, it is usually impossible for the facsimile machine on the reception side to know the binarization method adopted by the facsimile machine on the transmission side. Therefore, if the received image is discriminated such as "character region", "dither region", "error diffusion region", etc., and image processing suitable for each region characteristic is performed, it corresponds to all possible dither patterns. It is necessary to prepare the two-dimensional pattern and perform matching. However, it is not realistic to perform two-dimensional pattern matching corresponding to all dither patterns due to restrictions such as storage capacity and processing capacity.
On the contrary, if the determination is made only for a certain dither pattern, the accurate determination cannot be performed and a performance problem occurs.

The present invention has been made under such a background, and a pseudo halftone region having no periodicity is discriminated from an input binary image with a small processing scale and with high accuracy by an error diffusion method or the like. An object of the present invention is to provide an image processing device capable of performing the above.

[0009]

In order to solve the above-mentioned problems, the invention according to claim 1 detects an isolated pixel in which pixels of the same value are not adjacent to each other from an input image represented by a binary value. And a pixel for determining whether or not the value of a pixel located at a periodic distance in the input image from the position of the isolated pixel detected by the isolated pixel detection means and the value of the isolated pixel match. A value determining means, a correcting means for correcting the detection result of the isolated pixel detecting means based on the frequency with which the pixel value determining means determines that the pixel values match, and an isolated pixel detected by the isolated pixel detecting means. Area setting means for setting a predetermined area in the input image, and the total number of isolated pixels detected by the isolated pixel detection means in the area set by the area setting means. 1 accumulating means, a second accumulating means for accumulating the number of isolated pixels after being corrected by the correcting means in the area set by the area setting means, and the first and second accumulating means. Classification means for classifying the pixels forming the input image on the basis of the cumulative result of 1.

According to a second aspect of the present invention, in the first aspect of the present invention, the continuous pixel number recognizing means for recognizing the number of consecutive pixels having the same classification by the classification means, and the continuous pixel number. Classification correction means for changing the classification of pixels having a smaller number of continuous pixels than the adjacent pixel row to the same classification as the adjacent pixel row based on the number of continuous pixels recognized by the recognition means. I am trying.

(Operation) According to the first aspect of the present invention, the isolated pixel is detected from the input binary image to exclude the character region, and the periodicity judgment is performed only on the detected isolated point to dither. It is possible to exclude a pseudo-halftone region having periodicity by the method or the like and extract only a pseudo-halftone region having no periodicity by the error diffusion method or the like. As a result, it is possible to highly accurately determine a pseudo halftone region having no periodicity by the error diffusion method or the like without performing two-dimensional pattern matching or the like that causes an increase in processing scale.

According to the invention described in claim 2, in addition to the effect of the invention described in claim 1, erroneous detection can be eliminated,
The discrimination accuracy is further improved.

[0013]

DETAILED DESCRIPTION OF THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. A: Configuration of Embodiment FIG. 1 is a block diagram showing the configuration of an image processing apparatus according to an embodiment of the present invention. In this figure, reference numeral 101 denotes an isolated pixel detection unit that detects an isolated pixel from a binary image (hereinafter referred to as an input binary image) input from the outside and outputs the detection result. Here, the isolated pixel means a pixel in which no adjacent pixel having the same value as that pixel exists, and a pixel having a value “0” and pixels vertically and horizontally adjacent to each other have a value “1” and a value “ There is a case in which all the pixels adjacent to the pixel “1” in the vertical and horizontal directions become the value “0”.

Reference numeral 102 denotes an isolated pixel correction unit which takes in the input binary image and the detection result of the isolated pixel detection unit 101,
The detection result of the isolated pixel detection unit 101 is corrected. That is, the isolated pixel correction unit 102 compares the value of a pixel located at a periodic distance in the input image from the isolated pixel detected by the isolated pixel detection unit 101 with the value of the isolated pixel,
When both have the same value, the detection result of the isolated pixel detection unit 101 is corrected assuming that the isolated pixel is not an isolated pixel.

Both 103 and 104 are isolated pixel accumulators. The isolated pixel accumulator 103 includes an isolated pixel detector 10
The detected number of isolated pixels output from 1 is counted, and the cumulative result for a predetermined image area is output. On the other hand, the isolated pixel accumulating unit 104 counts the number of detected isolated pixels after correction output from the isolated pixel correcting unit 102, and outputs the accumulated result for the same image area as described above.

Reference numeral 105 denotes a pseudo-halftone image determination section which, based on the cumulative results supplied from the isolated pixel cumulative sections 103 and 104, determines the pixel for each pixel of the input binary image by the error diffusion method. (Hereafter, abbreviated as error diffusion area)
It is determined whether or not the pixel belongs to.

Reference numeral 106 denotes a main scanning direction correction unit, which is the number of pixels in which pixels determined by the pseudo-halftone determination unit 105 to be in the same classification (that is, whether they belong to the error diffusion area) are continuous in the main scanning direction. When the number of consecutive pixels is less than a certain value, the determination result of the pseudo halftone determination unit 105 is corrected.

Reference numeral 107 denotes a sub-scanning direction correction unit which recognizes the number of consecutive pixels in the sub-scanning direction which are determined by the pseudo-halftone determining unit 105 to be the same classification, and when the number of consecutive pixels is less than a certain value. In addition, the determination result of the pseudo halftone determination unit 105 after being corrected by the main scanning direction correction unit 106 is further corrected.

B: Operation of Embodiment Next, the operation of the image processing apparatus having the above configuration will be described with reference to FIG. Note that FIG. 2 is a diagram schematically showing the processing content of each unit shown in FIG.

As shown in FIG. 2, the isolated pixel detection unit 101
Indicates that the pixel currently being processed (hereinafter referred to as the target pixel) is “black (that is,“ 1 ”)”, and all the four pixels (that is, adjacent pixels) above, below, left and right of the target pixel are “black”. A value indicating that the target pixel is an isolated pixel when it is white (that is, “0”), or when the target pixel is “white” and all the pixels adjacent to the target pixel are “black” Outputs "1". On the other hand, the isolated pixel detection unit 10
When the target pixel does not correspond to any of the above, 1 outputs a value “0” indicating that the target pixel is not an isolated pixel.

Next, the isolated pixel correction unit 102 makes a cycle in the main scanning direction from the pixel of interest in the input image with respect to the pixel for which the output of the isolated pixel detection unit 101 is "1", that is, the pixel of interest that is an isolated pixel. It is determined whether the value of the pixel located at a specific distance is equal to the value of the pixel of interest, and the frequency when the values are equal is counted. For example, if the period T is 5 pixels,
The number of pixels having the same pixel value as the isolated pixel value out of the total 6 pixels of the fifth pixel, the 10th pixel, and the 15th pixel before and after the isolated pixel detected by the isolated pixel detection unit 101 is counted.
The determination of such periodicity is performed with 4 pixels, 6 pixels, 8 pixels, 12 pixels.
This is performed for five types of periods T of pixels and 16 pixels, and when the count result having the largest value in the period is equal to or greater than the threshold value (for example, when four or more pixels match), the pixel of interest is an isolated pixel. Cancel the judgment result that
The output of the isolated pixel detection unit 101 is changed from "1" to "0".

The output of the isolated pixel detection unit 101 is supplied to the isolated pixel accumulation unit 103, and the isolated pixel correction unit 102 is supplied.
Is supplied to the isolated pixel accumulator 104. The isolated pixel accumulating units 103 and 104 respectively count the number of isolated pixels included in a fixed area set around the pixel of interest, that is, the number of times the value “1” is supplied.

Next, the pseudo-halftone judging section 105 judges whether or not the pixel of interest belongs to the error diffusion area based on the output values of the isolated pixel accumulating sections 103 and 104. For example, if the output value of the isolated pixel accumulating unit 103 is V1 and the output value of the isolated pixel accumulating unit 104 is V2, and if the value of V1 is equal to or greater than a predetermined threshold value TH, the pixel of interest is determined to be “error diffusion area”. However, even if not, if the value of V2 is “1” or more, the pixel of interest is determined to be “the error diffusion area”. On the other hand, if none of the above applies, the pixel of interest is determined to be “not in the error diffusion area”. Then, if it is determined that the area is the error diffusion area, the value "1" is output, and if not, the value "0" is output.

Next, the main scanning direction correction unit 106 recognizes the number of pixels in the main scanning direction in which the pixels determined to be in the same classification by the pseudo-halftone determination unit 105 are continuous, and the continuous number is less than a certain value. In this case (that is, when the condition of the algorithm described later is satisfied), the determination result of the pseudo halftone determination unit 105 is corrected. That is, the number of consecutive pixels of "1" or "0" is counted in the one-dimensional window set in the main scanning direction, and for example, "... 1111111111100"
“1” on the left side, such as “01111111111 ……”
Is "11", the number of consecutive "0" s is "3",
When the number of consecutive "1" s on the right side becomes "10", "1" is set to match the consecutive pixels of "0" in the center with the pixel values on the left and right.
Replace with. In addition, for example, "... 0000000000
001110000000000000 ... ”,
Similarly, when "1" and "0" are reversed, the central "1" is replaced with "0".

This is because the "error diffusion area" is an area having a certain extent of spread due to its nature, and therefore, usually a small number of "error diffusion areas" are scattered or the "error diffusion area". It is considered that different regions are not scattered in the area. Therefore, the above correction is performed.

Further, when determining the pixel to be corrected, the following algorithm is used, for example. That is,
The number of consecutive pixels determined to be a certain classification is RLC, the number of consecutive pixels to the left of these pixels determined to be another classification is RLL, and the number of pixels to the right is a pixel determined to be another classification When the number of consecutive pixels is RLR and the maximum number of pixels to be corrected is RLmax, RLmax> RLC and RLL> RLC and RLR
The central pixel (or continuous pixel) is corrected on condition that> RLC is satisfied.

Next, the sub-scanning direction correction unit 107 performs the same processing as the main scanning direction correction unit 106 on the one-dimensional window set in the sub-scanning direction. In this way, the determination result of the pseudo halftone determination unit 105 is corrected in the main scanning direction and the sub scanning direction, and finally the sub scanning direction correction unit 10
Pixels for which the output of 7 is “1” are determined to belong to the “error diffusion area”.

Based on the determination result thus obtained, it is possible to perform resolution conversion switching and gradation conversion method switching corresponding to, for example, the characteristics of the region in the subsequent processing (not shown).

C: Modification Note that the present invention is not limited to the above-described embodiment,
For example, the following changes are possible. (1) The period T set by the isolated pixel correction unit 102 is
It is not limited to the above five types, and other cycles may be adopted. In short, it is desirable to do this for every dither period that is expected to be present in the input binary image.

(2) The value of the threshold value TH set by the pseudo-halftone judging section 105 varies depending on the size of the area around the target pixel set by the isolated pixel accumulating sections 103 and 104, but is shown in FIG. As described above, it has been confirmed by experiments that "4" is suitable for 7 × 7 pixels. Further, the determination algorithm in the pseudo halftone determination unit 105 is not limited to the above embodiment, and other determination algorithms may be adopted as long as they are suitable for determining the error diffusion area.

(3) The determination algorithm of the correction target pixel in the main scanning direction correction unit 106 and the sub scanning direction correction unit 107 is not limited to that of the above embodiment, and other algorithms may be adopted.

(4) In the above embodiment, the case where the pseudo halftone area is discriminated by the error diffusion method has been described. However, the present invention is not limited to this. It can also be applied to a case where the halftone region is distinguished from the pseudo halftone region having a periodicity as in the dither method.

[0033]

As described above, according to the present invention, a pseudo halftone region having no periodicity can be discriminated from an input binary image with a small processing scale and with high accuracy by an error diffusion method or the like. it can.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an image processing apparatus according to an embodiment of the present invention.

FIG. 2 is a schematic diagram for explaining the operation of the image processing apparatus.

FIG. 3 is a diagram showing an example of a pseudo halftone image by a dither method.

FIG. 4 is a diagram illustrating a conventional determination method using two-dimensional pattern matching.

5A and 5B are diagrams comparing an "error diffusion area" and a "dither area" in a highlighted portion of an image, where (a) shows the "error diffusion area" and (b) shows the "dither area". There is.

[Explanation of symbols]

Reference Signs List 101 isolated pixel detection unit (isolated pixel detection unit) 102 isolated pixel correction unit (pixel value determination unit, correction unit) 103 isolated pixel accumulation unit (region setting unit, first accumulation unit) 104 isolated pixel accumulation unit (region setting unit) , Second accumulating unit) 105 pseudo-halftone judging unit (classifying unit) 106 main scanning direction correcting unit (continuous pixel number recognizing unit, class correcting unit) 107 sub-scanning direction correcting unit (continuous pixel number recognizing unit, class correcting unit) )

Claims (2)

(57) [Claims] 1. An isolated pixel detecting means for detecting an isolated pixel in which pixels having the same value are not adjacent to each other from an input image represented by a binary value, and the position of the isolated pixel detected by the isolated pixel detecting means in the input image. And a pixel value determination unit that determines whether or not the value of a pixel located at a periodic distance and the value of the isolated pixel match, and the frequency at which the pixel value determination unit determines that the pixel values match. Correction means for correcting the detection result of the isolated pixel detection means, area setting means for setting a predetermined area in the input image including the isolated pixels detected by the isolated pixel detection means, and area setting means. A first accumulating means for accumulating the number of isolated pixels detected by the isolated pixel detecting means in the area set by Second accumulating means for accumulating the number of isolated pixels after being corrected by the correcting means, and classification means for classifying the pixels forming the input image based on the accumulation results of the first and second accumulating means An image processing apparatus comprising: 2. The image processing apparatus according to claim 1, wherein the continuous pixel number recognition means for recognizing the number of continuous pixels of the pixels classified into the same classification by the classification means, and the continuous pixel number recognition means for recognition. An image processing apparatus comprising: a classification correction unit that changes a classification of pixels having a smaller number of continuous pixels into the same classification as that of the adjacent pixel row based on the number of continuous pixels.