JPH11122493A - Picture processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a picture processor in which a texture level can be accurately detected and removed. SOLUTION: This device is provided with an edge judger 40 for judging the edge of inputted multi-value picture data, and deciding the weighting factor of error diffusion from the judged result, texture extractor 10 for detecting the texture components of the multi-value picture data by referring to the edge detected by the edge judger, subtractor 20 for subtracting the texture components from the multi-value picture data, output converter 30 for outputting the picture data by decreasing the concentration gradation of the inputted multi- level data, and detecting error components which can not be converted, and error arithmetic unit 50 for multiplying the detected error components by the weighting factor decided by the edge judger. Then, the error components multiplied by the weighting factor by the error arithmetic unit 50 are added to a difference value from the subtractor 20, and returned to the output converter 30 again. Thus, it is possible to accurately remove the texture components at the edge part.

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 applied to a digital copying machine, a printer and the like, and more particularly to an image processing apparatus capable of accurately detecting and removing a background level at an edge portion. .

[0002]

2. Description of the Related Art Conventionally, when processing image data input using an error diffusion method, the following problems occur. 1. Error diffusion is reproduced as a periodic output if the background level is not properly processed. 2. In an area where the high density area and the low density area are adjacent and the boundary is clear, the reproduced density cannot follow the change in density, and the effect of the area before the change remains for a while. (Edge) reproducibility deteriorates.

As a first conventional example proposed to solve the above-mentioned problem, there is “Image Processing Method and Image Processing Apparatus” of Japanese Patent Application Laid-Open No. Hei 8-228284. In this conventional example, in an image processing method of inputting multi-gradation pixel data, performing binary processing by an error diffusion method, and outputting a binary image,
The binarization processing order is set for each pixel in the pixel row existing in the line to perform the binarization processing, and binarization by the error diffusion method is performed using the pixel data for each pixel in the set order. Processing, and in the binarization processing by the error diffusion method, a plurality of error diffusion matrices are prepared to correspond to the binarization processing state of a pixel adjacent to the pixel being binarized in the main scanning direction. By selecting an error diffusion matrix according to whether or not a pixel adjacent to the pixel undergoing the binarization processing has been binarized, it is possible to prevent a texture generated in the binarization processing using the error diffusion method. It is intended for planning.

[0004]

However, the image processing method and the image processing apparatus of the above-mentioned prior art 1 input multi-tone image data and output binary image data. There is no description of a device that can output texture image data and prevent texture.

In the above-described error diffusion method of binary output, a pseudo contour becomes conspicuous in a region where the gradation changes slowly, particularly in a region represented by skin color.

SUMMARY OF THE INVENTION It is an object of the present invention to provide an image processing apparatus capable of converting data into data having a smaller number of gradations than input multi-gradation data and enabling stable reproduction of gradations.

[0007]

In order to achieve the above object, an image processing apparatus according to the present invention employs a multi-level error diffusion method for inputting multi-level image data and outputting multi-level image data. An edge determination unit that determines an edge of the input multi-valued image data and determines a weight coefficient of error diffusion from the determination result, and a background component of the multi-valued image data is determined by the edge determination unit. Background extraction means for detecting by referring to the edge; difference detection means for subtracting the background component extracted by the background extraction means from the multi-valued image data; An output conversion unit, an error detection unit that detects an error component that cannot be completely converted at the time of conversion by the output conversion unit, and an error component detected by the error detection unit determined by the edge determination unit. Multiplying means for multiplying only the coefficients by adding the error component multiplied by the weighting coefficient to the difference value detected by the difference detecting means, and performing output conversion again by the output converting means. I have.

[0008] The above image processing apparatus includes an input means for inputting an operation setting by an operator, a background component based on an instruction input from the input means, and a background component to be output to the difference detecting means. It is preferable to further include switching means for switching between the extracted background component and the extracted background component.

The edge detecting means may detect the edge of the image data and its direction based on the image density of the pixel of interest and its surrounding pixels, and change the error diffusion weighting coefficient based on the detected result.

[0010]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, an embodiment of the image processing apparatus of the present invention will be described in detail with reference to the accompanying drawings. FIG. 1 to FIG. 9 show an embodiment of the image processing apparatus of the present invention. FIG. 1 is a block diagram showing the overall configuration of the image processing apparatus according to the present invention, FIG. 2 is a block diagram showing the configuration of a background extractor, and FIG. 3 is a diagram showing the density change and background level of image data in the main scanning direction. FIG. 4 is a block diagram illustrating a configuration of an edge determiner, FIG. 5 is a diagram illustrating a configuration of an edge register, FIG. 6A is an example of weight distribution when edge determination is not performed, FIG. 6B is an example of a weight coefficient redistributed by an edge, FIG. 7 is a block diagram showing a configuration of an error calculator, FIG. 8 is an example of an output table, FIG.
FIG. 3 is a diagram illustrating an error component detected by an output converter.

Since the present invention is concerned with multi-level error diffusion,
The purpose is to remove textures that occur due to the background level of the input image, to reduce false contours that are likely to occur in areas where the gradation changes slowly, and to reduce so-called tailing due to delay in following in sharp density changes. .

As shown in FIG. 1, a main configuration of the present embodiment includes a background extractor 10 for extracting a background level Pb from input multi-value image data PI, a background level Pb and multi-value image data PI. , A difference unit 20 for calculating the difference value PS, an adder 60 for adding the difference value PS and the error output Eout,
The output addition value Pa is converted to a multi-value input Pout and an error input Er
The output converter 30, which divides the output into in, multiplies the error input Evin by a weight coefficient controlled by the output edge (Edge) of the edge determiner 40 that determines an edge from the multi-valued image data PI, thereby obtaining an error output Erout. Arithmetic unit 4
0.

Next, an operation example of the above configuration will be described.
The read multi-valued image data PI is input to the background extractor 10, the differentiator 20, and the edge determiner 40. The edge determiner 40 determines the direction and strength of the edge from the density distribution of the pixel of interest and the surrounding pixel data, and outputs an edge signal for controlling the weight coefficient. The edge signal is output to the error calculator 50 and the background extractor 10. The background extractor 10 accumulates the image data of the target pixel of the multi-valued image data PI and the surrounding pixels, and obtains the image data of the pixel whose density is lower than a certain level and the background level based on the change in the edge from the edge determiner 40. The background level is determined in consideration of the fluctuation of the background. Then, the detected background level Pb is sent to the differentiator 20.

The differentiator 20 receives the multivalued image data P
It is used for the purpose of reducing the background component from I. Differentiator 20
Subtracts the background level PB sent from the background extractor 10 from the multi-valued image data PI, and outputs the value Ps to the adder 60. The adder 60 calculates the difference data P from the differentiator 20.
s is the error output Eout detected by the error calculator 50
And the output converter 3 outputs the addition result as an addition value Pa.
Output to 0. The output converter 20 has a ROM or RA
A conversion table stored in M or the like is provided, and the input addition value Pa is converted into data of a gradation smaller than the addition value Pa by an output table to stabilize the reproduction of the gradation. The converted data is output as a multi-value output Pout, and is returned to the output converter 30 in order to detect an error that could not be converted when the conversion was performed using the output table. The multi-value output Pout returned to the output converter takes a difference from the added value Pa and is output to the error calculator 50 as an error input Erin.

An error calculator 50 multiplies the weight coefficient determined by the edge determiner 40 with the error input sent from the output converter 30 and uses the multiplied result as an error output as an adder 60.
Output to The adder 60 calculates the difference value Ps from the differentiator 20.
, And the error output Eout from the error calculator 50 is added thereto, and the added data is sent to the output converter 30.

Next, a detailed configuration and an operation example of the background extractor will be described with reference to FIG. In FIG. 2, reference numeral 11 denotes a background level register that stores a background level specified by a user, 12 denotes a background extraction register that stores multivalued image data PI, 13 denotes a line memory that stores image data for a plurality of lines, and 14 denotes a line memory. A background calculation unit for determining the background level based on the target pixel stored in the background extraction register 12 and the line memory 13 and the surrounding pixel data. Reference numeral 15 denotes a background level designated by the user from the background level register and a background level from the background calculation unit. A selection switch SW for switching the output with the background level.

Next, an operation example of the above configuration will be described.
The background extractor 10 stores the pixel of interest and surrounding pixel data in a background extraction register 12, and stores data for a plurality of lines in a line memory 13. Then, the background calculation unit 14 determines a background level by detecting a pixel having a density lower than a certain level, as a condition for detecting the background level, even for a change in the background level due to the edge detected by the edge determiner. FIG. 3 is a diagram showing a state when the background level determined by the above method is followed in the main scanning direction.

Next, a configuration and an operation example of the edge determiner will be described with reference to FIG. 4 includes an edge register 41 for storing multi-valued image data PI, a line memory 42 for storing image data for a plurality of lines, a pixel of interest stored in the edge register 41 and the line memory, and its periphery. The edge direction and its strength are obtained from the image data of the pixel of the above, and the edge determination arithmetic unit 43 which outputs an edge signal for controlling the weight coefficient is configured. The edge indicates the degree of density change between the target pixel and the adjacent pixel.

Next, an operation example of the above configuration will be described. First, the edge determiner 40 sequentially stores the multi-valued input image in the edge register 41 and stores the delay data for a plurality of lines in the line memory 42. Then, the edge determiner obtains the direction and strength of the edge from the density distribution of the target pixel and its surrounding pixels. Then, a signal edge for controlling a weight coefficient for diffusing an error according to the direction and strength of the detected edge is output to the background extractor and the error calculator.

FIG. 5 shows a 3 × 3 structure applied to the present embodiment.
The edge register of the matrix is shown, and the number described for each pixel represents the density of the pixel of interest and its surrounding pixels. In FIG. 5, "23" described in the middle
“0” is the density of the target pixel, and “250” indicated at the upper left of the target pixel indicates that there is a sharp edge at the upper left of the target pixel. The edge determiner 43 detects the density change for the target pixel. Then, a weight coefficient is determined based on the detected edge.

FIG. 6A shows the weight coefficient when the weight coefficient is determined without detecting the edge, and FIG. 6B shows the case where the weight coefficient is redistributed based on the detected edge. By setting the weight coefficient to 0, all error components shown below can be cut.

Next, the configuration and operation of the output converter and error calculator will be described with reference to FIG. The output converter 30 shown in FIG. 7 has an output table stored in the ROM and the RAM, and converts the input multi-gradation data into data having a smaller number of gradations. The input added data Pa is converted into data having low gradation by the output table, output as the multi-value output Pout, and returned to the output converter 30 again. The multivalued output Pout returned to the output converter detects a difference from the added data Pa in order to detect an error component that could not be converted in the output table. The detected error component is sent to the error calculator 50 as an error input Erin.

The multi-level output Pout has a stepwise characteristic corresponding to the number of output bits with respect to the multi-level input as shown in FIG. 8, and the error input Erin has the multi-level input Pa and the multi-level input Pa as shown in FIG. Since the difference between the multi-level outputs Pout is obtained, the output characteristics have a sawtooth shape. Since these characteristic values are described in tables (ROM and RAM), flexible characteristics can be provided.

The error converter 50 shown in FIG. 7 has an error memory 51 for storing an error input Erin, which is an error detected by the output converter 30, and an error register composed of a plurality of registers. 52, a weight register 53 for storing the weight coefficient determined by the edge determiner 40, and a multiplier 54 for multiplying the error input accumulated in the error register by the weight coefficient stored in the weight register. . The error memory 51 outputs an error input delayed by one line with respect to the error input input from the output converter 30 to the error register 52. This is because the edge determination method by the edge determiner determines two-stage weight coefficients with reference to the image data of the stage including the pixel of interest and the preceding stage.

Next, an operation example of the above configuration will be described. The error calculator accumulates the error input Erin from the output converter 30 in the error memory 51 and the error register 52. The error memory outputs an error input delayed by one line with respect to the error input input to the error register to the error register.
Then, the error input stored in the error register and the weight coefficient stored in the weight register are output to the multiplier 54 at the same time.

The multiplier 54 multiplies the error input by a weighting coefficient and outputs the result to the adder 60 as an error output Erout. Thus, for example, when the edge detector detects an edge, an error component from the output converter can be discarded by setting the weight coefficient to 0. The adder 60 adds the error output Eout to the difference value Ps from which the background component has been removed by the differentiator, and returns the result to the output converter 30 again.

With the above configuration and operation, the background level can be accurately detected and removed, so that the background portion can be prevented from being reproduced as a texture. Further, since the user can specify the background level in advance, it is possible to easily deal with image data whose characteristics are known.

Further, since the error coefficient can be changed by judging the edge of the image, a change in the background level at the edge can be properly grasped.

[0029]

As is apparent from the above description, according to the image processing apparatus of the present invention, the edge of the multi-valued image data input by the edge determination means is determined, and the weight coefficient of error diffusion is determined from the determination result. Determined, and the background extraction unit detects the background component of the multi-valued image data by adding the edge determined by the edge determination unit to the condition, thereby accurately detecting and removing the background component. Thereby, the background component can be prevented from being reproduced as a texture.

An image processing apparatus to which multi-valued image data is input and multi-valued error diffusion is applied to output multi-valued image data. By inputting multi-valued image data and outputting binary image data. The pseudo contour can be made inconspicuous as compared with an image processing apparatus that performs the above.

The edge detecting means detects the edge of the image data and its direction based on the image density of the pixel of interest and its surrounding pixels, and changes the weight coefficient of error diffusion from the detection result to thereby obtain the background component of the edge portion. Change can be controlled.

The characteristics can be understood by further providing a switching means for switching the background component output to the difference detecting unit between a background component based on data based on an instruction input from the input unit and a background component extracted by the background extracting unit. The processing for the image data can be switched to the user's instruction, and the processing can be facilitated.

[Brief description of the drawings]

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

FIG. 2 is a block diagram illustrating a configuration of a background extractor.

FIG. 3 is a diagram illustrating a change in density of image data in the main scanning direction and a background level.

FIG. 4 is a block diagram illustrating a configuration of an edge determiner.

FIG. 5 is a diagram illustrating a configuration and a storage method of an edge register.

FIG. 6A is a diagram illustrating an example of normal weight distribution, and FIG. 6B is a diagram illustrating an example of redistributing weight coefficients by edges.

FIG. 7 is a block diagram illustrating a configuration of an output converter and an error calculator.

FIG. 8 is a diagram illustrating an example of an output table.

FIG. 9 is a diagram illustrating an error component detected by an output converter.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 Background extractor 20 Difference device 30 Output converter 40 Edge determiner 50 Error converter 60 Adder

Claims (3)

[Claims] An image processing apparatus for applying a multi-level error diffusion method for inputting multi-level image data and outputting multi-level image data, comprising the steps of: determining edges of the input multi-level image data; Edge determination means for determining a weighting coefficient for error diffusion from the result; background extraction means for detecting a background component of the multi-valued image data with reference to the edge determined by the edge determination means; Difference detection means for subtracting the background component extracted by the background extraction means from the image data, output conversion means for lowering and outputting the density gradation of the input multi-tone data, conversion at the time of conversion by the output conversion means Error detecting means for detecting an error component that cannot be completed; and multiplying the error component detected by the error detecting means by the weight coefficient determined by the edge determining means. Multiplying means for adding the error component multiplied by the weighting coefficient to the difference value detected by the difference detecting means, and performing output conversion again by the output converting means. Characteristic image processing device. An input unit for inputting an operation setting by an operator; the background component based on an instruction input from the input unit for the background component output to the difference detection unit; and a background extraction unit. The image processing apparatus according to claim 1, further comprising: a switching unit configured to switch with the extracted background component. 3. An edge detecting means for detecting an edge of image data and its direction based on the image density of a pixel of interest and surrounding pixels, and changing the error diffusion weighting coefficient from the detected result. 3. The image processing apparatus according to claim 1, wherein