JP4029545B2 - Image processing apparatus and image processing method - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing apparatus and an image processing method, and more particularly to an image processing apparatus and an image processing method capable of creating an image with reduced gradation by using a threshold value.
[0002]
[Prior art]
Digital image handling is the mainstream of current image processing. When displaying or outputting a digital image, it is often necessary to express the gradation of the image with a smaller gradation level due to restrictions due to the characteristics of the output device. From the beginning, various digital halftoning image processing techniques have been studied, such as a binarization processing method that reproduces gradation using only white and black dots as pseudo halftone processing.
[0003]
The systematic dither method, error diffusion method, and various methods of those systems still in use have been developed and improved since the 1960s. In recent years, with the development of calculation processing hardware, a method for directly searching for an optimal pixel arrangement has been developed, such as a cost minimization method.
[0004]
Each of these halftoning methods has advantages and disadvantages depending on the purpose of use, and various issues and countermeasures have been studied. For example, the organized dither method is easy to process and easy to use, but the reproduced image quality is not good. Although the error diffusion method requires more computation than the dither method, the image quality is excellent.
[0005]
Various optimization methods such as neural networks, genetic algorithms, and simulated annealing are used in direct optimization methods such as cost minimization methods. By adopting these methods, it is easy to incorporate visual models and output device models into the process, and the degree of freedom of processing is expanded. However, there is a problem that the amount of calculation becomes enormous because it repeatedly calculates and searches for an optimal state.
[0006]
These challenges change as technology advances. The problem that the amount of calculation when the optimal search method is directly used becomes enormous may be solved by the advancement of hardware that defines the calculation processing speed. However, simpler calculation processing is desired from the viewpoint of widespread use of simple and high-quality output devices.
[0007]
Further, as a common problem, there is a problem of a trade-off between resolution and gradation. This may also be solved by increasing the output gradation level of the output device itself and improving the resolution characteristics. However, for example, an increase in the chance that characters are processed as an image may be considered, and it is desired to perform these processes as simply as possible.
[0008]
Conventionally, for example, a method of determining an image region requiring gradation and an image region requiring resolution and changing a processing method in each region according to the determination result, a method of using a plurality of processing methods in combination, etc. It has been studied as a means for improving image processing. However, in order to execute them, it is necessary to develop and add a new process such as area discrimination, which is not a simple method. From the standpoint of balance with hardware (output device), it would be desirable to achieve the object of maintaining good resolution and gradation by processing similar to the error diffusion method.
[0009]
FIG. 21 is a block diagram showing a configuration of an image processing apparatus that executes a conventional error diffusion method.
[0010]
Referring to the figure, the image processing apparatus includes an input unit 501 that inputs a pixel value of one pixel of a multi-valued image, a subtracter 503 that subtracts an error diffused from the input pixel value, and a subtracter 503. Are output as corrected pixel values, a threshold processing unit 507 that performs threshold processing on the output of the output unit 505 to form binary data, and a threshold processing unit 507 An output unit 509 that outputs the output as pixel data, a subtraction unit 511 that subtracts the output of the output unit 505 from the output of the threshold processing unit 507, and a pixel whose output is output from the subtraction unit 511 And an error memory 513 for diffusing the pixels around the pixel of interest.
[0011]
An image created by the error diffusion method has a unique texture. However, as the blue noise characteristic is pointed out, the texture is not visually noticeable. In the dither method, a method of setting a dither pattern so as to obtain the blue noise characteristic more easily has been studied. However, since the error diffusion method adaptively generates a dot pattern for the input image, the characteristics of the input image can be reflected more than the dither method.
[0012]
In this respect, the error diffusion method outperforms the dither method in terms of image quality, but there is also noise specific to the error diffusion method. This is because the texture changes in a gentle gradation change area, and there appears to be a boundary line even in an area without a boundary (texture shift), or white or black in a black or white gradation area. This is a phenomenon that makes it easier to arrange the dots in a line.
[0013]
In order to prevent these phenomena, various improved methods such as modulation of error diffusion weighting factors and threshold values have been developed. In addition, regarding the resolution, the inherent edge enhancement characteristic has been pointed out, but it cannot be said to be sufficient.
[0014]
In addition, the error diffusion method cannot freely control the γ characteristic simultaneously with the halftoning process.
[0015]
Further, the error diffusion method functions so as to reproduce the pixel values of the input image on average based on the algorithm. That is, it functions to reproduce the local zeroth order component of the image. On the other hand, the error diffusion method has been improved to emphasize the first and higher order components.
[0016]
[Problems to be solved by the invention]
SUMMARY OF THE INVENTION An object of the present invention is to provide an image processing apparatus and an image processing method capable of eliminating the above-described drawbacks of the image processing method, improving the image quality, and controlling the γ characteristic by simple processing. Yes.
[0017]
[Means for Solving the Problems]
  In order to achieve the above object, according to one aspect of the present invention, an image processing apparatus represents a density level of each pixel.Multi-valued imageBy comparing the first image signal input from the input means for sequentially inputting the first image signal with a threshold valueTwo types of on or offThreshold processing means for generating a second image signal, and generated by the threshold processing means, Corresponding to on and off respectivelySecond image signalValue (n-1, 0)And the threshold used to generate the second image signal(Th (x))Based onAccording to the prescribed operationCalculating means for calculating a threshold value used for subsequent threshold processing of the pixel; and changing means for changing a coefficient used in the calculating means based on the second image signal generated by the threshold value processing means; Is provided.The changing means is provided with a first coefficient set (a1, b1) and a second coefficient set (a2, b2) having different coefficient sets corresponding to the γ characteristics in advance, and the second image signal is turned on and off. The first coefficient group (a1, b1) and the second coefficient group (a2, b2) are changed correspondingly. The calculation means is a1 × Th (x) + b1 × ((n−1) −Th (x)): when the second image signal is on, a2 × Th (x) + b2 × ((n−1) −Th (X)): When the second image signal is off (a1, b1, a2, b2 are coefficients, Th (x) is a threshold value used when generating the second image signal, and n is an output of the calculation means. (The number of gradations) is calculated, the value calculated by the calculation is multiplied by the feedback coefficient β, and the initial threshold value is added..
[0019]
  According to another aspect of the present invention, an image processing method represents a density level of each pixel.Multi-valued imageAn input step for sequentially inputting the first image signal, and comparing the first image signal input in the input step with a threshold valueTwo types of on or offA threshold processing step for generating a second image signal, and the threshold processing step., Corresponding to on and off respectivelySecond image signalValue (n-1, 0)And the threshold used to generate the second image signal(Th (x))Based onAccording to the prescribed operationA calculation step for calculating a threshold value used for subsequent threshold processing of the pixel, and a change step for changing a coefficient used in the calculation step based on the second image signal generated in the threshold value processing step. Prepare.In the changing step, a first coefficient set (a1, b1) and a second coefficient set (a2, b2) having different coefficient sets according to the γ characteristics are provided in advance, and the second image signal is turned on and off. The first coefficient group (a1, b1) and the second coefficient group (a2, b2) are changed correspondingly. The calculation step is a1 × Th (x) + b1 × ((n−1) −Th (x)): when the second image signal is on, a2 × Th (x) + b2 × ((n−1) −Th (X)): When the second image signal is off (a1, b1, a2, b2 are coefficients, Th (x) is a threshold value used when generating the second image signal, and n is an output of the calculation means. The number of gradations) is calculated. Then, the value calculated by the calculation is multiplied by the feedback coefficient β. Further, addition with the initial threshold value is performed.
[0021]
DETAILED DESCRIPTION OF THE INVENTION
[Reference example]
FIG. 1 is a block diagram showing a configuration of an image creating apparatus according to a reference example of the present invention. The image binarization process executed by this apparatus is referred to as “threshold diffusion method”. In the drawing of this case, the part enclosed by a normal rectangle is a part that performs some arithmetic processing, and the part enclosed by a rounded rectangle is a part that does not perform arithmetic processing and only outputs a value. Is shown.
[0022]
Referring to the figure, an image creating apparatus includes an image (pixel value) input unit 101, a threshold processing unit 103, a binary image output unit 105, an inversion unit 113, and an initial threshold generation unit 107. , Subtractor 109, correction threshold value output unit 111, subtractor 115, coefficient multiplier 117, and correction value memory 119.
[0023]
One pixel value (0 to 1) of the multi-valued image is input to the image input unit 101. For example, when a 256-level multi-value image n (0 to 255) is handled, a value (n / 255) normalized to 0 to 1 is input to the image input unit 101. The threshold processing unit 103 compares the correction threshold Th (x) output from the correction threshold output unit 111 with the pixel value input to the image input unit 101. If pixel value ≧ correction threshold Th (x), the threshold processing unit 103 outputs “1”, and if pixel value <correction threshold Th (x), the threshold processing unit 103. Outputs “0”. As a result, the binary image output unit 105 outputs a binary image of “0” or “1”.
[0024]
The initial threshold value generator 107 outputs the initial threshold value Th (x) before correction. The initial threshold value Th (x) before correction may be a constant value or may be changed according to the pixel position so as to form a dither pattern.
[0025]
The subtractor 109 reads the correction value stored in the correction value memory 119 corresponding to the pixel to be processed (target pixel), and subtracts the correction value from the initial threshold value Th (x). The result is set as a correction threshold Th (x).
[0026]
The inversion unit 113 inverts the output of the threshold processing unit 103. That is, the inversion unit 113 outputs “1” if the output of the threshold processing unit 103 is “0”, and “0” if the output is “1”.
[0027]
The subtracting unit 115 subtracts the correction threshold value Th (x) from the output of the inverting unit 113 and outputs the result. The coefficient multiplier 117 multiplies the output of the subtractor 115 by a feedback coefficient β set between 0 and 1 and outputs the result. Note that β = 0 means that threshold value diffusion is not performed.
[0028]
The correction value memory 119 is a memory for distributing the output result of the coefficient multiplication unit 117 to the correction value of the threshold value for the peripheral pixels of the pixel to be processed. Referring to FIG. 2, when a pixel to be processed is indicated by a white circle, the output result of the coefficient multiplier 117 is a ratio (weighting coefficient) of 1 to 32 with the threshold correction value for the surrounding pixels. It is sorted and stored.
[0029]
As shown in FIG. 2, the number of pixels to which the output result of the coefficient multiplier 117 is to be distributed is increased compared to the normal error diffusion method. This is to eliminate the monotony of the value. In other words, the error diffusion method performs diffusion processing on input values (pixel values) that change in various ways, whereas the threshold diffusion method performs diffusion processing on a threshold value that is constant or nearly constant. It is because there are many cases to do. When the initial threshold value before correction is not a constant value but takes various values, the number of pixels to be distributed may be reduced.
[0030]
In comparison with the block diagram of the image processing apparatus using the error diffusion method (FIG. 21), in the apparatus using the threshold diffusion method, inversion processing is performed by the inversion unit 113 when the output is fed back. . This is a process for changing the output result to the output viewed from the threshold side because the output result is the output viewed from the input value side.
[0031]
Further, the feedback coefficient β is multiplied by the coefficient multiplier 117 when feedback to the threshold value. This is a threshold value diffusion method that functions only to reproduce the threshold value on average unless the feedback coefficient β is multiplied. Is a process for causing the input value to be reproduced on average.
[0032]
The operation and effect of the threshold value diffusion method are shown below by comparison with the error diffusion method. As can be seen by comparing the image processing apparatus using the error diffusion method shown in FIG. 21 and the apparatus using the threshold value diffusion method shown in FIG. 1, in the error diffusion method, the input pixel value and the output However, the threshold diffusion method is greatly different in that the difference between the threshold value and the output is fed back to the threshold value. In other words, the object for calculating the difference from the output and the object for feeding back the difference are input values (input pixel values) in the error diffusion method, whereas they are threshold values in the threshold diffusion method. Yes. In the threshold diffusion method, the same texture (blue noise characteristic) as that of the error diffusion method can be obtained by the feedback algorithm.
[0033]
FIG. 3 shows a halftone processing result using a systematic dither method using a 4 × 4 pixel fat-type pattern, FIG. 4 shows a halftone processing result using an error diffusion method, and FIG. 5 shows an initial threshold value. The halftone processing result by the threshold value diffusion method (feedback coefficient β = 0.5) when a constant value is used as Th (x) is shown.
[0034]
When the systematic dither method is employed, the gradation and resolution are the worst. When the error diffusion method is used, both gradation and resolution are improved as compared with the dither method. However, when the threshold value diffusion method is used, the error diffusion method exceeds the error diffusion method in particular. With regard to the gradation and texture, the threshold value diffusion method can obtain almost the same result as the error diffusion method. In particular, in the threshold value diffusion method, the occurrence of a texture shift is observed as in the error diffusion method. However, the defect that the dots are arranged in a line in a region close to black or white that is generated in the error diffusion method does not occur in the threshold diffusion method.
[0035]
FIG. 6 shows a halftone processing result by the threshold value diffusion method (feedback coefficient β = 0.5) using a fat-type dither pattern of 4 × 4 pixels as the initial threshold value Th (x). Shows a halftone processing result by an error diffusion method in which a fat-type pattern of 4 × 4 pixels is modified as a threshold value, and FIG. 8 shows a halftone processing result using an error diffusion method with edge enhancement. .
[0036]
By using the dither pattern as the initial threshold Th (x) in the threshold diffusion method, the texture shift can be improved. Similar improvements have been made in the error diffusion method. However, this improvement does not adversely affect other characteristics such as resolution in the threshold diffusion method, and the threshold diffusion method is still superior to the error diffusion method. By using the error diffusion method with edge enhancement, the resolution is still improved. However, even in this case, since it is restricted by the essential function of the error diffusion method of reproducing the average input value, sufficient reproducibility cannot be obtained particularly with a thin line with low contrast.
[0037]
The characteristics of the output image quality of the binarization processing by the threshold value diffusion method are as already described. The process for producing such image quality will be described in comparison with the error diffusion method.
[0038]
The error diffusion method uses the input value to the maximum, that is, it makes an image reflecting the input value by a method of adaptively determining the dot arrangement using the process of feeding back the error between output and input to the input. On the other hand, in the threshold diffusion method, the input value is not directly involved in the feedback. In the threshold diffusion method, the input value is only used for comparison to determine the output value. That is, the output on (“1”) or off (“0”) is fed back, but the input value itself does not enter the feedback process.
[0039]
However, in the threshold value diffusion method, as described above, by setting the feedback coefficient β to an appropriate value, the input value can be reflected in the output result, that is, the gradation of the input image can be reproduced. is there.
[0040]
Further, the error diffusion method essentially functions to reproduce the input value locally on the essence, but the threshold diffusion method does not. This also seems to be disadvantageous for reproduction of the input image, but conversely it is difficult to be constrained by the input value.
[0041]
A case will be described in which the function of the error diffusion method of average reproduction of input values is constrained. For example, assume a case where there is a gray line, ie, a low contrast thin line, against a white background. In this case, since it is gray, the dot density is determined according to the degree. Assuming 50% gray, half the dots of white and black must exist on average. Then, since it is a thin line, if half of the dots constituting the line are made white, the solid line may become a dotted line in an extreme case. In other words, we want to increase the number of black dots and bear white dots around them, but since the surroundings are originally white, it is impossible to distribute more white dots around them. That is, since the density of the thin gray line is reproduced on average, the characteristic as a line may be destroyed.
[0042]
In such a case, it is better to reproduce the line characteristic, that is, the low contrast edge characteristic, even if the gray level of the line is ignored to some extent. The function of the error diffusion method gives priority to the reproduction of the local zero-order component (low frequency component). However, depending on the nature of the image, it may be desirable to prioritize local first-order or higher-order components (high-frequency components). For example, in a portion where there are relatively small irregularities locally, It is desirable to give priority to the reproduction of the first or higher order component, that is, the unevenness, over the reproduction of the average level. Of course, the zero-order component may be reproduced in the gentle gradation portion.
[0043]
On the other hand, as described above, in the threshold value diffusion method, emphasis is placed on the reproduction of a local first-order or higher-order component. Therefore, even in the case where a thin gray line exists on the white background described above, which is limited by the function of the error diffusion method, emphasis is placed on the reproduction of a local first-order or higher-order component. In the threshold diffusion method, good results can be obtained because the edge characteristics of the line are reproduced.
[0044]
As described above, the threshold value diffusion method functions so as to preferentially reproduce local first-order or higher components of the image input value. However, it is nevertheless possible to reproduce the local zero-order component by setting parameters. The algorithm uses the same feedback as in the error diffusion method, and the texture of the output image has the same blue noise characteristics as in the error diffusion method. Also, the amount of calculation is the same as the error diffusion method.
[0045]
By using the threshold diffusion method, it is possible to eliminate the disadvantage of the error diffusion method that dots in the background portion close to white or black are easily arranged in a line while maintaining the output image quality similar to that of the error diffusion method. Further, the threshold diffusion method has various advantages such as reproduction of low contrast edge components.
[0046]
With regard to halftone reproduction by binarization, it is considered that attention will be given to visual characteristics in the future. In that case, the balance between the overall characteristics of the observed image and the local characteristics is important. That is, it is necessary to reproduce the entire gradation while maintaining local correlation in the image. This is because the person who observes the image is thought to be psychologically balanced so that the characteristics of both are maximized. Therefore, a method is required that can easily reproduce local zeroth order components and first order or higher components of the input image. The threshold value diffusion method contributes to an image creation method that satisfies such requirements.
[0047]
[Embodiment of the Invention]
An image creating apparatus according to one embodiment of the present invention will be described below. The image creating apparatus according to the present embodiment is characterized in that the gamma characteristic is also controlled by simple processing at the same time while performing halftoning processing.
[0048]
That is, with the error diffusion method that is a conventional technique, the γ characteristic cannot be freely controlled simultaneously with the halftoning process. In Japanese Patent Application No. 11-237492, which is an application of the present applicant, a technique is disclosed in which a threshold diffusion method is employed and the γ characteristic is freely controlled simultaneously with the halftoning process. According to the above, since it is necessary to change the coefficient to be multiplied by the feedback value depending on the input value, the calculation process is complicated.
[0049]
In the present embodiment, when a threshold value or its inverted value is fed back according to on / off of binary output of the image creating apparatus, a set of two types of coefficients corresponding to separately set on / off is used. The linear combination value of each feedback value of the threshold value and its inverted value is fed back to the threshold value. By performing such processing, the image creating apparatus according to the present embodiment can also control the γ characteristic by simple processing at the same time while performing halftoning processing.
[0050]
FIG. 9 is a block diagram showing the configuration of the image creating apparatus according to this embodiment.
Referring to FIG. 9, the image creating apparatus in the present embodiment is compared with the image creating apparatus in FIG. 1, a coefficient a multiplying unit 201 that multiplies the output of the correction threshold output unit 111 by the coefficient a, A coefficient b multiplication unit 203 that multiplies the output of the inverting unit 113 by the coefficient b. Note that if the value when the output of the threshold processing unit 103 is ON is Out and the correction threshold is Th (x), the inversion unit 113 calculates Out−Th (x).
[0051]
Further, in the present embodiment, instead of the subtracters 109 and 115 in FIG. 1, an adder 205 for adding the output of the correction value memory 119 and the output of the initial threshold value generator 107, and a coefficient a multiplier 201 And an adder 207 for adding the output of the coefficient b multiplier 203.
[0052]
The coefficient a multiplier 201 and the coefficient b multiplier 203 change the coefficients to be multiplied according to the output of the threshold processing unit 103. The coefficient a and the coefficient b are a set of coefficients, and two types of coefficient sets corresponding to the case where the output of the output unit 105 is on and the case where it is off are preliminarily set according to the desired γ characteristics. It is prepared. Alternatively, a large number of sets may be prepared as table data in advance, and the user may select table data corresponding to desired γ characteristics.
[0053]
FIG. 10 is a diagram showing specific examples of the coefficient a and the coefficient b. Referring to the figure, when the output of output unit 105 is on, numerical value a1 is selected as coefficient a and numerical value b1 is selected as coefficient b. This coefficient is multiplied by the correction threshold value and its inverted value, and then added by the adder 207.
[0054]
On the other hand, when the output of the output unit 105 is OFF, the numerical value a2 is selected as the coefficient a and the numerical value b2 is selected as the coefficient b, and processing is performed.
[0055]
As shown in FIG. 11, when the numerical values a1 = 1, b1 = 0, a2 = 0, b2 = −1, processing equivalent to processing by the normal threshold value diffusion method (the above-described reference example) is performed. It is.
[0056]
Although there is no restriction on the value of each coefficient, it is desirable that each coefficient take a value from −1 to 1 in order to appropriately control the γ characteristic. However, the absolute value of each coefficient may exceed 1 if the output image is intentionally given an extreme γ characteristic for a special purpose.
[0057]
For example, assuming that the output unit 105 outputs 255 when the output of the output unit 105 is on and 0 when it is off, and the correction threshold value at that time is Th (x), feedback is provided. (The output value of the adder 207) is expressed by the following equation.
[0058]
a1 × Th (x) + b1 × (255−Th (x)): output = on = 255
a2 × Th (x) + b2 × (255−Th (x)): output = off = 0
The value calculated by the above formula is multiplied by the feedback coefficient β, and the threshold value for processing the unprocessed pixels is diffused based on the weighting calculation.
[0059]
Next, the processing result in this Embodiment is shown with a graph.
The coefficients b1 and a2 shown in FIG. 12 were arbitrarily changed, and the characteristics of the output image were examined. That is, as shown in FIG. 12, the coefficient a when the output of the output unit 105 is on is always 1, and the coefficient b when the output of the output unit 105 is off is always -1. The coefficient b when the output of the output unit 105 is on is b1, and the coefficient a when the output of the output unit 105 is off is a2.
[0060]
In this process, an input image composed of 100 × 100 pixels was actually processed, and the relationship between the pixel value of the input image and the dot density of the output image was summarized in a graph. The pixel value of the input image is uniform for all the pixels (100 × 100 pixels), and the process was repeated while changing the value in the full range (ie, from 0 to 255).
[0061]
That is, 256 processes were performed for one processing condition (one coefficient setting condition), and 256 output images were obtained. The black and white dots of 256 images, which are the processing results, were counted to obtain 256 dot densities.
[0062]
FIGS. 13 to 18 are graphs showing the relationship between the pixel value (horizontal axis) of the input image and the dot density (vertical axis) of the output image obtained as described above. The curve shown in each graph is the output result when a specific set of coefficients is set. Although there are six graphs, the coefficient setting is changed in each. Thus, it can be seen that the γ characteristic of the output image can be effectively controlled by changing the setting of the coefficient.
[0063]
The setting of the coefficient group is as follows in each of FIGS.
[0064]
FIG. 13: b1 = 1, a2 = 1
FIG. 14: b1 = 0.8, a2 = 0.8
FIG. 15: b1 = 0.5, a2 = 0.5
FIG. 16: b1 = −0.8, a2 = 0.8
FIG. 17: b1 = −0.8, a2 = −0.8
FIG. 18: b1 = 1, a2 = 0
Next, the reason why the γ characteristic of the output image can be controlled by the image processing in the present embodiment will be described with reference to FIGS. 19 and 20.
[0065]
The horizontal axis of the graphs shown in FIGS. 19 and 20 represents the correction threshold Th (x) at that time, and the vertical axis represents the value to be fed back (the output of the adder 207). In the graph, a line segment indicated by ON indicates a relationship when the output is on, and a line segment indicated by OFF indicates a relationship when the output is off. In the graph, FB is an estimated value fed back when ON / OFF is mixed.
[0066]
FIG. 19 shows processing by normal threshold value diffusion (the above-mentioned reference example), and FIG. 20 is set in this embodiment as coefficient b1 = a2 = 0.5, coefficient a1 = 1, coefficient b2 = −1. It shows the processing in the case of.
[0067]
As shown in the figure, in the case of the normal threshold value diffusion (FIG. 19), the coefficient is changed in the example shown in FIG. 20 compared to the case where the FB value is linear with respect to the threshold value. The value of FB is non-linear. Thus, it can be seen that the output characteristic can be made non-linear in the present embodiment.
[0068]
As described above, by adopting the algorithm in the present embodiment, it is possible to control the γ characteristic of the output image simultaneously with the halftoning process by a simple process. Also, as the method, two sets of coefficients need only be set in advance or selected each time according to the desired γ characteristic, so that it is easy to use and the cost of the apparatus can be kept low. Furthermore, the calculation amount is small. In addition, according to the present embodiment, it is possible to provide an apparatus that is simpler and less loaded than an apparatus that separately performs γ correction prior to halftoning.
[0069]
Note that the algorithm in the above-described embodiment is applied to a scene where an output image is formed for a printer, a display, or another device that outputs a digital image, and the input image data is output to the output device. When converting to image data. Further, the present invention can be effectively applied when the output gradation level is limited, so-called halftoning processing is required, and when the output is not linear with respect to the input and γ correction is required. . In such a case, it is possible to easily carry out the necessary processing with a low load and to obtain an output image with good quality that meets the requirements.
[0070]
In the above description, only conversion from an input image with 256 gradations to an output image with two gradations is shown, but conversion from an arbitrary input gradation to an arbitrary output gradation is also possible with the same method. is there.
[0071]
The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration of an image creating apparatus according to a reference example.
FIG. 2 is a diagram for explaining the operation of a correction value memory 119;
FIG. 3 is a diagram showing a halftone processing result using a systematic dither method.
FIG. 4 is a diagram illustrating a halftone processing result using an error diffusion method.
FIG. 5 is a diagram showing a halftone processing result by a threshold diffusion method.
FIG. 6 is a diagram showing a halftone processing result by a threshold diffusion method.
FIG. 7 is a diagram illustrating a halftone processing result by an error diffusion method.
FIG. 8 is a diagram illustrating a halftone processing result using an error diffusion method with edge enhancement.
FIG. 9 is a block diagram illustrating a configuration of an image creating apparatus according to an embodiment of the present invention.
FIG. 10 is a diagram illustrating an example of a set of two types of coefficients to be set.
FIG. 11 is a diagram showing a set of coefficients having the same processing result as normal threshold value diffusion.
FIG. 12 is a diagram illustrating an example of setting coefficients.
FIG. 13 is a first diagram showing an effect in the present embodiment.
FIG. 14 is a second diagram showing an effect in the present embodiment.
FIG. 15 is a third diagram showing effects in the present embodiment.
FIG. 16 is a fourth diagram showing an effect in the present embodiment.
FIG. 17 is a fifth diagram showing an effect in the present embodiment.
FIG. 18 is a sixth diagram showing an effect in the present embodiment.
FIG. 19 is a diagram showing a relationship between a corrected threshold value and a value fed back in a normal threshold value diffusion method.
FIG. 20 is a diagram showing a relationship between a corrected threshold value and a value to be fed back in the threshold value diffusion method of the present embodiment.
FIG. 21 is a diagram illustrating a configuration of an image processing apparatus using an error diffusion method.
[Explanation of symbols]
101 Image Input Unit, 103 Threshold Processing Unit, 105 Binary Image Output Unit, 107 Threshold Generation Unit, 109 Subtraction Unit, 111 Correction Threshold Output Unit, 113 Inversion Unit, 115 Subtraction Unit, 117 Feedback Coefficient Multiplication 119 correction value memory, 201 coefficient a multiplier, 203 coefficient b multiplier, 205 adder, 207 adder.

Claims (2)

Input means for sequentially inputting a first image signal which is a multi-valued image representing the density level of each pixel;
Threshold value processing means for generating two types of second image signals that are on or off by comparing the first image signal input from the input means with a threshold value;
The value (n-1, 0) of the second image signal corresponding to each of on and off generated by the threshold processing means, and the threshold value used when generating the second image signal (Th ( according based rather predetermined calculation in the x)), and calculating means for calculating a threshold value used for thresholding the subsequent pixel,
Changing means for changing a coefficient used in the calculating means based on the second image signal generated by the threshold processing means ;
The changing means is provided with a first coefficient set (a1, b1) and a second coefficient set (a2, b2) having different coefficient sets according to the γ characteristics in advance, and the second image signal is turned on and off. Corresponding to the first coefficient group (a1, b1) and the second coefficient group (a2, b2),
The calculating means includes
a1 × Th (x) + b1 × ((n−1) −Th (x)): When the second image signal is on,
a2 × Th (x) + b2 × ((n−1) −Th (x)): When the second image signal is off
(A1, b1, a2, b2 are coefficients, Th (x) is a threshold value used when generating the second image signal, and n is the number of output gradations of the calculation means),
An image processing apparatus that performs an operation of multiplying the value calculated by the operation by a feedback coefficient β, and further adds an initial threshold value . An input step for sequentially inputting a first image signal which is a multi-valued image representing the density level of each pixel;
A threshold value processing step for generating two types of second image signals that are turned on or off by comparing the first image signal inputted in the input step with a threshold value;
The value (n-1, 0) of the second image signal corresponding to each of ON and OFF generated in the threshold processing step and the threshold value (Th ( a calculation step for calculating a threshold value used for threshold processing of a subsequent pixel according to a predetermined calculation based on x));
A changing step of changing a coefficient used in the calculating step based on the second image signal generated in the threshold processing step;
In the changing step, a first coefficient set (a1, b1) and a second coefficient set (a2, b2) having different coefficient sets according to γ characteristics are provided in advance, and the second image signal is turned on and off. Corresponding to the first coefficient group (a1, b1) and the second coefficient group (a2, b2),
The calculating step includes:
a1 × Th (x) + b1 × ((n−1) −Th (x)): When the second image signal is on,
a2 × Th (x) + b2 × ((n−1) −Th (x)): When the second image signal is off
(A1, b1, a2, b2 are coefficients, Th (x) is a threshold value used when generating the second image signal, and n is the number of output gradations of the calculation means),
An image processing method that performs an operation of multiplying the value calculated by the operation by a feedback coefficient β, and further adds an initial threshold value.

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