JP2003281534A - Image processor and image output device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To naturally improve acutance of an image by reasonably applying sharpness processing. <P>SOLUTION: The image processor 10 receives original image data So, and it applies the sharpness processing to output corrected image data Sc. To be specific, an image level value Sk is determined on the basis of the original image data So. A total correction amount Sa showing a degree of sharpness correction is determined in response to the determined image level value, and the sharpness processing is executed with respect to the original image data on the basis of the total correction amount. In such manner, the total correction amount by the sharpness processing with respect to the original image data is adjusted in response to the image level value of the original image data. Consequently, an inconvenience such as a level of the original image data being saturated by the sharpness processing can be prevented. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing device, and more particularly to a sharpness processing method suitable for displaying an image on a device having a relatively small display area such as a mobile phone or a portable terminal device.

[0002]

2. Description of the Related Art In image display devices and the like, so-called sharpness processing is performed on a displayed image so that the contour of the image is sharpened to make the image easier to see. By the sharpness processing, the slightly blurred original image can be displayed clearly. Also, in devices with a relatively small display area, such as mobile phones and portable terminal devices,
It is known that the sharpness of the sharpness processing makes the contour of the displayed image clear and makes it easier to see.

[0003]

However, in the case where sharpness processing is performed to some extent as in the case of the above-mentioned mobile phone, if the image data is normalized in the subsequent processing, the white and black portions of the image are close to each other. The pixel value may be saturated. That is, the pixel value is completely saturated on the white side in the part close to white in the image, and the pixel value is completely saturated on the black side in the part close to black. There are also cases where the display image is rather unnatural, which is also called "white jump" or "black jump." For example, when displaying an image of a human face, if the degree of sharpness processing is too strong, overexposure is likely to occur in a portion close to the contour of the face. On the other hand, if the degree of sharpness processing is suppressed too much, the sharpness of some images will be insufficient, resulting in a blurred image.

The present invention has been made in view of the above points, and it is an object of the present invention to naturally improve the sharpness of an image by appropriately performing sharpness processing.

[0005]

An image processing apparatus of the present invention comprises an image level determining means for determining an image level value of original image data, and a correction amount computing means for computing a correction amount based on the determined image level value. And sharpness processing means for performing sharpness processing on the original image data according to the correction amount to generate corrected image data.

The above image processing apparatus receives the original image data, performs sharpness processing, and outputs corrected image data. Specifically, the image level value is determined based on the original image data. A correction amount indicating the degree of sharpness correction is determined according to the determined image level value, and the sharpness processing is executed on the original image data based on the correction amount. In this way, the correction amount of the original image data by the sharpness processing is adjusted according to the image level value of the original image data. Therefore, it is possible to prevent a problem that the level of the original image data is saturated due to the sharpness processing, for example.

In one aspect of the above image processing apparatus, the image level determining means may determine the image level value according to the brightness level of the original image data.

In another aspect of the above image processing device, the image level determining means may determine the image level value based on the level of green data included in the original image data. When the image data is RGB image data, it is generally said that G (green) has the highest sensitivity and has the least noise. Therefore, green data is used instead of luminance data for image level. By determining the value, it is possible to reduce the processing load and speed up the processing.

The correction amount calculation means calculates the correction amount when the image level value is within a predetermined level range defined from the image level value corresponding to white, and the image level value is within the predetermined level range. The correction amount can be made smaller than the correction amount when it is outside. As a result, it is possible to prevent the image level value of the image data from being saturated on the white side and causing so-called overexposure by performing the sharpness processing.

Further, the correction amount calculating means determines the correction amount when the image level value is within a predetermined level range defined from the image level value corresponding to black, and the image level value is the predetermined level. It can be made smaller than the correction amount when it is outside the level range. This makes it possible to prevent the image level value of the image data from being saturated on the black side and causing so-called black overexposure by performing the sharpness processing.

Further, the correction amount calculating means determines the correction amount when the image level value is within a predetermined first level range defined from the image level value corresponding to black, and the image level value is the above-mentioned. The correction amount is set to be smaller than the correction amount when it is within the second level range defined around the intermediate value between the maximum value and the minimum value of the image level values, and the image level value is defined from the image level value corresponding to white. The correction amount when the image level value is within the predetermined third level range is smaller than the correction amount when the image level value is within the second level range. This allows
So-called white and black highlights can be prevented.

Furthermore, the correction amount calculation means calculates the correction amount when the image level value is within the third level range, and the correction amount when the image level value is within the first level range. Can be smaller than. This makes it possible to prevent whiteout more effectively.

In one aspect of the above image processing apparatus, the original image data is So, the image level value is Sk,
When the corrected image data is Sc, the correction amount is G
It is a function of the image level value represented by (Sk), and the corrected image data Sc can be represented by Sc = So + G (Sk).

Another image processing apparatus according to the present invention comprises an image level determining means for determining an image level value of original image data, a level change degree of the original image data, and the detected level change level based on the detected level change degree. A correction reference amount determining means for generating correction reference amount data indicating a reference amount for correction by sharpness processing, a correction amount calculating means for calculating a correction amount based on the image level value and the correction reference amount, and the correction amount. And sharpness processing means for performing sharpness processing on the original image data to generate corrected image data.

The above image processing apparatus receives the original image data, performs sharpness processing, and outputs corrected image data. Specifically, the image level value is determined based on the original image data. Further, the degree of the level change is detected from the original image data, and the correction reference amount data is generated. Then, a correction amount indicating the degree of sharpness correction is determined according to the image level value and the correction reference amount data, and the sharpness processing is performed on the original image data based on the correction amount. In this way, the correction amount of the original image data by the sharpness processing is adjusted according to the image level value of the original image data. Therefore, it is possible to prevent a problem that the level of the original image data is saturated due to the sharpness processing, for example. Further, it is possible to sharpen the original image data by performing the sharpness processing to an appropriate degree according to the level change of the original image data.

In one aspect of the above image processing apparatus, the original image data is So, the image level value is Sk,
The correction reference amount is Sr, and the correction image data is Sc.
Then, the correction amount is a function of the image level value represented by H (Sk) and a function of the correction reference amount represented by F (Sr), and the correction image data S
c can be represented by Sc = So + H (Sk) · F (Sr).

Further, an image output apparatus of the present invention comprises the above image processing apparatus, image size determination means for determining the image size when outputting the image data, and image size determined by the image size determination means. Is smaller than a predetermined image size, the image processing apparatus outputs control means for executing sharpness processing on the original image data and corrected image data obtained by the original image data or the sharpness processing. And output means.

According to the above image output device, the image size for outputting the image data is determined, and when the output image size is large, the sharpness processing according to the present invention is not performed. On the other hand, when the output image size is small, by performing the sharpness processing according to the present invention, appropriate sharpness correction is performed, and it is possible to obtain a clear image with sharpening even in a small output image.

[0019]

BEST MODE FOR CARRYING OUT THE INVENTION Preferred embodiments of the present invention will be described below with reference to the drawings.

[Device Configuration] FIG. 1 shows a schematic configuration of a main part of an image processing device according to an embodiment of the present invention. As shown in FIG. 1, the image processing apparatus 10 of the present invention includes a sharpness processing section 11, a driver 12, and a display section (or a printing section) 13. The image processing apparatus 10 of the present invention is preferably applied to a terminal device including a relatively small display unit such as a mobile phone or a mobile terminal device. Further, the image processing apparatus 10 of the present invention can be applied to a color printer or the like, and in that case, the printing unit 13 is provided.

The original image data So is input to the sharpness processing section 11. When the image processing device 10 is a mobile phone or the like, the original image data So is image data received by the mobile phone via the communication path. When the image processing device 10 is a printer, the original image data So is image data supplied to the printer from a personal computer or the like.

The sharpness processing unit 11 performs sharpness processing on the input original image data So to generate corrected image data Sc, and supplies the corrected image data Sc to the driver 12. The driver 12 uses the corrected image data S
The display unit (or print unit) 13 is driven according to c.
Thereby, the image is displayed or printed.

FIG. 2 shows the configuration of the sharpness processing section 11. As shown in FIG. 2, the sharpness processing unit 11 includes a correction reference amount determination unit 15, a correction amount calculation unit 16, a sharpness processing unit 17, and an image level value determination unit 18. The original image data So input to the sharpness processing unit 11 is supplied to the correction reference amount determination unit 15, the image level value determination unit 18, and the sharpness processing unit 17.

The correction reference amount determining means 15 determines the correction reference amount using the original image data So. Here, the correction reference amount is an amount indicating the degree of sharpness processing to be performed on the original image data So, and is specifically a concept including a Laplacian signal and a difference signal, which will be described later. The correction reference amount calculation means 15 generates a correction reference amount signal Sr based on the original image data So, and supplies this to the correction amount calculation means 16.

The image level value determining means 18 determines the image level value from the original image data So. The image level value is a value indicating the level of each pixel of image data which is digital data. When the image data is color image data, the image level value may be, for example, a brightness level,
Alternatively, it may be simply G data that most greatly affects the brightness, or another parameter that correlates with the brightness may be used. The image level value determination means 18 detects the level of the input original image data So and supplies the image level value signal Sk to the correction amount calculation means 16. In addition,
As will be described in detail later, the present invention prevents overexposure and overexposure by controlling the degree of sharpness processing according to the level value of image data.

The correction amount calculation means 16 includes a correction reference amount signal S.
The correction amount by the sharpness processing is calculated based on the correction reference amount indicated by r and the image level value indicated by the image level value signal Sk, and the correction amount signal Sa is supplied to the sharpness processing means 17. The correction amount corresponds to the amount by which the level of image data to be sharpened is changed by the sharpness processing. That is, when the absolute value of the correction amount is large, the level of the image data is greatly changed by the sharpness processing, and the sharpness of the image is greatly increased. On the other hand, when the absolute value of the correction amount is small, the level change of the image data due to the sharpness processing is small,
The sharpness of the image does not increase significantly. Further, whether the correction amount is positive or negative indicates whether the image data is changed to the white side or the black side by the sharpness processing.

The sharpness processing means 17 changes the level of the original image data So according to the correction amount indicated by the correction amount signal Sa supplied from the correction amount calculating means 16, and outputs the corrected image data Sc which is the image data after the sharpness processing. To generate. The sharpness processing means 17 supplies the generated corrected image data Sc to the driver 12 shown in FIG.

[Method of Determining Correction Amount] Next, a method of determining the correction amount by the correction amount calculation means will be described in detail. According to the basic idea of the present invention, the correction amount calculating unit 16 calculates the correction amount for correcting the original image data So by the sharpness processing.
Is determined according to the image level value.
In the present embodiment, the correction amount calculating means 16 determines the correction amount based on both the image level value and the correction reference amount, but in the present invention, the determined correction amount is a function of the image level value. The embodiment regarding the correction reference amount is merely for explaining the example,
Any correction amount that is determined according to the image level value is included in the scope of the present invention.

In the following description, the amount by which the original image data is finally corrected by the sharpness processing will be referred to as "total correction amount". Further, the total correction amount is determined according to the “correction coefficient” determined based on the image level value and the “basic correction amount” determined regardless of the image level value.

(Method for Determining Total Correction Amount Based on Image Level Value) First, a method for determining a total correction amount based on the image level value will be described. The total correction amount is an amount indicating a level change given to the original image data by the sharpness processing. The image level value is the level value of the original image data So, and when the original image data So is a color signal including a luminance signal, it indicates a gradation level between white and black. In general, the corrected image data obtained by the sharpness processing is generated by adding the total correction amount to the original image data. This total correction amount is therefore: corrected image data Sc = original image data So + total correction amount.

The total correction amount is an amount determined by the relative relationship between the pixel data of the pixel of interest and the pixel data of its peripheral pixels, which is conventionally obtained through a sharpness filter, and is directly related to the image level value. There was no In the present invention, this total correction amount is a function of the image level value. In other words, the total correction amount is defined as a function of the image level value with the total correction amount = G (Sk), and the total correction amount is changed according to the image level value Sk. That is, Becomes Therefore, by appropriately setting the function G (Sk) that defines the relationship between the image level value and the total correction amount, it is possible to perform appropriate sharpness correction.

According to one idea of the present invention, the image data So
When the level of each pixel is close to white or close to black, the total amount of correction by the sharpness processing is set to a small level to prevent the above-described whiteout and blackout defects. For example, the total correction amount can be made a function of the image level value by multiplying the basic correction amount which is not a function of the image level value by a correction coefficient which is a function of the image level value. That is, corrected image data Sc = original image data So + G (Sk) = original image data So + H (Sk) · Ra (where the basic correction amount Ra is a value that is not a function of the pixel level value, for example, F (Sr) described later. Here, G (Sk) is a total correction amount, and H (Sk) is called a correction coefficient or a correction coefficient function.

As a method of actually determining the total correction amount based on the image level value, as exemplified below,
Function of total correction amount with respect to image level value (correction coefficient function H
There is a method in which the characteristic of (Sk)) is stored in advance in a lookup table or the like and the total correction amount is obtained by referring to it. Instead, a correction coefficient function H (Sk) of the total correction amount with respect to the image level value is prepared as a function, and if necessary, this function is used to calculate the total correction amount from the image level value. There is also a method. Which one is actually adopted depends on the processing speed, processing accuracy, etc. required by the apparatus to which the present invention is applied.

3 and 4 show examples of the correction coefficient function H (Sk) that defines the relationship between the image level value and the correction coefficient. In FIGS. 3 and 4, the horizontal axis represents the image level value Sk.
Indicates. As the image level value Sk is closer to zero (that is, closer to the origin of the horizontal axis), the brightness of the image data is closer to black, and as the image level value Sk is larger (that is, the value of the horizontal axis is larger), the image data The brightness is close to white. The vertical axis represents the first correction amount H (Sk).

An example of the function H (Sk) shown in FIG.
It has a characteristic that the value of the correction coefficient function H (Sk) becomes small when the image level value is close to black and white. That is, the total correction amount by the sharpness process is suppressed in the portion close to white and black in the image data so that the above-described defects such as white and black highlights do not occur.

The correction coefficient function H (Sk) shown in FIG.
3A, like the example of FIG. 3A, in addition to suppressing the total correction amount by the sharpness processing in the part close to white and black in the image data, the part close to white (almost corresponding to skin color Yes, the part on the right side of the center of the horizontal axis in FIG. 3 (b))
Therefore, the total correction amount is slightly increased. As a result, while preventing overexposure and overexposure, sufficient sharpness correction is performed in the flesh color area so that a human face or the like can be displayed clearly.

The correction coefficient function H (Sk) shown in FIG.
3A, like the example shown in FIG. 3A, suppresses the total correction amount by the sharpness processing in a portion close to white and black in the image data, but the characteristic of the function is set discretely. Thus, it is possible to reduce the load and speed up the calculation process of the correction coefficient. That is, when the method of storing the correction coefficient function H (Sk) in the lookup table when actually determining the correction coefficient from the image level value is adopted, the amount of data to be stored in the lookup table is It will be reduced. Further, when the method of obtaining the correction coefficient from the image level value by using the correction coefficient function H (Sk) is adopted, the calculation processing can be speeded up.

The correction coefficient function H (Sk) shown in FIG.
Is similar to the example shown in FIG. 4A, the correction coefficient function H
The characteristic of (Sk) is discretely set to reduce the processing load and the processing speed, but it is set in consideration of only the prevention of blown-out highlights. Therefore, when the image level value is close to black, the total correction amount by the sharpness processing is not suppressed.

Contrary to FIG. 4B, the total correction amount by the sharpness process is suppressed when the image level value is close to black, and the total correction amount by the sharpness process is when the image level value is close to white. It is also possible to adopt a characteristic that does not suppress A correction coefficient function H (Sk) having such characteristics
Is, for example, depending on the source or processing of the original image data,
This is effective when there is a special characteristic that blackout is likely to occur in a region close to black in the image level value.

Correction coefficient function H (Sk) shown in FIG.
The example of is basically similar to FIG. 3 (a) or (b),
The total correction amount of the sharpness processing is suppressed in a portion close to white and black of the image data. When this characteristic is mathematically defined, it is assumed that the maximum value of the image level value Sk is M (corresponding to white), and the image level value Sk is from zero (corresponding to black) to a predetermined level value width T, the image level. Areas ΔB, ΔM, and ΔW of the predetermined level value width T in the middle of the maximum value and the minimum value of the value Sk and the area of the predetermined level value width T in which the image level value Sk has the maximum value M, respectively.
However, the correction coefficient function H (Sk) is set so as to have a relationship of ΔW <ΔB <ΔM. The predetermined level value width T is preferably 0.1 to
It is set to about 0.2M. With this characteristic, the total amount of correction by the sharpness processing is
The area close to black is set smaller, and
The area close to white is set to be smaller than the area close to black. As a result, whiteout and blackout are prevented, and in particular whiteout can be sufficiently prevented.

FIG. 5 shows an example of waveforms of the original image data So and the corrected image data Sc when the correction amount for the sharpness processing is determined based on the image level value. The horizontal axis direction of FIG. 5 is time, the vertical axis direction of the original image data Sc is the image level value, the vertical axis direction of the correction reference amount signal Sr is the correction reference amount signal value, and the vertical axis direction of the corrected image data Sc is the image. It is a level value. Therefore, the original image data So and the corrected image data Sc
Is closer to white when the image level is higher and closer to black when the image level is lower. The correction reference amount signal will be described later in detail.

As shown in FIG. 5, the original image data So is 2
When there is a step-like image level value increasing portion at a location, the correction reference amount signal S indicating the degree of change of the image level value
r has two S-shaped change points. Normally, the corrected image data Sc is obtained by adding a signal proportional to the correction reference amount signal Sr to the original image data So. However, in the present invention, as shown in FIGS. The total amount of sharpness correction in the black area is set to a small amount. Therefore, in FIG. 5, the corrected image data Sc
Focusing on the area close to black (that is, the image level is low)
The degree of correction by the sharpness processing is small at the change point 30, and the degree of correction by the sharpness processing is also small at the change point 33 in the area close to white (that is, the image level is high). On the other hand, at the change points 31 and 32 that are not close to white and black, the degree of correction by the sharpness processing is large. Thus, according to the present invention, in the area where the image level value of the image data is close to white and black, the total correction amount of the sharpness processing is suppressed, so that the image level value after the sharpness correction is saturated in the black or white direction. That is, it is possible to prevent whiteout and blackout.

(Method of Determining Total Correction Amount Based on Image Level Value and Correction Reference Amount) Next, a method of determining total correction amount based on the image level value and correction reference amount will be described. The correction reference amount is a reference amount for determining the total correction amount for performing the sharpness processing, and the correction reference amount signal Sr is a signal indicating this reference amount. Specifically, the correction reference amount signal Sr
For this, either the Laplacian signal of the original image data So or the difference signal generated from the original image data So can be used.

First, the case where the Laplacian signal is used as the correction reference amount signal will be described. FIG. 6A shows
An example of waveforms of the original image data So, the correction reference amount signal Sr, the correction amount signal Sa, and the correction image data Sc when the Laplacian signal is used as the correction reference amount signal is shown. In addition,
In FIG. 6A, the vertical axis direction of each waveform represents the luminance of the original image data, and the upper direction in the figure is white and the lower direction is black. The horizontal axis of each waveform represents time.

The Laplacian signal is obtained by applying the filtering by the Laplacian filter to the original image data So. The Laplacian filter is
It is a kind of differential filter used for spatial filtering of images, and is a filter that mathematically performs a process corresponding to a quadratic differential operation. Examples of coefficients of the Laplacian filter are shown in FIGS. 6B and 6C. The Laplacian filter has a function of emphasizing a contour portion or a high frequency component in which the density in the image changes abruptly. As shown in FIG. 6 (a),
The Laplacian signal has unevenness in the waveform at the portion where the level of the original image data So starts increasing and at the portion where the increase finishes and becomes a constant level. The Laplacian signal is negative in the portion where the waveform of the original image data So is convex downward, and is positive in the portion where the waveform of the original image data So is convex upward. The Laplacian signal can be used as a correction reference amount signal to determine the degree of sharpness processing.

Next, the difference signal will be described. Figure 7
Original image data So, unsharp data Su, correction reference signal S when a difference signal is used as the correction reference amount signal
The waveform examples of r, the correction amount signal Sa, and the correction image data Sc are shown. Note that, in FIG. 7, the vertical axis direction of each waveform represents the luminance of the original image data, and the upper direction in the figure is white and the lower direction is black. The horizontal axis of each waveform represents time. The difference signal is
This signal is obtained by subtracting the unsharp data Su from the original image data So, and is a signal indicating a contour portion or a high frequency portion in the image, like the Laplacian signal. The unsharp data Su is generated by applying an averaging filter to a predetermined range of the original image data So.

That is, as shown in FIG. 7, the original image data S
Unsharp data Su by locally averaging o
Is obtained, and the difference signal Sr is obtained by subtracting this from the original image data So. Therefore, Sr = So-Su
Becomes

Both the Laplacian signal and the difference signal are signals indicating the contour portion and the high frequency portion in the image, and both can be used as the correction reference amount signal in the present invention. However, depending on the method of generating the unsharp signal,
The Laplacian signal and the difference signal do not always match.

Next, a method of determining the total correction amount based on the image level value and the correction reference amount will be described. The total correction amount is an amount indicating a level change given to the original image data by the sharpness processing. In this example, a method of using the total correction amount as a function of the correction reference amount is adopted. In other words, the basic correction amount = F (Sr) is defined as a function of the correction reference amount, and the basic correction amount is set to the correction reference amount S.
It was decided to change according to r. That is, the corrected image data Sc = the original image data So + the total correction amount = the original image data So + H (Sk) · F (Sr) In this way, by changing the basic correction amount according to the correction reference amount It is possible to perform appropriate sharpness correction on a necessary portion while preventing the image itself from being unnatural due to excessive sharpness correction on the portion.

A specific method of determining the basic correction amount based on the correction reference amount Sr will be described below with reference to the basic correction amount determination patterns illustrated in FIGS. In each of the basic correction amount determination patterns in FIGS. 8 to 12, the horizontal axis represents the correction reference amount Sr, and the vertical axis represents the basic correction amount. That is, FIGS. 8 to 12 are graphs showing how the basic correction amount changes with respect to the change of the correction reference amount.

As a method for actually determining the basic correction amount based on the correction reference amount, a basic correction amount determination pattern as illustrated in FIGS. 8 to 12 is stored in advance in a lookup table or the like, There is a method of obtaining the basic correction amount by referring to it. Alternatively, there is also a method in which a function that defines a basic correction amount determination pattern is prepared in advance and the basic correction amount is calculated from the correction reference amount using the function. Which one is actually adopted depends on the processing speed, processing accuracy, etc. required by the apparatus to which the present invention is applied.

FIG. 8A shows an example 1a of the basic correction amount determination method.
Indicates. Example 1a shows a method in which the basic correction amount is proportional to the correction reference amount, that is, a constant multiple of the correction reference amount is used as the basic correction amount.

FIG. 8B shows an example 1b of the basic correction amount determination pattern. In Example 1b, the basic correction amount is basically proportional to the correction reference amount. However, when the correction reference amount is larger than the predetermined value a or smaller than the predetermined value −a, the increase rate of the basic correction amount is made smaller than when the correction reference amount is within the range of −a to + a. There is. The fact that the absolute value of the correction reference amount is large indicates that the level of the image in that portion is abruptly changed to the white or black side. Therefore, in such an area where the image level changes rapidly,
The increase rate of the basic correction amount due to the increase of the correction reference amount is suppressed to some extent. That is, the correction amount by the sharpness processing is suppressed in the region where the change in luminance is large, and the image is prevented from being unnatural due to excessive emphasis by the sharpness processing. According to this, as described above, it is possible to prevent the outline portion of the face image from being displayed in a white state due to excessive emphasis of the sharpness processing.

An example 1c shown in FIG. 8C is a modification of the example 1b. When the absolute value of the correction reference amount is less than the predetermined value b, the basic correction amount is set to zero, that is, the sharpness correction is not performed. That's the method. The correction reference amount indicates a sharp change in the level of the image data, which means that the level of the image data greatly changes in the region where the correction reference amount is large. On the other hand, in the area where the correction reference amount is small, the level of the image data does not change so much. That is, a relatively small change in the correction reference amount does not correspond to a level change due to the content of the image itself, but often corresponds to noise added to the image data. From this point of view, when the absolute value of the correction reference amount is less than the predetermined value b, it is considered that such a change in the correction reference amount is due to noise, and the sharpness processing is not performed. When sharpness is applied to a region with a large amount of noise in an image, the noise tends to be emphasized and increased to make the image difficult to see. Therefore, as in Example 1c, a change in the correction reference amount equal to or less than the predetermined value b is referred to as noise. It is effective to consider the image and exclude it from the sharpness processing target in order to easily display an image with a relatively large amount of noise.

The example 1d shown in FIG. 8D is basically the example 1c.
Based on the same idea, the sharpness processing is not performed in the area where the absolute value of the correction reference amount is less than the predetermined value c. However, in Example 1d, as the correction reference amount enters from the region where the sharpness processing is not performed to the region where the sharpness processing is performed (that is, when the correction reference amount increases from less than the predetermined value c and exceeds the predetermined value c). , The basic correction amount is gradually increased from zero. As a result, it is possible to prevent the image from becoming unnatural due to a sharp change in the application or non-application of the sharpness processing in the region where the absolute value of the correction reference amount is close to the predetermined value c.

In both of the example 2a and the example 2b shown in FIG. 9, the basic correction amount is proportional to the correction reference amount, but the basic correction is performed in the region where the correction reference amount is positive and the region where the correction reference amount is negative. The feature is that the rate of increase of the amount is different.

The correction reference amount is the target pixel and
It is determined by the relative level difference between pixels around the pixel, and is an amount irrelevant to the level of the target pixel itself. If the correction reference amount is positive, it indicates that the pixel data is convex upward, that is, the pixel is closer to the white side than the peripheral pixels. When the correction reference amount is negative, the pixel data is convex downward, that is, the pixel is closer to the black side than the peripheral pixels. In the sharpness processing, generally, when the correction reference amount is positive, the correction amount of the sharpness is also corrected to the positive side, that is, the white side, and when the correction reference amount is negative, the correction amount of the sharpness is also the negative side, that is, the black side. to correct. However, when the correction reference amount is small, the sharpness correction may not be performed in some cases. That is, even if the correction reference amount is positive, the correction is not performed on the white side, and even if the correction reference amount is negative, the correction is not performed on the black side in some cases.

In Example 2a, the area where the correction reference amount is positive (that is, the image data is convex upward, that is, the corresponding pixel is closer to the white side than the peripheral pixels, and the correction direction is the white direction) ) Is more negative than the area in which the correction reference amount is negative (that is, the image data is convex downward, that is, the pixel is closer to the black side than the surrounding pixels, and the correction direction is black). The increase rate of the basic correction amount is suppressed. This example is particularly effective for preventing the phenomenon that a part of the human face described above becomes white. Since the brightness level of human skin color is higher than that of white, at a place on the display image where there is a sudden change in brightness in the brightness region of white of the image data, the degree of sharpness enhancement should be suppressed,
It is possible to effectively prevent the defect that the flesh-colored area becomes white.

On the other hand, in Example 2b, the increase rate of the basic correction amount is suppressed in the area where the correction reference amount is negative as compared with the area where the correction reference amount is positive. This is the opposite of the idea of preventing the skin-colored portion from becoming white, but depending on the image data source and / or the image data processing method,
This is effective in suppressing such noise when the original image data to be sharpened has a unique characteristic that it is likely to contain noise at a luminance level higher than black.

Both Example 3a and Example 3b shown in FIG. 10 are methods of suppressing the increase rate of the basic correction amount when the correction reference amount exceeds a predetermined value d, and this also prevents the defect of white spots in the skin color part. Is effective to do. Note that Example 3a
Then, if the correction reference amount is equal to or smaller than the predetermined value d, the basic correction amount is determined with the same increase rate with respect to the correction reference amount even if the correction reference amount is negative. On the other hand, in Example 3b, the increase rate of the basic correction amount with respect to the correction reference amount is suppressed even when the correction reference amount is smaller than the predetermined value e.

In each of Examples 4a to 4d shown in FIG. 11, the increase rate of the correction amount in the region where the correction reference amount is positive is set smaller than that in the region where the correction reference amount is negative. As a result, the white spots of the skin color are prevented as described above. Further, in both cases, when the absolute value of the correction reference amount is less than the predetermined value f, the correction amount is set to zero and the sharpness processing is not performed to prevent noise from being emphasized. Example 4
In a, the correction amount is discontinuously determined in a region where the absolute value of the correction reference amount is the predetermined value f. That is, the sharpness process is not performed until the absolute value of the correction reference amount reaches f, but when the absolute value of the correction reference amount becomes f, the sharpness process is performed with a predetermined correction amount. On the other hand, in Examples 4b and 4c, when the absolute value of the correction reference amount becomes f, the correction amount gradually increases from zero, and the image is gradually emphasized by the sharpness process. . In Example 4b, after the absolute value of the correction reference amount becomes f, the correction amount first increases in a curve and then linearly, whereas in Examples 4c and 4d, the absolute value of the correction reference amount is After reaching f, the correction amount increases linearly from the beginning. In addition, the example 4c is an example in which the correction amount increases along two straight lines having different inclinations, and the example 4d is an example in which the correction amount increases along one straight line.

In Example 5 shown in FIG. 12A, sharpness processing is not performed when the absolute value of the correction reference amount is small, and the correction reference amount is in the positive direction (the degree of convexity in the white direction) and When increasing in the negative direction (the degree of convexity in the black direction), the value for starting the sharpness processing is made different. That is, when the correction reference amount increases in the positive direction, sharpness processing is not performed until the correction reference amount reaches the predetermined value g, whereas when the correction reference amount increases in the negative direction,
Sharpness processing is started with a predetermined value −h (h <g).
Further, when the correction reference amount is larger than the predetermined value j, the basic correction amount is set to a constant value L1, and when the correction reference amount is smaller than the predetermined value −k, the basic correction amount is set to a constant value L2. That is, when the correction reference amount exceeds a predetermined value, the basic correction amount is maintained at a constant value to prevent excessive emphasis due to sharpness processing. Also in that case, when the correction reference amount increases in the white direction, the basic correction amount is maintained at a value (L1) smaller than that in the case where the correction reference amount increases in the black direction, and white spots in the skin color part are prevented. ing.

In Example 6 shown in FIG. 12B, the basic correction amount is set stepwise with respect to the correction reference amount. According to this method, when the basic correction amount based on the correction reference amount is determined by using the lookup table, the amount of data to be stored in the lookup table can be reduced, and the correction reference amount can be reduced. When the basic correction amount is determined based on the calculation using the predetermined function, there is an advantage that the processing load and the processing time required for the calculation can be reduced.

Further, as shown in FIG. 12C, the change of the basic correction amount with respect to the correction reference amount is defined by a predesigned curve according to the characteristics of the image data to be sharpened, and is smoothed. It is also possible to control the correction value.

[Sharpness Processing] Next, the flow of sharpness processing according to the present invention will be described. Figure 13
It is a flowchart of the sharpness process of this invention. In the following description, a case where the image processing apparatus that executes the sharpness processing according to the present invention is applied to a mobile phone will be described.

First, the mobile phone receives the original image data So to be displayed and supplies it to the sharpness processing section 11 shown in FIG. 1 (step S1).

In the sharpness processing unit 11, the supplied original image data So is temporarily expanded in a work memory (not shown) or the like, and the correction reference amount determining means 15 determines the correction reference amount using the original image data So. Then, the correction reference amount signal Sr
Is generated (step S2). This correction reference amount signal Sr
6 may be the Laplacian signal shown in FIG. 6A or the difference signal shown in FIG. 7 as described above. If it is a Laplacian signal, the correction reference amount determining means 15
Performs filtering on the original image data So expanded in the working memory or the like by using, for example, a Laplacian filter as shown in FIG. 6B or 6C, generates a Laplacian signal, and outputs the correction reference amount signal Sr. And on the other hand,
When the correction reference amount signal Sr is a difference signal, the correction reference amount determining means 15 uses a predetermined unsharp filter to generate the unsharp data shown in FIG. 7, and subtracts the unsharp data from the original image data So. Then, a difference signal is generated and used as a correction reference amount signal Sr.

Next, the image level value determining means 1 shown in FIG.
8 determines the image level value from the original image data So,
The image level value signal Sk is generated and supplied to the correction amount calculation means 16 (step S3).

Next, the correction reference amount signal S thus obtained
The correction amount calculation means 16 shown in FIG. 2 calculates the total correction amount using r and the image level value signal Sk, and the correction amount signal Sa
Is generated (step S4). Specifically, the correction amount calculation means 16 uses the correction coefficient function H illustrated in FIG. 3 or 4.
The correction coefficient H (Sk) is calculated using any of (Sk), and the basic correction amount F (Sr) is calculated using any of the correction amount determination patterns illustrated in FIGS. , The total correction amount is determined as the product of them.

As described above, the correction coefficient function H (Sk) and the basic correction amount function F (Sr) are prepared in advance in the form of a lookup table or defined as a function. The correction amount calculation means 16 calculates a correction coefficient for each image level value included in the image level value signal Sk by referring to a lookup table or performing a calculation according to a function. Further, the correction amount calculation means 16 calculates the basic correction amount for each correction reference amount included in the correction reference amount signal Sr by referring to a look-up table or performing a calculation according to a function, and calculates the correction coefficient and the basic correction amount. A product with the correction amount is calculated to generate a correction amount signal Sa. Then, the correction amount calculation means 16 supplies the obtained correction amount signal Sa to the sharpness processing means 17.

The sharpness processing means 17 performs sharpness processing on the original image data So based on the correction amount signal Sa to generate corrected image data Sc (step S5). Specifically, as shown in FIGS. 6A and 7, the correction amount signal Sa is added to the original image data So to generate the correction image data Sc. Then, the generated corrected image data Sc is output to the driver 12.

The driver 12 displays the received corrected image data Sc on the display unit 13 (step S6). Thus, the sharpness processing according to the present invention is performed, and the corrected image data Sc corrected by the sharpness processing is displayed on the display unit 13.

[Modification] In the above description, the image data is color image data including color components. However, an image display device to which the sharpness processing of the present invention is applied processes the image data as image data for each RGB. In the case of displaying, the above sharpness processing can be individually applied to each of the image data of RGB colors.

In this case, the image level value determining means 18 in the sharpness processing section 11 may first calculate the luminance data from the image data of each color of RGB, and use the luminance data to detect the image level value. . In that case, if the original image data of each RGB color before correction by the sharpness process is R, G, and B, and the image level value calculated based on the brightness data Y calculated from the original image data is Yk, the corrected image data is corrected. Image data Rc, Gc of RGB colors,
Bc can be expressed as Rc = R + H (Yk) Gc = G + H (Yk) Bc = B + H (Yk).

There is a limit to the hardware capability of the image display device to which the sharpness processing of the present invention is applied,
Alternatively, when it is not desirable to calculate the luminance data from the image data of each color of RGB as described above due to the demand for high-speed processing, only G (green) data is used instead of the luminance data. be able to. That is,
The image level value determining means 18 can generate an image level value signal based on the G data. This is because it is generally said that G (green) has the highest sensitivity and little noise when the image data is RGB color image data. Therefore, if the image level value is obtained using G data, it is relatively easy to distinguish between the original contour and noise, and it is difficult to distinguish between the contour such as B and G and noise. Also has the advantage that it is easy to sharpen. Further, as described above, since it is not necessary to calculate the brightness data from the image data of each color of RGB, the processing load is naturally lightened and the processing can be speeded up.

As described above, when the image level value is obtained by using the G data, the image data R of each of the RGB colors after correction is obtained.
C, Gc, and Bc can be expressed as Rc = R + H (Gk) Gc = G + H (Gk) Bc = B + H (Gk), where Gk is the image level value generated based on the G data.

[Application Example] Next, an application example of the sharpness processing of the present invention will be described. First, a first application example will be described with reference to FIG.

FIG. 14A shows an example in which the sharpness processing of the present invention is applied to the transmission of image data via a network. The server, which is the image sender, transmits certain image data to the recipient A and the recipient B via the network.
Recipient A uses a mobile phone as an image display device, and recipient B uses a personal computer (P
C) is used. Here, the server grasps what the image display device used by each of the recipients A and B (whether it is a mobile phone or a PC) from the data included in the communication signal. That is, during communication with the recipient A, the server receives device type information indicating that the device used by the recipient A is a mobile phone, and during communication with the recipient B. The device type information indicating that the device used by the recipient B is a PC is received from the PC.

When the server receives a request to send specific image data from the receiver A, the server sends the requested image data to the image data. At that time, since the display area of the mobile phone is generally small, the image data to be transmitted is subjected to the sharpness processing according to the present invention and then transmitted. Since the mobile phone of the recipient A displays the image data received from the server as it is on the display unit of the mobile phone,
An image in which the image is effectively sharpened by the sharpness processing is displayed even on the display unit of a mobile phone having a relatively small display area.

When the server receives a transmission request for specific image data from the receiver B, the server transmits the requested image data for the image data. At that time, since the display area of the PC is generally relatively large, the image data to be transmitted is transmitted without being subjected to the sharpness processing according to the present invention. Since the recipient B's PC directly displays the image data received from the server on the display unit of the mobile phone, an image subjected to appropriate sharpness processing is displayed on the display unit of the mobile phone having a relatively large display area. To be done.

If the device type information that can be received by the server from the mobile phone or the PC includes information indicating the size of the image display area of the mobile phone or PC, the server device determines Depending on the size, it can be determined whether or not the sharpness processing of the present invention is applied to the image data to be transmitted.

Next, a second application example will be described with reference to FIG. The second application example is one in which the sharpness processing of the present invention is applied to print output of an image by a color printer. The printer receives print instructions and print data from an external PC or the like. The print instruction includes print size information indicating the size of image data to be printed. Therefore, the printer refers to the print size information and prints the print data without performing the sharpness processing according to the present invention when performing printing larger than a predetermined print size. On the other hand, when the print size information included in the print instruction specifies a print size smaller than the predetermined print size, the print data after the sharpness processing according to the present invention is applied is printed. This allows
When the size of the print data is small, the sharpened image data is printed by the sharpness processing of the present invention, so that a clear image can be printed even when the print size is small.

[Brief description of drawings]

FIG. 1 shows a schematic configuration of a main part of an image processing apparatus that performs sharpness processing according to the present invention.

FIG. 2 shows a configuration of a sharpness processing section shown in FIG.

FIG. 3 shows a characteristic example of a function that defines a relationship between an image level value and a correction coefficient.

FIG. 4 shows another characteristic example of a function that defines the relationship between an image level value and a correction coefficient.

FIG. 5 shows an example of an image signal waveform when a correction amount for sharpness processing is changed according to an image level value.

FIG. 6 shows an example of each signal when a Laplacian signal is used as a correction reference amount signal and an example of coefficients of a Laplacian filter.

FIG. 7 shows an example of each signal when a difference signal is used as a correction reference amount signal.

FIG. 8 shows an example of a basic correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 9 shows another example of the basic correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 10 shows still another example of the basic correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 11 shows still another example of the basic correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 12 shows still another example of the basic correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 13 is a flowchart of sharpness processing of the present invention.

FIG. 14 shows an application example of the sharpness processing of the present invention.

[Explanation of symbols]

10 Image processing device 11 Sharpness processing section 12 drivers 13 Display section (print section) 15 Correction reference amount determining means 16 Correction amount calculation means 17 Sharpness processing means 18 Image level value determining means

Continued front page    F-term (reference) 5B057 BA30 CA01 CA08 CA12 CA16                       CB01 CB08 CB12 CB16 CE03                       CE11 DA17 DB02 DB06 DB09                       DC25 DC36                 5C077 LL01 LL19 MP01 MP07 PP03                       PP09 PP43 PQ03 PQ05 PQ12                       PQ18                 5L096 AA02 AA06 GA05 GA38 GA40

Claims (11)

[Claims] 1. An image level determination means for determining an image level value of original image data, a correction amount calculation means for calculating a correction amount based on the determined image level value, and the original image according to the correction amount. An image processing apparatus comprising: a sharpness processing unit that performs sharpness processing on data to generate corrected image data. 2. The image processing apparatus according to claim 1, wherein the image level determining unit determines the image level value according to a brightness level of the original image data. 3. The image processing apparatus according to claim 1, wherein the image level determining unit determines the image level value based on a level of green data included in the original image data. 4. The correction amount calculation means calculates the correction amount when the image level value is within a predetermined level range defined from the image level value corresponding to white, and the image level value is the predetermined level. The image processing apparatus according to claim 2, wherein the correction amount is smaller than the correction amount when the correction amount is outside the level range. 5. The correction amount calculation means calculates the correction amount when the image level value is within a predetermined level range defined from an image level value corresponding to black, and the image level value is within the predetermined level. The image processing apparatus according to claim 2, wherein the correction amount is smaller than the correction amount when the correction amount is outside the level range. 6. The correction amount calculation means calculates the correction amount when the image level value is within a predetermined first level range defined from the image level value corresponding to black, and the image level value is the The image level value is set to be smaller than the correction amount in the second level range defined around the intermediate value between the maximum value and the minimum value, and the image level value is defined from the image level value corresponding to white. The correction amount when the image level value is within the predetermined third level range is set to be smaller than the correction amount when the image level value is within the second level range. The image processing device according to item 1. 7. The correction amount calculation means calculates the correction amount when the image level value is within the third level range,
The image processing apparatus according to claim 6, wherein the image level value is smaller than the correction amount when the image level value is within the first level. 8. When the original image data is So, the image level value is Sk, and the corrected image data is Sc, the correction amount is a function of the image level value represented by G (Sk). The image processing apparatus according to claim 1, wherein the corrected image data Sc is represented by Sc = So + G (Sk). 9. An image level deciding means for deciding an image level value of original image data, a degree of level change of the original image data is detected, and a reference amount of correction by a sharpness process based on the detected degree of level change. A correction reference amount determining means for generating correction reference amount data, a correction amount calculating means for calculating a correction amount based on the image level value and the correction reference amount, and the original image data according to the correction amount. Sharpness processing means for performing sharpness processing to generate corrected image data for the image processing apparatus. 10. When the original image data is So, the image level value is Sk, the correction reference amount is Sr, and the corrected image data is Sc, the correction amount is represented by H (Sk). Is a function of the correction reference amount represented by F (Sr), and the corrected image data Sc is Sc = So + H (Sk).
The image processing apparatus according to claim 9, wherein the image processing apparatus is represented by F (Sr). 11. The image processing apparatus according to claim 1, an image size determination unit that determines an image size when outputting the image data, and an image size determination unit that determines the image size. When the image size is smaller than a predetermined image size, the image processing device performs a sharpness process on the original image data, a control unit, and the corrected image data obtained by the original image data or the sharpness process. An image output device comprising: an output unit that outputs