JP2000032287A - Image processor and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To attain high speed processing by automatically discriminating a type of an input image and outputting the image with/without adaptive gradation correction processing depending on the result of discrimination to prevent deterioration in image quality being a side effect of gradation correction. SOLUTION: In the operation of discriminating a type of a received image, a lightness distribution information extract section 301 generates a histogram of lightness based on processing object image data and a color number count section 302 counts number of colors expressed based on combinations of R, G, B of the processing object image data. A feature variable analysis section 303 extracts minimum lightness, maximum lightness and number of peaks where the lightness takes maximum values as feature variables based on a distribution shape of the lightness histogram. In the case that the lightness distribution is not widely distributed to an all gradation area or the lightness is concentrated on a few specific gradation areas by comparing the feature variables with a prescribed threshold level, it is discriminated that the image is a graphical image and gradation correction processing is stopped. In the case that number of colors is less than a prescribed threshold value, it is discriminated that the image receives processing such as dither processing and the gradation correction processing is stopped.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus and an image processing method, and more particularly to an image processing apparatus for performing image correction processing such as gradation correction, contrast correction and color balance correction on an image to be processed. The present invention relates to an apparatus and an image processing method.

[0002]

2. Description of the Related Art With the spread of image input / output devices such as digital cameras, digital scanners and printers, and the development of digital image processing technology associated therewith, higher image quality is required in the market. I have. For example, an image captured by a digital camera
When displaying on an RT or outputting a color original read by a flatbed scanner with a printer such as an electrophotographic / inkjet system, not only the characteristics of each image input / output device are corrected but also, for example, There have been proposed various techniques for obtaining a higher-quality image by performing an appropriate correction process (hereinafter, referred to as an adaptive correction process) in consideration of an image capturing condition, an influence of a document storage state on an image, and the like. I have.

More specifically, a person's skin color photographed by a digital camera is converted into a healthier skin color to display an image, or an underexposed photograph photographed indoors is converted into a bright and sharp image. For example, a technique of cutting turbidity of a low-density portion on a newspaper or a magazine to obtain a more readable copy. Examples of the image correction processing include gradation correction, contrast correction, color balance correction, fineness correction, moiré removal, noise removal, and the like.

[0004]

However, if an adaptive correction process is applied to all images to be processed, a problem may occur. here,
Faults due to adaptive correction processing FIGS. 17 and 18
This will be described with reference to FIG. FIG. 17 shows an example in which the image contrast is automatically corrected. As the image contrast automatic correction method in FIG. 17, a method using a cumulative histogram, which is a known technique, as a correction table is used.
FIG. 17A shows an image to be processed, which is a gradation image of 8 bits / pixel which becomes brighter at the center and darker toward the left and right ends as shown in the figure.

FIG. 17 (b) shows a cumulative histogram of the image of FIG. 17 (a). As shown in FIG. 17B, the cumulative histogram has an upwardly convex curve. When the cumulative histogram is corrected by using the correction curve shown in FIG. 17C, FIG. ) Will be obtained. Such a correction has a contrast improving effect in the case of a natural image such as an image captured by a digital camera or a photograph captured by a scanner. However, the obtained gradation change becomes more stair-like than in the case of the original image, and thus, for example, an illustration or CG (Computer Graphics)
In the case of (1), the linearity of the gradation at the intermediate gradation often differs from that intended by the image creator, resulting in a disadvantage that the effect is adversely affected.

Next, an example of contrast correction by dynamic range conversion will be described with reference to FIG. FIG.
(A) is an image to be processed of 8 bits / pixel, and FIG.
FIG. 8B shows the gradation distribution. In the case of contrast correction by dynamic range conversion, FIG.
The quantization width is changed using the minimum gradation value A and the maximum gradation value B of the gradation distribution in (b) as conversion reference points. More specifically, in the case of 256 gradations (= gradation values 0 to 255), the minimum gradation value A is set to gradation value = 0, and the maximum gradation value B is set to gradation value =
The gradation conversion is performed using a correction curve of 255 (see FIG. 18C). The obtained processed image is as shown in FIG.

The contrast correction processing by the dynamic range conversion has an effect of enhancing the contrast of an image. When the processing target image is a character or a line drawing, the readability is improved. However, when a predetermined gradation state is required for the processing target image, for example, when the processing target image is a logo of a company or a product, the change in the gradation state and the color The disadvantage is that the change is not very desirable. In the above description,
As an example, the gradation property of a gray scale (256 gradation) gradation image has been taken up. However, the same problem also occurs in a color image, and a problem occurs in gradation property and color reproducibility. Also, in the case of a spatial filter that automatically corrects definition, over-emphasis of an edge portion may occur.

As described above, the adaptive image correction processing cannot always obtain an image intended by the user depending on an image to be processed. Therefore, prior to performing the adaptive correction processing, it is desired to automatically identify an image to be subjected to the adaptive correction processing and an image not to be subjected to the adaptive correction processing. In particular, in recent years, the number of opportunities to input and output image data via a network or via various recording media has increased, and under such circumstances, the image to be processed is input, photographed, or created under any conditions. It is difficult to specify whether the image has undergone the adaptive correction process, and it is desired that the image to be subjected to the adaptive correction process be automatically identified. Therefore, an object of the present invention is to provide an image processing apparatus and an image processing method capable of automatically identifying whether or not an image to be subjected to adaptive correction processing and performing appropriate correction processing.

[0009]

According to an aspect of the present invention, there is provided an image processing apparatus comprising: an image type determining unit configured to determine a type of an image corresponding to input image data; A feature amount extracting means for extracting a feature amount;
Image correction means for performing image correction processing corresponding to the feature amount on the image data, and correction processing prohibition means for prohibiting the image correction processing on the image data based on the determined type of the image. It is characterized by that.

According to a second aspect of the present invention, in the configuration of the first aspect, the image type determining means determines the type of the image based on the number of colors or the number of gradations of the image. I have.

According to a third aspect of the present invention, in the configuration of the first or second aspect, the image type determination unit includes a pictorial determination unit that determines whether the image is a pictorial image. The correction processing prohibiting means prohibits the image correction processing when the image is not a pictorial image by the discrimination of the pictorial determination means.

According to a fourth aspect of the present invention, in the configuration of the first or second aspect, the image type determining unit determines whether the image is a color image or a grayscale image. A gray scale determining unit, a color number determining unit that determines the number of colors of the image when the image is a color image, and a gray scale number of the image when the image is a gray scale image. A tone number discriminating means for discriminating, wherein the correction processing prohibiting means is a color image by the color / gray scale discriminating means, the color number discriminating means and the tone number discriminating means, And when the number of colors is less than a predetermined number of colors or when the image is a gray scale image and the number of gradations is less than a predetermined number of gradations, It is characterized by prohibiting the serial image correction processing.

According to a fifth aspect of the present invention, in the configuration of the fourth aspect, the correction processing inhibiting means inhibits the image correction processing when the image is neither a color image nor a grayscale image. It is characterized by.

According to a sixth aspect of the present invention, in the image processing apparatus according to any one of the first to fifth aspects, the image correction means includes a brightness correction processing means for performing brightness correction processing of the image. It is characterized by:

According to a seventh aspect of the present invention, in the image processing apparatus according to any one of the first to fifth aspects, the image correction means includes a saturation correction processing means for performing a saturation correction processing of the image. It is characterized by having.

According to an eighth aspect of the present invention, in the image processing apparatus according to any one of the first to fifth aspects, the image correction means includes a color balance correction processing means for performing a color balance correction processing of the image. It is characterized by having.

According to a ninth aspect of the present invention, there is provided an image type determining step of determining a type of an input image, a characteristic amount extracting step of extracting a characteristic amount representing a characteristic of the image, And a correction prohibition step of prohibiting the image correction processing on the input image based on the determined type of the image.

According to a tenth aspect of the present invention, in the configuration of the ninth aspect, the image type determining step includes a color / gray scale determining step of determining whether the image is a color image or a gray scale image. A color number determining step of determining the number of colors of the image when the image is a color image; and a gray scale number of determining the number of gray levels of the image when the image is a gray scale image. A correction step, wherein the correction processing prohibiting step is a step in which the image is a color image by the determination in the color / gray scale determination step, the color number determination step, and the gradation number determination step, and Is an image with less than a predetermined number of colors, or when the image is a grayscale image and the number of tones is less than a predetermined number of tones, It is characterized by prohibiting the correction process.

[0019]

Preferred embodiments of the present invention will now be described with reference to the drawings. [1] First Embodiment [1.1] Configuration Example of Image Processing Apparatus of First Embodiment FIG. 1 shows a configuration example of an image processing apparatus of a first embodiment. The image processing apparatus 100 is based on a CPU 101 for controlling the entire image processing apparatus 100, a bus controller 102 for controlling an internal bus of the image processing apparatus 100, and image data stored in an image storage unit 110 described later. CRT 103 for performing various displays on the display, a CRT controller 104 for controlling the CRT 103, an operation unit 105 including a pointing device such as a mouse and a digitizer for inputting various data, a keyboard, and the like;
Interface unit 106 for performing an interface operation between the control unit 05 and the bus, a ROM 107 for storing a control program or control data, a RAM 108 for temporarily storing various data, and for performing direct memory access control And a DMA controller 109.

Further, the image processing apparatus 100 includes an image storage unit 110 for storing various image data such as image data of an image to be processed, and an image for determining the type of the image stored in the image storage unit 110. A determination unit 111 and an image correction unit 112 that performs image correction processing on an image to be subjected to image correction based on the determination result of the image determination unit 111
A scanner 113 for reading an image original to generate image data, an electrophotographic or inkjet printer 114 for outputting an image, and a scanner 1
An interface unit 115 that performs an interface operation between the printer 13 and the internal bus; an interface unit 116 that performs an interface operation between the printer 114 and the internal bus; a network 118 and the image processing apparatus 100
And an interface unit 119 that performs an interface operation between the two.

In this case, the CPU 101 and the bus controller 102 control other components by the operator performing interactive input via the operation unit 105. In the above configuration, the operator reads a photographic image with, for example, the scanner 113 and stores it in the image storage
Appropriate coordinates are specified while being displayed on T102, and image processing such as transmitting an image in the specified area to the network 118 is performed. Further, the image data obtained from the network 118 is stored in the image storage unit 110, subjected to a correction process by the image correction unit 112, and
It is also possible to perform processing to output from.

[1.2] Operation of First Embodiment Next, the operation of the first embodiment will be described in detail with reference to the flowcharts of FIGS. First, a processed image is input via a bus and temporarily stored in the image storage unit 110. Here, the input of the image may be performed by scanning and inputting the original with the scanner 113, or the network 118
May be obtained via the communication means. Image storage unit 11
At 0, those input images are stored and held, for example, in the format of 8 bits / pixel for each color of BGR (step S1). The image determining unit 111 determines the characteristics and types of the images stored in the image storage unit 110 (step S2), and determines whether to perform adaptive image correction by the image correcting unit 112 (step S3). ). If it is determined in step S3 that image correction is to be performed (step S3; Yes), the target image stored in the image storage unit 110 is sent to the image correction unit 112, and the image correction unit 11
In step S2, adaptive correction processing was performed (step S
4) After that, the image data is output to the printer 114 (step S5). If it is determined in step S3 that image correction is not to be performed (step S3).
3; No), the image storage unit 1
Image data stored in the printer 10
14 (step S5).

[1.2.1] Operation of Image Correction Unit Next, the detailed operation of the image correction unit 112 will be described.
In the image correction unit 112, three corrections of color balance correction, contrast correction, and definition correction are performed, and a condition setting process is performed prior to these correction processes. Hereinafter, the color balance correction condition setting process, the contrast correction condition setting process, and the definition correction condition setting process will be described.

[1.2.1.1] Color Balance Correction Condition Setting Process FIG. 3 shows an outline of the color balance correction condition setting process. FIG. 3A is a histogram of the processing target image stored in the image storage unit 110. First, the minimum values Rmin, G of the respective distributions of R (red), green (G), and blue (B)
min, Bmin and maximum values Rmax, Gmax, Bm
ax is detected. Next, the three minimum values Rmin, Gmi
n, Bmin and three maximum values Rmax, Gmax, B
Select a representative value for max.

In this embodiment, the minimum value Rmin,
The smallest value among Gmin and Bmin is the minimum representative value R
GBmin. That is, RGBmin = min (Rmin, Gmin, Bmi
n) Here, the function min () is a function that outputs the minimum value. And Further, the maximum values Rmax, Gmax, Bma
The largest value of x is set as the maximum representative value RGBmax. That is, RGBmax = max (Rmax, Gmax, Bmax
x) Here, the function max () is a function that outputs the maximum value. And Subsequently, as shown in FIG.
B. Each quantization range conversion LUT (Look Up Table: see FIG. 3 (c)) so that the minimum value and the maximum value of each B signal coincide with RGBmin and RGBmax, respectively.
Ask for.

[1.2.1.2] Contrast Correction Condition Setting Process Next, the contrast correction condition setting process will be described with reference to FIG. In the first embodiment, a dynamic range conversion, which is a known technique, is used as a technique of the contrast correction processing. First, as shown in FIG. 4A, a minimum gradation distribution value Cmin and a maximum gradation distribution value Cmax of an image to be processed are detected. Here, since Cmin = RGBmin Cmax = RGBmax, contrast correction is performed based on the minimum representative value RGBmin and the maximum representative value RGBmax obtained by the color balance correction which is the preceding process. Then, as shown in FIG. 4B, the contrast conversion LUT is set so that the output value is 0 for an input equal to the minimum gradation distribution value Cmin, and the output value is 255 for an input equal to the maximum gradation distribution value Cmax. Ask for.

[1.2.1.3] Fineness Correction Condition Setting Process Next, the fineness correction condition setting process will be described with reference to FIGS. The adaptive definition correction performs edge detection in an image to be processed, and determines a correction coefficient of a spatial filter based on the density of an edge portion with respect to the density of all images. First, the gray level total value SUMall and the edge gray level total value SUMedg are initialized (step S11). That is, SUMall = 0 and SUMedg = 0. Next, the processing target images stored in the image storage unit 110 are read, and the gray level GL of each image is calculated (step S12).

Here, the gray level GL is a value representing the brightness level of the image, and is calculated based on the R, G, and B signals, for example, by the following equation. GL = 0.3 × R + 0.6 × G + 0.1 × B Subsequently, a convolution operation is performed on the gray level signal by an edge detection filter as shown in FIG. 6 (step S13), and a predetermined predetermined value is set. It is determined whether or not the pixel of interest is an edge pixel by comparing with a threshold value (step S14). If it is determined in step S14 that the pixel of interest is an edge portion (step S14; Y
es), the value of the gray level GL is added to the sum of the gray levels SUMedg of the edge parts in all the images (step S).
15). That is, it is assumed that SUMedg = SUMedg + GL.

If it is determined in step S14 that the pixel of interest is not an edge portion (step S14; N
o), the process proceeds to step S16. Subsequently, the gray level GL of the target pixel is added to the gray level total value SUAll of all images (step S16). Next, it is determined whether or not the processing of steps S12 to S16 has been performed on all pixels (step S17). If the processing of steps S12 to S16 has not been performed on all pixels (step S17; No), the process is repeated similarly. Steps S12 to S12 are performed for all the pixels.
When the processing of S16 is performed (Step S17; Ye
s), a sharpness scale S representing the sharpness of the image to be processed
HP is calculated by the following equation (step S18). SHP = 100.0 × SUMedg / SUMall A spatial filter coefficient for fineness correction adapted to each of the RGB colors set in advance for the calculated sharp scale SHP is set. After the above-described condition setting of the correction process is performed, the image to be processed is sent from the image storage unit 110 to the image correction unit 112, and after the image correction process is performed under the set correction conditions, the printer 114 Output from

[1.2.2] Image Discriminating Section Next, the image discriminating section 111 will be described with reference to FIGS. FIG. 7 shows a schematic block diagram of the image discriminating unit, and FIG. 8 shows a processing flowchart of the image discriminating unit.
The image discriminating unit 111 converts the input image data into
A brightness distribution information extracting unit 301 for extracting brightness distribution information (histogram); and R, G, B of a processing target image corresponding to the processing target image data stored in the image storage unit 110
And a feature number of an image corresponding to the image data based on the extracted lightness distribution information and the number N of colors of the counted image, and adapts. Amount analysis unit 30 that outputs a determination result as to whether or not to apply a dynamic correction process to the processing target image
3 is provided.

In this case, the brightness distribution information extracting unit 3
01 calculates the brightness from the processing target image data stored in the image storage unit 110 and creates a histogram thereof.
In the present embodiment, the following values are used as the lightness value Y. Y = min (R, G, B) Here, min () is a function that outputs a minimum value. The method of calculating the brightness value Y to be used is not limited to the above-described method, but may be any other widely known method such as a method of obtaining a linear operation of R, G, and B, or a method of substituting only the G signal. It is possible to calculate. Hereinafter, a determination process in which the feature amount analysis unit 304 determines whether or not to perform the correction process on the processing target image will be described with reference to FIG. First, the feature amount analysis unit 304 calculates the minimum brightness value Ymin, maximum brightness value Ymax, and maximum brightness value point number Ypeak of the brightness Y from the information extracted by the brightness distribution information extraction unit 301 (step S21).

Here, the lightness minimum value Ymin and the lightness maximum value Ymax are 5% distribution from the low gradation side and the high gradation side, respectively, taking into account the reading noise of the scanner and the deterioration due to compression during image communication. Are obtained, and are respectively defined as a lightness minimum value Ymin and a lightness maximum value Ymax (see FIG. 9). In addition, the brightness maximum value point number Ypeak
Is a numerical value obtained by calculating the number of gradations showing a distribution in which the brightness Y is equal to or more than a predetermined threshold (see FIG. 10). These feature values of the lightness minimum value Ymin, the lightness maximum value Ymax, and the lightness maximum value point number Ypeak are, for example, in the case of a “pictorial image” such as a landscape image or a human image, the lightness distribution is over the entire gradation range. Widely distributed. Further, for example, in the case of a “graphical image” such as an artificially created design image or logo, the distribution is not concentrated over the entire brightness range, and the distribution is often concentrated on a specific gradation.

Therefore, in this embodiment, the lightness minimum value Ym
in, brightness maximum value Ymax and brightness maximum value point number Y
The peak is used to determine whether the target image is a “pictorial image” or a “graphical image”. More specifically, first, the lightness minimum value Ymin and the lightness maximum value Ymax
And the predetermined threshold values Yth1 and Yth2 (> Yth1), that is, whether or not Yth1 ≧ Ymin and Yth2 ≦ Ymax is satisfied is determined, and whether the brightness distribution of the processing target image extends over the entire brightness range It is determined whether or not it is (step S22). When it is determined in step S22 that Yth1 <Ymin or Yth2> Ymax (step S22; No), the brightness distribution of the processing target image does not cover the entire brightness range, that is, the processing target Determines that the image is a "graphical image" and skips (does not perform) adaptive image correction processing
Is determined (step S25), and the process ends.

If it is determined in step S22 that Yth1 ≧ Ymin and Yth2 ≦ Ymax (step S22; Yes), the maximum brightness value point number Ypeak is compared with a predetermined threshold value Yth3, and Yth3 ≦ Ypeak. Is satisfied, that is, whether the gradation distribution is not concentrated on the specific gradation (step S2).
3). If it is determined in step S403 that Yth3> Ypeak (step S23; No), the distribution is concentrated on a specific gradation, that is, it is determined that the processing target image is a “graphical image” and the adaptive processing is performed. (Step S)
26), end the process.

If it is determined in step S23 that Yth3 ≦ Ypeak is satisfied (step S23;
Yes), the number of colors N in the image counted by the color number counting unit 303 is compared with a predetermined threshold Nth, and whether or not Nth ≦ N is satisfied, that is, whether or not the processing target image is a normal full-color image (Step S
24). If it is determined in step S24 that Nth> N is satisfied (step S24; No), that is, the image to be processed is subjected to the gradation lowering process by area gradation expression such as dithering or error diffusion. As a result, it is determined that the adaptive image correction process is skipped (not performed) (step S28), and the process is terminated.

If it is determined in step S24 that Nth ≦ N (step S24; Yes), the correction processing is performed assuming that the processing target image is a normal full-color image (step S24). Step S27). As described above, it is determined whether to perform the correction processing on the image stored in the image storage unit 110.

[1.3] Summary of First Embodiment FIG. 11 shows an outline of the above processing. According to the first embodiment, the correction processing is not performed when the processing target image stored in the image storage unit 110 is a graphical image such as a logo or an illustration. This is because, in the case of a graphical image, it is generally required to faithfully reproduce each color constituting the graphical image rather than the contrast and sharpness of the entire image.
Also, in the case of images created artificially, such as graphical images, the distribution of colors and brightness is often biased,
This is because, if an adaptive correction process suitable for a pictorial image such as a photograph is performed on such an image, the image correction process leads to a deterioration in image quality such as over-contrast enhancement.

Furthermore, in the case of a graphical image, an ideal state in which noise or rounding is not included even at a color boundary or an edge portion of a line drawing is often obtained, and such an image is subjected to definition correction. This is because it is not preferable. Further, in the first embodiment, when the number of colors constituting the image is equal to or smaller than the predetermined threshold Nth, the correction processing is not performed. This is because the image to be processed is determined to be an image expressed by area gradation such as multi-value dither or binary error diffusion. When an adaptive correction process is performed on an image in which these area gradations are expressed, graininess and gradation are often impaired,
In addition, in the fineness correction focusing on the edge portion, an appropriate correction value cannot often be obtained due to the influence of dither and error diffusion dots. Therefore, in the first embodiment, the adaptive correction processing is not performed in the above case, and the adaptive color processing is performed only when the image is determined to be a pictorial image and a color image having a predetermined number or more of colors. The balance correction, the contrast correction, and the definition correction are performed.

[1.4] Effects of the First Embodiment As described above, according to the first embodiment, after the type of an input image (image to be processed) is automatically determined, the type is determined according to the result. It is determined whether or not to perform the adaptive correction process. If it is determined that the adaptive correction process is to be performed, the input image is output after performing the adaptive correction process, and the adaptive image is output to the input image. If it is determined that no appropriate correction processing is performed, by directly outputting the input image, it is possible to prevent the side effect of image quality deterioration caused by the adaptive correction processing and to shorten the image processing time. .

[2] Second Embodiment Next, a second embodiment of the present invention will be described. The difference between the second embodiment and the first embodiment is that, in addition to the process of automatically determining the type of the processing target image and determining whether or not to perform the image correction process according to the result, the process It is characterized in that whether the target image is a color image or a grayscale image is determined, and if the processing target image is a grayscale image, processing specific to the color image is not performed. Thus, according to the second embodiment, it is possible to prevent a problem such as coloring due to performing a correction process on an adaptive color balance and a saturation range on a black-and-white document input by the scanner 113, for example. it can. The configuration of the image processing apparatus according to the second embodiment is the same as the configuration and the general processing procedure of the image processing apparatus according to the first embodiment. Therefore, in the following description, referring to FIGS. explain.

[2.1] Operation of Second Embodiment Next, the operation of the second embodiment will be described in detail. First, an image to be processed is input via a bus and temporarily stored in the image storage unit 110. Here, the image may be input by, for example, scanning and inputting the original with the scanner 113, or may be obtained via communication means of the network 118. FIG. 12 shows a processing flowchart of the second embodiment. The image storage unit 110 stores and holds those input images in the format of 8 bits / pixel for each color of BGR (step S3).
1). The image discriminating unit 111 discriminates the characteristics and types of the images stored in the image accumulating unit 110, and
It is determined whether or not to execute the adaptive image correction according to the above, and what kind of correction processing is to be performed (step S32). If it is determined in step S32 that the color image correction process is to be performed (step S32).
33; Yes1), if the result is obtained, the target image stored in the image storage unit 110 is
2 and the image correction unit 112 performs an adaptive correction process (step S34).

In the color image correction processing in step S34, color balance correction (step S34a),
Four types of correction processing are executed: brightness contrast correction (step S34b), chroma contrast correction (step S34c), and definition correction (step S34d). After the execution of the correction process, the image data after the correction process is output to the printer 114 (step S36). If it is determined in step S32 that the grayscale image correction process is to be performed (step S33; Ye).
s2), the target image stored in the image storage unit 110 is sent to the image correction unit 112, and the image correction unit 112 performs an adaptive correction process (step S35).

In the correction processing for the gray scale image in step S35, the correction processing unique to the color image, that is, the color balance correction (corresponding to step S34a) and the saturation contrast correction (corresponding to step S34c) are not performed, and the lightness contrast correction ( Two types of corrections are executed: step S35a) and definition correction (step S35b). After executing the correction processing, the printer 11
The image data is output to No. 4 (step S36). If it is determined in step S32 that the image correction process is not to be performed (step S32; N
o), the image data stored in the image storage unit 110 is sent to the printer 114 as it is, and the image is output (step S36).

[2.1.2] Correction condition setting process [2.1.2.1] Color balance correction condition setting process FIG. 3 is an explanatory diagram of the color balance correction condition setting process. FIG. 3A is a histogram of the processing target image stored in the image storage unit 110. First, the minimum values Rmin, G of the respective distributions of R (red), green (G), and blue (B)
min, Bmin and maximum values Rmax, Gmax, Bm
ax is detected. Next, the three minimum values Rmin, Gmi
n, Bmin and three maximum values Rmax, Gmax, B
Select a representative value for max. In the present embodiment, the smallest value among the minimum values Rmin, Gmin, and Bmin is set as the minimum representative value RGBmin. That is, RGBmin = min (Rmin, Gmin, Bmi
n) Here, the function min () is a function that outputs the minimum value. And Further, the maximum values Rmax, Gmax, Bma
The largest value of x is set as the maximum representative value RGBmax. That is, RGBmax = max (Rmax, Gmax, Bmax
x) Here, the function max () is a function that outputs the maximum value. And Subsequently, as shown in FIG.
B. Each quantization range conversion LUT (Look Up Table: see FIG. 3 (c)) so that the minimum value and the maximum value of each B signal coincide with RGBmin and RGBmax, respectively.
Ask for.

[2.1.2.2] Brightness Contrast Correction Condition Setting Process and Chroma Contrast Correction Condition Setting Process Next, the brightness contrast correction condition setting process and the saturation contrast correction condition setting process will be described with reference to FIG. I do. In the first embodiment, as a method of the lightness contrast correction processing and the saturation contrast correction processing, R
After converting the GB3 primary color signals into signals representing lightness and chromaticity, a known technique of dynamic range conversion is used. First, an RGB image represented by a coordinate system as shown in FIG. 13A is converted into a Lab signal which represents lightness and chromaticity as shown in FIG. Convert. Here, the transformation of the coordinate system is approximately performed by the following equation.

(Equation 1) Next, a saturation signal Ca representing saturation by the a signal and the b signal
Generate b. As shown in FIG. 13C, the saturation signal Cab is obtained from the distance from the gray point on the two-dimensional plane defined by the signal axis a and the signal axis b. Then, based on the gradation distribution minimum value Cmin and the gradation distribution maximum value Cmax of the processing target image shown in FIG. 4, an LUT for contrast conversion is set for each of the lightness L and the saturation Cab. Here, for the lightness L, the same contrast conversion as the gradation distribution minimum value Cmin and the gradation distribution maximum value Cmax of FIG. 4 is performed, and for the saturation signal Cab, the range conversion is performed only for the saturation signal maximum value Cabmax. Is carried out. The saturation correction is performed only for a color image.

[2.1.2.3] Definition Correction Condition Setting Process Next, the definition correction condition setting process will be described with reference to FIGS. The adaptive definition correction performs edge detection in a target image, and determines a correction coefficient of a spatial filter based on the density of an edge portion with respect to the density of all images. First, the gray level total value SUMall and the edge gray level total value SUMedg are initialized (step S11). That is, SUMall = 0 and SUMedg = 0. Next, the processing target image stored in the image storage unit 110 is read, and the gray level GL of the image is calculated (step S12). Here, the gray level signal GL is a value representing the brightness level of the image, and is calculated by the following equation using the R, G, B signals and the brightness signals of the brightness / chromaticity signals. GL = a11.times.R + a12.times.G + a13.times.B + b1 Then, the gray level signal GL is subjected to a convolution operation using an edge detection filter as shown in FIG. 6 (step S13), and is compared with a predetermined threshold value. It is determined whether or not the target pixel is an edge pixel (Step S14).

If it is determined in step S14 that the pixel of interest is an edge portion (step S14; Ye
s), the value of the gray level GL is added to the sum of the gray levels SUMedg of the edge parts in all the images (step S1).
5). That is, it is assumed that SUMedg = SUMedg + GL. If it is determined in step S14 that the pixel of interest is not an edge portion (step S14; No), the process proceeds to step S16. Subsequently, the gray level GL of the target pixel is added to the gray level total value SUAll of all images (step S16). Next, step S1
It is determined whether or not the processing of steps S2 to S16 has been performed on all pixels (step S17). If the processing of steps S12 to S16 has not been performed on all pixels (step S17; No), The processing is repeated in the same manner. Steps S12 to S16 are performed for all the pixels.
(Step S17; Yes),
A sharpness measure SHP representing the sharpness of the processing target image is calculated by the following equation (step S18). SHP = 100.0 × SUMedg / SUMall A spatial filter coefficient for fineness correction adapted to each of the RGB colors set in advance for the calculated sharp scale SHP is set. After the correction processing conditions are set as described above, the processing target image is sent from the image storage unit 110 to the image correction unit 112.

If it is determined in step S33 (see FIG. 12) that the correction for a color image is to be performed (step S33; Yes1), the color image to be processed stored in the image storage unit 110 is added to the color image to be processed. Is subjected to a color balance correction process (step S34a).
Then,

(Equation 2) , A brightness signal contrast correction and a definition correction are performed on the L signal, and a saturation contrast correction is performed on the a signal and the b signal. Thereafter, the Lab signal is inversely converted into an RGB signal by the inverse conversion of the RGB signal → Lab signal conversion, and output from the printer 114. If it is determined in step S33 that the grayscale image correction is to be performed (step S33; Yes2), any of the R, G, and B images is processed from the processing target image stored in the image storage unit 110. Is transmitted to the image processing unit 112, brightness contrast correction and definition correction are performed in accordance with the correction conditions set as described above, and output from the printer 114.

[2.1.3] Image Discrimination Unit Next, the image discrimination unit 111 will be described with reference to FIGS. FIG. 14 shows a schematic block diagram of the image discriminating unit, and FIG. 15 shows a processing flowchart of the image discriminating unit. The image determination unit 111 includes a brightness distribution information extraction unit 401 that extracts brightness distribution information (histogram) from input image data, and a saturation distribution information extraction unit 402 that extracts saturation distribution information (histogram). The number of colors N1 represented by a combination of R, G, and B of the processing target image corresponding to the processing target image data stored in the image storage unit 110 and the number of gradations N2 represented by the calculated lightness value are counted. The color number counting unit 403 analyzes the feature amount of the image corresponding to the image data based on the extracted brightness distribution information and the number N of colors of the counted image, and performs adaptive correction processing on the image to be processed. And a feature amount analysis unit 404 that outputs a determination result as to whether or not to apply.

In this case, the brightness distribution information extraction unit 4
In step 01, a brightness is calculated from the processing target image data stored in the image storage unit 110, and a histogram thereof is created.
In the present embodiment, the following values are used as the lightness value Y. Y = min (R, G, B) Here, min () is a function that outputs a minimum value. The method of calculating the brightness value Y to be used is not limited to the above-described method, but may be any other widely known method such as a method of obtaining a linear operation of R, G, and B, or a method of substituting only the G signal. It is possible to calculate. Further, the saturation distribution information extraction unit 402 calculates saturation from the processing target image data stored in the image storage unit 110 and generates a histogram thereof. In the present embodiment, the following values are used as the saturation values C. C = max (R, G, B) −min (R, G, B) where min () is a function that outputs a minimum value,
max () is a function that outputs the maximum value. Note that the calculation method of the saturation value C to be used is not limited to the above-described method, but may be configured to use another calculation method.

The number-of-colors counting section 403 is means for counting the number of colors from the image data to be processed stored in the image storage section 110. The number of colors is represented by a combination of R, G, and B of the image to be processed. The number N1 and the number of gradations N2 represented by the lightness value Y are counted. [2.1.4] Hereinafter, according to FIG.
In 04, a description will be given of a determination process of determining whether or not to perform a correction process on the processing target image. The feature amount analysis unit 404 first calculates a lightness minimum value Ymin, a lightness maximum value Ymax, and a lightness maximum value point number Ypeak from the information extracted by the lightness distribution information extraction unit 401 (step S41). Here, the lightness minimum value Ym
The in and brightness maximum value Ymax are obtained by taking into account the reading noise in the scanner and the deterioration due to compression during image communication, etc., to find the gradation levels showing 5% distribution from the low gradation side and the high gradation side, respectively. The lightness minimum value Ymin and the lightness maximum value Ymax are respectively set (see FIG. 9).

Also, the brightness maximum value point number Ypeak
Is a numerical value obtained by calculating the number of gradations showing a distribution in which the brightness Y is equal to or more than a predetermined threshold (see FIG. 10). These feature values of the lightness minimum value Ymin, the lightness maximum value Ymax, and the lightness maximum value point number Ypeak are, for example, in the case of a “pictorial image” such as a landscape image or a human image, the lightness distribution is over the entire gradation range. Widely distributed. Further, for example, in the case of a “graphical image” such as an artificially created design image or logo, the distribution is not concentrated over the entire brightness range, and the distribution is often concentrated on a specific gradation. Therefore, in the present embodiment, the target image is a “pictorial image” using the lightness minimum value Ymin, the lightness maximum value Ymax, and the lightness maximum value point number Ypeak.
Or "graphical image".

More specifically, first, the lightness minimum value Ymin
And brightness maximum value Ymax and predetermined threshold values Yth1 and Yth
2 (> Yth1), that is, it is determined whether or not Yth1 ≧ Ymin and Yth2 ≦ Ymax are satisfied, and it is determined whether or not the brightness distribution of the image to be processed covers the entire brightness range. (Step S42). If it is determined in step S42 that Yth1 <Ymin or Yth2> Ymax (step S42; No), the brightness distribution of the processing target image does not extend over the entire brightness range, that is, the processing target Determines that the image is a "graphical image" and skips (does not perform) adaptive image correction processing
Is determined (step S48), and the process ends.

If it is determined in step S42 that Yth1 ≧ Ymin and Yth2 ≦ Ymax (step S42; Yes), the maximum brightness value point number Ypeak is compared with a predetermined threshold value Yth3, and Yth3 ≦ Ypeak. Is satisfied, that is, whether the gradation distribution is not concentrated on the specific gradation (step S4).
3). If it is determined in step S43 that Yth3> Ypeak (step S43; No), the distribution is concentrated on a specific gradation, that is, the processing target image is determined to be a “graphical image”, and the adaptive processing is performed. (Step S)
49), and the process ends.

If it is determined in step S43 that Yth3 ≦ Ypeak is satisfied (step S43;
Yes), the saturation feature amount Cn is calculated from the information extracted by the saturation distribution information extraction unit 402 (step S4).
4). More specifically, the saturation feature amount Cn is a feature amount indicating the ratio of the number of color pixels in the image, and counts the number of pixels having a value equal to or greater than a predetermined threshold value and divides by the total number of pixels of the target image It is required by doing. Subsequently, by comparing the calculated saturation feature amount Cn with a predetermined threshold value Cth, it is determined whether the processing target image is a color image or a gray scale image (step S45). If it is determined in step S45 that Cth ≦ Cn (step S45; Yes), the number of colors N1 in the image counted by the color number counting unit 403 is compared with a predetermined threshold Nth1 to satisfy Nth1 ≦ N1. That is, it is determined whether or not the processing target image is a normal full-color image (Step S).
46).

If it is determined in step S46 that Nth1> N1 (step S46; No), the image to be processed is subjected to the process of reducing the number of tones by area gradation expression such as dithering or error diffusion. It is determined that the image has been subjected to adaptive image correction processing to be skipped (not performed) (step S51), and the processing ends. If it is determined in step S45 that Cth> Cn (step S45; No), the number of tones N2 in the image counted by the color number counting unit 403 is compared with a predetermined threshold value Nth2, and Nth2 ≦ It is determined whether or not N1 is satisfied, that is, whether or not the processing target image is a gray scale image (step S4).
7).

If it is determined in step S47 that Nth2> N2 (step S47; No), the image to be processed is subjected to the process of lowering the number of tones by area gradation expression such as dithering or error diffusion. As in the case of the above-described full-color image, it is determined that the adaptive image correction process is skipped (not performed) (Step S).
53), end the process. If it is determined in step S47 that Nth2 ≦ N2 (step S47; Yes), the correction process is performed assuming that the processing target image is a grayscale image (step S52). As described above, it is determined whether to perform the correction processing on the image stored in the image storage unit 110.

[2.2] Summary of Second Embodiment FIG. 16 shows an outline of the above processing. According to the second embodiment, the correction processing is not performed when the processing target image stored in the image storage unit 110 is a graphical image such as a logo or an illustration. This is because, in the case of a graphical image, it is generally required to faithfully reproduce each color constituting the graphical image rather than the contrast and sharpness of the entire image.
Also, in the case of images created artificially, such as graphical images, the distribution of colors and brightness is often biased,
This is because, if an adaptive correction process suitable for a pictorial image such as a photograph is performed on such an image, the image correction process leads to a deterioration in image quality such as over-contrast enhancement. Furthermore, in the case of a graphical image, it is often the case that an ideal state does not include noise or rounding even at a color boundary or an edge portion of a line drawing. This is because it is not preferable.

Further, in the second embodiment, when the number of colors is equal to or less than a predetermined threshold value Nth for a color image, and when the number of gradations is equal to or less than a predetermined threshold value Nth2 for a grayscale image, a correction process is performed. There is no configuration. this is,
This is because the image to be processed is determined to be an image expressed in area gradation such as multi-value dither or binary error diffusion. When an adaptive correction process is performed on these area-graded images, the granularity and gradation are often impaired, and the fineness correction focusing on the edge portion is performed by dithering or error diffusion. This is because an appropriate correction value cannot often be obtained due to the influence of dots. Further, when the processing target image is determined to be a pictorial image and a color image,
Adaptive color balance correction, lightness contrast correction, saturation contrast correction, and fineness correction are performed on the target image. The processing target image is a pictorial image,
If it is determined that the image is a grayscale image, it is not necessary to perform color balance correction and saturation contrast correction. Therefore, adaptive contrast correction and definition correction are performed.

[2.3] Effects of Second Embodiment As described above, according to the second embodiment, after the type of an input image (image to be processed) is automatically determined, the type is determined according to the result. It is determined whether or not to perform the correction processing, and when it is determined that the adaptive correction processing is to be performed, the input image is subjected to the adaptive correction processing and then output, and the adaptive correction is performed for the input image. If it is determined that the processing is not performed, the input image is output as it is to prevent the side effect image quality deterioration caused by the adaptive correction processing,
Image processing time can be reduced. Further, by determining whether the input image is a color image or a grayscale image, a good correction process can be realized in a short time without being conscious of whether the target image is monochrome or color. .

[0061]

According to the present invention, after automatically determining the type of an input image (image to be processed), it is determined whether or not to perform adaptive correction processing in accordance with the determination result. When the adaptive correction processing is performed, the input image is subjected to adaptive gradation correction and then output. When the adaptive correction processing is not performed, the input image is output as it is. It is possible to prevent a side effect of image quality deterioration caused by the correction processing, and realize high-speed image processing.

[Brief description of the drawings]

FIG. 1 is a schematic configuration block diagram of an image processing apparatus according to an embodiment.

FIG. 2 is a flowchart illustrating an outline of image processing according to the embodiment;

FIG. 3 is an explanatory diagram of a general process of a color balance correction condition setting process.

FIG. 4 is an explanatory diagram of a schematic process of a contrast correction condition setting process.

FIG. 5 is a schematic processing flowchart of a definition correction condition setting process.

FIG. 6 is an explanatory diagram of a convolution operation by an edge detection filter.

FIG. 7 is a schematic configuration block diagram of an image determination unit according to the first embodiment.

FIG. 8 is a processing flowchart of an image determining unit according to the first embodiment.

FIG. 9 shows a minimum lightness value Ymin and a maximum lightness value Ymax.
FIG.

FIG. 10 is an explanatory diagram of a brightness maximum value point number Ypeak.

FIG. 11 is an explanatory diagram of a process outline according to the first embodiment.

FIG. 12 is a processing flowchart of the second embodiment.

FIG. 13 is an explanatory diagram of a brightness contrast correction condition setting process and a saturation contrast correction condition setting process.

FIG. 14 is a schematic configuration block diagram of an image determination unit according to a second embodiment.

FIG. 15 is a processing flowchart of an image determining unit according to the second embodiment.

FIG. 16 is an explanatory diagram of a process outline according to the second embodiment.

FIG. 17 is a diagram illustrating a conventional correction process for automatically correcting the contrast of an image.

FIG. 18 is a diagram illustrating a conventional contrast correction process based on dynamic range conversion.

[Explanation of symbols]

 REFERENCE SIGNS LIST 100 image processing device 101 CPU 102 bus controller 103 CRT 104 CRT controller 105 operation unit 106 interface unit 107 ROM 108 RAM 109 DMA controller 110 image storage unit 111 image determination unit 112 image Correction unit 113 Scanner 114 Printer 115 Interface unit 116 Interface unit 118 Network 119 Interface unit 301 Brightness distribution information extraction unit 302 Color count unit 303 Feature analysis unit 401 Lightness distribution information extraction unit 402 ... Saturation distribution information extraction unit 403 ... Color number counting unit 404 ... Feature amount analysis unit

 ──────────────────────────────────────────────────続 き Continued from the front page F-term (reference) PP51 PQ08 PQ19 SS06 TT02 TT06 5C079 HB01 HB06 LA01 LA02 LA06 LA12 LA31 LB02 NA03 NA05 PA02 PA03 PA05

Claims (10)

[Claims] An image type determination unit configured to determine a type of an image corresponding to input image data; a feature amount extraction unit configured to extract a feature amount representing a feature of the image; Image processing means for performing image correction processing corresponding to an amount, and correction processing prohibition means for prohibiting the image correction processing on the image data based on the determined type of the image. apparatus. 2. The image processing apparatus according to claim 1, wherein the image type determining unit determines the type of the image based on the number of colors or the number of gradations of the image. . 3. The image processing apparatus according to claim 1, wherein the image type determination unit includes a pictorial determination unit that determines whether the image is a pictorial image, and the correction process is prohibited. An image processing apparatus for prohibiting the image correction process when the image is not a pictorial image according to the determination by the pictorial determination unit. 4. The image processing apparatus according to claim 1, wherein the image type determination unit determines whether the image is a color image or a grayscale image. A color number determining unit that determines the number of colors of the image when the image is a color image; and a gradation that determines the number of gradations of the image when the image is a gray scale image. Number correction means, wherein the correction processing prohibiting means is a color image based on the color / gray scale determination means, the color number determination means and the gradation number determination means, and the color If the number of images is less than a predetermined number of colors or if the image is a grayscale image and the number of gradations is less than a predetermined number of gradations, the image complement is performed. The image processing apparatus characterized by prohibiting processing. 5. The image processing apparatus according to claim 4, wherein the correction processing prohibiting unit prohibits the image correction processing when the image is neither a color image nor a grayscale image. Image processing device. 6. The image processing apparatus according to claim 1, wherein the image correction unit includes a brightness correction processing unit that performs a brightness correction process on the image. Processing equipment. 7. The image processing apparatus according to claim 1, wherein the image correction unit includes a saturation correction processing unit that performs a saturation correction process on the image. Image processing device. 8. The image processing apparatus according to claim 1, wherein the image correction unit includes a color balance correction unit that performs a color balance correction process on the image. Image processing device. 9. An image type determining step of determining a type of an input image; a feature amount extracting step of extracting a feature amount representing a feature of the image; and an image correction process corresponding to the feature amount for the input image. An image processing method, comprising: an image correction step of performing the following, and a correction processing prohibition step of prohibiting the image correction processing on the input image based on the determined type of the image. 10. The image processing method according to claim 9, wherein the image type determining step includes a color / gray scale determining step of determining whether the image is a color image or a gray scale image; If the image is a color image, a number-of-colors determining step of determining the number of colors of the image; and if the image is a grayscale image, a number-of-gradations determining step of determining the number of gray levels of the image. Wherein the correction processing prohibiting step is a color image based on the determination in the color / gray scale determining step, the color number determining step, and the gradation number determining step, and
Prohibiting the image correction process when the number of colors is less than a predetermined number of colors or when the image is a grayscale image and the number of gradations is less than a predetermined number of gradations. Characteristic image processing method.