JP2000102033A - Automatic gradation correction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily perform the automatic gradation correction of color images without using a neural network. SOLUTION: A luminance histogram feature amount obtained by equally dividing an input luminance level axis into (n) (n) is an integer >=4) is fetched (step 101) and the various kinds of curve data are fetched (step 102). When the luminance histogram feature amount L is larger than a lower limit value, an under processing is performed (step 104). When it is L<=(lower limit value) and the luminance histogram feature amount H is larger than an upper limit value, an over processing is performed (step 106). When it is H<=(upper limit value), a linear processing is performed (step 107). When the under processing, the over processing or the linear processing is ended, gradation corrected curve data are obtained and stored in a gradation corrected curve data storage part 34 (step 108).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an automatic gradation correcting method, and more particularly to an automatic gradation correcting method for automatically correcting the gradation of a color image when an output color image of a digital camera, a video camera or the like is printed by a printer. It relates to a key correction method.

[0002]

2. Description of the Related Art FIG. 11 is a block diagram showing an example of a printing system using a color video printer. In the figure, a color image signal obtained by imaging with a digital camera 11 is directly supplied to a video printer 16 and printed, while being taken in a personal computer (hereinafter, referred to as a personal computer) 14 and a cathode ray tube of a display monitor. (CRT) 19 and the personal computer 14
It is processed by a central processing unit (CPU) 15 and then supplied to a video printer 16 for printing.

A color image signal obtained by imaging with a video camera 12 is also used in the digital camera 11.
Video printer 1 as well as the color image signal from
While being supplied to the printer 6 and printed, it is taken into the personal computer 14 and displayed on the CRT 19 of the display monitor, processed by the CPU 15 in the personal computer 14 and then supplied to the video printer 16 for printing. Further, the color image signal reproduced by the video deck 13 is supplied to the video printer 16 and printed.

As described above, the video printer 16 includes the digital camera 11, the video camera 12, and the VCR 1
3 to print the color image signal input directly from
The output color image signal of the digital camera 11 or the video camera 12 processed by the CPU 15 in the personal computer 14 can be printed.

By the way, usually, the digital camera 11,
When shooting with the video camera 12, automatic adjustment is performed so that the camera body optimizes exposure. However, in a printing system such as that shown in FIG. 11, the image photographed by them does not always reproduce a desired gradation. In particular, the brightness and contrast that were not bothersome because the video was scanned by the video camera 12 were moving images.
Printing as a still image on the video printer 16 often does not provide satisfactory results.

Therefore, in order to obtain a desired gradation in the video printer 16, it has been conventionally performed to perform gradation correction of a color image and then print. This gradation correction method for a color image is a gradation correction method using a luminance histogram of an image.
This method uses a cumulative luminance distribution as shown in FIG.
Dimensional Signals and Image Processing, "issued December 25, 1996, published by the Society of Instrument and Control Engineers.

As shown in FIG. 12 (A), when a given image has an arbitrary density value k between 0 and M−1, the total number of pixels having that value is _nk. , H (k) = n _k k∈ ｛0, 1, 2,. . . , M−1} is called a histogram.
Assuming that the number of pixels of a given image is N,

[0008]

(Equation 1) It is.

[0009] Since an image in which the histogram is flattened and quantized has the highest information amount, an image in which the histogram is not flat and in which there is a bias in information is flattened by gradation conversion to enhance the contrast of the image. Becomes The conversion function at this time is shown in FIG.
By obtaining a cumulative luminance distribution as shown in FIG. That is, when the pixel value of x (i, j) is K, and the cumulative frequency of pixels in the entire image up to the pixel value K is H (K),

[0010]

(Equation 2) Is calculated. When the pixel value after the conversion of x (i, j) is y (i, j), the value is calculated by the following formula: y (i, j) = H (K) · (M−1) / N By performing the above calculation, the histogram can be flattened.

As a conventional gradation correction method for performing gradation correction using the above-mentioned accumulated luminance distribution, a method realized by hardware is known (Japanese Patent Application Laid-Open No. 7-250339). According to this conventional method, an adaptive gradation correction table is generated based on the luminance distribution of an image, the gradation correction of the luminance signal is performed in pixel units based on the table, and the Y / C ratio is kept constant. When performing tone correction on color signals to maintain
At the time of calculating the color correction data, the operation rate of the color correction signal is set to half of the luminance correction, the gradation correction data obtained based on the luminance level of each pixel is averaged, and the average value is multiplied by C / Y. In this configuration, the influence of noise superimposed on the luminance signal on the color gradation correction is reduced.

As a conventional gradation correction method, there is also known a gradation correction method using a neural network (see Akihiro Tamura et al., "Digitalization of Camera by Neuro-gamma Processing").
Range Expansion '', Technical Report of the Institute of Television Engineers of Japan, Vol. 18, No.
72, pp13-18, announced on November 29, 1994). This is a camera gradation correction method using a learning function of a neural network. The luminance histogram is divided into three regions of low / medium / high luminance to calculate a feature amount, and the gradation correction curve is calculated by the neural network. Is determined. Conventional methods based on trial and error use a neural network because it is difficult to create an optimal backlight determination rule.

[0013]

However, the conventional gradation correction method described in Japanese Patent Laid-Open No. 7-250339 is realized by hardware using a cumulative luminance distribution as the gradation correction method. However, it is not possible to flexibly cope with a change in gradation correction for an image, and the configuration is expensive. For this reason, it is conceivable that the above-described conventional tone correction method is processed by software. However, if the method is used as it is, high-speed processing by software causes some problems.

First, when the accumulated luminance distribution is used as it is, smooth gradation cannot be obtained unless processing such as passing through a filter is performed. Also, if the image has a lot of flesh or sky blue parts, if the process of flesh and sky color extraction is not performed using information other than luminance, many gradations will be assigned to it, and the smoothness of the skin will be poor. It often results in an unnatural image in which the sky disappears and only the clouds in the sky are emphasized. In order to perform high-speed processing using only software, it is necessary to further simplify the processing.

On the other hand, in the conventional gradation correction method using a neural network, the neural network is a method in a research stage, and requires a lot of technology accumulation and research time before it can be fully used.

The present invention has been made in view of the above points,
An object of the present invention is to provide an automatic gradation correction method capable of realizing automatic gradation correction of a color image without using a neural network.

[0017]

In order to achieve the above object, the present invention performs automatic gradation correction of color image data to be corrected in the first to sixth steps, and performs the seventh step in the seventh step. This is for writing image data that has been subjected to automatic gradation correction to an image memory. However, the third, fourth,
In the fifth step, any one of the steps is executed.

That is, first, in the first step, a luminance histogram is created from the color image data whose gradation is to be corrected, and the input luminance level axis is divided into n equal parts (n is an integer of 4 or more) from the luminance histogram. A first feature amount indicating a ratio of the number of pixels in each region to all pixels,
A second feature quantity indicating the ratio of the number of pixels exceeding a certain limiter value to all pixels in each area divided into n equal parts, and the total number of pixels in each area obtained by dividing the input luminance level axis into three parts from the luminance histogram A third feature amount indicating a ratio to a pixel is calculated and stored in the first storage unit.

Subsequently, in a second step, the first to third feature values stored in the first storage unit are fetched,
Among them, the third feature amount of the lowest luminance region is larger than the preset lower limit value, and the third feature value of the highest luminance region is
It is determined whether or not the feature value of is smaller than a preset upper limit value. In the third step, when the determination result that the third feature amount of the lowest luminance side area is larger than the preset lower limit value is obtained in the second step, it is determined that the exposure is under, and the first step is performed. After calculating the inclination information of the contrast using the characteristic amount of the above, and obtaining the under intensity from the inclination information, various curve data indicating various characteristic curves of the output gradation with respect to the input gradation are obtained in advance according to the under intensity. An under process is performed to select, from the stored second storage section, curve data having a characteristic of increasing the output gradation with respect to the input gradation.

In the fourth step, it is determined that the exposure is over when the result of the determination in the second step is that the third characteristic amount in the highest luminance side area is larger than a preset upper limit value. After calculating the gradient information of the contrast by using the first feature amount and calculating the over-intensity from the tilt information, the output gray scale is compared with the input gray scale from the second storage unit according to the over-magnitude. An over process for selecting curve data having characteristics to be reduced is performed. In the fifth step, according to the second step, the third feature value of the lowest luminance region is equal to or less than a preset lower limit value, and the third feature value of the highest luminance region is set in advance. When a determination result that is smaller than the upper limit value is obtained, the gradient information of the contrast is obtained using the first feature amount, and the strength is obtained according to the second feature amount. A linear process for selecting curve data from the second storage unit is performed.

The sixth step is to combine the gradient information of the contrast created in the third, fourth or fifth step with the selected curve data to generate gradation correction curve data, and store it in the third storage unit. Store. Then, in the seventh step,
The color correction curve data from the third storage unit is reflected in the color image data to be corrected read out from the image memory in which the color image data to be corrected is stored. Store again.

According to the present invention, the luminance histogram of the input color image data is divided into n equal parts, the approximate shape of the curve is known, the slope information of the contrast is calculated according to the shape, and the curve data is optimized. After choosing one,
These are combined to generate gradation correction curve data.

Further, according to the present invention, the number of pixels for which the color difference signal of the color image data overflows is determined by using the gradation correction curve data in the third storage unit and the input color image data to be corrected. And correcting at least one of the contrast gradient information stored in the third storage unit and the selected curve data based on the overflow pixel number detected in the eighth step. A corrected gradation correction curve data obtained by synthesizing in the ninth step.
Is stored again in the storage unit.

According to the present invention, after the gradation correction curve is automatically determined from only the element of the luminance histogram, the rate at which the color difference signal overflows when the gradation correction curve is reflected on the color image is checked to select the gradation correction curve. Fix it.

Further, according to the present invention, the color image data to be subjected to gradation correction is image data output from a video device, the image memory is provided in the printer, and the first to ninth steps are performed. It is executed by software processing by a central processing unit in a printer or a personal computer outside the printer, and prints, by the printer, image data that has been subjected to automatic gradation correction and stored in an image memory.

[0026]

Next, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram of a correction device for realizing an embodiment of the automatic gradation correction method according to the present invention. In the printing system as shown in FIG.
The CPU 15 of the personal computer 14 analyzes the image captured by the personal computer 14 and automatically corrects the gray scale by analyzing the image captured by the image memory 18. Yes, the corrected image is printed by the video printer 16.

In the embodiment shown in FIG.
1 is an image memory 18 in the video printer 16 of FIG.
The other blocks 22 to 37 in FIG. 1 show functional blocks realized by software of the CPU 17 in the video printer 16. In FIG. 1, color image data input from an external video device such as a digital camera, a video camera, a personal computer, or a video deck is stored in an image memory 21. In consideration of the processing speed, the image sampling processing unit 22
, And image data of every g pixels (g> 0) is sampled and stored in the image sampling data storage unit 23. g
May be determined as a value such as 2 or 4 according to the total number of pixels.

The luminance histogram creating section 24 creates a luminance histogram from the image data stored in the image sampling data storage section 23 and stores it in the luminance histogram storage section 25. Brightness histogram feature value calculation unit 2
6 calculates a brightness histogram feature from the brightness histogram stored in the brightness histogram storage 25 and stores the calculated feature in the brightness histogram feature storage 27. There are three types of the above-described luminance histogram feature amounts to be stored.

First, a feature quantity indicating the ratio of the number of pixels in each area to all pixels obtained by dividing data in the luminance histogram storage unit 25 into n equal parts (where n> 3) on the input luminance level axis. Sep [0] to Sep [n-1].
The second is a feature amount indicating the ratio of all pixels over the number of pixels exceeding a certain limit value obtained by dividing data in the luminance histogram storage unit 25 into n equal parts (where n> 3) on the input luminance level axis. LimitSep [0]-LimitSep [n-1]
It is. Third, feature amounts L, M, and H indicating the ratio of the number of pixels in each region to all pixels obtained by dividing the data of the brightness histogram storage unit 25 into three on the input brightness level axis.
It is.

The overexposure / underexposure detection section 28 reads out the luminance histogram characteristic amounts stored in the luminance histogram characteristic amount storage section 27, and detects overexposure and underexposure from the luminance histogram characteristic amounts. Subsequently, the contrast inclination determining unit 29 determines contrast inclination information based on the luminance histogram characteristic amount stored in the luminance histogram characteristic amount storage unit 27 and the detection result by the over-exposure / under-detection unit 28. .

Next, the contrast data creating section 30
Contrast data is created based on the information on the contrast inclination determined by the contrast inclination determining unit 29. The over / under intensity determination unit 31 is determined by the luminance histogram feature stored in the luminance histogram feature storage 27, the detection result of the overexposure / under detection unit 28, and the contrast inclination determination unit 29. The over-intensity and under-intensity (Level) of the exposure are determined based on the contrast inclination information, and the curve data is selected from the various curve data stored in the various curve data storage unit 32 according to the intensity (Level). 1
Choose one.

The contrast straight line / curve data synthesizing section 33 synthesizes the contrast straight line data created by the contrast data creating section 30 and the curve data selected by the over / under intensity judging section 31 to produce gradation correction curve data. And stores it in the gradation correction curve data storage unit 34
To be stored. Assuming that the variable i changes from 0 to 255, if the contrast straight line data is cont [i] and the curve data is curve [i], the synthesis result autog
am [i] is autogam [i] = curve [c
ont [i]].

The color difference signal overflow detection unit 35 uses the sampling data stored in the image sampling data storage unit 23 and the gradation correction curve data stored in the gradation correction curve data storage unit 34 to calculate the color difference. The number of pixels (Count) at which the signal overflows is detected, and the detection result is input to the gradation correction curve correction unit 36.

The gradation correction curve correction unit 36 includes the number of overflow pixels input from the color difference signal overflow detection unit 35, various curve data from the various curve data storage unit 32, and the data from the gradation correction curve data storage unit 34. In response to the linear level and the curve level, it is determined how much the intensity of overexposure (Level) should be reduced according to the number of overflow pixels, and the data in the gradation correction curve data storage unit 34 is corrected based on the determination result. As described above, the inclination information input to the contrast data creation unit 30 is corrected, and if correction is not possible, the correction curve data is further changed and input to the contrast straight line and curve data synthesis processing unit 33.

The image processing section 37 includes a gradation correction curve correcting section 3
6, the image data from the image memory 21 is corrected based on the gradation correction curve data in the gradation correction curve data storage unit 34, and the corrected image data is stored in the image memory 21 again. The image data subjected to the automatic gradation correction stored in the image memory 21 is read out and printed by the printer.

Next, the operation of the main part of the embodiment of FIG. 1 will be described in more detail with reference to FIGS. FIG. 2 receives the respective stored data of the histogram feature amount storage unit 27 and the various curve data storage units 32 of FIG. 1 as input, generates gradation correction curve data based on these, and stores the data in the gradation correction curve data storage unit 34. 3 to 8 are flowcharts for detailed explanation of each unit in FIG. 2, and FIG. 9 is a flowchart showing the gradation correction curve data stored in the gradation correction curve data storage unit 34 in FIG. Flow chart from receiving data from the image sampling data storage unit 23 as input, passing through the color difference signal overflow detection unit 35 and the gradation correction curve correction unit 36, and outputting corrected data to the gradation correction curve data storage unit 34 It is. FIG. 10 is a conceptual diagram of selecting a gradation correction curve.

The luminance histogram feature quantity calculator 26 shown in FIG.
When calculating the luminance histogram feature amount by using, when the input luminance level axis is divided into eight equal parts (n = 8), Sep [0] to [7], LimitSep are used as the luminance histogram feature amount.
[0] to [7] are prepared. Sep [] contains the ratio of the number of pixels in each area to the total number of pixels. In addition, LimitSep [] contains the ratio of the number of pixels exceeding a certain limiter value to the total number of pixels. Thus, in the case of a luminance signal as shown in FIG. 10A, it can be roughly represented by a luminance histogram as shown in FIG. In this embodiment, the luminance histogram feature amounts L, M, and H are L = Sep [0].
+ Sep [1] + Sep [2], M = Sep [3] + S
ep [4], H = Sep [5] + Sep [6] + Sep
[7].

The various curve data stored in the various curve data storage section 32 of FIG. 1 include * Up [1] to
[5], * Down [1] to [5] are prepared. * U
p [] and * Down [] are YY 'look-up tables (LUTs) as shown in FIGS. 10 (C) and 10 (D). * Up [1] is a linear LUT, where 0
It is assumed that the value of the output Y ′ corresponding to the value of the input Y from 255 to 255 is stored. * The correction strength of the LUT increases as the number in [] of Up [] increases. Similarly, * Do
wn [1] is the LUT closest to linear, * Dow
As the number in [] of n [] increases, the correction strength of the LUT increases.

The over / under intensity judging section 31 in FIG. 1 fetches the luminance histogram feature from the luminance histogram feature storage 27 (step 10 in FIG. 2).
1) Also, various curve data * Up [1] to [5] and * Down [1] to [5] are fetched from the various curve data storage unit 32 (step 102 in FIG. 2). It is determined whether the value is larger than a preset lower limit (underLimit) (step 103 in FIG. 2).

Since the luminance histogram feature L is a feature of the region on the lowest luminance side when the input luminance level axis is divided into three equal parts, if L> (lower limit), it is determined that exposure is under and under exposure is performed. The processing is performed (step 104 in FIG. 2),
If L ≦ (lower limit), it is determined whether or not the luminance histogram feature value H is larger than a preset upper limit (overLimit) (step 105 in FIG. 2). The luminance histogram feature H is a feature of the region on the highest luminance side when the input luminance level axis is divided into three equal parts. Therefore, if H> (upper limit), it is determined that overexposure has occurred and overprocessing is performed. (Step 106 in FIG. 2), if H ≦ (upper limit value),
It is determined that the processing is linear (or other), and linear processing is performed (step 107 in FIG. 2).

The upper and lower limits are determined by conducting experiments on many images. For example, upper limit = 6
When 0 and the lower limit = 60, it is determined that L is under if the pixels are more than 60% biased, and it is judged that the pixels are over when H is more than 60% biased. When the under processing, the over processing, or the linear processing is completed, tone correction curve data is obtained and stored in the tone correction curve data storage unit 34 (step 108 in FIG. 2).

The under processing in step 104 is as follows.
According to the flowcharts of FIGS. 3 and 4, a contrast straight line and a correction curve * based on the luminance histogram feature amount.
The level of Up [] is determined and combined to form a gradation correction curve. The basic strategy is to flatten the histogram. The flattening of the histogram itself is known (Shin Nagao,
"Digital Image Processing", Modern Science, p177-p181, 197
8).

First, the initial value of the level (Level) is set to, for example, "5" (step 201 in FIG. 3), and the variable s
um is set to "0" (step 20 in FIG. 3).
2) The variable i is set to "7" (step 20 in FIG. 3).
3) The luminance histogram feature amount Sep is set in the variable sum.
[I] is added (step 204 in FIG. 3), and it is determined whether or not the added value sum is greater than 0 (step 205 in FIG. 3). If sum ≦ 0, the value of i is set to 6 Similar addition and determination processing is performed. The above addition and determination processing is repeated while the value of i is reduced by 1 until sum> 0.

When sum> 0, the variable secthig
The value of h is set to 7-i, and the value of the variable sectlow is set to 0 (step 206 in FIG. 3). That is, first,
In consideration of the fact that the pixels are biased toward the lower luminance, an area where the pixels are 0% is searched from the luminance histogram feature amount Sep [7] having the highest luminance. The 0% area is set to Ycon-Yco in order not to assign a gradation to an area without pixels.
In the graph of n ', Ycon' is attached to 255. The variable second indicates the area to be attached to 255,
The variable “sectlow” indicates an area pasted to “0”.

If only 1% from the top is 0%, su
Since m becomes larger than 0 when i = 6, the variable sect
The high is 1 (= 7−6) section. It is to be noted that the reason why seclow = 0 is set in step 206 is that the probability of 0% counted from below in an under image is low and the process is simplified. The inclination of the contrast straight line is determined up to this point. The value to be compared with sum in step 205 may be other than 0. This value can be variably set by experience. If it is desired to increase the contrast correction strength, set this to a large value.

The variable sechigh is the result information of the contrast gradient determining section 29. If this value is large, it becomes a straight line that increases the output gradation with respect to the input gradation, and the under image is improved. Next, a variable Level indicating the strength of the under is represented by Level = Level-sect.
The contrast is subtracted from the level of the correction curve based on the arithmetic expression “high” (step 20 in FIG. 3).
7).

Next, a method using a cumulative luminance distribution is applied to determine the correction intensity. In under processing,
Since it is determined that the pixels are biased toward the lower luminance, the histogram shape of that region is used as a material for the determination.
That is, first, Sep [0]> Sep [1]> Sep
It is determined whether the condition [2] is satisfied (step 20 in FIG. 3).
8) If this condition is not satisfied, the level is determined to be a weak level, and the variable Level indicating the strength is set to Level-2 (step 209 in FIG. 3). Sep [0]> Sep
If [1]> Sep [2], then Sep [0] is S
Compare whether the value is greater than ep [1] * 1.5 (FIG. 3)
Step 210), if Sep [1] * 1.5 or less, set the variable Level indicating the intensity as a medium level to Level
-1 (step 211 in FIG. 3).

Also, Sep [0]> Sep [1]> Se
p [2] and Sep [0]> Sep [1] * 1.
In the case of 5, it is determined that the inclination is the maximum, and the variable Level indicating the intensity of the under is left as it is. The value "1.5" can be changed to one or more values by experience. When the value is increased, the correction is weak, and the ratio of being judged as the medium level is increased.

Next, paying attention to the lower luminance, it is determined whether or not the ratio of pixels exceeding the limiter value is larger than 0% (that is, LimitSep [0]> 0) (step 212 in FIG. 4). In this case, the level is kept as it is, otherwise, the value of Level is reduced by 1 (step 213 in FIG. 4).

As described above, as a result of subtraction from Level = 5, Level may become 0 or less. Since the image was originally determined to be under, it is corrected so that Level = 1 at least as the final correction. Therefore, it is determined whether Level is larger than 0 (see FIG. 4).
Step 214), if it is 0 or less, the value of Level is corrected to 1 (step 215 in FIG. 4).

Subsequently, since the contrast inclination is determined from the inclination information sechigh and sectLow determined by the contrast determination section 29, the contrast data of the input Ycon and the output Ycon 'is obtained, and * Cont is determined from j = 0 to j of Ycon. = 255 is stored (step 216 in FIG. 4). The equation is a linear equation as follows. n is the number of divided histograms, which is “8” here.

X1 = (256 / n) * sectlow (when x1> 255, x1 = 255) x2 = 256- (256 / n) * secthigh (when x2> 255, x2 = 255) Ycon '[J] = 255 * (j−x1) / (x2−x1) Subsequently, since the Level has been determined, the curve data * Up [1] stored in the various curve data storage units 32
~ [5] of Level * Up [Level]
Is selected as correction curve data, and is combined with the contrast straight line data obtained in step 216 and stored in * Cont to obtain a gradation correction curve (step 21 in FIG. 4).
7). Thus, the under process is completed.

Next, the over process in step 106 will be described in more detail with reference to the flowcharts in FIGS. In the case of the over process, a contrast straight line and a correction curve * Do based on the luminance histogram feature amount
It is a large flow to determine and combine the levels of wn [] to form a gradation correction curve.

First, the initial value of the level (Level) is set to, for example, “5” (step 301 in FIG. 5), and the variable s
The initial value of um is set to “0” (step 30 in FIG. 5).
2) The variable i is set to "0" (step 30 in FIG. 5).
3) The luminance histogram feature amount Sep is set in the variable sum.
[I] is added (step 304 in FIG. 5), and it is determined whether or not the added value sum is greater than 0 (step 305 in FIG. 5). If sum ≦ 0, the value of i is set to 2 Similar addition and determination processing is performed. The above addition and determination processing is repeated while incrementing the value of i by 1 until sum> 0.

If sum> 0, the variable setlow
Is set to i, and the value of the variable second is set to 0 (step 306 in FIG. 5). That is, first, in consideration of the fact that the pixels are biased toward the higher luminance, an area where the pixels are 0% is searched from Sep [0] having the lowest luminance. 0% to avoid assigning gradations to areas without pixels
In the area, Ycon 'is pasted to 0 in the Ycon-Ycon' graph.

It should be noted that, in step 306, the second
= 0 is counted from the top in the over image and is 0
The probability of% is low to simplify the processing. The inclination of the contrast straight line is determined up to this point. The value to be compared with sum in step 305 may be other than 0. This value can be variably set by experience. If it is desired to increase the contrast correction strength, set this to a large value. Variable se
ctlow is the result information of the contrast gradient determination unit 29. If this value is large, it becomes a straight line that decreases the output gradation with respect to the input gradation, and the oversized image is improved.

Next, a variable Level indicating the overstrength is set.
1 is subtracted from the level of the correction curve by the amount of contrast based on an arithmetic expression of Level = Level-sectlow (step 307 in FIG. 5). Next, a method using a cumulative luminance distribution is applied to determine the correction intensity. In the over process, since it is determined that the pixels are biased toward the higher luminance, the histogram shape of the region is used as a material for the determination. That is, first, Sep
It is determined whether the condition [7]> Sep [6]> Sep [5] is satisfied (step 308 in FIG. 5). If the condition is not satisfied, it is determined that the level is weak, and the variable Le indicating the strength is determined.
The level is set to Level-2 (step 30 in FIG. 5).
9).

Sep [7]> Sep [6]> Sep
If [5], then Sep [7] is Sep [6] *
Compare whether it is greater than 1.5 (step 3 in FIG. 5).
10), if it is equal to or less than Sep [6] * 1.5, a variable Level indicating the intensity is set to Level-1 as a medium level (step 311 in FIG. 5). Also, Sep [7]> Se
p [6]> Sep [5], and Sep [7]> Se
In the case of p [6] * 1.5, it is determined that the inclination is the maximum, and the variable Level indicating the intensity of over is kept as it is. "1.
The value of 5 "can be changed to a value of 1 or more by experience. If the value is increased, the correction becomes weak, and the ratio of being judged as the medium level increases.

Next, paying attention to the higher luminance, it is determined whether or not the ratio of pixels exceeding the limiter value is larger than 0% (that is, LimitSep [7]> 0) (step 312 in FIG. 6). In this case, the level is kept as it is, otherwise, the value of Level is reduced by 1 (step 313 in FIG. 6). Next, Level
Is greater than 0 (step 31 in FIG. 6).
4) If the value is 0 or less, the value of Level is corrected to 1 (step 315 in FIG. 6).

Subsequently, since the contrast gradient is determined from the gradient information sechigh and sectLow determined by the contrast determining section 29, the contrast data of the input Ycon and the output Ycon 'is obtained, and * Cont is determined from j = 0 to j of Ycon. = 255 is stored (step 316 in FIG. 6). The equation is the same linear equation as in the case of the under processing.

Subsequently, since Level has been determined,
* Down [Left] among the curve data * Down [1] to [5] stored in the various curve data storage units 32
1] is selected as the correction curve data, and is combined with the contrast straight line data obtained in step 316 and stored in * Cont to obtain a gradation correction curve (step 317 in FIG. 6). Thus, the over process is completed.

Next, the linear processing in step 107 will be described in more detail with reference to the flowcharts of FIGS. In the linear processing, it is a large flow to determine a level of a straight line for contrast and a correction curve * Up [] or * Down [] based on a luminance histogram feature amount and combine them to obtain a gradation correction curve.

First, in order to determine the contrast, the upper and lower
Find the% area. That is, the data is added in the direction from Sep [7] to Sep [0] of the luminance histogram feature amount storage unit 27 to check which section from 7 to 0 or less and stores it in the variable sechigh (step 401 in FIG. 7). ~
405). Subsequently, the luminance histogram feature storage unit 27
Is added in the direction from Sep [0] to Sep [7], and it is checked how many sections from 0 to 0 or less.
It is stored in low (steps 406 to 410 in FIG. 7).
However, a value of 0 or less can be adjusted by experience. If it is desired to increase the contrast correction strength, set it higher.

Next, in order to determine the correction intensity, the data LimitSe in the luminance histogram feature storage 27 is stored.
Look at the value of p []. It is determined whether LimitSep [0]> 0 and LimitSep [7]> 0 (FIG. 8).
Step 411), if this condition is satisfied, it is determined that there is backlight. When it is determined that the subject is backlit, it is determined that the subject is under light, ignoring that a bright portion is crushed in order to simplify the processing. In the case of analyzing by the cumulative luminance distribution, a curve having an inflection point is often obtained in the case of such a histogram, but here, a curve of * Up [] is simply selected.

If it is determined that the value is under, the data LimitSe in the luminance histogram feature quantity storage unit 27 is stored.
Looking only at p [0], when the value is small, the Level is low, and when the value is large, the Level is high (Step 41 in FIG. 8).
2). The level assignment may be adjusted by investigating many images.

Subsequently, sechigh, sectro
From w, the gradient of the contrast line * Cont is calculated based on the same formula as the under processing, and stored in the contrast line * Cont (step 413 in FIG. 8). Thereafter, the contrast line * Cont and the correction curve * Up [Lev
el] to obtain a gradation correction curve (step 414 in FIG. 8).

On the other hand, LimitSep [0] and Limi
If only LimitSep [0] of tSep [7] satisfies LimitSep [0]> 0 (steps 415 and 416 in FIG. 8), it is determined to be under and the same processing as steps 412 to 414 described above is performed. (Steps 417 to 419 in FIG. 8). Also, LimitSep [0]
And LimitSep [7], LimitSep
If only [7] has LimitSep [7]> 0 (steps 415 and 416 in FIG. 8), it is determined to be over and the data Limi in the luminance histogram feature amount storage unit 27 is determined.
Looking only at tSep [7], the level is lowered when the level is small, and the level is raised when the level is large (step 420 in FIG. 8). The level assignment may be adjusted by investigating many images.

Subsequently, sechigh, sectro
From w, the gradient of the contrast line * Cont is calculated based on the same formula as the under process, and stored in the contrast line * Cont (step 421 in FIG. 8). Thereafter, the contrast line * Cont and the correction curve * Up [Lev
el] to obtain a gradation correction curve (step 422 in FIG. 8). Note that LimitSep [0] is also limited to LimitSep [0].
If Sep [7] is also equal to or less than 0, it is completely determined to be linear, and a correction curve is formed using only the contrast straight line * Cont (step 423 in FIG. 8).

After the gradation correction curve is determined as described above, the gradation correction curve is corrected based on the image sampling data (Y, U, V). The operation of correcting the gradation correction curve will be described in detail with reference to the flowchart of FIG.
An image is information composed of a three-dimensional signal of (Y, U, V). When the luminance signal Y becomes Y ′ / Y times as a result of gradation correction, the color difference signals U and V are generally the same. Multiply Y '/ Y times.
The same applies to the present embodiment.

The gradation correction curve correction unit 36 first fetches the data of the gradation correction curve (Y, Y ′) before correction from the gradation correction curve data storage unit 34 (step 501 in FIG. 9). When the color difference signals U and V are Y '/ Y, the number of overflows is calculated in advance (step 502 in FIG. 9). The color difference signals U and V are -128
To 127. Also, limit values 127 * Y / Y 'and -128 * Y / Y' for input luminance signal Y are calculated in advance. The luminance signal Y has a value of 0 to 255. With this preparation, the image sampling data (Y, U, V) is checked.

If the data in the image memory 21 shown in FIG. 1 is used as it is, the processing becomes slow. Therefore, sampling data is used. Subsequently, while the number of sampling data is always the same, the number of pixels where U and V overflow is counted by the color difference signal overflow detection unit 35 and the variable Count is counted.
(Steps 503 and 504 in FIG. 9). Subsequently, the variable Count is compared with the threshold value UVLimit (step 505 in FIG. 9), and Count <UV
In the case of Limit, the gradation correction curve is used as it is (FIG. 9).
Step 506). Otherwise, after lowering the Level according to the value of Count, * Up [Level
l] and * Cont are combined to form a new correction curve (steps 507 and 508 in FIG. 9).

The UV Limit is adjusted by experience.
UVLimit is a value greater than 0. The larger the value, the greater the color difference signal collapse. The smaller the value, the less the color difference signal collapses. The rate at which the Level is lowered is also adjusted by experience.

For example, when Level is lowered by one level, C
When the number of “outs” drops from 100 to 5, or when “1000” drops to 50, the level is lowered by considering that one level is reduced by 1/20. If UVLimit is 6, the level is lowered by one when the initial Count is 100. 10
In the case of 00, the processing is to lower by two levels. The number of pixels in the image sampling data storage unit 23 is 19
In the case of 200 pixels, when Count> 1000, the pixel is unconditionally set to linear. 1000 ≧ Count> 10
Multiply by 0.25 during 0. When Count ≦ 100, multiply by 0.03. Lowers one level each time you ride.
Multiplication and level reduction are performed until Count becomes smaller than UVLimit.

The method of lowering the level when correcting the data in the gradation correction curve data storage section 34 is to lower the Level according to the value of the Count.
First, determine the contrast line so that
Lower until cthight becomes 0, and still target Le
If the value does not decrease to the value of vel, the curve data having a lower level than the selected curve data is selected from the various curve data obtained from the various curve data storages 32. The curve data and the contrast straight line data are combined to generate correction curve data. The image is (Y, U, V)
Since the correction curve determined from only the Y component is corrected in this process, a well-balanced image can be obtained.

The present invention is not limited to the above embodiment. For example, the input luminance level axis can be divided into n equal parts of 4 or more and the correction level can be set in m steps of 1 or more. In the above embodiment, the processing of the blocks in FIG. 1 other than the image memory 21 from the image sampling processing unit 22 to the image processing unit 37 is performed by the video printer 1 in FIG.
Although the description has been made with the software of the CPU 17 in the computer 6, the software may be performed with the software of the CPU 15 in the personal computer 14. Alternatively, the brightness histogram feature value calculation unit 26 and the brightness histogram feature value storage unit 2 in FIG.
7, over-exposure / under-detection unit 28, contrast inclination determination unit 29, contrast data creation unit 30, over / under intensity determination unit 31, various curve data storage units 32, contrast straight line and curve data synthesis processing unit 33,
The respective processes of the color difference signal overflow detection unit 35 and the gradation correction curve correction unit 36 are performed by the CPU 17 in the video printer 16.
And the other blocks may be configured by hardware.

[0076]

As described above, according to the present invention,
After dividing the luminance histogram of the input color image data into n equal parts, knowing the approximate shape of the curve, calculating the slope information of the contrast according to the shape, and selecting the curve data to be optimal, Is used to generate gradation correction curve data by software processing, so that a natural gradation can be obtained as compared with the case where the accumulated luminance distribution is used as it is. Is not used, complicated calculations are not required, and cost reduction and speeding up can be achieved. Further, since the processing is software processing, it is possible to easily cope with a change in image data.

Further, according to the present invention, after automatically determining a tone correction curve from only the elements of the luminance histogram, the rate of overflow of the color difference signal when the tone correction curve is reflected on a color image is checked. Since the selection of the curve is corrected, there is no collapse in the saturation direction that may occur when the gradation correction curve is automatically determined from only the elements of the luminance histogram, and a good gradation image is obtained by software processing. be able to.

[Brief description of the drawings]

FIG. 1 is a block diagram of a correction apparatus for realizing an embodiment of an automatic gradation correction method according to the present invention.

FIG. 2 is a flowchart for explaining a gradation correction curve determination process according to an embodiment of the method of the present invention.

FIG. 3 is a flowchart (part 1) for explaining an example of an under process in FIG. 2;

FIG. 4 is a flowchart (part 2) for explaining an example of an under process in FIG. 2;

FIG. 5 is a flowchart (part 1) for explaining an example of an over process in FIG. 2;

FIG. 6 is a flowchart (part 2) for explaining an example of the over process in FIG. 2;

FIG. 7 is a flowchart (part 1) for explaining an example of the linear processing of FIG. 2;

FIG. 8 is a flowchart (part 2) for explaining an example of the linear processing of FIG. 2;

FIG. 9 is a flowchart illustrating a tone correction curve correction process according to an embodiment of the present invention.

FIG. 10 is a conceptual diagram of an approximate shape of a luminance histogram and selection of a gradation correction curve according to the present invention.

FIG. 11 is a block diagram illustrating an example of a printing system using a color video printer.

FIG. 12 is an explanatory diagram of a luminance histogram and a cumulative luminance distribution.

[Explanation of symbols]

 Reference Signs List 14 personal computer (personal computer) 15, 17 central processing unit (CPU) 16 video printer 18, 21 image memory 22 image sampling processing unit 23 image sampling data storage unit 24 luminance histogram creation unit 25 luminance histogram storage unit 26 luminance histogram feature amount calculation Unit 27 luminance histogram feature amount storage unit 28 overexposure / under exposure detection unit 29 contrast inclination determination unit 30 contrast data creation unit 31 over / under intensity determination unit 32 various curve data storage units 33 contrast straight line and curve data synthesis processing unit 34 gradation Correction curve data storage unit 35 Color difference signal overflow detection unit 36 Tone correction curve correction unit 37 Image processing unit 104 Under processing step 106 Over processing step 107 Linear processing step

Continuation of the front page (72) Inventor Kenji Tanaka 3-12 Moriyacho, Kanagawa-ku, Yokohama-shi, Kanagawa Japan F-term (reference) 5B057 CA01 CA16 CB01 CB16 CE11 CH11 DC23 5C055 BA03 BA06 BA08 CA07 EA02 EA04 FA22 HA17 HA31 5C066 AA01 AA05 AA13 CA17 EC05 EF00 GA02 GA05 KA12 KD02 KD04 KE07 5C077 LL01 LL19 MP01 NN02 PP10 PP47 PQ08 PQ19 PQ25 TT02

Claims (3)

[Claims] 1. A luminance histogram is created from color image data to be subjected to gradation correction, and an input luminance level axis is divided into n equal parts (n is an integer of 4 or more) from the luminance histogram. A first feature value indicating a ratio to pixels, a second feature value indicating a ratio of the number of pixels exceeding a certain limiter value to all pixels in each of the n equally divided regions, and an input brightness level from the brightness histogram. A first step of calculating a third feature amount indicating a ratio of the number of pixels of each area divided into three equal parts to all pixels, and storing the calculated third feature amount in a first storage unit; The first to third feature values stored in the first and third feature values are taken in, and the third feature value in the lowest brightness region is larger than a preset lower limit value, and the third feature value in the highest brightness region is 3 features A second step of comparing and determining whether the amount is smaller than a preset upper limit value; and the second step, wherein the third feature value of the lowest luminance side region is larger than a preset lower limit value. When a certain determination result is obtained, it is determined that the exposure is underexposure, the inclination information of the contrast is obtained by using the first feature amount, and the under intensity is obtained from the inclination information. Under processing for selecting, from a second storage unit in which various curve data indicating various characteristic curves of an output gradation with respect to an input gradation are stored, in advance, a curve data having a characteristic of increasing the output gradation with respect to the input gradation. When the result of the determination that the third feature value of the highest luminance side region is larger than a preset upper limit value is obtained by the second step, it is determined that the exposure is over. hand Using the first feature amount, the gradient information of the contrast is obtained, the over-intensity is obtained from the inclination information, and the output gray-scale is compared with the input gray-scale from the second storage unit in accordance with the over-intensity. A fourth step of performing an over process of selecting curve data having a characteristic of reducing the characteristic, and the second step is such that the third feature value in the lowest luminance region is equal to or less than a preset lower limit value, and When a determination result is obtained in which the third feature amount of the highest luminance side region is smaller than a preset upper limit, contrast inclination information is obtained using the first feature amount, and A fifth step of performing a linear process of selecting curve data from the second storage unit according to the strength after obtaining the strength according to the second feature amount; and the third, fourth, or fifth step. Before created in step A sixth step of generating gradation correction curve data by combining the inclination information of the contrast and the selected curve data and storing the data in the third storage unit; and storing the color image data to be corrected for the gradation. A seventh step of reflecting the gradation correction curve data from the third storage unit on the color image data to be corrected for gradation read out from the image memory and storing the color image data again in the image memory. An automatic gradation correction method characterized by including: 2. Using the gradation correction curve data in the third storage unit and the input color image data to be corrected, the number of pixels at which the color difference signal of the color image data overflows is detected. An eighth step of correcting at least one of the contrast inclination information and the selected curve data stored in the third storage unit based on the number of overflow pixels detected in the eighth step. A ninth step of combining, wherein the corrected gradation correction curve data obtained by combining in the ninth step is stored again in the third storage unit. Automatic gradation correction method described. 3. The color image data to be corrected for gradation is image data output from a video device, the image memory is provided in a printer, and the first
The ninth to ninth steps are executed by software processing by a central processing unit in the printer or a personal computer external to the printer, and print the image data with the automatic gradation correction stored in the image memory by the printer. 3. The automatic gradation correction method according to claim 2, wherein