KR20130002698A - A method of enhancing contrast using bezier curve - Google Patents

Abstract

PURPOSE: A contrast improving method which uses a Bezier curve is provided to improve the quality of an image by supplying contrast. CONSTITUTION: An RGB value of an image is converted to a YCbCr(S12). An image type is determined based on a pixel count by section(S14). A moving direction of control points is determined(S15). The moving direction of the control points is calculated based on histogram distribution of a luminance value(S16). A three-dimensional Bezier curve is generated(S17). The contrast of an image is improved by using a reference curve or the generated bezier(S18). [Reference numerals] (AA) Start; (BB) End; (S11) Reading an image; (S12) Converting an RGB value into a YCbCr; (S13) Calculating a pixel count for low luminance, medium luminance, and high luminance sections; (S14) Determining an image type based on a pixel count value for each section; (S15) Determining a moving direction of control points based on the image type; (S16) Calculating a moving distance of the control points based on histogram distribution of a luminance value; (S17) Forming a 3D Bezier curve based on the moved control point; (S18) Improving contrast

Description

A contrast method using Bezier curves {A METHOD OF ENHANCING CONTRAST USING BEZIER CURVE}

The present invention relates to a method for improving contrast of an image. More specifically, the present invention calculates the histogram distribution from the image taken or read into the storage medium, classifies the image by type according to the contrast state, determines the direction of movement of the control point of the three-dimensional Bezier curve by the type The control point moving distance of the 3D Bezier curve is calculated based on the number of luminance values present in the histogram distribution. The two control points of the three-dimensional Bezier curve are moved based on the determined control point movement direction and the control point movement distance, and a Bezier curve is generated using the two moved control points and two fixed control points. Then, the generated Bezier curves are applied to perform image enhancement.

Contrast has a significant impact on the quality of an image in human visual perception. Image contrast improvement is basically to improve the intertranslation or recognition of image information for the human eye, and to provide "better" input for other automated image processing techniques. The main purpose of image enhancement is to change the attributes of the image to make it more appropriate for a given task and a particular observer. In this process, one or more attributes of the image are changed.

Typically, the contrast of an image is defined as the range of luminance intensities available in a given image. Mathematically, the image contrast can be expressed as follows.

Contrast = (Lmax-Lmin) / (Lmax + Lmin) ... (Equation 1)

Where Lmax and Lmin are the maximum and minimum luminance values of a given image, respectively.

Contrast improvement is a process that makes the image feature clearer by using the maximum range of intensities provided by the number of bits used to quantify the intensities.

Among these contrast improvement methods, a method of improving the contrast is required so that the human eye can be more optimized.

It is an object of the present invention to provide a method for improving contrast in a spatial region that adaptively improves contrast, depending on the type of image.

According to one embodiment of the present invention, a method for improving contrast using Bezier curves is provided. The method comprises: reading an image; Calculating a luminance value distribution of the image; Calculating pixel counts for each of the low, medium, and high luminance sections of the luminance value; Determining a type of the image based on the pixel count for each section; Determining a control point movement direction based on the type of the image; Calculating a moving distance of the control point based on a distribution of luminance values of the image; Moving a control point based on the control point moving direction and the control point moving distance and generating a 3D Bezier curve based on the moved control point; And performing a contrast improvement using the generated three-dimensional Bezier curve.

According to the present invention, the contrast can be improved so that the human eye can be more optimized with a small amount of calculation.

1 illustrates types of an image according to a histogram distribution of luminance values of an image.
2 illustrates a histogram distribution of an image according to an embodiment of the present invention.
3 illustrates a method of setting a control point of a three-dimensional Bezier curve according to an embodiment of the present invention.
4 illustrates a method of moving a control point for each image type according to an embodiment of the present invention.
FIG. 5 is a diagram for describing the number N of luminance values present in a luminance value distribution according to one embodiment of the present invention.
6 illustrates a method of moving a control point by a calculated movement distance according to an embodiment of the present invention.
7 is a reference curve using the finally obtained Bezier curve according to an embodiment of the present invention.
8 is a flowchart illustrating an adaptive contrast improvement method according to an embodiment of the present invention.

With reference to the drawings, embodiments of the present invention will be described in more detail.

1 illustrates types of an image according to a histogram distribution of luminance values of an image. As shown in FIG. 1, (a) is a dark image, (b) is a bright image, (c) is a backlit image, (d) is a low contrast image, and (e) is a high contrast image Can be classified as

As shown in FIG. 2, the histogram distribution of the image may be divided into three regions, that is, low luminance (L), medium luminance (M), and high luminance (H) region. Preferably, the section lengths of the low luminance section, the medium luminance section, and the high luminance section are set to be substantially the same. That is, the luminance values included in the low luminance section, the middle luminance section, and the high luminance section may be 85, 85, or 86 of 256 discrete luminance values.

Bezier curves, on the other hand, are intermediate curves that are frequently used in computer graphics and related fields. Bezier curves are widely used in the field of computer graphics to model smooth curves. In the present invention, a three-dimensional Bezier curve is used to improve contrast.

The three-dimensional Bezier curve can be defined by the following equation.

... (Equation 2)

Here, P0, P1, P2, and P3 are control points of the three-dimensional Bezier curve.

Three-dimensional Bezier curves can be used to model contrast improvement problems for dynamically different scenarios based on scene or image content. By changing the position of the control points, all necessary curve shapes are obtained to improve contrast. Depending on the positions of the control points P0, P1, P2, and P3, the shape of the Bezier curve to be generated varies.

Hereinafter, according to an embodiment of the present invention, how to set the position of the control point to obtain a curve to be used for contrast improvement. 3 illustrates a method of setting a control point of a three-dimensional Bezier curve according to an embodiment of the present invention.

First, among the four control points P0, P1, P2, and P3, the control points P0 and P3 are fixed in diagonal directions facing each other. That is, in FIG. 3, P0 is fixed to (0,0) and P3 is fixed to (1,1). P1 and P2 are located at the same position as P0 and P3, respectively, and move in a direction away from P0 and P3 by a distance determined by three regions of the luminance value histogram distribution, that is, the pixel count of the low luminance, medium luminance, and high luminance interval. do. The direction in which P1 and P2 move away from P0 and P3 may vary depending on the type of image.

The type of the image may be classified into five types as shown in FIG. 1, and which type belongs to which is determined according to the pixel count belonging to each section of the luminance value histogram.

If N (L) is a pixel count belonging to a low luminance section, N (M) is a pixel count belonging to a medium luminance section, and N (H) is a pixel count belonging to a high luminance section, it can be determined as follows.

If (N (L)> N (M)) && ((N (L)> N (H))) then dark image;

Else If (N (H)> N (M)) && ((N (H)> N (L))) then bright image;

Else If (N (L)> N (M)) && ((N (H)> N (M))) then backlight image;

Else If (N (M)> N (L)) && ((N (M)> N (H))) then low contrast image;

Else high contrast image.

Preferably, when classifying an image into a type other than a high contrast image, there are many sizes of N (L), N (M), and N (H), such as a difference of more than 10% of the total number of pixels of the image. It is limited to. Since there is a difference in the values of N (L), N (M), and N (H), it is arithmetically determined to be of a type other than high contrast, but when the difference is not large, all are classified as high contrast images. This is because it is preferable. For example, if the total number of pixels in the image is 1780, if N (L) = 600, N (M) = 590, N (H) = 590, the classification above is classified as a dark image, but this is because Because it's not large, you can just classify it as a high contrast image. When the pixel count difference per section differs by 1780 * 10% = 178 or more, the total number of pixels may be classified into types other than high contrast images. The above 10% criterion is a criterion for determining whether the pixel count difference in each section is large. The value may be changed to 20% or 30% according to an embodiment.

Referring to FIG. 4, when the image is classified as (a) a dark image, P2 is fixed and is at the same position as P3, and P1 moves upward. As a result, a Bezier curve can be generated by P0, P1, P2, and P3.

If the image is (b) classified as a bright image, P1 is fixed and at the same position as P0, and P2 moves downward.

If the image is classified as (c) a backlit image, then P1 moves up and P2 moves down.

If the image is classified as (d) a low contrast image, then P1 moves to the right and P2 moves to the left.

If the image is classified as (e) a high contrast image, the control points do not move and a straight Bezier curve is created. Contrast enhancement using a straight Bezier curve ensures that the input and improved values are the same, resulting in virtually no change in the image.

The distance that P1 and P2 travel is determined by the tone contrast ratio of (Equation 3).

Tone Contrast Ratio (TC) = TC = N / (Lmax-Lmin +1) ... (Equation 3)

N is the number of luminance values present in the luminance value distribution, Lmax is the maximum luminance value of the luminance value distribution, and Lmin is the minimum luminance value of the luminance value distribution.

The number N of luminance values present in the luminance value distribution will be described with reference to FIG. 5. Assuming that the histogram distribution as shown in FIG. 5 is calculated, the pixel count may not exist in all luminance values from 0 to 255, but the pixel count may exist only in specific luminance values. In FIG. 5, pixel counts are concentrated in nine luminance values. In this case, N = 9. N may range from 1 to 256 for a particular image.

The moving distance D of the control points P1 and P2 is determined by (Expression 4).

D = (2 ^ N-1) * TC ... (Equation 4)

For example, from the histogram distribution of an image, if the type of image is determined to be a backlight image, and N = 9, Lmax = 240, Lmin = 5, the tone contrast ratio TC is as follows.

TC = 9 / (240-5 + 1) = 0.038135

Since the image type is a backlight image, P1 will move up, P2 will move down, and the moving distance is determined as follows.

D = (2 ^ 9-1) * 0.038135 = 9.72457

Thus, P1 and P2 are each shifted by 9.72457 units, i.e. by 9.72457 luminance values. At this time, in order to reduce the amount of calculation, it is possible to round the movement amount of P1 and P2 to determine the integer value. For example, you can move 9.72457 rounded by 10 units.

Referring to FIG. 6, a case in which P1 and P2 move up and down by 10 units, respectively. In FIG. 6, the x-axis and the y-axis are scaled to [0,255]. If the x-axis and the y-axis are scaled to [0,1], the calculated moving distance may be moved by the distance divided by 256.

A three-dimensional Bezier curve is generated from four control points obtained through the above process, as shown in FIG. 7. The x axis is the input luminance value and the y axis is the output luminance value. Contrast can be improved by mapping a given image to a new luminance value using a reference curve as shown in FIG. 7.

8 is a flowchart illustrating an adaptive contrast improvement method according to an embodiment of the present invention.

In step S11, an image is read. The image may be photographed by a digital camera, or image data stored in a storage medium may be read.

In step S12, the RGB value of the read image is converted into YCbCr, and the Y value which is the luminance component for each pixel is extracted.

In operation S13, the pixel counts of the low luminance, the medium luminance, and the high luminance interval are calculated based on the extracted Y for each pixel.

In step S14, the image type is determined based on the pixel count for each section. The image type may be one of the five types of FIG. 4 as described above.

In step S15, the moving direction of the control point is determined based on the determined image type. As described above, P0 and P3 are fixed to (0,0) and (1,1), and P1 and P2 are moved up, down, right or left, respectively.

In step S16, the moving distances of the control points P1 and P2 are calculated based on the histogram distribution of the luminance values. In this case, as described above, the moving distance may be calculated in the same manner as described above by extracting the number N of luminance values having a pixel count among the luminance values of 0 to 255 in the histogram distribution of the luminance values.

In step S17, a three-dimensional Bezier curve is generated based on the moved control points P1 and P2 and the fixed control points P0 and P3.

Contrast improvement is performed using the Bezier curve generated in step S18 or a reference curve scaled from 0 to 255.

Claims (5)

In the contrast improvement method using a Bezier curve,
Reading an image;
Calculating a luminance value distribution of the image;
Calculating pixel counts for each of the low, medium, and high luminance sections of the luminance value;
Determining a type of the image based on the pixel count for each section;
Determining a control point movement direction based on the type of the image;
Calculating a moving distance of the control point based on a distribution of luminance values of the image;
Moving a control point based on the control point moving direction and the control point moving distance and generating a 3D Bezier curve based on the moved control point; And
And using the generated three-dimensional Bezier curve to perform contrast enhancement. The method of claim 1,
And the image type is one of a dark image, a bright image, a backlight image, a low contrast image, or a high contrast image. The method of claim 1,
Determining the type of the image based on the pixel count for each section,
If the pixel count of the low luminance period is greater than the pixel count of the medium luminance period, and the pixel count of the low luminance period is greater than the pixel count of the high luminance period, a dark image is determined.
If the pixel count of the high brightness period is greater than the pixel count of the medium brightness period and greater than the pixel count of the high brightness period, a bright image is determined.
If the pixel count of the low brightness period is greater than the pixel count of the medium brightness period and the pixel count of the high brightness period is greater than the pixel count of the medium brightness period, the backlight image is determined.
If the pixel count of the medium brightness section is greater than the pixel count of the low brightness section, and the pixel count of the medium brightness section is greater than the pixel count of the high brightness section, the low contrast image is determined.
Otherwise including the step of determining a high contrast image. The method of claim 1,
Calculating a moving distance of the control point based on the luminance value distribution,
Extracting the number N of luminance values present in the luminance value distribution;
Calculating a tone contrast ratio (TC) based on the number of luminance values;
Calculating a moving distance based on the tone contrast ratio (TC),
The tone contrast ratio (TC) is,
TC = N / (Lmax-Lmin + 1) is determined,
The moving distance (D),
Determined by D = (2 ^ N-1) * TC,
Wherein Lmax is the maximum luminance value of the luminance value distribution, and Lmin is the minimum luminance value of the luminance value distribution. 5. The method of claim 4,
And the control point is moved by an integer rounded off the calculated movement distance (D).

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