KR101418521B1 - Image enhancement method and device by brightness-contrast improvement - Google Patents

Abstract

A method and an apparatus for improving image quality through brightness contrast enhancement are disclosed. An image quality enhancement apparatus includes a brightness enhancement unit and an image quality enhancement unit. The image enhancement unit includes: an image analysis unit for analyzing a cumulative distribution function of a conditional contrast probability with respect to a previous frame image; A compensation curve generation unit for generating a final compensation curve using the cumulative distribution function for the analyzed conditional contrast probability; And a curve application unit for applying the generated final compensation curve to the current frame image for the previous frame image to output a current frame image having improved image quality.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image enhancement method and an image enhancement method,

BACKGROUND OF THE INVENTION 1. Field of the Invention [0002] The present invention relates to a method and an apparatus for improving image quality through improvement of brightness contrast.

Recently, a still image or a moving image can be conveniently obtained in various environments by using a portable terminal or a digital camera. In most cases, photographing is performed in a shooting environment of a non-uniform light source such as a fluorescent lamp in a building or a sunlight or a streetlight in a building, rather than in a shooting environment such as a studio that maintains an artificially prepared uniform lighting condition. The obtained image may be deteriorated in image quality due to darkness or excessively bright state due to one side being shadowed.

In addition, since a conventional digital image device is lost due to an inadequate quantity of light or a dynamic range that can be represented in the image acquisition process, information of a dark part or a bright part is lost, There is also a problem that can not be done. To solve these problems, digital imaging devices having various functions such as AE (Auto Exposure) and AWB (Auto White Balance) have been developed. However, in general digital imaging devices, There is a problem that the satisfaction of the acquired image is formed differently depending on the amount of the roughness.

As a method for solving the above problems and improving the image quality of images photographed in various environments, there is proposed a SSR (Single Scale Retinex) method which reduces the influence of illumination components based on Retinex characteristics of a human visual model There is. However, the SSR method is dependent on the Gaussian filter, which is a preprocessing process, and the degree of improvement in the degree of improvement in the input image is very large, and there are drawbacks such as a halo effect and deterioration of a color signal.

In order to solve the problem of the SSR method, a MSR (Multi Scale Retinex) method using a luminance channel, which is a main component of a light source, has been proposed.

The MSR scheme has a merit that the result is stable and shows high saturation because the contrast for the luminance component channel is improved while the balance of the RGB channel is maintained. However, since only the luminance channel is used, the Retinex algorithm can not remove the chromaticity value of the light source, which is a unique characteristic, and has a disadvantage in that the representation of the chroma component is not natural.

In order to solve the problem of color distortion caused by the MSR method, a multi-scale retinex with color restoration (MSRCR) method with a color restoration function has been proposed. The MSRCR method is described in detail in the prior art Impact of multi-scale retinex computation on performance of segmentation algorithm (Proceedings of SPIE, Volume 5438, pp. 171-182 (July 2004)).

In the preceding paper, the following equation (1) is presented as a calculation formula of the MSR basic form and the restoration factor (α).

Here, i represents an i-th spectral band, and S represents an image type (for example, a gray image when S is 1 and a color image when S is 3). (x ₁ , x ₂ ) denotes the coordinates of the image, I denotes the input image, and R denotes the output image according to the MSRCR scheme. F _k denotes a kth Gaussian surround function, σ _k denotes standard deviations of the kth Gaussian surround function, W _k denotes a weight for the k th Gaussian surround function, K is the value of the surround function, α _i represents the color restoration factor of the i-th spectral band (color restoration coefficients).

However, since the MSRCR scheme according to the prior art performs operations in the frequency domain, it is troublesome to perform a Fourier transform, and there is a problem that a plurality of frame memories are required to store input images and Gaussian functions there was.

In addition, additional memory is required to store the table in advance for log operation, and real-time operation processing is not possible.

Therefore, there is a need for a method capable of real-time image enhancement processing by minimizing computational complexity.

The present invention is to provide a method and apparatus for enhancing image quality through brightness contrast enhancement so that information of an image having a wide range of brightness change can be expressed better within a limited dynamic range.

The present invention relates to a method for improving image quality through brightness contrast enhancement that can compensate for loss of sharpness that can be generated by simply scaling a brightness value at a certain ratio by efficiently scaling an actual brightness value to match a dynamic range of a display device, Device.

The present invention eliminates the hassle of Fourier transforms and the like which increase the computational complexity in the conventional MSRCR method, and provides a method and apparatus for enhancing image quality by improving brightness contrast capable of performing image enhancement processing with only a minimum memory space without using a plurality of frame memories .

The present invention relates to a method and apparatus for enhancing image quality through brightness contrast enhancement capable of performing real-time image quality enhancement processing by performing tone mapping on a pixel-by-pixel basis using information obtained from previous image information .

Other objects of the present invention will become readily apparent from the following description.

According to an aspect of the present invention, there is provided an image quality enhancement apparatus including a brightness contrast enhancement unit, the apparatus comprising: an image analyzer for analyzing a cumulative density function of a conditional contrast probability with respect to a previous frame image; A compensation curve generation unit for generating a final compensation curve using the cumulative distribution function for the analyzed conditional contrast probability; And a curve application unit for applying the generated final compensation curve to the current frame image with respect to the previous frame image to output a current frame image of improved image quality.

Wherein the compensation curve generation unit comprises: a first area curve generation unit for generating a first compensation curve for improving brightness contrast in a dark region by using an accumulated distribution function for the conditional contrast probability; A second area curve generation unit for generating a second compensation curve for improving brightness contrast in a bright area by using an accumulated distribution function for the conditional contrast probability; A weight curve generator for generating a weight curve corresponding to an average of the first compensation curve and the second compensation curve; And using the first compensation curve, the second compensation curve, and the weight curve to improve the contrast of brightness by the first compensation curve in a region smaller than an intermediate value of all weight values of the weight curve, And a final compensation curve in which the brightness contrast of the second compensation curve is improved in an area where the brightness value is greater than a middle value among the total brightness values of the first and second compensation curves.

The first compensation curve may have a curved shape in which the tangential slope decreases as the brightness increases.

The first compensation curve YL (x) may be generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YL (x l} ) is in the x _l Is the value of the compensation curve, and Y _L (x _{l -1} ) may be the value of the compensation curve at x _{l -1} .

The second compensation curve may have a curved shape in which the tangential slope increases as the brightness increases.

The second compensation curve YH (x) may be generated by the following equation.

여기서, x는 화소의 밝기값이고, x_l은 l번째 빈(bin)의 중심이며, x_{l -1}은 l-1번째 빈(bin)의 중심이고, YH(x_l)은 x_l에서의 보상 커브의 값이며, YH(x_{l -1})는 x_{l -1}에서의 보상 커브의 값일 수 있다.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YH (x l} ) is in the x _l Is the value of the compensation curve, and Y H (x _{l -1} ) may be the value of the compensation curve at x _{l -1} .

The final compensation curve (Y (x)) can be generated by the following equation.

Here, W (x) is the weight curve, YH (x) is the second compensation curve, and YL (x) is the first compensation curve.

Wherein when the image information for improving the image quality is a moving image, the immediately preceding frame image is an image frame positioned temporally before the current frame image, and if the image information is a still image, And may be an image frame captured and stored.

According to another aspect of the present invention, there is provided a method of improving image quality through brightness contrast enhancement, the method comprising: analyzing a cumulative density function of a conditional contrast probability with respect to an immediately preceding frame image; Generating a final compensation curve using a cumulative distribution function for the analyzed conditional contrast probabilities; And a curve applying unit applying the generated final compensation curve to the current frame image with respect to the immediately preceding frame image to output a current frame image of improved image quality.

The generating of the final compensation curve may include using a cumulative distribution function for the conditional contrast probability to calculate a first compensation curve to improve brightness contrast in the dark region and a second compensation curve to improve brightness contrast in the bright region ; Generating a weight curve corresponding to an average of the first compensation curve and the second compensation curve; And using the first compensation curve, the second compensation curve, and the weight curve to improve the contrast of brightness by the first compensation curve in a region smaller than an intermediate value of all weight values of the weight curve, And generating the final compensation curve in which the brightness contrast by the second compensation curve is improved in an area larger than the middle value among the total brightness values of the first compensation curve.

The first compensation curve may have a curved shape in which the tangential slope decreases as the brightness increases, and the second compensation curve may be curved in which the tangential slope increases as the brightness increases.

The first compensation curve YL (x) may be generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YL (x l} ) is in the x _l Is the value of the compensation curve, and Y _L (x _{l -1} ) may be the value of the compensation curve at x _{l -1} .

The second compensation curve YH (x) may be generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YH (x l} ) is in the x _l Is the value of the compensation curve, and Y H (x _{l -1} ) may be the value of the compensation curve at x _{l -1} .

The final compensation curve (Y (x)) can be generated by the following equation.

Here, W (x) is the weight curve, YH (x) is the second compensation curve, and YL (x) is the first compensation curve.

Wherein when the image information for improving the image quality is a moving image, the immediately preceding frame image is an image frame positioned temporally before the current frame image, and if the image information is a still image, And may be an image frame captured and stored.

Other aspects, features, and advantages will become apparent from the following drawings, claims, and detailed description of the invention.

According to the embodiment of the present invention, there is an effect that information of an image having a wide brightness change can be expressed better in a limited dynamic range.

In addition, there is also an effect of compensating the loss of sharpness that can be generated by simply scaling the brightness value at a certain ratio by efficiently scaling the actual brightness value to match the dynamic range of the display device.

In addition, there is also an effect of eliminating the hassle of Fourier transform and the like which increased computational complexity in the conventional MSRCR method, and performing image enhancement processing with only a minimum memory space without a plurality of frame memories.

Also, it is possible to perform tone mapping on a pixel-by-pixel basis with respect to the current image by using the information obtained from the previous image information, thereby realizing the real-time image quality improvement process.

1 is a block diagram schematically showing a configuration of a brightness contrast enhancing unit according to an embodiment of the present invention;
2 is a diagram illustrating compensation curve information generated by each curve generation unit according to an embodiment of the present invention;
3 is a flowchart illustrating a method of improving image quality through brightness contrast enhancement according to an exemplary embodiment of the present invention.
FIGS. 4 to 6 are views illustrating an input image and an output image, respectively, according to the processing of the brightness contrast enhancing unit according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like refer to the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

Also, the terms " part, "" unit," " module, "and the like, which are described in the specification, refer to a unit for processing at least one function or operation and may be implemented by hardware or software or a combination of hardware and software .

In the following description of the present invention with reference to the accompanying drawings, the same components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

Hereinafter, in describing an embodiment according to the present invention, the current frame is denoted as X _t and the (t-1) th frame as the immediately preceding frame is denoted as X _{t -1} .

If the object of the current image quality improvement process is a moving image, the current frame to be processed in real time is X _t , and the frame positioned immediately before in the current frame is X _{t -1} .

If, however, if the target is a still image of the current image quality improvement processing, and if the previously read out to the image analysis and compensation curve generation will be described later from the image stored in the image memory (i.e., the preceding frame) it is X _{t -1,} stored in the memory The image (ie, the current frame) read out later by the compensation curve generated for X _{t -1} in the image to improve the image quality and output is X _t . Among the images stored in the memory, X _{t -1} and X _t may be the respective images stored sequentially in time.

The symbol X is the pixel value of the input image (i.e., the current frame), where the input image is an image of each channel (R, G, B) of an RGB image, a Y image of YCbCr (or YUV) A V (or I, L) image representing brightness in HSV (or HSI, HSL), and the like.

For example, in a case where the image quality improvement process is performed in real time, a value directly input from the image sensor may be input information, and in the case of a still image, a value read from image data stored in the memory may be input information .

FIG. 1 is a block diagram schematically showing a configuration of a brightness contrast enhancing unit according to an embodiment of the present invention. FIG. 2 is a diagram illustrating compensation curve information generated by each curve generating unit according to an embodiment of the present invention. to be.

Referring to FIG. 1, the brightness contrast enhancing unit 100 may include an image analyzer 110, an individual curve generator 120, a full image curve generator 130, and a curve applicator 140.

The image analyzing unit 110 calculates histograms for the immediately preceding frame X _{t -1} , Cumulative Density Function (CDF) of the histogram, Conditional Contrast Probability (CCP) Distribution function (CDF of CCP).

A cumulative distribution function (CDF) of a histogram, a cumulative distribution function (CDF), a conditional contrast probability (CCP), and a cumulative distribution function (CDF of a conditional contrast probability) of a histogram performed by the image analysis unit 110, CCP) is obvious to those skilled in the art and will be briefly described.

For the analysis of the image analysis unit 110, the dynamic range of the input image X can be divided into n bins having the same size, and the center x _j of the corresponding bins is 0,?, 2? (n-1) δ, j is an arbitrary value from 0 to n-1, and (n-1) δ <256. Delta representing the width of each bin may be 4, for example.

Also, a laplacian pyramid of image pyramids can be applied to find the contrast value in the input image.

The contrast for the ith frequency band can be calculated by G ⁱ = X ^{i -1} - X ⁱ . Herein, the input image X ⁰ is a previous frame X _{t -1} for image analysis and compensation curve generation, X ⁱ is an input image processed by a Gaussian filter of i-th (i is a pyramid level of any natural number from 1 to m) Image.

The image analysis by the image analysis unit 110 can be understood as a conditional contrast probability (CCP) value G ⁱ , which is a conditional probability of the contrast depending on the luminance according to the pyramid level i (i.e., the input image, X ⁱ ) .

Accordingly, if the contrast value G ⁱ belongs to the j th bin at the luminance X ^{i -1} , if the contrast value G ⁱ has a value capable of affecting the tone mapping, the image analyzing unit 110 accumulates the j th bin and outputs the conditional contrast probability (CCP).

의 j번째 bin 에 누적하여 히스토그램을 계산하고, 반대로 Gⁱ가 미리 지정된 임계값보다 작다면 X^{i -1}가 Xⁱ보다 어두운 값을 가지면서 콘트라스트 에 기여하는 것으로 판단하여

의 j번째 bin에 누적하여 조건부 콘트라스트 확률(CCP)을 계산한다. At this time, if the contrast value G ⁱ is larger than the predetermined threshold value, that is, the luminance X ^{i -1} has a value lighter than the background luminance X ⁱ , it is determined that the contrast contributes to the contrast,

For calculating a cumulative histogram by the j-th bin, if contrary G ⁱ is less than the pre-specified threshold, it is determined that the X ^{i -1} while having a darker value than X ⁱ contribute to the contrast

And accumulates the conditional contrast probability (CCP) in the j &lt; th &gt;

The cumulative distribution function (CDF of CCP) for the conditional contrast probability shown in Equation (3) is used as a basic curve for contrast enhancement. That is, in a section where the value of the conditional contrast probability (CCP) is large, the gradient of the cumulative distribution function (CDF) is increased to widen the dynamic range. In the section where the value of the conditional contrast probability (CCP) (CDF) to reduce the dynamic range.

Here, P denotes a probability value, G ⁱ denotes a contrast value,? Denotes a width of each bin, and x _j denotes a center of the corresponding bins.

Here, l is an index indicating an arbitrary bin among n bins.

In addition, the image analysis unit 110 further performs a hole filling process to interpolate and fill the intermediate bin data to reconstruct the dynamic range of the input image into 256 bins (i.e., 8 bits) in n bins can do.

The individual curve generation unit 120 generates a plurality of curves using the cumulative distribution function (CDF of CCP) for the conditional contrast probability analyzed by the image analysis unit 110 using the immediately preceding frame X _{t -1} . Information on the generated curve can be stored in any storage space.

The individual curve generation unit 120 includes a first region (i.e., a dark region) curve generation unit 150, a second region (i.e., a bright region) curve generation unit 155 and a weight curve generation unit 160 .

The first region curve generation unit 150 generates a first region curve using a cumulative distribution function (CDF of CCP) for the conditional contrast probability analyzed by the image analysis unit 110 using the immediately preceding frame X _{t -1} , The dark region) of the first compensation curve (YL _t-1 ).

The generated first compensation curve (YL _{t -1} ) is shown in (a) of FIG. 2. The first compensation curve (YL _{t -1} ) is a curved line graph in which the width of the dynamic range indicated by the Y axis sharply decreases as the X- Can be expressed in the form. That is, the first compensation curve for improving the brightness contrast in the dark region is expressed in a curve shape in which the tangential slope greater than 0 is sequentially decreased as the brightness is increased. The method of generating the first compensation curve is described in Equation (4) below.

Here, x is the brightness value of the pixel (pixel), x _l is the center of the l th bin (bin), x is _{l -1} l-1 and the center of the second bin (bin), YL (x _l) is x _l , and YL (x _{l -1} ) may be the value of the compensation curve at x _l-1 .

The second area curve generation unit 155 generates a second area curve using the cumulative distribution function (CDF of CCP) for the conditional contrast probability analyzed by the image analysis unit 110 using the immediately preceding frame X _{t -1} , A bright region) is generated by the second compensation curve (YH _t-1 ).

The generated second compensation curve (YH _{t -1} ) is shown in FIG. 2 (b) and is expressed in the form of a curve graph in which the width of the dynamic range indicated by the Y axis increases sharply as the brightness value indicated by the X axis increases . That is, the second compensation curve for improving the brightness contrast in the bright region is expressed in a curve shape in which the tangential slope greater than zero is sequentially increased as the brightness is increased. The method of generating the second compensation curve is described in Equation (5) below.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YH (x l} ) is in the x _l Is the value of the compensation curve, and Y H (x _{l -1} ) may be the value of the compensation curve at x _{l -1} .

The weight curve generating unit 160 generates the weighting curve by using the first compensation curve YL _{t -1} and the second compensation curve YH _{t -1} generated by the first region curve generating unit 150 and the second region curve generating unit 155, _{- 1)} and generates a multiplication weighting curve (W _t-1) to be.

As shown in FIG. 2 (c), for example, the weight curve is a maximum point corresponding to the maximum value of the Y-axis representing the maximum value of the X-axis representing the magnitude of the brightness and the magnitude of the dynamic region, ) In the form of a one-dimensional straight line.

The weighting curve generating unit 160 generates a weighting curve W _{t -1} by Equation (6).

The generated weight curve increases the value multiplied by the first compensation curve in the first region (i.e., the dark region), while the weight curve increases the value multiplied by the second compensation curve in the second region (bright region), as shown in Equation (7) It can be applied in a form of increasing the multiplication value.

Full curve image generating portion 130 includes a first region curve generation module 150, a second area curve generator 155 and the weight curve generator generates respectively by 160, the first compensation curve (YL _{t -1} ), A second compensation curve (YH _{t- 1} ) and a weight curve (W _{t- 1} ) to generate a final compensation curve (Y _{t -1} ) for improving the brightness contrast of the current frame X _t (D)).

The full image curve generating unit 130 generates a final compensation curve Y _{t -1} by using the first compensation curve YL _{t -1} , the second compensation curve YH _{t -1} and the weight curve W _{t -1} . ) Is as described in Equation (7) below.

As shown in Equation (7), the final compensation curve Y (x) for improving the brightness contrast is weighted to YL (x) in the dark region based on the brightness value corresponding to W (x) = 0.5, Contrast is improved for pixels contributing to a contrast while having a darker value. On the other hand, in a bright region, a weight is applied to YH (x) so that pixels contributing to contrast, The contrast is improved.

The curve applying unit 140 applies the final compensation curve (Y _{t -1} ) generated for the previous frame X _{t -1} to the current frame X _t as shown in Equation (8) by the overall image curve generating unit 130 And outputs the current frame Z _t whose brightness contrast is improved.

FIG. 3 is a flowchart illustrating a method of improving image quality through brightness contrast enhancement according to an exemplary embodiment of the present invention. FIGS. 4 to 6 illustrate a method of enhancing brightness enhancement according to an exemplary embodiment of the present invention, Respectively.

Referring to FIG. 3, in step 310, the image analyzing unit 110 calculates a cumulative distribution function (CDF of CCP) for the conditional contrast probability for the immediately preceding frame X _{t -1} .

In step 320, the individual curve generating unit 120 generates the individual curve generating unit 120 using the cumulative distribution function (CDF of CCP) for the conditional contrast probability analyzed by the image analyzing unit 110 using the immediately preceding frame X _{t -1} that is, the first compensation curve (second compensation curve (YH _t-1) to improve the contrast of the brightness in the YL _{t -1)} and a second region (that is, the light area) to improve the contrast of the brightness in a dark area) And generates a weight curve (W _t-1 ) which is an average value of the first compensation curve and the second compensation curve.

In step 330, the entire image curve generating unit 130 generates a first compensation curve YL _{t -1} , a second compensation curve YH _{t -1} , and a weight curve W _{t -1} ) is used to generate the final compensation curve (Y _{t -1} ).

In step 340, the curve applying unit 140 outputs the current frame Z _t whose brightness contrast is improved by applying the final compensation curve generated in step 330 to the input current frame X _t .

4 to 6 show an output image ((b) of each drawing) in which the input image ((a) in each drawing) and the final compensation curve are applied and the contrast of brightness is improved.

By applying the final compensation curve generated based on the cumulative distribution function (CDF of CCP) for the conditional contrast probability calculated by the analysis of the immediately preceding frame to the current frame when referring to the respective drawings, image data of better image quality can be obtained .

It is a matter of course that the image quality improvement method through the brightness contrast enhancement described above may be performed in an automated procedure in a time series sequence by a built-in or installed program in the digital processing apparatus. The codes and code segments that make up the program can be easily deduced by a computer programmer in the field. In addition, the program is stored in a computer readable medium readable by the digital processing apparatus, and is read and executed by the digital processing apparatus to implement the method. The information storage medium includes a magnetic recording medium, an optical recording medium, and a carrier wave medium.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the following claims And changes may be made without departing from the spirit and scope of the invention.

100: Brightness contrast enhancement unit
110: Image analysis section
120: Individual curve generation unit
130: full image curve generation unit
140: Curve application part
150: first area curve generation unit
155: second area curve generation unit
160: Weight Curve Generator

Claims (15)

An apparatus for improving image quality,
An image analyzer for analyzing a cumulative distribution function of a conditional contrast probability for a previous frame image;
A compensation curve generation unit for generating a final compensation curve using the cumulative distribution function for the analyzed conditional contrast probability; And
And a curve applying unit for applying the final compensation curve generated for the previous frame image to the current frame image to output an updated current frame image,
Wherein the compensation curve generation unit comprises:
A first area curve generator for generating a first compensation curve for increasing the contrast of brightness in the first area using the cumulative distribution function for the conditional contrast probability;
A second area curve generator for generating a second compensation curve for increasing the brightness contrast in the second area using the cumulative distribution function for the conditional contrast probability;
A weight curve generator for generating a weight curve corresponding to an average of the first compensation curve and the second compensation curve; And
Using the first compensation curve, the second compensation curve, and the weighting curve, the brightness contrast is increased by the first compensation curve in the first region, which is smaller than the middle value among the total brightness values of the weighting curve, And a total image curve generator for generating a final compensation curve in which the brightness contrast is increased by the second compensation curve in the second region, which is larger than the middle value among the total brightness values of the weight curve,
delete The method according to claim 1,
Wherein the first compensation curve has a curved shape in which a tangent slope decreases as the brightness increases.
The method of claim 3,
Wherein the first compensation curve YL (x) is generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YL (x l} ) is in the x _l Is the value of the compensation curve, and YL (x _{l -1} ) is the value of the compensation curve at x _{l -1} .
The method according to claim 1,
Wherein the second compensation curve has a curve shape in which a tangential gradient increases as the brightness increases.
6. The method of claim 5,
Wherein the second compensation curve (YH (x)) is generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YH (x l} ) is in the x _l Is the value of the compensation curve, and Y H (x _{l -1} ) is the value of the compensation curve at x _{l -1} .
The method according to claim 1,
Wherein the final compensation curve (Y (x)) is generated by the following equation.

Where W (x) is the weight curve, YH (x) is the second compensation curve, and YL (x) is the first compensation curve.
The method according to claim 1,
If the image information to be processed is a moving image, the immediately preceding frame image is an image frame positioned temporally immediately before the current frame image,
Wherein when the image information is a still image, the immediately preceding frame image is an image frame captured and stored immediately before the current frame image.
A method for improving image quality,
Analyzing a cumulative distribution function (Conditional Contrast Probability) of the conditional contrast probability with respect to the immediately preceding frame image;
Generating a final compensation curve using a cumulative distribution function for the analyzed conditional contrast probabilities; And
Wherein the curve applying unit applies the final compensation curve generated for the previous frame image to the current frame image to output an updated current frame image,
Wherein generating the final compensation curve comprises:
Generating a first compensation curve for increasing brightness contrast in the first region and a second compensation curve for increasing brightness contrast in the second region using the cumulative distribution function for the conditional contrast probability;
Generating a weight curve corresponding to an average of the first compensation curve and the second compensation curve; And
Using the first compensation curve, the second compensation curve, and the weighting curve, the brightness contrast is increased by the first compensation curve in the first region, which is smaller than the middle value among the total brightness values of the weighting curve, And generating the final compensation curve in which the brightness contrast is increased by the second compensation curve in the second region that is greater than the median of the total brightness values of the weighting curve.
delete 10. The method of claim 9,
Wherein the first compensation curve has a curved shape in which the tangential slope decreases as the brightness increases,
Wherein the second compensation curve has a curve shape in which a tangential gradient increases as the brightness increases.
12. The method of claim 11,
Wherein the first compensation curve YL (x) is generated by the following equation.

x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YL (x l} ) is a compensation curve in x _l And YL (x _{l -1} ) is the value of the compensation curve at x _{l -1} .
12. The method of claim 11,
Wherein the second compensation curve YH (x) is generated by the following equation.

Here, x is the brightness value of the pixel, x _l is l and the center of the second bin (bin), x is _{l -1} l-1 and the center of the second bin _{(bin), YH (x l} ) is in the x _l Is the value of the compensation curve, and Y H (x _{l -1} ) is the value of the compensation curve at x _{l -1} .
10. The method of claim 9,
Wherein the final compensation curve (Y (x)) is generated by the following equation.

Where W (x) is the weight curve, YH (x) is the second compensation curve, and YL (x) is the first compensation curve.
10. The method of claim 9,
If the image information to be processed is a moving image, the immediately preceding frame image is an image frame positioned temporally immediately before the current frame image,
Wherein when the image information is a still image, the immediately preceding frame image is an image frame captured and stored immediately before the current frame image.

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