KR100195120B1 - Image Quality Improvement Method Using Quantized Mean-Separated Histogram Equalization with Brightness Compensation and Its Circuit - Google Patents

Abstract

The image quality improving method and the circuit of the present invention quantize an input video signal, average the input video signal by screen unit, and quantize the quantized video signal according to a quantized average level. The cumulative density function of each of the predetermined number of quantized sub-images is calculated, and based on the cumulative density function obtained for each quantized sub-image, the cumulative density function value interpolated for each sub-image is interpolated. Compensation value according to a predetermined correction function based on the average brightness of the video signal is added to the average level to output the compensated average level, and quantized based on the interpolated cumulative density function value and the compensated average level for each sub-image. Independent histogram equalization for each sub-image, effectively reducing abrupt brightness changes and artifacts While improving agent maintains the overall brightness of the given image, and, in particular, excessively dark or light to improve the contrast of an input video signal.

Description

Image Quality Improvement Method Using Quantized Mean-Separated Histogram Equalization with Brightness Compensation and Its Circuit

The present invention relates to a method for improving image quality using a quantized average-separated histogram equalization with a brightness compensation function and a circuit thereof, and more particularly, to a quantized video signal by quantizing an input video signal based on the average of a predetermined number of sub-images. The present invention relates to a method and a circuit for improving image quality by dividing by and performing historam equalization independently while compensating for brightness of the divided sub-images.

The basic operation of histogram equalization is to convert a given input image based on the histogram of the input image, where a histogram represents a gray level distribution in a given input image.

This gray level histogram provides an overall depiction of the appearance of the image. Gray levels appropriately adjusted according to the sample distribution of the image improve the appearance or contrast of the image.

Among many methods for improving contrast, histogram equalization, the method of improving the contrast of a given image according to the sample distribution of the image, is the most widely known and described in [1] and [2]: [1] JSLim , Two-Dimensional Signal and Image Processing, Prentice Hall, Englewood Cliffs, New Jersey, 1990, [2] RC Gonzalez and P. Wints, Digital Image Processing, Addison-Wesley, Reading, Massachusetts, 1977.

In addition, useful applications of histogram equalization methods, including medical image processing and radar image processing, are disclosed in [3] and [4] below: [3] J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement, IEEE Tr.on Medical Imaging, pp. 304-312, Dec. 1988, [4] Y.Li, W. Wang , and DYYu, Application of adaptive histogram equalization to x-ray chest image, Proc. of the SPIE, pp. 513-514, vol. 2321,1994.

Therefore, the technique using the histogram of a given image has been usefully applied in various fields such as medical image processing, infrared image processing, radar image processing.

In general, histogram equalization has the effect of stretching the dynamic range, so histogram equalization flattens the distribution density of the resulting image, and as a result improves the contrast of the image.

This property of well-known histogram equalization is a drawback in practical cases. That is, since the output density of the histogram equalization is constant, the average brightness of the output image is close to the intermediate gray level. In practice, for histogram equalization of analog images, the average brightness of the output image in histogram equalization is exactly intermediate gray level regardless of the average brightness of the input image. Clearly, this property is undesirable in practical applications. For example, a scene taken at night may appear to look like a scene taken during the day after histogram equalization.

In addition, the conventional histogram equalization circuit requires a configuration capable of storing all occurrences of all gray levels, resulting in a problem of high hardware cost. For example, if the gray level L is L = 256, 256 memory elements are required to store the occurrences of all levels, and 256 accumulators are required to accumulate the occurrences of all levels. It was.

It is an object of the present invention to quantize a level of an input image and divide the quantized input image into a predetermined number of quantized sub-images based on the average, and a compensated average obtained by adding a correction value to the average level according to the average brightness of the input image. The present invention provides a method of improving image quality by independently histogram equalization for each quantized sub-image using a level.

Another object of the present invention is to quantize the level of the input image to divide the quantized input image into a predetermined number of quantized sub-images based on the average, and to compensate for adding the correction value to the average level according to the average brightness of the input image. The present invention provides a circuit for improving image quality by independently histogram equalization for each quantized sub-image using an average level.

In order to achieve the above object, the image quality improving method according to the present invention is a method for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: (a) Quantizing an input video signal Outputting a quantized video signal; (b) outputting a quantized average level by obtaining and quantizing an input video signal in units of screens; (c) dividing the quantized video signal into a predetermined number of quantized sub-pictures according to the quantized average level; (d) obtaining a cumulative density function of each of the predetermined number of quantized sub-images; (e) outputting an interpolated cumulative density function value for each subimage by interpolation based on the cumulative density function obtained for each quantized subimage; (f) adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level and outputting a compensated average level; And (g) independently histogram equalization for each quantized sub-image based on the cumulative density function value interpolated for each sub-image and the compensated average level.

In order to achieve the above another object, the image quality improvement circuit according to the present invention is a circuit for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: quantizing and quantizing an input video signal. First quantization means for outputting the received video signal; First calculating means for calculating a gray level distribution of the quantized video signal in units of screens; Second calculating means for calculating an average level of the input video signal in units of screens; Second quantization means for quantizing the calculated average level and outputting a quantized average level; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level; Dividing means for dividing the gray level distribution calculated by the first calculating means into a predetermined number of quantized sub-images according to the quantized average level; Third calculating means for obtaining a cumulative density function for each quantized sub-image; Interpolation means for outputting an interpolated cumulative density function value by interpolation based on the cumulative density function calculated for each quantized subimage; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input image signal to the average level; And output means for outputting an improved signal by mapping to the new gray level according to the interpolated cumulative density function value corresponding to each of the quantized sub-images and the compensated average level.

1 is a diagram illustrating an example of quantizing an L-level discrete signal into a Q-level discrete signal in order to explain the quantization concept of the present invention.

2 is a view for explaining an interpolation concept applied to the present invention.

3 is a block diagram according to an embodiment of an image quality improvement circuit using quantized average-separated histogram equalization with brightness compensation according to the present invention.

4A and 4B are diagrams illustrating examples of brightness correction functions applied to the present invention.

5A and 5B illustrate an example of a relationship between an average level compensated by the brightness correction function illustrated in FIGS. 4A and 4B and an average level of an input image.

6 is a block diagram according to another embodiment of an image quality improvement circuit using quantized average-separated histogram equalization with brightness compensation according to the present invention.

A method of improving image quality using quantized mean-separate histogram equalization with a brightness compensation function proposed by the present invention will be described.

A given image {X} consists of L discrete gray levels {X ₀ , X ₁ , ..., X _L-1 }, where X ₀ = 0 represents a black level, and X _L-1 = 1 indicates white level. And X _m ∈ {X ₀ , X ₁ , ..., X _L-1 }.

Quantize the original discrete input level {X ₀ , X ₁ , ..., X _L-1 } to the Q discrete level defined by {Z ₀ , Z ₁ , ..., Z _Q-1 }, where Z _Q-1 = X _L-1 , and also Q 1 L, {Z ₀ , Z ₁ , ..., Z _Q-1 } 1C {X ₀ , X ₁ , ..., X _L-1 } Is assumed.

Thus, an example of quantizing the L-level discrete signal to the Q-level discrete signal is shown in FIG.

Q [X _k ] is called a quantization operation and is defined as follows.

Q [X _k ] = Z _q , if Z _q-1 X _K 1Z _q

When {Z} = Q [{X}] and Z _m = Q [X _m ], X _m is the mean level of the original image, {Z} is the quantized input image, and Z _m is the quantized mean level. Respectively, and divides the quantized input image {Z} into two sub-images {Z} _L and {Z} _U around Z _m . Here, all samples in the quantized sub-image {Z} _L are less than or equal to the quantized average level (Z _m ), and all samples in the quantized sub-image {Z} _U are larger than the quantized average level (Z _m ).

Each quantized probability density function (PDF) of the sub-images {Z} _L , {Z} _U may be represented by Equations 1 and 2 below.

[Equation 1]

[Equation 2]

Here, P _L (Z _q) is the quantized sub-image {Z} and _L probability of the q th quantized gray level (Z _q) In, P _U (Z _q) is the q th quantized by the quantization sub-image {Z} _U the gray level and the (Z _q) probability, N _q ^L, N _q ^U represent the number of times the level (Z _q) is represented in the sub-image {Z} _L, the quantized sub-image {Z} _U, respectively quantized, N _L , N _U represents the total number of samples of each of the quantized subpicture {Z} _L and the quantized subpicture {Z} _U.

At this time, each cumulative density function (CDF) of the quantized sub-image {Z} _L and the quantized sub-image {Z} _U is defined as in Equations 3 and 4 below.

[Equation 3]

[Equation 4]

Here, C _L (Z _m ) = 1 and C _U (Z _Q-1 ) = 1.

The interpolated cumulative density functions c _L (X _k ), c _U (X _k ) are approximately calculated from the quantized cumulative density functions C _L (Z _q ), C _U (Z _q ) through the linear interpolation shown in FIG. 2. Can be.

Assuming that Q [X _k ] = Z _q 1 Z _m , Z _-1 = 0, and c _L (X _k ) is linearly interpolated as shown in Equation 5 below.

[Equation 5]

Similarly, assuming Q [X _k ] = Z _q Z _m , c _U (X _k ) is linearly interpolated as shown in Equation 6 below.

[Equation 6]

On the other hand, the biggest problem of histogram equalization is that the average brightness between input and output signals can vary significantly depending on the cumulative density function value used as the conversion function.

In addition, the present invention proposes the following mapping operation that combines brightness compensation when the average brightness of a given image is too dark or too bright.

Based on the interpolated cumulative density function, the improved output Y _H is given by Equation 7 for the input image X _k .

[Equation 7]

here,

[Equation 8]

In other words, if B _m is a compensated average level and Δ is a correction value obtained by a predetermined correction function according to average brightness, this compensated average level B _m is a correction value at a given image average level X _m . It is a result of adding (triangle | delta). In this case, it is assumed that B _m ⊂ {X ₀ , X ₁ , ..., X _L-1 }.

And,

[Equation 9]

to be. B _m ′ represents the first gray level mapped in the level region higher than the compensated average level B _m .

In conclusion, than Equation (7) is if the samples are equal to or less than the quantized mean level (Z _m) of the input image is (0, B _m) in which the results, and the samples are the mean-level quantization of the input image (Z _m) are mapped to If large, the result is mapped to (B _m ', X _L-1 ).

Thus, if the correction value is greater than zero (Δ 0), the average brightness of the improved output Y _H will be bright, and if the correction value is smaller than zero (Δ 0), the average brightness of the improved output Y _H is dark. Will lose. As Δ increases, the dynamic range of the lower level region will improve, and as Δ decreases, the dynamic range of the upper level region will improve. Quantized average-separated histogram equalization using the average level B _m appropriately compensated according to the average level (X _m ) of a given image, that is, bright and dark, can greatly improve the quality of the input image.

Herein, splitting into a predetermined number of quantized sub-images according to the average level of the input image and independently histogram equalization for each quantized sub-image is referred to as quantized mean-separated histogram equalization in the present invention.

Next, an embodiment of an image quality improvement circuit using quantized average-separated histogram equalization with a brightness compensation function according to the present invention will be described with reference to FIGS. 3 to 6.

3 is a block diagram according to an embodiment of an image quality improvement circuit using quantized average-separated histogram equalization with brightness compensation according to the present invention.

In FIG. 3, the first quantizer 102 quantizes the input video signal X _k having the L discrete level to the Q discrete level to output the quantized video signal Z _q . The frame histogram calculator 104 calculates a gray level distribution of the quantized video signal Z _{q in} units of one screen. Here, the screen unit may be a field but is a frame.

The frame average calculator 106 calculates an average level X _m of the input image in units of frames. The second quantizer 108 quantizes the average level X _m of the input image signal X _k to output the quantized average level Z _m .

The divider 110 generates a predetermined number (here two) based on the quantized gray level distribution calculated by the frame histogram calculator 104 based on the quantized average level Z _m output from the second quantizer 108. Probability density functions P _L (Z _q ) and P _U (respectively) of the quantized sub-images {Z} _L , {Z} _U by dividing them into quantized sub-images {Z} _L , {Z} _U ) Z _q )), and the quantized probability density functions P _L (Z _q ) and P _U (Z _q ) may be calculated by Equations 1 and 2 above.

Here, all samples in the quantized sub-image {Z} _L are less than or equal to the quantized average level (Z _m ), and all samples in the quantized sub-image {Z} _U are larger than the quantized average level (Z _m ).

The first CDF calculator 112 quantizes the sub-image based on the probability density function P _L (Z _q ) of the quantized sub-image {Z} _{L that} is equal to or less than the quantized average level Z _m from the divider 110. The cumulative density function value C _L (Z _q ) of {Z} _L is calculated using Equation 3 above.

The second CDF calculator 114 quantizes based on the probability density function P _U (Z _q ) of the quantized sub-image {Z} _{U that} is greater than the quantized mean level Z _m output from the divider 110. The cumulative density function value C _U (Z _q ) of the obtained sub-image {Z} _U is calculated by using Equation 4 above.

The CDF memory 116 stores the cumulative density function values C _L (Z _q ) and C _U (of the quantized sub-images {Z} _L , {Z} _U ) calculated by the first and second CDF calculators 112 and 114. Z _q )) is updated in units of screens according to the sync signal SYNC, and the quantized cumulative density function values C _L (Z _q ) and C _U (Z _q ) stored during the update are first and second interpolated. To 118 and 120. Here, the synchronization signal SYNC becomes a field synchronization signal when the screen unit is a field, becomes a frame synchronization signal when it is a frame, and the CDF memory 116 is used as a buffer.

Based on the cumulative density function value C _L (Z _q ) of the quantized sub-image {Z} _L , the first interpolator 118 is interpolated by the interpolation cumulative density function value (c). _L (X _k )) Where k = 0,1, ..., m.

The second interpolator 120 is based on the cumulative density function value (C _U (Z _q )) of the quantized sub-image {Z} _U based on the cumulative density interpolated by the equation (6) (c) _U (X _k )) Where k = m + 1, m + 2, .., L-1.

The video signal X _k input to the first and second interpolators 118 and 120 is the video signal of the next frame of the video signal X _k input to the first quantizer 102 and the frame averaging calculator 106. . However, the hardware can be reduced by omitting the frame memory by utilizing the property of having high correlation between adjacent frames.

Meanwhile, the brightness compensator 122 inputs an average level X _m output from the frame average calculator 106 and converts the correction value? According to the average brightness of the input image to the average level X as shown in Equation (8). _m ) to output the compensated average level (B _m ).

This correction value? Is determined by a correction function as shown in Figs. 4A and 4B. The present invention is not limited to the example of the correction function shown in FIGS. 4A and 4B but may have other applications.

The brightness of the improved signal Y _H is adjusted by the correction value according to the correction function shown in FIGS. 4A and 4B. In other words. If the average level (X _m ) of the input image is very small, that is, a very dark image, the correction value (△) greater than 0 is added to the average level (X _m ) and the quantum proposed by the present invention using Equation 7 above. Once averaged-separated histogram equalization, the average brightness of the improved signal Y _H is brightened.

In addition, if the average level (X _m ) of the input image is very large, that is, a very bright image, the correction value (△) smaller than 0 is added to the average level (X _m ), which is proposed in the present invention using Equation 7 above. The quantized mean-separated histogram equalization darkens the average brightness of the improved signal Y _H. Therefore, if the quantized average-separated histogram equalization is performed using the average level B _m compensated by a predetermined appropriate correction value Δ according to the average level X _m , the image quality of the input image can be greatly improved.

5A and 5B are diagrams illustrating a relationship between a compensated average level B _m to which a correction value Δ is added according to the brightness correction function illustrated in FIGS. 4A and 4B and an average level X _m of an input image. .

Meanwhile, the first mapper 124 inputs the interpolated cumulative density function value c _L (X _k ), the input image signal X _k , and the compensated average level B _m to quantize the average level Z _m. ) Samples of the quantized sub-image {Z} _{L which} are equal to or less than are mapped to gray levels from X ₀ to B _m according to the interpolated cumulative density function value c _L (X _k ).

The second mapper 126 inputs the interpolated cumulative density function value c _U (X _k ), the input image signal X _k , and the compensated average level B _m to determine the quantized average level Z _m . Samples of the large quantized subimage {Z} _U are mapped to gray levels from B _m ′ to X _L−1 according to the interpolated cumulative density function value c _U (X _k ).

The outputs mapped in the first and second mappers 124 and 126 are represented by equation (7), and B _m 'is represented by equation (9).

The comparator 128 compares the input image signal X _k with the quantized average level Z _m and if the input image signal X _k is less than or equal to the quantized average level Z _m , the first mapper 124. Is selected, otherwise a selection control signal for selecting the second mapper 126 is generated.

The selector 130 selects the first mapper 124 according to the selection control signal, that is, if the input image signal X _k is less than or equal to the quantized average level Z _m , otherwise the second mapper 126 is selected. Therefore, the improved signal Y _H , which can be represented by Equation 7, is output.

Here, the present invention does not use the frame histogram calculator 104 and the first and second CDF calculators 112 and 114 separately, and the sub-image quantized by the first and second CDF calculators 112 and 114 without the frame histogram calculator 104. The CDF can be calculated based on the gray level distribution of.

6 is a block diagram according to another embodiment of an image quality improvement circuit using quantized average-separated histogram equalization with brightness compensation according to the present invention.

In FIG. 6, the first quantizer 202 quantizes the input video signal X _k having the L discrete level to the Q discrete level to output the quantized video signal Z _q . The frame histogram calculator 204 calculates a gray level distribution of the quantized video signal Z _{q in} units of frames.

The frame average calculator 206 calculates an average level X _m of the input image in units of frames. The second quantizer 208 quantizes the average level X _m of the input video signal X _k to output the quantized average level Z _m .

The divider 210 divides the quantized gray level distribution calculated by the frame histogram calculator 204 based on the quantized average level Z _m outputted from the second quantizer 208. Z} _L , {Z} _U ) to output each of the probability density functions P _L (Z _q ) and P _U (Z _q ) of the quantized subimages {Z} _L , {Z} _U ). The quantized probability density functions P _L (Z _q ) and P _U (Z _q ) may be calculated by Equations 1 and 2 above.

Claim 1 CDF calculator 212 is the probability density function (P _L (Z _q)) of the quantized sub on the basis of the image {Z} cumulative density function value of _L of the sub-image {Z} _L quantization from the divider 210, (C _L (Z _q )) is calculated using Equation (3).

Claim 2 CDF calculator 214 is the probability density function of the quantized sub-image {Z} _U (P _U (Z _q)) of the sub-image {Z} _U quantized as a basis the cumulative density function value (C _U (Z _q )) is calculated using Equation 4 above.

The CDF memory 216 stores cumulative density function values C _L (Z _q ) and C _U (of the quantized sub-images {Z} _L , {Z} _U ) calculated by the first and second CDF calculators 212 and 214. Z _q )) is updated frame by frame according to the frame synchronization signal SYNC, and the quantized cumulative density function values C _L (Z _q and C _U (Z _q )) stored during the update are first and second. Supplied to interpolators 218 and 220.

The frame memory 200 delays the input image signal X _k by one frame.

Here, the quantized cumulative density function values C _L (Z _q ) and C _U (Z _q ) are cumulative density function values of the video signal delayed by one frame relative to the currently input video signal X _k . Frames the image signal X _k inputted to input the image signals of the same frame as the cumulative density function values C _L (Z _q ) and C _U (Z _q ) to the first and second interpolators 218 and 220. The memory 200 delays one frame.

Based on the cumulative density function value C _U (Z _q ) of the quantized sub-image {Z} _L , the first interpolator 218 interpolates the cumulative density function value c interpolated by linear interpolation according to Equation (5). _L (X _k )) Where k = 0,1, ..., m.

The second interpolator 220 of the linear interpolation by the following formula 6 for interpolation on the basis of the cumulative density function value of the quantized sub-image {Z} _U (C _U (Z _q)), the cumulative density function value (c _U (X _k )) Where k = m + 1, m + 2, .., L-1.

The brightness compensator 222 inputs an average level X _m output from the frame average calculator 206 and adds a correction value according to the average brightness of the input image to the average level X _m as shown in Equation (8). The compensated average level B _m is output.

The first mapper 224 is an interpolated cumulative density function value c _L (X _k ) output from the first interpolator 218, an image signal X _k output from the frame memory 200, and a brightness compensator ( X ₀ according to the interpolated cumulative density function value (c _L (X _k )) of samples of the sub-image {Z} _{L that} are equal to or less than the quantized average level Z _m by inputting the compensated average level B _m . To gray levels from to B _m .

The second mapper 226 is an interpolated cumulative density function value c _U (X _k ) output from the second interpolator 220, an image signal X _k output from the frame memory 200, and a brightness compensator ( Input the compensated average level (B _m ) output from 222 to the interpolated cumulative density function value (c _U (X _k )) of the samples of the sub-image {Z} _U larger than the quantized average level (Z _m ) Therefore, it maps to gray levels from B _m 'to X _L-1 .

The outputs mapped in the first and second mappers 224 and 226 are represented by equation (7), and B _m 'is represented by equation (9).

The comparator 228 compares the delayed video signal X _k output from the frame memory 200 with the quantized average level Z _m , and the averaged video signal X _k output from the frame memory 200 is quantized. If it is less than or equal to the level Z _m , a selection control signal is generated that selects the first mapper 224, otherwise selects the second mapper 226.

The selector 230 selects the first mapper 224 according to the selection control signal, that is, when the image signal X _k output from the frame memory 200 is equal to or less than the quantized average level Z _m , 2 selects the mapper 226 and outputs an improved signal Y _H , which can be represented by Equation 7 above.

The present invention can be applied to a wide range of fields related to the improvement of image quality of a video signal. That is, the present invention can be applied to broadcasting equipment, radar signal processing systems, medical engineering, home appliances, and the like.

The method of the present invention using the quantized average-separated histogram equalization in consideration of the correction value according to the average brightness of the input image effectively reduces the sudden brightness change and artifacts that occur in the conventional histogram equalization and improves the contrast while improving the overall brightness of the given image. It is effective to maintain. In addition, the method of the present invention greatly improves the image quality by improving the contrast of an excessively dark or light input video signal.

Furthermore, the circuit of the present invention quantizes the input video signal and independently histogram equalizes the sub-picture divided into a predetermined number to store and accumulate only the number of occurrences of the quantized level for CDF calculation, thereby simplifying hardware and reducing cost. It works.

Claims (21)

A method for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: (a) quantizing the input video signal and outputting a quantized video signal; (b) outputting a quantized average level by obtaining and quantizing an input video signal in units of screens; (c) dividing the quantized video signal into a predetermined number of quantized sub-pictures according to the quantized average level; (d) obtaining a cumulative density function of each of the predetermined number of quantized sub-images; (e) outputting an interpolated cumulative density function value for each subimage by interpolation based on the cumulative density function obtained for each quantized subimage; (f) adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level and outputting a compensated average level; And and (g) independently histogram equalization for each quantized subimage based on the cumulative density function value interpolated for each subimage and the compensated average level. The method of claim 1, wherein the step (c) divides the quantized video signal into two sub-pictures according to the quantized average level. The method of claim 1, wherein the interpolation in the step (e) is linear interpolation. The method of claim 1, wherein in the step (f), if the average level is very small, that is, a very dark image, a correction value larger than zero is added to the average level to output a compensated average level. In other words, if the image is very bright, the correction level smaller than zero is added to the average level to output the compensated average level. The method of claim 1, wherein step (g) (g1) If the input image signal is less than or equal to the quantized average level, the input image signal is grayed out from the minimum gray level (X ₀ ) to the compensated average level (B _m ) according to the corresponding interpolated cumulative density function value. Mapping to a level; And (g2) If the input video signal is larger than the quantized average level, the input video signal is mapped to a gray level from B _m 'to a maximum gray level (X _L-1 ) according to the corresponding interpolated cumulative density function value. But here Quality improvement method comprising the step of. 6. The image quality improving method according to claim 5, further comprising the step (g3) of delaying the input video signal in units of screens and inputting the delayed video signal in the steps (g1) and (g2). A method for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: (a) quantizing the input video signal and outputting a quantized video signal; obtaining a gray level distribution of the quantized video signal in units of screens; (c) outputting a quantized average level by obtaining and quantizing the input video signal in units of screens; (d) dividing the gray level distribution obtained in step (b) into a predetermined number of quantized sub-images according to the quantized average level; (e) obtaining a quantized cumulative density function based on the gray level distribution of the predetermined number of quantized sub-images; (f) outputting an interpolated cumulative density function value for each subimage by interpolation based on the cumulative density function obtained for each quantized subimage; (g) adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level and outputting a compensated average level; And and (h) outputting an improved signal by mapping to a new gray level independently for each subimage based on the cumulative density function value interpolated for each subimage and the compensated average level. Way. 8. The method of claim 7, wherein, in the step (d), the gray level distribution obtained in the step (b) is divided into two quantized sub-images according to the quantized average level. 8. The method of claim 7, wherein the interpolation in the step (f) is linear interpolation. The method of claim 7, wherein in the step (g), if the average level is very small, that is, a dark image, a correction value larger than zero is added to the average level to output a compensated average level. That is, if the image is very bright, the correction value smaller than zero is added to the average level to output the compensated average level. And the brightness of the improved signal is adjusted according to the compensated average level. The method of claim 7, wherein step (h) (h1) If the input image signal is less than or equal to the quantized average level, the input image signal is grayed out from the minimum gray level (X ₀ ) to the compensated average level (B _m ) according to the corresponding interpolated cumulative density function value. Mapping to a level; And (h2) If the input video signal is larger than the quantized average level, the input video signal is mapped to a gray level from B _m 'to a maximum gray level (X _L-1 ) according to the corresponding interpolated cumulative density function value. But here Quality improvement method comprising the step of 12. The method of claim 11, further comprising the step (h3) of delaying the input video signal in units of screens and inputting the delayed video signal in steps (h1) and (h2). A circuit for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: First quantization means for quantizing the input video signal and outputting a quantized video signal; First calculating means for calculating a gray level distribution of the quantized video signal in units of screens; Second calculating means for calculating an average level of the input video signal in units of screens; Second quantization means for quantizing the calculated average level and outputting a quantized average level; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level; Dividing means for dividing the gray level distribution calculated by the first calculating means into a predetermined number of quantized sub-images according to the quantized average level; Third calculating means for obtaining a cumulative density function for each quantized sub-image; Interpolation means for outputting an interpolated cumulative density function value by interpolation based on the cumulative density function calculated for each quantized subimage; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input image signal to the average level; And And an output unit for mapping the quantized sub-image to the new gray level according to the interpolated cumulative density function value corresponding to the quantized sub-images and the compensated average level, and outputting an improved signal. The screen memory of claim 13, wherein the input image signal is delayed in units of screens to input an image signal of the same screen as the quantized cumulative density function value calculated for each sub-image by the third calculating means to the interpolation means. The image quality improvement circuit further comprises. The buffer of claim 13, further comprising updating a quantized cumulative density function value calculated for each sub-image in units of screens by the third calculating means, and supplying a pre-stored quantized cumulative density function value to the interpolation means. Image quality improvement circuit comprising. The image improving circuit according to claim 13, wherein the screen unit is a frame and the predetermined number is two. The image quality improvement circuit of claim 13, wherein the interpolation is linear interpolation. The method of claim 13, wherein the brightness compensating means outputs a compensated average level by adding a correction value greater than zero to the average level if the average level is very small, that is, a very dark image, and if the average level is very large, And if the image is very bright, adding a correction value smaller than zero to the average level to output the compensated average level. The method of claim 13, wherein the output means If the input video signal is less than the quantized average level, the input video signal is mapped from the minimum gray level (X ₀ ) to the compensated average level (B _m ) according to the corresponding interpolated cumulative density function value. A first mapper; If the input video signal is larger than the quantized average level, the input video signal is mapped to a gray level from B _m 'to a maximum gray level (X _L-1 ) according to the corresponding interpolated cumulative density function value. A second mapper; A comparator for comparing the input video signal with the quantized average level and outputting a selection control signal; And And a selector for selecting a first mapper according to the selection control signal, i.e., selecting the first mapper if the input image signal is less than or equal to the quantized average level, and otherwise selecting the second mapper. The method of claim 14, wherein the output means If the delayed video signal output from the screen memory is less than or equal to the quantized average level, the delayed video signal is reduced from the minimum gray level (X ₀ ) to the compensated average level (B _m ) according to the corresponding interpolated cumulative density function value. A first mapper that maps to a gray level of; If the delayed video signal is larger than the quantized average level, the delayed video signal is mapped to a gray level from B _m 'to a maximum gray level (X _L-1 ) according to the corresponding interpolated cumulative density function value. A second mapper; A comparator for comparing the delayed video signal with the quantized average level and outputting a selection control signal; And And a selector for selecting a first mapper according to the selection control signal, that is, if the delayed video signal is less than or equal to the quantized average level, and otherwise selecting a second mapper. A circuit for improving image quality by histogram equalizing a video signal represented by a predetermined number (L) of gray levels: First quantization means for quantizing the input video signal and outputting a quantized video signal; First calculating means for calculating an average level of the input video signal in units of screens; Second quantization means for quantizing the calculated average level and outputting a quantized average level; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input video signal to the average level; Dividing means for dividing the quantized video signal into a predetermined number of quantized sub-pictures according to the quantized average level; Second calculating means for calculating a cumulative density function for each quantized sub-image based on the gray level distribution calculated by calculating the gray level distribution for each quantized sub-image; Interpolation means for outputting an interpolated cumulative density function value by interpolation based on the cumulative density function calculated for each quantized subimage; Brightness compensation means for outputting a compensated average level by adding a correction value according to a predetermined correction function based on the average brightness of the input image signal to the average level; And And output means for mapping the quantized sub-images to the new gray level according to the interpolated cumulative density function value corresponding to the quantized sub-images and outputting an improved signal.