KR100195119B1 - Image quality enhancement method and circuit thereof based on the quantized mean-matching histogram equalization - Google Patents

Abstract

The image quality improving method and the circuit of the present invention quantize the level of the input video signal, obtain the gray level distribution of the quantized video signal in units of screens, and accumulate the density based on the obtained gray level distribution of the quantized video signal. Calculates a function, outputs an interpolated cumulative density function value based on the quantized cumulative density function value, calculates an average level of the input image signal in units of screen, and converts the interpolated cumulative density function into a transform function. Using the histogram according to the cumulative density function approximated through interpolation from the quantized cumulative density function of a given image, including the step of mapping the input image signal to a gray level using an average level mapped to itself. When equalizing improves the contrast by mapping the average level of a given image to itself It kept constant on the average brightness of a given image.

Description

Image Quality Improvement Method Using Quantized Mean-Matched Histogram Equalization and Its Circuit

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for improving image quality using a quantized mean-matching histogram equalization and a circuit thereof, and more particularly to a method for improving image quality while maintaining a constant average brightness of a given image and a circuit thereof.

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, which is disclosed in [1] and [2] below: [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 a histogram of a given image has been usefully applied in various fields such as medical image processing, infrared image processing, and 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 problem may occur such that a scene taken at night looks too bright 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 high cost of hardware. 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.

An object of the present invention is to use the quantized cumulative density function of a given image as a transform function, and to improve contrast by adjusting the transform function such that the average level of a given image is mapped to itself upon histogram equalization, while improving the contrast, The present invention provides a method of improving image quality by keeping it constant.

Another object of the present invention is to improve the contrast by adjusting the transform function such that the average level of a given image is mapped to itself upon histogram equalization using the quantized cumulative density function of the given image as a transform function, The present invention provides a circuit that improves image quality by maintaining a constant average brightness of a given image.

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 of gray levels: (a) by quantizing the level of the input video signal Outputting a quantized video signal; (b) obtaining a gray level distribution of the quantized video signal in units of screens, and calculating a cumulative density function based on the obtained gray level distribution of the quantized video signal and outputting a quantized cumulative density function value; (c) outputting the interpolated cumulative density function value by interpolation based on the quantized cumulative density function value; (d) calculating an average level of the input video signal in units of screens; And (e) mapping the input video signal to gray level using the interpolated cumulative density function as a conversion function, and adjusting the conversion function such that the average level obtained in step (d) is mapped to itself. It is characterized by.

In order to achieve the above another object, an 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 of gray levels: quantizing the level of an input video signal Quantization means for outputting a video signal; First calculating means for obtaining a gray level distribution of the quantized video signal in units of screens, calculating a cumulative density function based on the obtained gray level distribution of the quantized video signal, and outputting a quantized cumulative density function value; Second calculating means for calculating an average level of the input video signal in units of screens; Interpolation means for outputting interpolated cumulative density function values by interpolating the quantized cumulative density function values; And using the interpolated cumulative density function as a conversion function, mapping the input image signal to gray level, and adjusting the conversion function such that the average level obtained by the second calculating means is mapped to itself, and outputs the improved signal. It characterized in that it comprises an output means to.

1 illustrates 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 mean-matching histogram equalization according to the present invention.

4 is a block diagram according to another embodiment of a method for improving image quality using quantized mean-matching histogram equalization according to the present invention.

First, an image quality improvement method using quantized mean-matching histogram equalization proposed by the present invention will be described.

{X} represents a given image, and X _m represents an average level of a given image {X}.

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 into a 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}], {Z} represents the quantized input image.

Probability density function (PDF) of the quantized input image {Z} may be represented by Equation 1 below.

[Equation 1]

Where P (Z _q ) is the probability of the q th quantized gray level (Z _q ) in the quantized image {Z}, and N _q represents the number of times this level (Z _q ) appears in the quantized image {Z}, N represents the total number of samples of the quantized image {Z}.

At this time, the cumulative density function (CDF) of the quantized image {Z} is defined as in Equation 2 below.

[Equation 2]

Where C (Z _Q-1 ) = 1.

The cumulative density function c (X _k ) of the samples before quantization can be approximated through linear interpolation as shown in FIG. 2 from the cumulative density function C (Z _q ) of the quantized sample.

When Q [X _k ] = Z _q , assuming Z ₋₁ = 0, c (X _k ) is linearly interpolated as in Equation 3 below.

[Equation 3]

In addition, c (X _m ) can be approximately obtained from Equation 3 above.

The biggest problem with histogram equalization is that the average brightness between input and output signals can vary significantly depending on the cumulative density function used as the conversion function.

In order to solve this problem, the present invention proposes the following mapping operation based on the average of the input image in combination with the cumulative density function obtained through linear interpolation.

[Equation 4]

This means that input samples below average level (X _m ) Is mapped to gray level of from X _m on the X ₀ by a large input sample than the mean level (X _m) is a transfer function It is mapped to gray levels from X _{m + 1} to X _L-1 . X _m in Equation (4) it can be seen that the re-mapping to X _m.

Thus, when a given image is histogram equalized according to the interpolated cumulative density function computed through linear interpolation from the quantized cumulative density function, the mean level of the given image is transformed based on the interpolated cumulative density function to be mapped back to itself. Is adjusted as in Equation 4 so that the average brightness of a given image is not changed by histogram equalization. In the present invention, quantized average-matched histogram equalization is called.

3 and 4, an embodiment of the image quality improvement circuit using the quantized average-matching histogram equalization according to the present invention will be described.

3 is a block diagram according to an embodiment of an image quality improvement circuit using quantized mean-matching histogram equalization according to the present invention.

In FIG. 3, the quantizer 102 quantizes the L-level input video signal X _k to a Q level and outputs a quantized video signal Z _q .

The frame histogram calculator 104 calculates a probability density function P (Z _q ) using Equation 1 by calculating a gray level distribution on a quantized image signal Z _q in units of one screen. Here, the screen unit may be a field, but here, it is a frame.

The CDF calculator 106 calculates the cumulative density function quantized using Equation (2) based on the probability density function P (Z _q ) of the quantized video signal Z _q calculated by the frame histogram calculator 104. Calculate ((C (Z _q )).

In the CDF interpolator 108, based on the cumulative density function value C (Z _q ) of the quantized image signal Z _q , the interpolated cumulative density function value c (X _k) Output)). Where k = 0,1, ..., L-1.

On the other hand, the frame mean calculator 110 in calculating the mean level (X _m) of the input image (X _k) of the frame unit, and a synchronization signal (here, the frame synchronizing signal: SYNC) an average level calculated according to the (X _m) Output to the CDF memory 114, the first and the second mapper 116, 118.

The frame memory 112 stores the input image signal X _{k in} units of one frame.

Here, the interpolated cumulative density function value c (X _k ) is an interpolated cumulative density function value c (X (X _k )) since it is a cumulative density function value of the video signal delayed by one frame compared to the currently input video signal X _k . The video signal X _k is delayed by one frame by the frame memory 112 so that the video signal of the same frame as _k )) is input to the first and second mappers 116 and 118.

The CDF memory 114 updates the cumulative density function c (X _k ) interpolated in the CDF interpolator 108 in units of frames according to the synchronization signal SYNC, and stores the interpolated cumulative density function value ( c (X _k )) and the cumulative density function value c (X _m ) with respect to the average level X _m output from the frame average calculator 110. Here, the CDF memory 114 is used as a buffer and k = 0, 1, ..., L-1.

The first mapper 116 may include the interpolated cumulative density function value c (X _k ) output from the CDF memory 114, the cumulative density function value c (X _m ) with respect to the average level, and the frame average calculator 110. The delayed input video signal (X _m ) is equal to or less than the average level (X _m ) by inputting the average level (X _m ) outputted from the < _{RTI ID} = 0.0 &_gt; 1 < / _RTI &_gt; _k ) maps to gray levels from X ₀ to X _m .

The second mapper 118 may include the interpolated cumulative density function value c (X _k ) output from the CDF memory 114, the cumulative density function value c (X _m ) with respect to the average level, and the frame average calculator 110. ) expression the mean level (X _m) and a frame memory 112, one-frame delayed input video signal (X _k) largest delayed input video signal (X _k) than the mean level (X _m) by entering the output from the output from the Use (4) to map gray levels from X _{m + 1} to X _L-1 .

The comparator 120 generates a selection control signal by comparing the image signal X _k output from the frame memory 112 with the average level X _m output from the frame average calculator 110.

The selector 122 selects the first mapper 116 according to the selection control signal, that is, if the video signal X _k output from the frame memory 112 is equal to or less than the average level X _m , otherwise the second mapper is selected. Select 118 to output the improved video signal Y _H.

Here, without using the frame histogram calculator 104 and the CDF calculator 106 separately, the gray level distribution is calculated in units of one screen with respect to the quantized video signal Z _q and the quantized video signal Z _q ) may be composed of one block for calculating the CDF.

4 is a block diagram according to another embodiment of an image quality improvement circuit using quantized mean-matching histogram equalization according to the present invention.

In FIG. 4, the quantizer 202 quantizes the L-level input video signal X _k to a Q level and outputs a quantized video signal Z _q .

A frame histogram calculator 204 is the probability density function of calculating a gray level distribution in a frame unit by quantization using equation 1 for a quantized video signal (Z _q) video signal (Z _q), (P (Z _q) Calculate

The CDF calculator 206 is a cumulative density function quantized using Equation (2) based on the probability density function P (Z _q ) of the quantized video signal Z _q calculated by the frame histogram calculator 204. Calculate ((C (Z _q )).

In the CDF interpolator 208, based on the cumulative density function C (Z _q ) of the quantized video signal Z _q , the interpolated cumulative density function value c (X _k ) is linearly interpolated by equation (3). Output)). Where k = 0,1, ..., L-1.

On the other hand, the frame mean calculator 210, the calculated average level (X _m) of the input image (X _k) of the frame unit, and calculating in accordance with the frame synchronizing signal (SYNC), an average level (X _m) CDF memory (212 ) And the first and second mappers 214 and 216.

The CDF memory 212 updates the cumulative density function value c (X _k ) interpolated in the CDF interpolator 208 in units of frames according to the frame synchronization signal SYNC, and stores the interpolated cumulative density function stored during the update. The cumulative density function value c (X _m ) with respect to the value c (X _k ) and the average level X _m output from the frame average calculator 210 is output. Where k = 0,1, ..., L-1.

The first mapper 214 is an interpolated cumulative density function value c (X _k ) output from the CDF memory 212, a cumulative density function value c (X _m ) with respect to an average level, and a frame average calculator 210. ) of the mean level (X _m) and the input video signal (the average level using equation (4) to enter the X _k) (X _m) or less input video signal (X _k) output from the from X ₀ to X _m Map to gray level.

The second mapper 216 may include the interpolated cumulative density function value c (X _k ) output from the CDF memory 212, the cumulative density function value c (X _m ) with respect to the average level, and the frame average calculator 210. ), an average level (X _m) and the input video signal (X _k) by entering a large number of input image signals (X _k) than the mean level (X _m) from X _{m + 1} using equation 4 X _L outputted from the _Map to gray levels up to _-1

At this time, the image signal X _k input to the first and second mappers 214 and 216 is an image signal of the next frame compared to the interpolated cumulative density function value c (X _k ) output from the CDF memory 212. .

However, the hardware is reduced by omitting the frame memory using the property of having a high correlation between adjacent frames.

The comparator 218 compares the input image signal X _k with the average level X _m output from the frame average calculator 210 to generate a selection control signal.

The selector 220 selects the first mapper 214 according to the selection control signal, that is, if the input image signal X _k is equal to or less than the average level X _m , otherwise selects the second mapper 218. Output the improved signal Y _H as shown in (4).

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.

As described above, the present invention adjusts a transform function such that the average gray level of a given image is mapped to itself when histogram equalization is performed according to the cumulative density function approximated through linear interpolation from the quantized cumulative density function of a given image signal. It has the effect of keeping the average brightness of a given image constant while improving the contrast.

In addition, the circuit of the present invention saves and accumulates only the number of occurrences of the quantized level for CDF calculation during histogram equalization by quantizing the input video signal, thereby simplifying hardware and reducing cost.

Claims (12)

A method for improving image quality by histogram equalization of video signals represented by a predetermined number of gray levels: (a) quantizing the level of the input video signal and outputting a quantized video signal; (b) obtaining a gray level distribution of the quantized video signal in units of screens, and calculating a cumulative density function based on the obtained gray level distribution of the quantized video signal and outputting a quantized cumulative density function value; (c) outputting the interpolated cumulative density function value by interpolation based on the quantized cumulative density function value; (d) calculating an average level of the input video signal in units of screens; And (e) mapping the input video signal to gray level using the interpolated cumulative density function as a conversion function, and adjusting the conversion function such that the average level obtained in step (d) is mapped to itself. Quality improvement method characterized in that. The image quality improving method according to claim 1, further comprising the step (e1) of delaying the input video signal in units of screens and outputting the delayed video signal in the step (e). The method of claim 1, wherein the interpolation in step (c) is linear interpolation. A method for improving image quality by histogram equalization of video signals represented by a predetermined number of gray levels: (a) quantizing the level of the input video signal and outputting a quantized video signal; obtaining a gray level distribution of the quantized video signal in units of screens, and obtaining a cumulative density function based on the obtained gray level distribution of the quantized video signal; (c) calculating an average level of the input video signal in units of screens; (d) obtaining a cumulative density function value for the average level based on the quantized cumulative density function; (e) outputting an interpolated cumulative density function value by interpolation based on the quantized cumulative density function; And (f) mapping the input image signal to the gray level according to the corresponding interpolated cumulative density function value and the cumulative density function value for the average level, and the average level obtained in step (c) mapping to itself. Image quality improvement method comprising the. 5. The image quality improving method according to claim 4, further comprising the step (f1) of delaying the input video signal in units of screens and outputting the delayed video signal in the step (f). 5. The method of claim 4, wherein the interpolation in the step (e) is linear interpolation. A circuit for improving image quality by histogram equalization of a video signal represented by a predetermined number of gray levels: Quantization means for quantizing the level of an input video signal and outputting a quantized video signal; First calculating means for obtaining a gray level distribution of the quantized video signal in units of screens, calculating a cumulative density function based on the obtained gray level distribution of the quantized video signal, and outputting a quantized cumulative density function value; Second calculating means for calculating an average level of the input video signal in units of screens; Interpolation means for outputting interpolated cumulative density function values by interpolating the quantized cumulative density function values; And By using the interpolated cumulative density function as a conversion function, the input image signal is mapped to a gray level, and the conversion function is adjusted to output an improved signal by adjusting the conversion function such that the average level obtained by the second calculation means is mapped to itself. And an output means. The image quality improvement of claim 7, further comprising a screen memory for delaying the input video signal by a screen unit in order to input the video signal having the same frame as the interpolated cumulative density function value to the output means. Circuit. 8. The image quality improvement circuit according to claim 7, wherein the interpolation is linear interpolation. 8. The method of claim 7, further comprising a buffer for updating the interpolated cumulative density function value on a screen basis and outputting the interpolated cumulative density function value stored during the update and the cumulative density function value for the average level. Image quality improvement circuit. The method of claim 10, wherein the output means The average level calculated by the second calculating means is mapped to itself, and the input image signal below the average level has a first range according to the corresponding interpolated cumulative density function value and the cumulative density function value for the average level. A first mapper that maps to a gray level; A second mapper for mapping the input image signal larger than the average level to a gray level in a second range according to an interpolated cumulative density function value corresponding to the average level and a cumulative density function value for the average level; A comparator for comparing the input video signal with the average level to generate a selection control signal; And And a selector for selecting a first mapper according to the selection control signal, that is, selecting a first mapper if the input image signal is less than the average level, and otherwise selecting a second mapper. The method of claim 8, wherein the output means The average level calculated by the second calculating means is mapped to itself, and an image signal below the average level output from the screen memory is applied to the corresponding interpolated cumulative density function value and the cumulative density function value for the average level. A first mapper to map to a gray level of a first range according to the first mapper; A second mapper which maps an image signal larger than the average level output from the screen memory to a gray level in a second range according to an interpolated cumulative density function value corresponding to the average level and a cumulative density function value for the average level; A comparator for comparing a video signal output from the screen memory with the average level to generate a selection control signal; And And a selector for selecting a first mapper according to the selection control signal, i.e., if the video signal output from the screen memory is equal to or less than the average level, and otherwise selecting a second mapper.