Patent Description:
As shown in <FIG>, the existing histogram drawing method is as follows:.

Collect data: M statistics X values are collected. Determine a data range (R): the maximum value and the minimum value defined of X are respectively set as X(max) and X(min); if the minimum unit amount of X is ΔX = <NUM>a > <NUM> , R = X(max) - X(min) + ΔX. Determine a class interval (h): a class number of a histogram is determined at first, then the range is divided by the class number, and the width of each class of the histogram can be obtained, that is, the class interval. In this example, the class number is set as N, and the class interval h = R/N. The class number should be determined properly, wherein if the class number is too small, it will lead to a greater calculation error; if the class number is too much, it will affect prominence of a data grouping rule and increase the cost of hardware, and may increase the work load of calculation and affect a response speed. Determine a boundary value of each class: to be able to make statistics on all the X values, a lower limiting value of the first class and an upper limiting value of the Nth class are special, respectively being X(min) - a and X(max) + a. An upper limiting value of the first class is the lower limiting value of the first class plus the range, that is, X(min) - a + h; and a lower limiting value of the second class is the upper limiting value of the first class, an upper limiting value of the the second class is the lower limiting value of the second class plus the range, that is, X(min) - a + <NUM>h, and boundaries of respective classes are deduced by such analogy. Draw out a frequency distribution table: statistics is made on the M X values, which are listed into corresponding classes according to sizes, and finally frequencies of each class of X values are calculated, the frequencies are set as Pi, wherein i=<NUM>, <NUM>. N, and the following equation needs to be satisfied: <MAT> As shown by the following Table <NUM>, the existing histogram statistic:.

Generate a histogram: a horizontal ordinate X and a vertical ordinate Pi are drawn respectively according to a scale of data value, and the height of each statistical bar is drawn according to the vertical ordinate.

The histogram drawn according to the aforementioned method has defects, that is, when applied to data statistics in the field of image processing, the histogram cannot reflect features of image quality more accurately and more comprehensively if a user has higher requirements for precision of image quality processing.

<CIT> discloses an apparatus, method, computer useable medium, and processor programmed to automatically generate tone mapping curves in a digital camera based on image metadata are described. Rather than having a static tone mapping curve for all images, the curve can be varied automatically based on, e.g., the brightness histogram of the image. In one embodiment, a certain percentage of the least bright pixels and a certain percentage of the brightest pixels can be disregarded, while the remaining pixels can be linearly stretched to encompass the original range of brightness values. Based on the distribution of the resultant stretched brightness histogram, slopes for the low end (S0) and high end (S1) of the tone mapping curve can be independently determined, and the tone mapping curve can be automatically generated. The improved, automatically generated tone mapping curve may be able to lift shadows more aggressively and/or increase the dynamic range of the image.

The above contents are merely used for assisting in understanding the technical solution of the present invention, which does not mean admitting that the above contents are prior arts.

A main objective of the present invention is to solve the technical problem in the prior arts that the histogram drawn cannot embody features of image quality more accurately and more comprehensively.

To achieve the foregoing objective, the present invention provides a method for drawing a histogram for an image of a dynamic video, wherein the method includes the following steps:.

Preferably, the step of expanding a luminance range of the luminance data to obtain an expanded luminance range includes:
decreasing the minimum luminance value and increasing the maximum luminance value, or decreasing the minimum luminance value, or increasing the maximum luminance value.

In addition, to achieve the foregoing objective, the present invention further provides a method for dynamically adjusting luminance, wherein the method for dynamically adjusting luminance includes the steps of the above mentioned method for acquiring a histogram, and further includes the following steps:.

Preferably, the step of luminance-level stretching luminance of the image of the dynamic video according to the histogram and the main distribution range includes:.

Preferably, the step of luminance-level stretching luminance of the image of the dynamic video according to the histogram and the main distribution range further includes:
synchronously adjusting a blue chrominance component Cb and a red chrominance component Cr.

In addition, to achieve the foregoing objective, the present invention further provides an image processing apparatus, wherein the apparatus includes:.

Preferably, the stretching module includes:.

Preferably, the stretching module further includes:
a synchronization unit, used for synchronously adjusting a blue chrominance component Cb and a red chrominance component Cr.

With the method for acquiring a histogram, the method for dynamically adjusting luminance and the image processing apparatuses of the present invention, according to characteristics of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value, the two kinds of luminance data are specifically used as a statistical bar for statistics, and a histogram is drawn. When a new histogram is used for processing image quality, image features can be understood more accurately and more comprehensively from the new histogram, for example, light and shade degrees of an image, distribution situations of luminance of pixels and the like are understood through luminance changes, and the image can be stretched in luminance levels accordingly.

The objective implementation, functional characteristics and advantages of the present invention are further described with reference to the accompanying drawings in combination with embodiments.

It should be understood that the specific embodiments described herein are merely used to explain the present invention, but are not used to limit the present invention, which is defined by the appended claims.

The present invention provides a method for acquiring a histogram, and referring to <FIG>, in one embodiment, the method for acquiring a histogram includes:
Step S101. Acquire luminance data of pixels of an image of a dynamic video.

The method for acquiring a histogram of this embodiment is applied to an image processing apparatus, the apparatus is disposed in a terminal, and the terminal may be a device having video playback and image processing functions such as computers, smart TVs or smartphones.

In this embodiment, when video or continuous images are played on the terminal, luminance data of pixels of an image of a dynamic video, that is, luminance of each pixel, is acquired.

This embodiment is not limited to making statistics on the luminance data, but also may make statistics on other data of the image of the dynamic video.

Perform separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value in the luminance data, to draw a first histogram corresponding to the luminance data after the statistical processing.

In this embodiment, when video or continuous images are played on the terminal, if a user has higher requirements for the dynamic image, the existing histogram cannot reflect image features more accurately and more comprehensively when processing the image quality. In this embodiment, when the existing histogram is used to make statistics on luminance data of the dynamic image, usually, the place with the minimum luminance value is embodied as a black edge of the dynamic image, while the place with the maximum luminance value is embodied as a white edge of the dynamic image, the viewing effect is not ideal, and at the same time, it is also easy to misjudge average pixel luminance of the dynamic image.

This embodiment performs separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value, and the histogram drawn can overcome the aforementioned shortcomings.

This embodiment performs separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value in the following two manners: the manner of expanding a luminance range and the manner of narrowing a luminance range. For the manner of expanding a luminance range, referring to <FIG>, the lower limiting value of the 1st statistical bar of the histogram is X(min)-a, and the upper limiting value of the Nth statistical bar of the histogram is X(max)+a. The group interval of each statistical bar from 1st to Nth is h respectively. Considering that all the pixel points actually fall within the range from X(min) to X(max), the number of pixels fall between X(min)-a and X(min) is <NUM>, and the number of pixels fall between X(max) and X(max)+a is <NUM>.

This embodiment, according to characteristics of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value, specifically uses the two kinds of luminance data as a statistical bar for statistics, and draws a histogram. When a new histogram is used for processing image quality, image features can be understood more accurately and more comprehensively from the new histogram, for example, light and shade degrees of an image, distribution situations of luminance of pixels and the like are understood through luminance changes, and the image can be stretched in luminance levels accordingly.

In this embodiment, as shown in <FIG>, on the basis of the embodiment of <FIG>, step S102 includes:.

In this embodiment, as the number of pixels fall between X(min)-a and X(min) is <NUM>, and the number of pixels fall between X(max) and X(max)+a is <NUM>, the first statistical bar P1 in <FIG> and the existing statistical bar have the same statistical frequency result, and the same is true of the final statistical bar PN.

However, as this embodiment has made separate statistics on frequencies of the first luminance data and the second luminance data, statistics on the first luminance data has been repeated in the first statistical bar P1, and statistics on the second luminance data has been repeated in the final statistical bar PN; therefore, it is necessary to remove the luminance data on which statistics has been repeated in P1 and PN.

In this embodiment, the manner of expanding a luminance range is not limited to the manner of decreasing the minimum luminance and increasing the maximum luminance as shown in <FIG>, and the manner of only decreasing the minimum luminance or only increasing the maximum luminance is also feasible.

In a preferred embodiment, as shown in <FIG>, on the basis of the embodiment of <FIG>, step S <NUM> may further include:.

In this embodiment, as shown in <FIG>, considering that all the pixel points actually fall within the range from X(min) to X(max), the number of pixels fall between X(min) and X(min)+a is not <NUM>, and the number of pixels fall between X(max)-a and X(max) is not <NUM>, that is, X(min)+a is slightly greater than X(min), therefore, the first statistical bar in <FIG> and the existing statistical bar do not have the same statistical frequency result, which is (P1-Pmin), and the same is true of the final statistical bar, which is (PN-Pmax).

As this embodiment has made separate statistics on frequencies of the first luminance data and the second luminance data, in <FIG>, statistics on the first luminance data is not repeated in the first statistical bar (P1-Pmin), and statistics on the second luminance data is not repeated in the final statistical bar (PN-Pmax) either; therefore, each statistical bar can be obtained directly in this manner, which does not require the later removal processing as the expanding a luminance range and does not require additional calculation.

In this embodiment, the manner of narrowing a luminance range is not limited to the manner of increasing the minimum luminance value and decreasing the maximum luminance value as shown in <FIG>, and the manner of only increasing the minimum luminance value or only decreasing the maximum luminance value is also feasible.

The present invention further provides a method for dynamically adjusting luminance, and as shown in <FIG>, in one embodiment, the method for dynamically adjusting luminance includes the following steps:
Step S201. Acquire luminance data of pixels of an image of a dynamic video.

The method for dynamically adjusting luminance of this embodiment is applied to an image processing apparatus, the apparatus is disposed in a terminal, and the terminal may be a device having video playback and image processing functions such as computers, smart TVs or smartphones.

Perform separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value in the luminance data, to draw a first histogram corresponding to the luminance data after the statistical processing.

This embodiment performs separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value in the following two manners: the manner of expanding a luminance range and the manner of narrowing a luminance range. For the manner of expanding a luminance range, as the number of pixels fall between X(min)-a and X(min) is <NUM>, and the number of pixels fall between X(max) and X(max)+a is <NUM>.

Acquire a main distribution range of luminance according to the first histogram.

In this embodiment, luminance values of first luminance data Y(min), second luminance data Y(max) and pixels nearby are eliminated, and a main distribution range of luminance can be analyzed using the first histogram. It is set that the luminance is mainly distributed on jth to kth statistical bars, and it is easy to know that j and k should satisfy the following condition: <NUM> ≤ j ≤ k ≤ N.

By taking the first histogram as an example, M is the total number of pixels, P' is the frequency, the eliminated first luminance data and the number u of pixels nearby, the eliminated second luminance data and the number v of pixels nearby are introduced, and j and k need to satisfy the following conditions:.

the minimum value Y1 of the luminance is a lower limiting value of the jth statistical bar, and the maximum value Y2 is an upper limiting value of the kth statistical bar, therefore: <MAT> <MAT> <MAT> <MAT> thus, the main distribution range of the luminance is determined as: <MAT>.

Step <NUM>. Grey level-stretch luminance of the image of the dynamic video according to the first histogram and the main distribution range.

In this embodiment, the stretched y can be obtained according to the first histogram and the main distribution range, for example, for an 8bits video YCbCr signal image, a value range of the luminance is set as <NUM> to <NUM>, that is: <MAT>.

it is set that a main distribution range of luminance of all pixels of a certain image is from Y1 to Y2, and satisfies: <NUM> ≤ Y<NUM> ≤ Y ≤ Y<NUM> ≤ <NUM>,.

In one specific implementation process, if the image has pixels of which Y are <NUM> and <NUM>, at this point, if statistics is made according to the existing histogram, there is: <MAT> that is, it is insufficient to perform stretching processing according to the condition now.

In another specific implementation process, according to the histogram of this embodiment, it is feasible to first eliminate pixels whose luminance is <NUM> and <NUM> and make: <MAT> that is, at this point, it is feasible to, through y = f(Y) = k × Y + b , make the value range of the luminance expanded, that is, stretched, respective grey levels thereof are also stretched, and such processing will not sacrifice image levels and details.

In this embodiment, in combination with <FIG>, on the basis of the embodiment of <FIG>, step <NUM> includes:.

In one preferred embodiment, in combination with <FIG>, on the basis of the embodiment of <FIG>, step S202 may further include:.

In this embodiment, as shown in <FIG>, the number of pixels fall between X(min) and X(min)+a is not <NUM>, and the number of pixels fall between X(max)-a and X(max) is not <NUM>, that is, X(min)+a is slightly greater than X(min), therefore, the first statistical bar in <FIG> and the existing statistical bar do not have the same statistical frequency result, which is (P1-Pmin), and the same is true of the final statistical bar, which is (PN-Pmax).

In another preferred embodiment, as shown in <FIG>, on the basis of the embodiment of <FIG>, step S204 includes:.

The first difference ΔY is a difference of the maximum luminance and the minimum luminance in the main distribution range, the second difference Δy1 is a difference between luminance on the first tensile curve y1 and the minimum luminance in the first histogram, and the third difference Δy2 is a difference between the maximum luminance in the first histogram and luminance on the second tensile curve y2.

As shown in <FIG>, the first tensile curve y1 and the second tensile curve y2 form the stretched curve y in this embodiment.

The tensile coefficient r is: r = ΔY/ΔYm. wherein ΔY = Y<NUM> - Y<NUM>, and ΔYm = Y(max) - Y(min).

The tensile coefficient r reflects the concentration degree of main distribution data, the greater the tensile coefficient r is, the greater the main distribution range is, and stretching can be performed; the smaller the tensile coefficient r is, the smaller r the main distribution range is, and stretching is limited or not performed.

As shown in <FIG>, the first function relationship is a r-ΔY curve, obviously, <NUM> < r < <NUM>, and r increases with increase of ΔY.

The first degree of deviation r1 is the degree to which the first tensile curve y1 deviates from Y(min), and the second degree of deviation r2 is the degree to which the second tensile curve y2 deviates from Y(max).

The first degree of deviation r1 is: <MAT>, wherein Δy<NUM> = y<NUM> - Y(min), ΔY<NUM> = Y<NUM> - Y(min), and herein <NUM> < r<NUM> < <NUM>, which needs to decrease with increase of Δy<NUM>, as shown in <FIG>. Certainly, the second function relationship r<NUM> - Δy<NUM> curve may be set as the optimal according to adjustment effects of various actual scenes during adjustment of image quality.

In combination with the tensile coefficient r and the first degree of deviation r1, <MAT> can be obtained,
wherein Y(max) and Y(min) are constants, values of Y2 and Y1 are decided by a current video dynamic image, and the value of r1 can be found out according to the r<NUM> - Δy<NUM> curve in <FIG>.

The first degree of deviation r2 is: <MAT>, wherein Δy<NUM> = Y(max)- y<NUM>, ΔY<NUM> = Y(max) - Y<NUM>, and herein <NUM> < r<NUM> < <NUM>, which decreases with increase of Δy<NUM>, as shown in <FIG>. Certainly, the third function relationship r<NUM> - Δy<NUM> curve may be set as the optimal according to adjustment effects of various actual scenes during adjustment of image quality.

In combination with the tensile coefficient r and the first degree of deviation r2, <MAT> can be obtained,
wherein Y(max) and Y(min) are constants, values of Y2 and Y1 are decided by a current video dynamic image, and the value of r2 can be found out according to the r<NUM> - Δy<NUM> curve in <FIG>.

As shown in <FIG>, the luminance is grey level-stretched through y = f(Y) = c × Y + b, stretching is performed within a range of [y1, y2], and y1 and y2 satisfy: Y(min) ≤ y<NUM> < Y<NUM> ≤ Y ≤ Y<NUM> < y<NUM> ≤ Y(max),.

For the image of the dynamic video, the main distribution range [Y1, Y2] of the luminance is constantly changing, y1 and y2 adapting thereto can be obtained in real time, and values of c and b are obtained finally, thereby dynamically adjusting intercept b and gain c of the luminance, to achieve grey level stretching of the luminance, which improves light and dark levels and depth of field effects of the image and achieves adaptive dynamic luminance adjustment, and at the same time, according to the luminance adjustment, contrast may also be properly adjusted.

In one preferred embodiment, on the basis of the embodiment of <FIG>, the method for dynamically adjusting luminance further includes the following step: synchronously adjusting a blue chrominance component Cb and a red chrominance component Cr.

In combination with <FIG>, grey level stretching of Y is achieved through the equation <MAT>, and although grey levels of the luminance within the main distribution range [Y1, Y2] is stretched, at this point, saturation obviously occurs in the luminance in intervals [Y(min), Y1] and [Y2, Y(max)], that is, cut-off occurs, which indicates that grey level stretching of Y in an interval [Y1, Y2] is established on the basis of sacrificing luminance levels of the intervals [Y(min), Y1] and [Y2, Y(max)].

As separate grey level stretching for the luminance will change the size of saturation of the pixels, at this point, it is necessary to synchronously adjust a blue chrominance component Cb and a red chrominance component Cr of the dynamic image.

In this embodiment, when cut-off occurs in the intervals [Y(min), Y1] and [Y2, Y(max)], it is necessary to make <MAT> or <MAT>, wherein Y' is the luminance after Cb and Cr are synchronously adjusted;.

So far, synchronous adjustment of Cb, Cr and the luminance has been achieved, to make saturation of respective pixels of the dynamic image unchanged before and after adjustment.

The present invention further provides an image processing apparatus, as shown in <FIG>, the apparatus including:
An acquisition module <NUM>, used for acquiring luminance data of pixels of an image of a dynamic video.

The image processing apparatus of this embodiment is disposed in a terminal, and the terminal may be a device having video playback and image processing functions such as computers, smart TVs or smartphones.

A drawing module <NUM>, used for performing separate statistical processing on frequencies of first luminance data with the minimum luminance value and second luminance data with the maximum luminance value in the luminance data, to draw a first histogram corresponding to the luminance data after the statistical processing.

In this embodiment, on the basis of <FIG>, the drawing module <NUM> includes:.

In this embodiment, as the number of pixels fall between X(min)-a and X(min) is <NUM>, and the number of pixels fall between X(max) and X(max)+a is <NUM>, the first statistical bar P1 and the existing statistical bar have the same statistical frequency result, and the same is true of the final statistical bar PN.

In this embodiment, the manner of expanding a luminance range is not limited to the manner of decreasing the minimum luminance and increasing the maximum luminance, and the manner of only decreasing the minimum luminance or only increasing the maximum luminance is also feasible.

In one preferred embodiment, on the basis of <FIG>, the drawing module <NUM> may further include:.

In this embodiment, the number of pixels fall between X(min)-a and X(min) is <NUM>, and the number of pixels fall between X(max) and X(max)+a is <NUM>, that is, X(min)+a is slightly greater than X(min), therefore, the first statistical bar and the existing statistical bar do not have the same statistical frequency result, which is (P1-Pmin), and the same is true of the final statistical bar, which is (PN-Pmax).

As this embodiment has made separate statistics on frequencies of the first luminance data and the second luminance data, statistics on the first luminance data is not repeated in the first statistical bar (P1-Pmin), and statistics on the second luminance data is not repeated in the final statistical bar (PN-Pmax) either; therefore, each statistical bar can be obtained directly in this manner, which does not require the later removal processing as the expanding a luminance range and does not require additional calculation.

In this embodiment, the manner of narrowing a luminance range is not limited to the manner of increasing the minimum luminance value and decreasing the maximum luminance value, and the manner of only increasing the minimum luminance value or only decreasing the maximum luminance value is also feasible.

The present invention further provides an image processing apparatus, as shown in <FIG>, the apparatus including:
A first acquisition module <NUM>, used for acquiring luminance data of pixels of an image of a dynamic video.

A second acquisition module <NUM>, used for acquiring a main distribution range of luminance according to the first histogram.

A stretching module <NUM>, used for grey level-stretching luminance of the image of the dynamic video according to the first histogram and the main distribution range.

In another specific implementation process, according to the histogram of this embodiment, it is feasible to first eliminate pixels whose luminance is <NUM> and <NUM> and make: <MAT> that is, at this point, it is feasible to, through y = f(Y) = k × Y + b, make the value range of the luminance expanded, that is, stretched, respective grey levels thereof are also stretched, and such processing will not sacrifice image levels and details.

In another preferred embodiment, on the basis of <FIG>, the stretching module <NUM> includes:.

The tensile coefficient r is: = ΔY/ΔYm;
wherein ΔY = Y<NUM> - Y<NUM>, and ΔYm = Y(max) - Y(min).

The first degree of deviation r1 is: <MAT>,
wherein Δy<NUM> - y<NUM> - Y(min), ΔY<NUM> = Y<NUM> - Y(min), and herein <NUM> < r<NUM> < <NUM>, which needs to decrease with increase of Δy<NUM>, as shown in <FIG>. Certainly, the second function relationship r<NUM> - Δy<NUM> curve may be set as the optimal according to adjustment effects of various actual scenes during adjustment of image quality.

The first degree of deviation r2 is: <MAT>,
wherein Δy<NUM> = Y(max)- y<NUM>, ΔY<NUM> = Y(max) - Y<NUM>, and herein <NUM> < r<NUM> < <NUM> , which decreases with increase of Δy<NUM>, as shown in <FIG>. Certainly, the third function relationship r<NUM> - Δy<NUM> curve may be set as the optimal according to adjustment effects of various actual scenes during adjustment of image quality.

In one preferred embodiment, on the basis of the embodiment of <FIG>, the stretching module <NUM> further includes: a synchronization unit, used for synchronously adjusting a blue chrominance component Cb and a red chrominance component Cr.

Grey level stretching of Y is achieved through the equation <MAT>, and although grey levels of the luminance within the main distribution range [Y1, Y2] is stretched, at this point, saturation obviously occurs in the luminance in intervals [Y(min), Y1] and [Y2, Y(max)], that is, cut-off occurs, which indicates that grey level stretching of Y in an interval [Y1, Y2] is established on the basis of sacrificing luminance levels of the intervals [Y(min), Y1] and [Y2, Y(max)].

Claim 1:
A computer implemented method for drawing a histogram for an image of a dynamic video, wherein the method comprises the following steps:
acquiring the luminance data of the pixels of the image of the dynamic video, wherein the luminance data ranges from a first luminance data (Xmin) to a second luminance data (Xmax);
characterized in that, when drawing statistical bars of the histogram,
expanding a luminance range of the luminance data, by luminance-level stretching, to obtain an expanded luminance range of the statistical bars covering along x-axis of the histogram;
counting a frequency (Pmin) of the first luminance data (Xmin) and a frequency (Pmax) of the second luminance data (Xmax);
performing frequency statistics processing on the expanded luminance range to obtain the frequencies (P1, P2, ... PN) of N statistical bars; and
removing the frequency (Pmin) of the first luminance data (Xmin) from the frequency (P1) of the first statistical bar, and removing the frequency (Pmax) of the second luminance data (Xmax) from the frequency (PN) of the last statistical bar, to obtain the histogram, wherein, in addition to the statistical bars, the histogram further includes separately the frequency (Pmin) of the first luminance data (Xmin) and the frequency (Pmax) of the second luminance data (Xmax).