CN114882125A - Method and device for graying image data, terminal device and readable storage medium - Google Patents

Abstract

The present application is applicable to the technical field of image processing, and provides a grayscale method, device, terminal device and readable storage medium for image data. The method includes: determining a grayscale conversion coefficient, pixel value and color contrast of the image data to be processed The first relationship between, determine the pixel value and color contrast of multiple pixel pairs in the image data to be processed, determine the grayscale conversion coefficient according to the first relationship, the pixel value and color contrast of the multiple pixel pairs, and determine the grayscale conversion coefficient according to the grayscale conversion. The coefficients perform grayscale transformation on the image data to be processed to obtain target grayscale image data. Through the pixel values and color contrasts of multiple pixel pairs in the image data to be processed, the conversion coefficients are calculated and obtained, and the grayscale conversion of the image coefficients to be processed is performed to obtain the target grayscale image data. The amount of calculation is small, and the converted target grayscale The image data can retain the contrast information of the image data to be processed, thereby improving the grayscale conversion efficiency and the quality of the target grayscale image data.

Description

Grayscale method, device, terminal device and readable storage medium for image data

technical field

The present application belongs to the technical field of image processing, and in particular, relates to a grayscale method, apparatus, terminal device and readable storage medium for image data.

Background technique

In image application technology, image grayscale technology is a common method, and grayscale images are often used in digital printing, photo rendering and other fields.

Existing image grayscale techniques usually cannot preserve the contrast of the original image data, resulting in the problem of information loss in the converted grayscale image.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a grayscale method, device, terminal device, and readable storage medium for image data, which can solve the problem that the existing image grayscale technology cannot preserve the contrast of the original image data, resulting in the converted grayscale The image suffers from loss of information.

In a first aspect, an embodiment of the present application provides a grayscale method for image data, including:

determining the first relationship between the grayscale conversion coefficient, pixel value and color contrast of the image data to be processed;

determining pixel values and color contrasts of a plurality of pixel pairs in the image data to be processed; wherein, the pixel pairs include image block pairs or pixel point pairs;

determining a grayscale conversion coefficient according to the first relationship, the pixel value, and the color contrast;

Perform grayscale conversion on the to-be-processed image data according to the grayscale conversion coefficient to obtain target grayscale image data.

In one embodiment, performing grayscale conversion on the to-be-processed image data according to the grayscale conversion coefficient to obtain target grayscale image data includes:

Determine the grayscale value of the preset pixel point in the image data to be processed according to the grayscale conversion coefficient;

The target grayscale image data corresponding to the to-be-processed image data is determined according to the grayscale value.

In one embodiment, the determining pixel values and color contrasts of multiple pixel pairs in the image data to be processed includes:

Selecting multiple image blocks in the image data to be processed, and constructing multiple image block pairs according to the multiple image blocks;

According to the pixel values of all pixels in each image block, the pixel value and color contrast of each image block pair are obtained by calculation.

In one embodiment, calculating and obtaining the pixel value and color contrast of each image block pair according to the pixel values of all pixels in each image block includes:

According to the RGB three-channel pixel values of all pixels in each image block, calculate and obtain the pixel average value of each image block in the RGB three-channel respectively, as the RGB three-channel pixel value of each image block;

According to the Lab three-channel pixel values of all pixels in each image block, calculate and obtain the pixel average value of each image block in the Lab three-channel, as the Lab three-channel pixel value of each image block;

According to the Lab three-channel pixel value of each image block in each image block pair, the color contrast corresponding to each image block pair is calculated and determined.

In one embodiment, the determining pixel values and color contrasts of multiple pixel pairs in the image data to be processed further includes:

Selecting a plurality of pixel points in the image data to be processed, and constructing a plurality of pixel point pairs according to the plurality of pixel points;

Determine the RGB three-channel pixel value and color contrast for each pixel pair.

In one embodiment, the determining a grayscale conversion coefficient according to the first relationship, the pixel value and the color contrast includes:

Substitute the RGB three-channel pixel value and color contrast of the plurality of pixel pairs into the first relationship to obtain a grayscale conversion coefficient matrix;

The least squares solution of the grayscale conversion coefficient matrix is obtained by calculation according to the pixel values of the plurality of pixel pairs and the color contrast, as the grayscale conversion coefficient.

In one embodiment, the determining the first relationship between the grayscale conversion coefficient, the pixel value and the color contrast of the image data to be processed includes:

The first relationship is determined according to the second relationship and the third relationship of the image data to be processed; wherein, the second relationship is the relationship between the gray value and the color contrast, and the third relationship is the The relationship between the grayscale conversion coefficient, the pixel value, and the grayscale value.

In a second aspect, an embodiment of the present application provides a grayscale device for image data, including:

a first determination module, configured to determine the first relationship between the grayscale conversion coefficient, pixel value and color contrast of the image data to be processed;

a second determination module, configured to determine pixel values and color contrasts of a plurality of pixel pairs in the image data to be processed; wherein, the pixel pairs include image block pairs or pixel point pairs;

a third determining module, configured to determine a grayscale conversion coefficient according to the first relationship, the pixel value and the color contrast;

A conversion module, configured to perform grayscale conversion on the to-be-processed image data according to the grayscale conversion coefficient to obtain target grayscale image data.

In one embodiment, the conversion module includes:

a conversion unit, configured to determine the grayscale value of a preset pixel point in the to-be-processed image data according to the grayscale conversion coefficient;

A traversing unit, configured to determine target grayscale image data corresponding to the to-be-processed image data according to the grayscale value.

In one embodiment, the second determining module includes:

a first selection unit, configured to select multiple image blocks in the image data to be processed, and construct multiple image block pairs according to the multiple image blocks;

The first calculation unit is configured to calculate and obtain the pixel value and color contrast of each image block pair according to the pixel values of all pixel points in each image block.

In one embodiment, the first computing unit includes:

The first calculation subunit is used to calculate and obtain the pixel average value of each image block in the RGB three channels according to the RGB three-channel pixel values of all pixels in each image block, as the RGB three-channel pixel of each image block. value;

The second calculation subunit is used to calculate and obtain the pixel average value of each image block in the Lab three channels according to the Lab three-channel pixel values of all pixels in each image block, as the Lab three-channel pixels of each image block. value;

The third calculation subunit is configured to calculate and determine the color contrast corresponding to each image block pair according to the Lab three-channel pixel value of each image block pair in each image block pair.

In one embodiment, the second determining module includes:

a second selection unit, configured to select a plurality of pixel points in the to-be-processed image data, and construct a plurality of pixel point pairs according to the plurality of pixel points;

The determining unit is used to determine the RGB three-channel pixel value and color contrast of each pixel pair.

In one embodiment, the third determining module includes:

a second calculation unit, configured to substitute the RGB three-channel pixel values and color contrasts of the plurality of pixel pairs into the first relationship to obtain a grayscale conversion coefficient matrix;

The third calculation unit is configured to calculate and obtain the least squares solution of the grayscale conversion coefficient matrix according to the pixel values of the plurality of pixel pairs and the color contrast, as the grayscale conversion coefficient.

In one embodiment, the determining module includes:

a determining unit, configured to determine the first relationship according to the second relationship and the third relationship of the image data to be processed; wherein, the second relationship is the relationship between the gray value and the color contrast, and the first relationship The three relationships are the relationship between the grayscale conversion coefficient, the pixel value, and the grayscale value.

In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program The grayscale method for image data according to any one of the above first aspects is implemented.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the above-mentioned first aspects is implemented. The grayscale method of the image data described above.

In a fifth aspect, an embodiment of the present application provides a computer program product that, when the computer program product runs on a terminal device, enables the terminal device to execute the grayscale method for image data according to any one of the first aspects above.

Through the pixel value and color contrast of multiple pixel pairs in the image data to be processed, the grayscale conversion coefficient is calculated and obtained, and the grayscale conversion is performed on the coefficient of the image to be processed according to the grayscale conversion coefficient to obtain the target grayscale image data. The amount of calculation is small, At the same time, the converted target grayscale image data can retain the contrast information of the to-be-processed image data, thereby improving the grayscale conversion efficiency and the quality of the target grayscale image data.

It can be understood that, for the beneficial effects of the second aspect to the fifth aspect, reference may be made to the relevant description in the first aspect, which is not repeated here.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present application. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a schematic flowchart of a grayscale method for image data provided by an embodiment of the present application;

2 is a schematic flowchart of step S104 of the grayscale method for image data provided by an embodiment of the present application;

3 is a schematic flowchart of step S102 of the grayscale method for image data provided by an embodiment of the present application;

4 is a schematic flowchart of step S1022 of the grayscale method for image data provided by an embodiment of the present application;

5 is another schematic flowchart of step S102 of the grayscale method for image data provided by an embodiment of the present application;

6(a), 6(b), and 6(c) are schematic diagrams of grayscale image data obtained based on different image grayscale methods provided by the embodiments of the present application;

7 is a schematic structural diagram of a grayscale device for image data provided by an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a terminal device provided by an embodiment of the present application.

Detailed ways

In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are set forth in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It is to be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described feature, integer, step, operation, element and/or component, but does not exclude one or more other The presence or addition of features, integers, steps, operations, elements, components and/or sets thereof.

It will also be understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items.

As used in the specification of this application and the appended claims, the term "if" may be contextually interpreted as "when" or "once" or "in response to determining" or "in response to detecting ". Similarly, the phrases "if it is determined" or "if the [described condition or event] is detected" may be interpreted, depending on the context, to mean "once it is determined" or "in response to the determination" or "once the [described condition or event] is detected. ]" or "in response to detection of the [described condition or event]".

In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and should not be construed as indicating or implying relative importance.

References in this specification to "one embodiment" or "some embodiments" and the like mean that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in other embodiments," etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless specifically emphasized otherwise. The terms "including", "including", "having" and their variants mean "including but not limited to" unless specifically emphasized otherwise.

The grayscale method for image data provided by the embodiments of the present application may be applied to terminal devices such as mobile phones, tablet computers, and notebook computers, and the embodiments of the present application do not impose any restrictions on specific types of terminal devices.

FIG. 1 shows a schematic flow chart of a grayscale method for image data provided by the present application. As an example but not a limitation, the method can be applied to the above-mentioned notebook computer.

S101. Determine a first relationship among grayscale conversion coefficients, pixel values, and color contrasts of the image data to be processed.

In specific applications, the gray value can be determined and obtained according to the pixel value and conversion coefficient of the pixel pair in the image data to be processed, and the corresponding color contrast can be determined according to the gray value of the pixel pair, and then the gray conversion coefficient, pixel value can be determined. The first relationship between value and color contrast. A pixel point pair refers to a pair of pixel points located at different positions in the image data to be processed.

The image data to be processed refers to the real image data that needs to be processed in grayscale. It can be understood that in the process of image grayscale, it is necessary to determine the corresponding conversion coefficients WR, WG, and WB of the three channels _R , _G , and _B respectively, and then gray the RGB three channels of the pixels in the image data to be processed. degree conversion.

In one embodiment, the step S101 includes:

The first relationship is determined according to the second relationship and the third relationship of the image data to be processed; wherein, the second relationship is the relationship between the gray value and the color contrast, and the third relationship is the The relationship between the grayscale conversion coefficient, the pixel value, and the grayscale value.

In a specific application, in order to keep the color contrast of the image data to be processed as much as possible in the converted target grayscale image data, the conversion target is set as: the color contrast between the pixel pairs of the image data to be processed is minimized, and then the color contrast between the pixel pairs of the image data to be processed can be minimized The second relationship between the gray value of the pixel pair and the color contrast is determined as:

min∑ _x,y (g _x -g _y -δ _x,y ) ² ; formula (1)

In practical applications, the gray value of a pixel is determined by gray-scale conversion of the pixel values of the three RGB channels of the pixel according to the three-channel conversion coefficient. Therefore, it can be determined that the third relationship is expressed as:

In the formula, x and y represent a pair of pixel points in the image data to be processed, g _x represents the gray value of the pixel point x in the image data to be processed, g _y represents the gray value of the pixel point y in the image data to be processed, δ _{x, y} represents the corresponding color contrast; W _R , W _G , and W _B represent the conversion coefficients of the three RGB channels, respectively, and R _x , G _x , and B _x represent the R, G, The pixel values of the three channels of B, R _y , G _y , and By represent the pixel values of the three channels of R, G, and B of the pixel point _y in the image data to be processed, respectively.

In a specific application, by substituting the third relationship between the grayscale conversion coefficient, pixel value and grayscale value into the second relationship between grayscale value and color contrast, and setting the polynomial of the second relationship equal to 0, it can be obtained:

W _R R _x +W _G G _x +W _B B _x -(W _R R _y +W _G G _y +W _B B _y )=δ _x,y ; Equation (3)

By arranging formula (3), we can obtain:

W _R ·(R _x -R _y )+W _G ·(G _x -G _y )+W _B ·(B _x -B _y )=δ _x,y ; formula (4)

Let I _R =R _x -R _y , IG = _{G x} -G _y , I _B =B _x -B _y , obtain the first relationship between grayscale conversion coefficient, pixel value and color contrast:

W _R · I _R + W _G · I _G + W _B · I _B = δ _x,y ; formula (5).

Among them, the color contrast of the pixel pair can be obtained by the following formula:

In the formula, L _x represents the L channel pixel value of the pixel x in the image data to be processed, a _x represents the a channel pixel value of the pixel x in the image data to be processed, b _x represents the b of the pixel x in the image data to be processed Channel pixel value; _Ly represents the L channel pixel value of the pixel point y in the image data to be processed, a _y represents the a channel pixel value of the pixel point y in the image data to be processed, and b _y represents the pixel point y in the image data to be processed. b channel pixel value.

S102. Determine pixel values and color contrasts of multiple pixel pairs in the image data to be processed; wherein, the pixel pairs include image block pairs or pixel point pairs.

In a specific application, by selecting a plurality of pixel pairs in the image data to be processed (pixel pairs include but not limited to image block pairs or pixel point pairs), the optimal pixel value and color contrast of the plurality of pixel pairs can be calculated and determined. Grayscale conversion coefficient, and then grayscale conversion of the image data to be processed to obtain target grayscale image data. An image block pair refers to a pair of image blocks located at different positions in the image data to be processed.

S103. Determine a grayscale conversion coefficient according to the first relationship, the pixel value, and the color contrast.

In a specific application, by selecting a plurality of pixel pairs in the image data to be processed, the pixel values and color contrasts of the plurality of image pixel pairs are determined, and the first relationship is converted into grayscale according to the pixel values and color contrasts of the plurality of pixel pairs. The conversion coefficient matrix is then calculated to obtain the least squares solution of the grayscale conversion coefficient matrix, and the grayscale conversion coefficient is obtained.

S104. Perform grayscale conversion on the to-be-processed image data according to the grayscale conversion coefficient to obtain target grayscale image data.

In a specific application, each pixel in the to-be-processed image data is gray-scaled by a gray-scale conversion coefficient to obtain target gray-scale image data corresponding to the to-be-processed image data.

As shown in FIG. 2, in one embodiment, the step S104 includes:

S1041. Determine the grayscale value of a preset pixel point in the to-be-processed image data according to the grayscale conversion coefficient;

S1042. Determine target grayscale image data corresponding to the to-be-processed image data according to the grayscale value.

In a specific application, when determining the conversion coefficients of the three channels of R, G, and B, the pixel values of the three channels of R, G, and B in the preset pixel points in the image data to be processed are obtained, and the pixel values of the three channels of R, G, and B are obtained through the R, G, B The conversion coefficients of the three channels respectively perform grayscale conversion on the R, G, and B three channel values of the preset pixel point to obtain the grayscale value of the preset pixel point after conversion, and obtain the grayscale value of the preset pixel point according to the grayscale value of the preset pixel point. Target grayscale image data corresponding to the image data to be processed.

It can be understood that in the process of grayscale conversion, each pixel point in the image data to be processed needs to be grayscale converted separately, so the preset pixel points are correspondingly set as all the pixel points in the image data to be processed. That is: through the conversion coefficients of the three channels of R, G, and B, the R, G, and B three-channel values of each pixel in the image data to be processed are converted to grayscale, and each pixel in the converted image data is obtained. The grayscale value of the point is obtained, and then the target grayscale image data corresponding to the image data to be processed is obtained.

Among them, the grayscale conversion of the pixel value through the conversion coefficient can be expressed as:

g(o,c)=W _R ·R(o,c)+W _G ·G(o,c)+W _B ·B(o,c) Formula (7);

In the formula, R(o,c) represents the pixel value of the R channel of the pixel in the oth row and the cth column of the image data to be processed, and G(o,c) represents the oth row and the cth column of the image data to be processed. The pixel value of the G channel of the pixel point, B(o,c) represents the pixel value of the B channel of the pixel point in the oth row and the cth column of the image data to be processed, and g(o,c) represents the converted oth row The gray value of the pixel point in the c-th column.

As shown in FIG. 3, in one embodiment, the step S102 includes:

S1021, selecting multiple image blocks in the image data to be processed, and constructing multiple image block pairs according to the multiple image blocks;

S1022: Calculate and obtain the pixel value and color contrast of each image block pair according to the pixel values of all pixel points in each image block.

In a specific application, multiple image blocks in the image data to be processed are randomly selected, and randomly paired according to multiple image blocks to construct and obtain multiple image block pairs, and the pixel values of all pixel points in each image block are identified, Calculate the pixel average value of all pixels in each image block in the R, G, B three channels respectively, as the pixel value of each image block R, G, B three channels; and obtain all pixels in each image block through calculation The pixel average value of the three channels of L, a, and b in the CIELab color space is taken as the pixel value of the three channels of L, a, and b for each image block. The pixel values of the three channels , b (specifically brought into formula 6), and the color contrast of each image block pair is obtained by calculation.

As shown in FIG. 4, in one embodiment, the step S1022 includes:

S10221, according to the RGB three-channel pixel values of all pixels in each image block, calculate and obtain the pixel average value of each image block in the RGB three-channel, respectively, as the RGB three-channel pixel value of each image block;

S10222, according to the Lab three-channel pixel values of all pixels in each image block, calculate and obtain the pixel average value of each image block in the Lab three-channel respectively, as the Lab three-channel pixel value of each image block;

S10223: Calculate and determine the color contrast corresponding to each image block pair according to the Lab three-channel pixel value of each image block pair in each image block pair.

In a specific application, the pixel values of the R, G, and B channels of all pixels in each image block are obtained respectively, and according to the pixel values of the R, G, and B channels of all pixels in each image block, Calculate and obtain the pixel average value of the R channel, the pixel average value of the G channel and the pixel average value of the B channel in each image block, as the pixel values of the R, G, and B channels of each image block; The pixel values of the L, a, b channels of all pixels in each image block in the CIELab color space are calculated separately according to the pixel values of the L, a, b channels of all pixels in each image block. The pixel average value of the L channel, the pixel average value of the a channel and the pixel average value of the b channel in each image block are taken as the pixel values of the L, a and b channels of each image block. The pixel values of the L, a, and b channels of each image block are calculated to obtain the color contrast of each image block pair.

Among them, the pixel average value of the R channel, the pixel average value of the G channel and the pixel average value of the B channel in the image block pair x, y can be obtained by the following formula:

In the formula, R _x,i represents the pixel value of the R channel of the ith pixel in the image block x in the image data to be processed, and G _x,i represents the G of the ith pixel in the image block x in the image data to be processed. The pixel value of the channel, B _x,i represents the pixel value of the B channel of the ith pixel in the image block x in the image data to be processed. R _x-block represents the pixel average value of the R channel of the image block x in the image data to be processed; G _x-block represents the pixel average value of the G channel of the image block x in the image data to be processed; B _x-block represents the image to be processed. The pixel average value of the B channel of the image block x in the data; correspondingly, the pixel average value of the R, G, and B channels of the image block y in the image data to be processed R _y-block , G _y-block , _By- The calculation method of the color contrast δ _x,y-block of the _block and the corresponding image block y is the same as the calculation method of R _x-block , G _x-block , and B _x-block , and will not be repeated here.

As shown in FIG. 5, in one embodiment, the step S102 further includes:

S1023, selecting a plurality of pixel points in the image data to be processed, and constructing a plurality of pixel point pairs according to the plurality of pixel points;

S1024. Determine the RGB three-channel pixel value and color contrast of each pixel pair.

In a specific application, randomly select multiple pixels in the image data to be processed, and perform random pairing according to multiple pixel points to construct multiple pixel pairs, and identify the RGB three-channel of each pixel in each pixel pair. The pixel value, and the Lab three-channel pixel value of each pixel point in each pixel point pair, and according to the Lab three-channel pixel value of each pixel point in each pixel point pair, each pixel point is calculated and determined by the above formula 6 correct color contrast.

In practical applications, since the pixel value of a single pixel point is easily affected by noise and becomes unstable, the accuracy of the calculation result obtained by the calculation of the randomly determined pixel point pair is unstable. Corresponding setting By selecting the image block pairs in the image data to be processed, the average value of the pixel values of all pixels in each image block pair is taken as the pixel value of each image block pair, and the average value of all pixel values in each image block pair The average value of the color contrast of the pixel points is used as the color contrast of each image block pair, and then the corresponding conversion coefficient is calculated and obtained according to the pixel value and color contrast of each image block pair, which improves the accuracy and stability of the calculation result.

In one embodiment, the step S103 includes:

Substitute the RGB three-channel pixel value and color contrast of the plurality of pixel pairs into the first relationship to obtain a grayscale conversion coefficient matrix;

The least squares solution of the grayscale conversion coefficient matrix is obtained by calculation according to the pixel values of the plurality of pixel pairs and the color contrast, as the grayscale conversion coefficient.

In a specific application, by selecting multiple pixel pairs, and substituting the RGB three-channel pixel values and color contrasts of the multiple pixel pairs into the first relationship (that is, the above formula 5), the corresponding equation system can be obtained, and then the corresponding grayscale can be obtained. The conversion coefficient matrix is calculated according to the pixel values and color contrasts of multiple pixel pairs to obtain the least squares solution of the grayscale conversion coefficient matrix, which is used as the corresponding grayscale conversion coefficient.

Taking pixel pairs as image block pairs as an example, substituting the RGB three-channel pixel values and color contrasts of m image block pairs into the first relationship, we can obtain:

Wherein, IR _-block =Rx _{-block- Ry-block} , _IG-block =Gx _-block -Gy _-block , _IB-block =Bx _-block -By _-block ;

By converting formula (9), the grayscale conversion coefficient matrix is obtained as follows:

可简化获得灰度转化系数矩阵为：UV＝k；make

The grayscale conversion coefficient matrix can be simplified as: UV=k;

When U ^T · U is invertible, the least squares solution of the grayscale conversion coefficient matrix can be obtained by calculating V=(U ^T · U) ^-1 U ^T k

By randomly selecting a plurality of pixel pairs in the image data to be processed, the stability of the calculated grayscale conversion coefficient is better, and the contrast of the image data to be processed can be preserved to the greatest extent, so that the contrast of the target grayscale image data is different from the one to be processed. The contrast of the image data is the closest.

6 exemplarily provides a schematic diagram of grayscale image data obtained based on different image grayscale methods;

For the same image to be processed, Fig. 6(a) is the first grayscale image data obtained by calculating the RGB three-channel pixel value and color contrast based on the pixel point pair to determine the conversion coefficient, and then the first grayscale image data obtained by graying the image; Fig. 6(b) Determine the conversion coefficient based on the RGB three-channel pixel value and color contrast calculation based on the image block pair, and then obtain the target grayscale image data through image grayscale; Figure 6(c) is based on the existing grayscale method. Grayscale image data.

In this embodiment, grayscale conversion coefficients are obtained by calculating the pixel values and color contrasts of multiple pixel pairs in the image data to be processed, and grayscale conversion is performed on the coefficients of the image to be processed according to the grayscale conversion coefficients to obtain target grayscale image data. At the same time, the converted target grayscale image data can retain the contrast information of the to-be-processed image data, thereby improving the grayscale conversion efficiency and the quality of the target grayscale image data.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Corresponding to the grayscale method for image data described in the above embodiments, FIG. 7 shows a block diagram of the structure of the device for grayscale image data provided by the embodiment of the present application. Example relevant part.

Referring to FIG. 7 , the grayscale apparatus 100 for image data includes:

a first determination module 101, configured to determine a first relationship between grayscale conversion coefficients, pixel values, and color contrasts of the image data to be processed;

The second determination module 102 is configured to determine pixel values and color contrasts of multiple pixel pairs in the image data to be processed; wherein, the pixel pairs include image block pairs or pixel point pairs;

a third determining module 103, configured to determine a grayscale conversion coefficient according to the first relationship, the pixel value and the color contrast;

The conversion module 104 is configured to perform grayscale conversion on the to-be-processed image data according to the grayscale conversion coefficient to obtain target grayscale image data.

In one embodiment, the conversion module 104 includes:

a conversion unit, configured to determine the grayscale value of a preset pixel point in the to-be-processed image data according to the grayscale conversion coefficient;

A traversing unit, configured to determine target grayscale image data corresponding to the to-be-processed image data according to the grayscale value.

In one embodiment, the second determining module 102 includes:

a first selection unit, configured to select multiple image blocks in the image data to be processed, and construct multiple image block pairs according to the multiple image blocks;

The first calculation unit is configured to calculate and obtain the pixel value and color contrast of each image block pair according to the pixel values of all pixel points in each image block.

In one embodiment, the first computing unit includes:

The first calculation subunit is used to calculate and obtain the pixel average value of each image block in the RGB three channels according to the RGB three-channel pixel values of all pixels in each image block, as the RGB three-channel pixel of each image block. value;

The second calculation subunit is used to calculate and obtain the pixel average value of each image block in the Lab three channels according to the Lab three-channel pixel values of all pixels in each image block, as the Lab three-channel pixels of each image block. value;

The third calculation subunit is configured to calculate and determine the color contrast corresponding to each image block pair according to the Lab three-channel pixel value of each image block pair in each image block pair.

In one embodiment, the second determining module 102 includes:

a second selection unit, configured to select a plurality of pixel points in the to-be-processed image data, and construct a plurality of pixel point pairs according to the plurality of pixel points;

The determining unit is used to determine the RGB three-channel pixel value and color contrast of each pixel pair.

In one embodiment, the third determining module 103 includes:

a second calculation unit, configured to substitute the RGB three-channel pixel values and color contrasts of the plurality of pixel pairs into the first relationship to obtain a grayscale conversion coefficient matrix;

The third calculation unit is configured to calculate and obtain the least squares solution of the grayscale conversion coefficient matrix according to the pixel values of the plurality of pixel pairs and the color contrast, as the grayscale conversion coefficient.

In one embodiment, the determining module 101 includes:

a determining unit, configured to determine the first relationship according to the second relationship and the third relationship of the image data to be processed; wherein, the second relationship is the relationship between the gray value and the color contrast, and the first relationship The three relationships are the relationship between the grayscale conversion coefficient, the pixel value, and the grayscale value.

In this embodiment, the grayscale conversion coefficients are obtained by calculating the pixel values and color contrasts of multiple pixel pairs in the image data to be processed, and grayscale conversion is performed on the coefficients of the image to be processed according to the grayscale conversion coefficients to obtain target grayscale image data. At the same time, the converted target grayscale image data can retain the contrast information of the to-be-processed image data, thereby improving the grayscale conversion efficiency and the quality of the target grayscale image data.

It should be noted that the information exchange, execution process and other contents between the above-mentioned devices/units are based on the same concept as the method embodiments of the present application. For specific functions and technical effects, please refer to the method embodiments section. It is not repeated here.

FIG. 8 is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As shown in FIG. 8 , the terminal device 8 of this embodiment includes: at least one processor 80 (only one is shown in FIG. 8 ), a memory 81 , and a processor 81 stored in the memory 81 and capable of processing in the at least one The computer program 82 running on the processor 80, when the processor 80 executes the computer program 82, implements the steps in any of the foregoing embodiments of the grayscale method for image data.

The terminal device 8 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The terminal device may include, but is not limited to, a processor 80 and a memory 81 . Those skilled in the art can understand that FIG. 8 is only an example of the terminal device 8, and does not constitute a limitation on the terminal device 8. It may include more or less components than the one shown, or combine some components, or different components , for example, may also include input and output devices, network access devices, and the like.

The so-called processor 80 may be a central processing unit (Central Processing Unit, CPU), and the processor 80 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (Application Specific Integrated Circuits) , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 81 may be an internal storage unit of the terminal device 8 in some embodiments, such as a hard disk or a memory of the terminal device 8 . The memory 81 may also be an external storage device of the terminal device 8 in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital Card (Secure Digital, SD), flash memory card (Flash Card), etc. Further, the memory 81 may also include both an internal storage unit of the terminal device 8 and an external storage device. The memory 81 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as program codes of the computer program. The memory 81 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion, that is, dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

The embodiments of the present application provide a computer program product, when the computer program product runs on a mobile terminal, the steps in the foregoing method embodiments can be implemented when the mobile terminal executes the computer program product.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the present application realizes all or part of the processes in the methods of the above embodiments, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When executed by a processor, the steps of each of the above method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, and the like. The computer-readable medium may include at least: any entity or device capable of carrying the computer program code to the photographing device/terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, disk or CD, etc. In some jurisdictions, under legislation and patent practice, computer readable media may not be electrical carrier signals and telecommunications signals.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units. Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still be used for the above-mentioned implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the application, and should be included in the within the scope of protection of this application.

Claims (10)

1. A method of graying image data, comprising:

determining a first relation among a gray conversion coefficient, a pixel value and color contrast of image data to be processed;

determining pixel values and color contrasts of a plurality of pixel pairs in the image data to be processed; wherein the pixel pairs comprise image block pairs or pixel point pairs;

determining a gray scale conversion coefficient according to the first relation, the pixel value and the color contrast;

and carrying out gray level conversion on the image data to be processed according to the gray level conversion coefficient to obtain target gray level image data.

2. The method for graying image data according to claim 1, wherein said performing grayscale conversion on the image data to be processed according to the grayscale conversion coefficient to obtain target grayscale image data comprises:

determining the gray value of a preset pixel point in the image data to be processed according to the gray conversion coefficient;

and determining target gray image data corresponding to the image data to be processed according to the gray value.

3. The method for graying image data according to claim 1, wherein the determining the pixel values and the color contrasts of the plurality of pixel pairs in the image data to be processed includes:

selecting a plurality of image blocks in the image data to be processed, and constructing a plurality of image block pairs according to the plurality of image blocks;

and calculating to obtain the pixel value and the color contrast of each image block pair according to the pixel values of all the pixel points in each image block.

4. The method for graying image data according to claim 3, wherein said calculating to obtain the pixel value and color contrast of each image block pair according to the pixel values of all pixel points in each image block comprises:

respectively calculating and obtaining the pixel average value of each image block in RGB three channels according to the RGB three-channel pixel values of all pixel points in each image block, and taking the pixel average value as the RGB three-channel pixel value of each image block;

respectively calculating and obtaining the pixel average value of each image block in the Lab three channels according to the Lab three-channel pixel values of all the pixel points in each image block, and using the pixel average value as the Lab three-channel pixel value of each image block;

and calculating and determining the color contrast corresponding to each image block pair according to the Lab three-channel pixel value of each image block in each image block pair.

5. The method for graying image data according to claim 1, wherein the determining of the pixel values and the color contrasts of the plurality of pixel pairs in the image data to be processed further comprises:

selecting a plurality of pixel points in the image data to be processed, and constructing a plurality of pixel point pairs according to the pixel points;

the RGB three-channel pixel values and color contrast for each pixel point pair are determined.

6. The method of graying image data according to claim 1, wherein said determining a grayscale conversion coefficient according to the first relation, the pixel value, and the color contrast includes:

substituting the RGB three-channel pixel values and the color contrast of the plurality of pixel pairs into the first relation to obtain a gray conversion coefficient matrix;

and calculating to obtain a least square solution of a gray conversion coefficient matrix according to the pixel values and the color contrast of the plurality of pixel pairs as the gray conversion coefficient.

7. The method for graying image data according to claim 1, wherein the determining of the first relationship among the grayscale conversion coefficient, the pixel value, and the color contrast of the image data to be processed includes:

determining the first relation according to the second relation and the third relation of the image data to be processed; wherein the second relation is a relation between a gray value and the color contrast, and the third relation is a relation between the gray conversion coefficient, the pixel value, and the gray value.

8. An apparatus for graying image data, comprising:

the first determining module is used for determining a first relation among a gray conversion coefficient, a pixel value and color contrast of image data to be processed;

a second determining module, configured to determine pixel values and color contrasts of a plurality of pixel pairs in the image data to be processed; wherein the pixel pairs comprise image block pairs or pixel point pairs;

a third determining module, configured to determine a gray scale conversion coefficient according to the first relationship, the pixel value, and the color contrast;

and the conversion module is used for carrying out gray level conversion on the image data to be processed according to the gray level conversion coefficient to obtain target gray level image data.

9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

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