Image color correction method, device, projection equipment and storage medium

Abstract

The present disclosure relates to the field of projection technology, and in particular to an image color correction method, device, projection equipment and storage medium, the method comprising: receiving first color data of each pixel of a source image; performing white balance processing on the first color data to obtain second color data and a gain coefficient of the first color data; determining color offset data according to the first color data and the second color data; adjusting the first color data according to the color offset data and the gain coefficient to obtain third color data; and performing color correction on the source image according to the third color data. The present disclosure can finely adjust the image color while ensuring the original white balance effect.

Description

Technical Field

[0001] The present disclosure relates to the field of projection technology, and in particular to an image color correction method, device, projection equipment and storage medium. Background Art

[0002] White balance (WB) refers to making the projected image of the projection device close to the source image seen by the human eye under different color temperatures. In daily use, the projection device can be set to automatic white balance to remove the influence of environmental information on image factors. However, the existing automatic white balance solution can only perform full white balance processing on the affected colors in the image, and cannot make fine adjustments to the image color, resulting in poor projection imaging effect. Summary of the invention

[0003] Disclosed are an image color correction method, device, projection equipment and storage medium, which can finely adjust the image color while ensuring the original white balance effect.

[0004] In a first aspect, the present disclosure relates to an image color correction method, comprising: receiving first color data of each pixel in a source image; performing white balance processing on the first color data to obtain second color data and a gain coefficient of the first color data; determining color offset data based on the first color data and the second color data; adjusting the first color data based on the color offset data and the gain coefficient to obtain third color data; and performing color correction on the source image based on the third color data.

[0005] In a second aspect, the present disclosure also provides an image color correction device, comprising: a receiving module, used to receive first color data of each pixel of a source image; a white balance module, used to perform white balance processing on the first color data to obtain second color data and a gain coefficient of the first color data; a first calculation module, used to determine color offset data based on the first color data and the second color data; a second calculation module, used to adjust the first color data based on the color offset data and the gain coefficient to obtain third color data; and a correction module, used to perform color correction on the source image based on the third color data.

[0006] In a third aspect, the present disclosure also provides a projection device, comprising: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the image color correction method as described above.

[0007] In a fourth aspect, the present disclosure further provides a computer storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the image color correction method as described above.

[0008] The present disclosure relates to an image color correction method, device, projection equipment and storage medium. The method first obtains the first color data of each pixel of the source image, performs white balance processing on the first color data, obtains the second color data and the gain coefficient, and then calculates the color offset data of each pixel by comparing the first color data with the second color data, thereby obtaining the specific color offset of each pixel through the color offset data, and completing the fine processing of the first color data. At the same time, the source image is corrected according to the third color data obtained by the color offset data and the gain coefficient, so that the white balance effect of the source image can be guaranteed through the gain coefficient, and the color of each pixel of the source image can be finely adjusted through the color offset data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the disclosed embodiments or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 Schematic diagram of the application environment of the image color correction method according to the embodiment of the present disclosure.

[0011] Figure 2 It is a flowchart of an image color correction method according to an embodiment of the present disclosure.

[0012] Figure 3 Schematic diagram of the color space of the embodiment of the present disclosure.

[0013] Figure 4 Schematic diagram of a color space according to an embodiment of the present disclosure.

[0014] Figure 5 Schematic diagram of a color space according to another embodiment of the present disclosure.

[0015] Figure 6 It is a cross-sectional schematic diagram of a color space of yet another embodiment of the present disclosure.

[0016] Figure 7 It is a schematic diagram of the values ​​of the component color weights of the first color protection algorithm of the embodiment of the present disclosure.

[0017] Figure 8 It is a schematic diagram of the values ​​of the characteristic color weights of the second color protection algorithm of the embodiment of the present disclosure.

[0018] Fig. 9 This is a schematic diagram of the values ​​of the low-saturation color weights of the third color protection algorithm in the embodiment of the present disclosure.

[0019] Fig.10 It is a principle block diagram of the image correction and inspection device according to an embodiment of the present disclosure.

[0020] Fig.11 It is a block diagram of the internal structure principle of the projection device according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] The disclosed embodiments will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the disclosure are shown in the accompanying drawings, it should be understood that the disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein, which are instead provided for a more thorough and complete understanding of the disclosure. It should be understood that the disclosed drawings and embodiments are only for exemplary purposes and are not intended to limit the scope of protection of the disclosure.

[0022] It should be understood that the various steps described in the disclosed method embodiments may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the disclosure is not limited in this respect.

[0023] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0024] It should be noted that the concepts such as "first" and "second" mentioned in the disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0025] It should be noted that the modifications of "one" and "plurality" mentioned in the disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0026] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0027] To facilitate understanding, some descriptions of concepts related to the disclosed embodiments are exemplarily provided for reference.

[0028] Color space, also known as color space, color space, color system, color space is an abstract mathematical model that describes the method of representing color using a set of values ​​(usually three or four values ​​or color components). Common color spaces include: RGB color space, HSV color space and Lab color space.

[0029] Among them, the RGB color space is composed of three colors: R (Red), G (Green) and B (Blue). The RGB color space can be regarded as a unit cube in a three-dimensional rectangular coordinate system. D (Dark) is located at the origin, R (Red), G (Green) and B (Blue) are located at the endpoints of the three coordinate axes, generating three-dimensional coordinate axes of three colors. By adjusting the three-dimensional coordinate axes of the three colors, 16.77 million different colors can be generated. In this model, color can be regarded as a point in a three-dimensional rectangular coordinate system. The value range of each color component (R, G, B) is usually 0 to 255. In this way, 16.77 million different colors can be represented. For example, pure red can be represented as (255, 0, 0), pure green (0, 255, 0), pure blue (0, 0, 255), and white is (255, 255, 255).

[0030] Among them, the HSV color space is a color space created based on the intuitive characteristics of color, and the HSV color space is also called the hexagonal pyramid model. The color parameters in the HSV color space are hue H (Hue), saturation S (Saturation) and brightness V (SValue); among them, the value range of hue H is 0-180, the value range of saturation S is 0-255, and the value range of brightness V is 0-255.

[0031] Among them, the parameters in the Lab color space are brightness L, color component a represents a color component from green to magenta, and color component b represents a color component from blue to yellow; among them, the value range of brightness L is 0 to 100, the value range of color component a is -128 to 127, and the value range of color component b is -128 to 127. In the Lab color space, the brightness L channel is independent of the color information. The brightness L channel only encodes the brightness, and the color component a channel and the color component b channel encode the color information.

[0032] R (Red), G (Green) and B (Blue) can form various colors. Taking the maximum R color value and the maximum G color value at the same time can get Y (Yellow), taking the maximum R color value and the maximum B color value at the same time can get M (Magenta), and taking the maximum G color value and the maximum B color value at the same time can get C (Cyan). Gain coefficient (Gain), the gain coefficient refers to the brightness of the image by adjusting the brightness of the image to increase or decrease the brightness of the image, thereby changing the visual effect of the image. In image processing, the gain coefficient is usually used to adjust the brightness gain and contrast of the image to achieve the ideal visual effect.

[0033] 3DRGB is a three-dimensional RGB lookup table. The RGB value of any color will generate a new RGB value after passing through this lookup table.

[0034] White balance (WB) is a technique used to adjust the color balance of an image. Its main purpose is to restore white objects to white under different light sources. The basic principle of white balance is to compensate for the effect of the light source on white objects by adjusting the ratio of red, green and blue in the image. For example, a white object photographed under a fluorescent light may appear yellow. By adjusting the white balance, the white object can appear pure white in the image. The adjustment of white balance is usually achieved by changing the color temperature of the image. Color temperature refers to the color temperature of the light source. Different light sources have different color temperature values.

[0035] In daily use, the existing automatic white balance solution can only white balance all colors in the source image, and cannot make fine adjustments to the image color, making it difficult to meet the wide range of projection needs of users.

[0036] For example Figure 1 , Figure 1 FIG. 1 is an application environment diagram of the image processing method provided in the disclosed embodiment. When a projection device (such as Figure 1 The projection device 101 in the projection device 101 projects the original image to be projected (hereinafter referred to as the source image) onto the projection plane (eg Figure 1 When the plane 102 in the projection plane is visualized, the color and other attributes of the image displayed on the projection plane will change accordingly as the plane material of the projection plane changes, and the brightness of the light source of the projection device is closely related to the light source controller of the projection device. Therefore, it is necessary to correct the colors of the source image in the projection device.

[0037] For example, in some projection scenes, the projection device directly projects a source image on the wall, but because the wall is bluish, the projection imaging effect of the unprocessed source image on the wall is yellowish overall. In order to make the picture display normal, the source image needs to be white balanced to eliminate the yellow projection imaging effect of the source image. At this time, a white balance gain coefficient Gain needs to be applied to the image, and the color value of each color in the source image is multiplied by the gain coefficient Gain to complete the white balance processing of the yellow image. For example, in some white balance processing, when the picture is yellow, it is necessary to maintain the color value of the R color, reduce the color value of the G color and the color value of the B color, and set the gain coefficient Gain for the R color, G color and B color respectively, where the gain coefficient Gain R of the R color=1, the gain coefficient Gain G of the G color<1, and the gain coefficient Gain B of the B color<1. Since the gain coefficient of the R color and the gain coefficient of the B color are both less than 1, the gain coefficient and the color value of each color are multiplied respectively, so that the color value of the B color and the color value of the G color can be reduced while maintaining the color value of the R color. However, in the white balance process, the gain coefficient is determined based on the color that is most easily affected by the environment. However, some colors are not sensitive to the environment. Colors insensitive to the environment do not require gain. Therefore, on the basis of white balance, the color value of each color must be refined to make colors insensitive to the environment more vivid and lossless, thereby improving the projection imaging effect.

[0038] The present application discloses an image color correction method, device, projection equipment and storage medium, which can finely adjust the image color while ensuring the original white balance effect. Figure 2 As shown, the method includes: Step 110: Receive first color data of each pixel in the source image.

[0039] The first color data is color data in RGB color format, each pixel in the source image includes multiple sub-pixels, and the multiple sub-pixels include R sub-pixels, G sub-pixels, and B sub-pixels; wherein the first color data includes the color value of the R sub-pixel (hereinafter referred to as the R color value), the color value of the G sub-pixel (hereinafter referred to as the G color value), and the color value of the B sub-pixel (hereinafter referred to as the B color value). For example, in the source image, if the first color data of a pixel is (128, 64, 64), it means that the R color value of the pixel is 128, the G color value is 64, and the B color value is 64.

[0040] Step 120: Perform white balance processing on the first color data to obtain gain coefficients of the second color data and the first color data.

[0041] The first color data is white balanced using a preset white balance algorithm to obtain second color data and a gain coefficient Gain in the white balance process. In some embodiments, the second color data can be calculated by multiplying the color value of each color sub-pixel in the first color data by the gain coefficient Gain.

[0042] It should be noted that the preset white balance algorithm is a gain coefficient Gain calculated based on the consistency of the projection device components and the influence of the environment (such as ambient light, wall color, etc.). For example, when the projection device projects the source image, the projection device first needs to identify the environmental information during projection based on the sensor, and then determine the gain coefficient of the interference color based on the environmental information to obtain Gain. By setting the gain coefficient Gain < 1, the color value compression of the interference color is completed to reduce the projection effect of the interference color on the source image. It should be noted that in each calibration process, at most only two colors of the R color, G color and B color are subjected to gain processing.

[0043] Since the preset white balance algorithm performs the same white balance processing on all first color data, performing the same white balance processing on some colors that are less affected by the environment or device consistency will cause the brightness or saturation of the color to decrease, resulting in the second color data appearing dark when projected. Therefore, the following steps 130 to 150 require fine adjustment of the first color data after the white balance processing.

[0044] Step 130: Determine color shift data according to the first color data and the second color data.

[0045] The color offset data generally refers to the color offset of each pixel in the three color channels of R, G, and B. By comparing the first color data with the second color data, the color offset data of each pixel in the white balance can be determined, so as to facilitate the subsequent color correction of each pixel.

[0046] Step 140: Adjust the first color data according to the color offset data and the gain coefficient to obtain third color data.

[0047] The first color data of the source image are multiplied by the corresponding gain coefficients Gain, and the color offset data is added to the first color data multiplied by the gain coefficients Gain to obtain third color data.

[0048] For example, pixel 1 is selected from the first color data, and the color value of pixel 1 is represented by the first color coordinate (X, Y, Z), where the R color value is X, the G color value is Y, and the B color value is X. The colors to be adjusted are R color and G color, and the gain coefficient Gain-R of the R color and the gain coefficient Gain-G of the G color are obtained. X is adjusted according to the gain coefficient Gain-R, and Y is adjusted according to the gain coefficient Gain-G to obtain the second color coordinate (X·Gain-R, Y·Gain-G, Z). The first color coordinate and the second color coordinate are compared to obtain the color offset data x-offset of the R color and the color offset data y-offset of the G color. The second color data is processed according to the color offset data to obtain the third color coordinate (X·Gain-R+x-offset, Y·Gain-B+y-offset, Z) of pixel 1. Similarly, the third color coordinates of each pixel in the first color data are respectively obtained in the above manner to generate the third color data. The source image outputs the third color data to complete the color correction of the pixel.

[0049] Step 150: Perform color correction on the source image according to the third color data.

[0050] The color value of each pixel of the source image is converted into third color data, and the projection device projects the source image according to the third color data. Since the third color data is calculated based on the gain coefficient and the color offset data, the third color data can not only ensure the white balance effect of the source image through the gain coefficient, but also fine-tune the color of each pixel of the source image through the color offset data, thereby improving the projection imaging effect of the projection device.

[0051] In one embodiment, the color shift data includes a color shift value. Step 130 determines the color shift data according to the first color data and the second color data, including steps 1310 and 1320, specifically including: Step 1310: Obtain a first coordinate set corresponding to the first color data and a second coordinate set corresponding to the second color data in the color space.

[0052] The color space is in 3DRGB form. The color space includes three-dimensional coordinate axes, including R color coordinate axis, G color coordinate axis and B color coordinate axis. The value on the R color coordinate axis indicates the R color value, the value on the G color coordinate axis indicates the G color value, and the value on the B color coordinate axis indicates the B color value. The color space also includes 8 vertices, including: R vertex, G vertex, B vertex, C vertex, M vertex, Y vertex, W vertex and D vertex. Among them, the maximum R color value on the R color coordinate axis is taken to obtain the R vertex, the maximum G color value on the G color coordinate axis is taken to obtain the G vertex, the maximum B color value on the B color coordinate axis is taken to obtain the B vertex, the maximum R color value and the maximum G color value are taken at the same time to obtain the Y vertex (indicating yellow), the maximum R color value and the maximum B color value are taken at the same time to obtain the M vertex (indicating magenta), the maximum G color value and the maximum B color value are taken at the same time to obtain the C vertex (indicating cyan), and the maximum R color value, the maximum B color value and the maximum G color value are taken at the same time to obtain the W vertex (indicating white). The origin where the three-dimensional coordinate axes intersect is set as vertex D, where the R color takes the minimum value, the G color takes the minimum value, and the B color takes the minimum value, indicating black.

[0053] Specifically, Figure 3 As shown, Figure 3 is an example of a color space. Figure 3 In the example, D is the origin, G color is the X axis, and the G color coordinate axis is obtained; R color is the Y axis, and the R color coordinate axis is obtained; B color is the Z axis, and the B color coordinate axis is obtained. For example, if the first color data of a pixel point in the source image is (128, 64, 64), then the position of the pixel point is 128 on the R color coordinate axis, 64 on the G color coordinate axis, and 64 on the B color coordinate axis. It should be noted that Figure 3 This is only an example. According to different requirements, the R color, G color and B color may be arranged in another coordinate axis arrangement.

[0054] In this embodiment, Figure 4 Take the first color data as an example. Get the first coordinate set in the color space. The first coordinate set is distributed in Figure 4 The second color data is obtained by multiplying the first color data by the gain coefficient. For the second color data, when the R color does not need gain, and the G color and the B color need gain, the color data requiring gain is multiplied by the gain coefficient, and the first cube is compressed to obtain the second cube 200, and the second color coordinates in the second coordinate set are distributed on the second cube 200.

[0055] Step 1320: Calculate the color shift value according to the first coordinate set and the second coordinate set.

[0056] This step obtains the first color coordinates of the same pixel in the first coordinate set and the second color coordinates in the second coordinate set, and compares the first color coordinates and the second color coordinates of each pixel to obtain the color offset value of each pixel, thereby achieving fine color adjustment for each pixel.

[0057] In one embodiment, step 1320 calculates the color shift value according to the first coordinate set and the second coordinate set, including steps 1321 to 1326, specifically including: Step 1321, comparing the first coordinate set and the second coordinate set to obtain a comparison result.

[0058] like Figure 4 As shown, Figure 4 is different from Figure 3 Another example of a color space is Figure 4 In the figure, D is taken as the origin, G color is taken as the Y axis, the G color coordinate axis is obtained, R color is taken as the Z axis, the R color coordinate axis is obtained, and B color is taken as the X axis, the B color coordinate axis is obtained. The first coordinate set is distributed on the cube 100, and the second coordinate set is distributed on the cube 200.

[0059] By comparing the first coordinate set and the second coordinate set, the displacement change of each pixel point in the color space can be determined to obtain the color offset value of each pixel point.

[0060] In one embodiment, before comparing the first coordinate set and the second coordinate set, the first coordinate set needs to be corrected to obtain a third coordinate set; accordingly, in the above step 1321, the comparison result is obtained by comparing the third coordinate set and the second coordinate set.

[0061] like Figure 5 As shown, the correction of the first coordinate set includes: according to the W vertex (white) of the first coordinate set and the W' vertex of the second coordinate set, the W (W') point is obtained, and after the W vertex is overlapped, the coordinates of the pixel points in the first coordinate set are offset as a whole to obtain the third coordinate set. Figure 4 The first coordinate set before correction is distributed on 100 in , Figure 5 A corrected third coordinate set is distributed on 300 in FIG. Figure 6 The figure is a cross-sectional schematic diagram disclosed, in which the pixel point a is part of the pixel points in the second coordinate set, and the pixel point b is part of the pixel points in the third coordinate set. The color offset value required is the pixel displacement of the same pixel point from a to b.

[0062] By comparing the third coordinate set with the second coordinate set, the displacement change of each pixel in the color space can be determined to obtain the color offset value of each pixel. Since the third coordinate set is obtained by calibrating the first coordinate set with W color, the third coordinate set can eliminate the color cast of the source image relative to the first coordinate set.

[0063] Step 1322: determine a target gain plane in the color space according to the comparison result.

[0064] In one embodiment, the color space includes three-dimensional coordinate axes; and the target gain plane includes a first gain plane and a second gain plane.

[0065] Determining a target gain plane in a color space according to the comparison result includes: determining at least two gain coordinate axes from three-dimensional coordinate axes according to the comparison result; determining a first gain plane according to the at least two gain color coordinate axes; and determining a second gain plane in the color space that is parallel to the first gain plane.

[0066] exist Figure 5 In the example of gaining the G color and the B color, the G color value and the B color value are multiplied by the gain coefficient respectively, and the R color value is kept unchanged. At this time, the G color coordinate axis is the gain color coordinate axis, the B color coordinate axis is the gain color coordinate axis, and the R color coordinate axis is the non-gain coordinate axis. The first gain plane is the CBDG plane composed of the G color coordinate axis and the B color coordinate axis.

[0067] The first gain plane is completely affected by the G color to be gained and the B color to be gained. The gain color G color and the R color are mixed to obtain the Y color, and the gain color and the R color are mixed to obtain the M color. The Y color and the M color are also gain colors that are greatly affected by white balance. Therefore, a second gain plane parallel to the first gain plane is found in the color space. The second gain plane represents a plane composed of the Y color and the M color that are greatly affected by white balance when the maximum R color value is taken. Based on the Y color and the M color, the second gain plane WYRM is obtained.

[0068] Step 1323, selecting the target pixel coordinates from the second coordinate set, and calculating the target relative distance between the target pixel coordinates and the target gain plane.

[0069] Since the gain color plane is composed of two gain color coordinate axes, the target relative distance between the target pixel coordinate and the gain color plane is obtained, and the white balance influence of the gain color coordinate axis on the target pixel coordinate can be determined according to the distance value of the target relative distance. It can be understood that the target relative distance is negatively correlated with the degree of white balance, that is, the larger the target relative distance, the smaller the white balance influence on the pixel point corresponding to the target pixel coordinate, and the smaller the target relative distance, the greater the white balance influence on the pixel point corresponding to the target pixel coordinate.

[0070] For example, taking pixel 2 corresponding to the target pixel coordinate as an example, the smaller the distance value of the target relative distance of pixel 2, the greater the influence of white balance on pixel 2, and the color coordinate of pixel 2 represents the color that needs to be corrected during the white balance process. The larger the distance value of the target relative distance of pixel A, the less the influence of white balance on pixel A, and the color coordinate of pixel 2 represents the insensitive color during the white balance process.

[0071] In one embodiment, the target relative distance includes a first target relative distance and a second target relative distance, and calculating the target relative distance between the target pixel coordinates and the target gain plane includes: calculating the first target relative distance based on the target pixel coordinates and the first gain plane, and calculating the second target relative distance based on the target pixel coordinates and the second gain plane.

[0072] Step 1324: Determine the first influence weight corresponding to the second coordinate set according to the target relative distance.

[0073] In one embodiment, the second coordinate set includes multiple pixel coordinates; selecting the target pixel coordinates from the second coordinate set includes: selecting the target pixel coordinates from the multiple pixel coordinates; determining the first influence weight corresponding to the second coordinate set according to the target relative distance, including: determining the first sub-influence weight of the target pixel coordinate according to the first target relative distance, the first target relative distance is negatively correlated with the first sub-influence weight; determining the second sub-influence weight of the target pixel coordinate according to the second target relative distance, the second target relative distance is negatively correlated with the second sub-influence weight; calculating the third sub-influence weight of each pixel coordinate according to the first sub-influence weight and the second sub-influence weight; and combining the third sub-influence weights of each pixel coordinate to obtain the first influence weight.

[0074] Since the smaller the target relative distance is, the more likely the pixel point corresponding to the target pixel coordinate is to be affected by the gain color, the influence weight of the gain coefficient can be set for each pixel point in turn according to the target relative distance. Understandably, the first target relative distance is negatively correlated with the first sub-influence weight, that is, the larger the first target relative distance is, the smaller the weight of the first sub-influence weight corresponding to the first target relative distance is; the second target relative distance is negatively correlated with the second sub-influence weight, that is, the larger the second target relative distance is, the smaller the weight of the second sub-influence weight corresponding to the second target relative distance is.

[0075] Specifically, Figure 5Take the first gain plane CBDG plane in as an example, and calculate the first sub-influence weight β on the first gain plane according to the first target relative distance from the target pixel to the first gain plane. For example, each pixel coordinate corresponds to a different pixel point. Take the target pixel coordinate corresponding to the target pixel point 3 as an example. When the first target relative distance of the target pixel point 3 is 0, the target pixel point 3 is on the first gain plane. At this time, the gain color G and the gain color B have a huge influence on the target pixel point 3, and the corresponding maximum first sub-influence weight in multiple pixel coordinates is obtained. Optionally, the maximum first sub-influence weight is 1. As the distance value of the first target relative distance increases, the influence of the gain color G and the gain color B becomes smaller and smaller. When the first target relative distance value is the largest, the gain color G and the gain color B have almost no influence on the pixel point 2. At this time, the corresponding minimum first sub-influence weight in multiple pixel coordinates is obtained. Optionally, the minimum first sub-influence weight is 0.

[0076] In this embodiment, the maximum first target relative distance is selected from all pixels in the second coordinate set, and the relative proportion of the first target relative distance and the maximum first target relative distance of the target pixel is obtained. For example, the first target relative distance m of the target pixel 4 is obtained, and the maximum first target relative distance corresponding to the second coordinate set is l. According to the relative proportion of the first target relative distance m and the maximum first target relative distance l, m / l is obtained. At this time, the minimum first sub-influence weight p corresponding to the maximum first target relative distance is obtained; the maximum sub-influence weight p is obtained, and the calculation formula of the first type of influence weight β is: .

[0077] Specifically, Figure 5 Taking the second gain plane WYRM plane in as an example, the second sub-influence weight α on the second gain plane is calculated according to the second target relative distance from the target pixel to the second gain plane. For example, when the second target relative distance of the target pixel 5 is 0, the target pixel 5 is on the second gain plane. At this time, the gain color M and the gain color Y have a huge influence on the target pixel 5, and the corresponding maximum second sub-influence weight in multiple pixel coordinates is obtained, and optionally, the maximum second sub-influence weight is 1. As the distance value of the second target relative distance increases, the influence of the gain color M and the gain color Y decreases. When the second target relative distance value is the largest, the gain color M and the gain color Y have almost no influence on the pixel 5. At this time, the corresponding minimum second sub-influence weight in multiple pixel coordinates is obtained, and optionally, the minimum second sub-influence weight is 0. It can be understood that the second target relative distance is negatively correlated with the second sub-influence weight, that is, the larger the second target relative distance, the smaller the second sub-influence weight, and the smaller the target relative distance, the larger the second sub-influence weight.

[0078] In this embodiment, the maximum second target relative distance is selected from all pixel points in the second coordinate set, and the relative proportion of the second target relative distance of the target pixel point and the maximum second target relative distance is obtained. For example, the second target relative distance m of the target pixel point 6 is obtained, and the maximum second target relative distance corresponding to the second coordinate set is l. According to the relative proportion of the second target relative distance m and the maximum second target relative distance l, m / l is obtained. At this time, the minimum second sub-influence weight p corresponding to the maximum second target relative distance is obtained; the maximum sub-influence weight p is obtained, and the calculation formula of the second type of influence weight α is: .

[0079] In a specific embodiment, the gain color plane includes: a first gain plane CBDG and a second gain plane WYRM. According to the first sub-influence weight and the second sub-influence weight, the third sub-influence weight of each target pixel coordinate is calculated. According to the first sub-weight α of the first gain plane CBDG and the second sub-weight β of the second gain plane WYRM, the third sub-weight is calculated. For the target pixel point 7, the first target relative distance of the target pixel point from the first gain plane and the second target relative distance from the second gain plane are obtained, α is calculated according to the first target relative distance, and β is calculated according to the second target relative distance. For example, when the target pixel point 7 is in the WYRM plane, the color of this layer is dominated by Y color and M color, β=1, α=0. When the target pixel point 7 is in the CBDG plane, G color and B color are dominant at this time, α=1, β=0, and for the weights of the remaining levels between the first gain plane and the second gain plane, the first sub-weight and the second sub-weight are interpolated according to the above method. Specifically, the relative distance of the pixel point to the first target of the first gain plane and the relative distance of the pixel point to the second target of the second gain plane are obtained. When the relative distance of the first target is 0, the first sub-influence weight is 1. As the relative distance of the first target increases, the first sub-influence weight gradually decreases until the first sub-influence weight is 0; when the relative distance of the second target is 0, the second sub-influence weight is 1. As the relative distance of the second target increases, the second sub-influence weight decreases until the second sub-influence weight is 0.

[0080] Step 1325: Compress the gain coefficient according to the preset color gain range to obtain a second influence weight.

[0081] If the gain coefficient is too small, the first color data will be submerged by noise during processing, and if the gain coefficient is too large, the first color data will amplify the noise during image processing. Therefore, the gain coefficient needs to be compressed within a preset color gain range to reduce the impact of noise during image processing.

[0082] In one embodiment, the preset color gain range is from a first threshold to a second threshold; according to the preset color gain range, the gain coefficient is compressed to obtain a second influence weight, including: comparing the gain coefficient with the preset color range; when the gain coefficient is less than the first threshold, using the first gain coefficient as the second influence weight; when the gain coefficient is greater than the second threshold, using the second gain coefficient as the second influence weight, wherein the first gain coefficient is less than the second gain coefficient; when the gain coefficient is greater than the first threshold and less than the second threshold, the gain coefficient is processed based on a preset monotonically increasing function to obtain a second type of influence weight.

[0083] In one embodiment, the second impact weight As shown in the following formula (1): Formula (1); Wherein, X is the gain coefficient, threshold_L is the first threshold, mininum Gain is the first gain coefficient, threshold_H is the second threshold, maximum gain is the second gain coefficient, and linear or nonlinearincreasing function represents a linear or nonlinear monotonically increasing function.

[0084] It can be understood that when the gain coefficient x is very small, the first gain coefficient is set In order to avoid the amplification effect of the gain coefficient being unclear. When the gain coefficient x is very large, set the second gain coefficient maximum gain to avoid excessive amplification of the gain coefficient, which may cause image distortion. When the gain coefficient x is between the first threshold and the second threshold, use a linear or nonlinear monotonically increasing function to process x, so that the gain coefficient of the pixel points of the entire source image is gradually amplified, so that the source image is smoother after the gain amplification process.

[0085] Step 1326: Calculate the color shift value according to the first influence weight and the second influence weight, wherein the first influence weight, the second influence weight, and the color shift value are positively correlated.

[0086] In the color space, taking the gain color as B and the gain color as G as an example, the color offset value includes the offset of the pixel point on the B color coordinate axis and the G color coordinate axis. x-offset is the color offset value in the B color coordinate axis, and y-offset is the color offset value on the G color coordinate axis.

[0087] Furthermore, the color offset value x-offset is shown in the following formula (2), and the color offset value y-offset is shown in the following formula (3): Formula (2); Formula (3); Among them, x-offset is the color offset value in the B color coordinate axis, y-offset is the color offset value on the G color coordinate axis, is the second influence weight on the B color coordinate axis, is the second influence weight α on the B color coordinate axis and is the first influence weight, SPG-Gain[B] is the gain coefficient of color B, SPG-Gain[M] is the gain coefficient of color M, SPG-Gain[Y] is the gain coefficient of color Y, and SPG-Gain[G] is the gain coefficient of color G.

[0088] Understandably, after white balance processing, the gain value SPG-Gain[B] of the B color and the gain value SPG-Gain[G] of the G color are obtained. Since the M color is composed of the R color and the B color, the color gain value of the R color remains unchanged, SPG-Gain[R]=1, and the gain value of the M color SPG-Gain[M]=(SPG-Gain[R]+ SPG-Gain[B]) / 2. The Y color is composed of the R color and the G color, and the color gain value of the R color remains unchanged, R-Gain=1, and SPG-Gain[Y]=(SPG-Gain[R]+ SPG-Gain[G]) / 2. is the second impact weight calculated according to SPG-Gain[B], (G_Gain) is the second influence weight calculated based on Gain[G], and α and β are the first influence weights.

[0089] In one embodiment, before step 140 adjusts the first color data according to the color offset data and the gain coefficient, the step further includes: correcting the color offset value, where correcting the color offset value includes: Step 210: Perform a preset color format conversion process on the first color data to obtain fourth color data.

[0090] Step 220: Determine protection color data according to the first color data and the fourth color data, wherein the protection color data includes a third influence weight corresponding to the protection color.

[0091] Step 230, correcting the color offset data based on the third impact weight.

[0092] When performing white balance processing, some colors need to be specially protected to avoid color distortion caused by excessive adjustment. For example, the human eye is very sensitive to changes in skin color, and even a slight color shift can cause color distortion of the person in the image. Since white balance is a uniform adjustment of the source image based on projection environmental factors, it is easy to pay too much attention to the impact of projection environmental factors during the white balance process, resulting in excessive color shift of the skin color. Therefore, it is necessary to find the skin color as protected color data and perform protection processing on these protected color data.

[0093] This embodiment corrects the color shift value by determining the color to be protected, so that when correcting the image, the color shift in the image can be repaired more accurately, and the true color and details of the color to be protected can be restored, thereby improving the overall quality of the image.

[0094] In this embodiment, the preset color format is the HSV format, and the fourth color data is the HSV data. It should be noted that the preset color format does not necessarily have to use the HSV space, it can also be the HSL space, the Lab space or even the RGB space. This embodiment takes into account the different performances of different colors under the influence of the environment, subdivides the colors, distinguishes the colors that are not easily affected by the environment, and protects the colors that are not easily affected by the environment by setting weights, so as to avoid the colors that are not easily affected by the environment from being over-white balanced. Therefore, it is necessary to add three color protection algorithms, respectively protecting the hue protection HP of the component color, the skin color protection SKP of the characteristic color, and the low saturation protection GP of the low saturation color. Since the skin color is a non-continuous color, it is difficult to frame the skin color in the RGB space, so it is transferred to the HSV, HSL, Lab and other spaces, and then framed in three dimensions in these spaces according to the hue, saturation and brightness characteristics of the skin color.

[0095] Each color protection algorithm generates a third sub-influence weight. The third sub-influence weight is obtained by taking the maximum value FW from multiple third sub-influence weights. The calculation formula is shown in formula (4), where the third sub-influence weight ranges from 0 to 1.

[0096] Formula (4); Among them, FW is the third impact weight, and HPW, SKPW and GPW correspond to a sub-influence weight respectively. It can be understood that the color offset values ​​x-offset and y-offset refer to FW to obtain new offset values, and the specific calculations of x-offset-new and y-offset-new are shown in formula (5) and formula (6) respectively. When FW is very large, it means that this color is protected, so no color offset is performed on this color, and the color offset is allowed to tend to 0.

[0097] Formula (5); Formula (6); Among them, x-offset-new and y-offset-new are the corrected color offset values, x-offset and y-offset are the original offset values, and FW is the third influence weight.

[0098] In one embodiment, the protection color data includes component colors, characteristic colors and low-saturation colors, and the third influence weight includes component color weights of component colors, characteristic weights of characteristic colors and low-saturation color weights of low-saturation colors.

[0099] Step 220 determines the protection color data according to the first color data and the fourth color data, wherein the protection color data includes a third influence weight corresponding to the protection color, and the third influence weight includes: component color weight, characteristic color weight and low saturation color weight. Step 220 includes steps 2210 to 2230, specifically including: Step 2210: determine component colors from the first color data based on the first color protection algorithm, and find component color weights corresponding to the component colors.

[0100] The first color protection algorithm is used to protect the component colors. The color hue protection of the component colors is to protect those colors that are easily affected by the environment and white balance, such as C color, M color, and Y color, because R color, G color, and B color are all single component colors, while C color, M color, Y color and other colors are multi-component colors. Therefore, different weights need to be assigned to different colors.

[0101] Figure 7 is a component color weight search curve of the first color protection algorithm in the disclosed embodiment, the horizontal axis is the brightness of the component color, and the vertical axis corresponding to the brightness of all component colors is searched to obtain the component color weight of each component color weight. Figure 7 It can be seen that lower weights are assigned to the R color, the G color, and the B color, and higher weights are assigned to the C color, the M color, and the Y color. Each color is input into the first color protection algorithm to obtain a component color weight HPW.

[0102] Step 2220: Based on the second color protection algorithm, determine the characteristic color from the fourth color data, and find the characteristic color weight of the characteristic color.

[0103] The second color protection algorithm is used to protect the characteristic color, where the characteristic color is skin color protection, which is to protect the color related to skin color, in order to avoid the skin color protection being over-stretched and causing strange effects. Since skin color does not refer to a specific color, the three components of HSV Hue (hue), Saturation (saturation), and Value (brightness) are used to give weights (if it is HSL or Lab, other components are used).

[0104] The first feature weight SKHW is calculated according to the hue, the second feature weight SKSW is calculated according to the saturation, and the third feature weight SKVW is calculated according to the brightness. The calculation formula of the feature color weight is: SKPW=SKHW·SKSW·SKVW.

[0105] like Figure 8 As shown, in the second color protection algorithm, different curves are set for different colors. For curve 1 SKHW (protecting hue, the horizontal axis is hue, and the vertical axis is the first feature weight), curve 2 SKSW (protecting saturation, the horizontal axis is saturation, and the vertical axis is the second feature weight) and curve 3 SKVW (protecting brightness, the horizontal axis is brightness, and the vertical axis is the third feature weight), the first feature weight corresponding to Hue (hue) is found according to curve 1, the second feature weight corresponding to the color difference S of Saturation (saturation) is found according to curve 2, and the third feature weight corresponding to Value (brightness) is found according to curve 3. The first feature weight, the second feature weight and the third feature weight calculate the feature color weight.

[0106] Step 2230: Based on the third color protection algorithm, determine the low-saturation color from the fourth color data, and find the low-saturation color weight of the low-saturation color.

[0107] This embodiment uses the third color protection algorithm to protect low-saturation colors. Low-saturation colors are easily affected by the environment. Therefore, after obtaining the correct color after color gain, stretching is avoided as much as possible.

[0108] like Fig. 9 As shown, Fig. 9 The corresponding relationship of the low saturation color weight in the third color protection method is publicly provided. The horizontal axis is the low saturation value and the vertical axis is the low saturation color weight. After obtaining the low saturation color of the current source image, according to Fig. 9 Finds the weight of low-saturation colors, where low-saturation colors with low saturation have high weights, and high-saturation colors with low saturation have low weights.

[0109] Second, Fig.10 is a principle block diagram of the image correction and inspection device according to an embodiment of the present disclosure. Fig.10As shown, the present disclosure provides an image color correction device 140, comprising: The receiving module 1410 is used to receive the first color data of each pixel in the source image.

[0110] The white balance module 1420 is used to perform white balance processing on the first color data to obtain the gain coefficients of the second color data and the first color data.

[0111] The first calculation module 1430 is used to determine color shift data according to the first color data and the second color data.

[0112] The second calculation module 1440 is used to adjust the first color data according to the color offset data and the gain coefficient to obtain third color data.

[0113] The correction module 1450 is used to perform color correction on the source image according to the third color data.

[0114] Thirdly, Fig.11 As shown, the present disclosure provides a projection device 200, including: a projection unit 210 and a driving unit 220 driving the projection unit 210. The projection unit 210 can form an optical image and project the optical image onto an imaging medium SC.

[0115] The projection unit 210 includes a light source unit 211 , a light modulator 212 and an optical system 213 .

[0116] The driving section 220 includes a light source driving section 221 and a light modulator driving section 222 .

[0117] The light source unit 211 may include a solid light source such as a light emitting diode (LED), a laser, or a pump lamp.

[0118] The light source unit 211 may include optical elements such as a lens and a polarizing plate for improving the optical characteristics of the projected light, and a dimming element for adjusting the light flux.

[0119] The light source driving unit 221 can control the light source in the light source unit 211 to operate, including lighting and extinguishing, according to the instruction of the control unit 250 .

[0120] The light modulator 212 includes a display panel 215 , which may be a transmissive liquid crystal display (LCD) panel, a reflective liquid crystal on silicon (LCOS) panel, or a digital micromirror device (DMD).

[0121] The light modulator 212 is driven by a light modulator driving unit 222 , and the light modulator driving unit 222 is connected to the image processing unit 245 .

[0122] The image processing section 245 inputs image data to the light modulator driving section 222 .

[0123] The light modulator driving unit 222 converts the input image data into a data signal suitable for the operation of the display panel 215 .

[0124] The light modulator driving unit 222 applies a voltage to each pixel of each display panel 215 according to the converted data signal, and draws an image on the display panel 215 .

[0125] The optical system 213 includes a lens, a reflection mirror, and the like for forming an image of the incident image light PLA on the imaging medium SC.

[0126] The optical system 213 may also include a zoom mechanism for magnifying or reducing the image projected onto the imaging medium SC and a focus adjustment mechanism for performing focus adjustment.

[0127] The projection device 200 further includes an operation unit 231 , a signal receiving unit 233 , an input interface 235 , a storage unit 237 , a data interface 241 , an interface unit 242 , a frame memory 243 , an image processing unit 245 , and a control unit 250 .

[0128] The input interface 235 , the storage unit 237 , the data interface 241 , the interface unit 242 , the image processing unit 245 , and the control unit 250 can mutually communicate data via the internal bus 207 .

[0129] The operation unit 231 may generate corresponding operation signals according to operations of various buttons and switches on the housing surface of the projection device 200 , and output the signals to the input interface 235 .

[0130] The input interface 235 includes a circuit that outputs an operation signal input from the operation unit 231 to the control unit 250 .

[0131] After receiving a signal (such as an infrared signal or a Bluetooth signal) sent from a control device 5 (such as a remote controller), the signal receiving unit 233 may decode the received signal to generate a corresponding operation signal.

[0132] The signal receiving unit 233 outputs the generated operation signal to the input interface 235 .

[0133] The input interface 235 outputs the received operation signal to the control unit 250 .

[0134] The storage unit 237 may be a magnetic recording device such as a hard disk drive (HDD) or a storage device using a semiconductor storage element such as a flash memory.

[0135] The storage unit 237 stores programs executed by the control unit 250 , data processed by the control unit 250 , image data, and the like.

[0136] The data interface 241 includes a connector and an interface circuit, and can be connected to other electronic devices 100 by wire.

[0137] The data interface 241 may be a communication interface for performing communication with other electronic devices 100. The data interface 241 receives image data, sound data, etc. from other electronic devices 100. In the present embodiment, the image data may be a content image.

[0138] The interface unit 242 is a communication interface for communicating with other electronic devices 100 according to the Ethernet standard.

[0139] The interface unit 242 includes a connector and an interface circuit that processes a signal transmitted from the connector.

[0140] The interface section 242 is an interface substrate including a connector and an interface circuit and is connected to the main substrate of the control section 250 on which the processor 253 and other components are mounted. The connector and the interface circuit constituting the interface section 242 are mounted on the main substrate of the control section 250.

[0141] The interface unit 242 may receive setting information or instruction information sent by other electronic devices 100 .

[0142] The control unit 250 includes a memory 251 and a processor 253 .

[0143] The memory 251 is a storage device that non-volatilely stores programs and data executed by the processor 253. The memory 251 is composed of a semiconductor storage element such as a magnetic storage device, a flash read-only memory (ROM), or other types of non-volatile storage devices. The memory 251 may also include a random access memory (RAM) that constitutes a work area of ​​the processor 253.

[0144] The memory 251 stores data processed by the control unit 250 and a control program executed by the processor 253. The processor 253 may be composed of a single processor or a combination of a plurality of processor groups.

[0145] The processor 253 executes the control program to control various parts of the projection device 200. For example, the processor 253 performs corresponding image processing according to the operation signal generated by the operation unit 231, and outputs the parameters used in the image processing (such as the parameters for performing trapezoidal correction on the image) to the image processing unit 245. In addition, the processor 253 can control the light source in the light source unit 211 to turn on, turn off, or adjust the brightness by controlling the light source driving unit 221.

[0146] The image processing unit 245 and the frame memory 243 may be formed by an integrated circuit. The integrated circuit includes a large-scale integrated circuit (LSI), an application-specific integrated circuit (ASIC), and a programmable logic device (PLD), wherein the PLD may include a field-programmable gate array (FPGA). The integrated circuit may also include a portion of an analog circuit, or a combination of a processor and an integrated circuit.

[0147] The combination of a processor and an integrated circuit is called a microcontroller unit (MCU), system on chip (SoC), system LSI, chipset, etc.

[0148] The image processing unit 245 may store the image data received from the data interface 241 in the frame memory 243. The frame memory 243 includes a plurality of memory banks, each of which includes a storage capacity that can write image data of one frame. The frame memory 243 may be composed of a synchronous dynamic random access memory (SDRAM) or a dynamic random access memory (DRAM).

[0149] The image processing unit 245 may perform image processing on the image data stored in the frame memory 243 , including resolution conversion, size adjustment, distortion correction, shape correction, digital zoom, image tone adjustment, image brightness adjustment, and the like.

[0150] The image processing unit 245 can also convert the input frame frequency of the vertical synchronization signal into a drawing frequency and generate a vertical synchronization signal having the drawing frequency. The generated vertical synchronization signal is called an output synchronization signal. The image processing unit 245 then outputs the output synchronization signal to the optical modulator driving unit 222.

[0151] The above description is only a preferred embodiment of the disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the disclosure.

[0152] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0153] Although the subject matter has been described in the language of specific method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. A method for image color correction, characterized in that: include: Receiving first color data of each pixel in a source image; Performing white balance processing on the first color data to obtain gain coefficients of second color data and the first color data; determining color shift data according to the first color data and the second color data; Adjust the first color data according to the color shift data and the gain coefficient to obtain third color data; Color correction is performed on the source image according to the third color data.

2. The method according to claim 1, characterized in that The color shift data includes a color shift value; and determining the color shift data according to the first color data and the second color data includes: Acquire a first coordinate set corresponding to the first color data and a second coordinate set corresponding to the second color data in a color space; A color shift value is calculated according to the first coordinate set and the second coordinate set.

3. The method according to claim 2, characterized in that The calculating the color shift value according to the first coordinate set and the second coordinate set includes: Comparing the first coordinate set and the second coordinate set to obtain a comparison result; Determining a target gain plane in the color space according to the comparison result; Selecting a target pixel coordinate from the second coordinate set, and calculating a target relative distance between the target pixel coordinate and the target gain plane; Determining a first influence weight corresponding to the second coordinate set according to the target relative distance; Compressing the gain coefficient according to a preset color gain range to obtain a second influence weight; The color shift value is calculated according to the first influence weight and the second influence weight, wherein the first influence weight, the second influence weight, and the color shift value are positively correlated.

4. The method according to claim 3, characterized in that The color space includes three-dimensional coordinate axes; the target gain plane includes a first gain plane and a second gain plane; and determining the target gain plane in the color space according to the comparison result includes: According to the comparison result, determining at least two gain coordinate axes from the three-dimensional coordinate axes; Determining the first gain plane according to the at least two gain color coordinate axes; Determining a second gain plane in the color space that is parallel to the first gain plane; The target relative distance includes a first target relative distance and a second target relative distance; and the calculating the target relative distance between the target pixel coordinate and the target gain plane includes: A first target relative distance is calculated according to the target pixel coordinates and the first gain plane, and a second target relative distance is calculated according to the target pixel coordinates and the second gain plane.

5. The method according to claim 4, characterized in that The second coordinate set includes a plurality of pixel coordinates; and selecting the target pixel coordinates from the second coordinate set includes: Selecting a target pixel coordinate from the plurality of pixel coordinates; The determining, according to the target relative distance, a first influence weight corresponding to the second coordinate set includes: Determining a first sub-influence weight of the target pixel coordinate according to the first target relative distance, wherein the first target relative distance is negatively correlated with the first sub-influence weight; determining a second sub-influence weight of the target pixel according to the second target relative distance, wherein the second target relative distance is negatively correlated with the second sub-influence weight; Calculating a third sub-influence weight of each of the pixel coordinates according to the first sub-influence weight and the second sub-influence weight; The third sub-influence weights of each of the pixel coordinates are combined to obtain a first influence weight.

6. The method according to claim 3, characterized in that The preset color gain range includes a lower threshold and an upper threshold; The step of compressing the gain coefficient according to the preset color gain range to obtain a second influence weight includes: comparing the gain coefficient with the preset color range; When the gain coefficient is less than the lower limit threshold, using the first gain coefficient as the second influence weight; When the gain coefficient is greater than the upper threshold, using a second gain coefficient as the second influence weight, wherein the first gain coefficient is less than the second gain coefficient; When the gain coefficient is greater than the lower threshold and less than the upper threshold, the gain coefficient is processed based on a preset monotonically increasing function to obtain the second influence weight.

7. The method according to any one of claims 1 to 6, characterized in that Before adjusting the first color data according to the color shift data and the gain coefficient, the method further includes: Performing a preset color format conversion process on the first color data to obtain fourth color data; Determine protection color data according to the first color data and the fourth color data, wherein the protection color data includes a third influence weight corresponding to the protection color; The color offset data is modified from the third influence weight.

8. An image color correction device, characterized in that: include: A receiving module, used for receiving first color data of each pixel in a source image; A white balance module, used to perform white balance processing on the first color data to obtain a gain coefficient of the second color data and the first color data; A first calculation module, configured to determine color shift data according to the first color data and the second color data; A second calculation module, configured to adjust the first color data according to the color offset data and the gain coefficient to obtain third color data; A correction module is used to perform color correction on the source image according to the third color data.

9. A projection device, characterized in that: The projection device comprises: one or more processors; A memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the image color correction method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored thereon, and the computer program is loaded by a processor to execute the image color correction method according to any one of claims 1 to 7.

Images