CN115426485A - Color correction matrix adjustment method, camera equipment, electronic equipment and storage medium - Google Patents

Abstract

The application discloses a method for adjusting a color correction matrix, an imaging device, an electronic device, and a computer-readable storage medium. The method for adjusting the color correction matrix includes: acquiring calibration data of the imaging equipment; acquiring real-time gamma curve data of the imaging equipment based on the environment where the imaging equipment is located; and generating real-time color correction matrix data based on the calibration data and the real-time gamma curve data. Through the above method, the present application can dynamically adjust the generated new color correction matrix in real time based on the real-time scene where the camera device is located.

Description

Color correction matrix adjustment method, camera equipment, electronic equipment and storage medium

technical field

The present application relates to the technical field of video monitoring, in particular to a method for adjusting a color correction matrix, an imaging device, an electronic device, and a computer-readable storage medium.

Background technique

In the existing color correction matrix (Color Correction Matrix, CCM), generally a group of target CCMs are calibrated in advance under three color temperatures of high, medium and low, and used in real time in the camera equipment.

In actual use, the action of the CCM is calibrated in advance to achieve the uniform color style of the debugged camera equipment and the target. Theoretically, in order to keep the color reproduction consistent with the laboratory calibration stage in any scene, it is best to use all possible gamma curves for calibration in the laboratory calibration stage, and the CCM obtained from all calibrations They are all stored in the camera device, and the corresponding CCM is selected and used according to the gamma curve currently used during actual use.

However, in actual use, a limited number of gamma curves are not pre-stored in the camera device, but are dynamically adjusted and generated by the camera device in real time according to the current scene. Therefore, it is impossible to estimate the gamma curve that will be used in advance, nor It is impossible to perform pre-calibration and obtain the corresponding CCM.

Contents of the invention

The present application proposes a color correction matrix adjustment method, device, electronic equipment, and computer-readable storage medium to solve the above-mentioned problem that the color correction matrix cannot be dynamically generated in real time.

In order to solve the above technical problems, a technical solution adopted by the present application is to provide a color correction matrix adjustment method, which includes:

Obtain the calibration data of the imaging equipment; acquire the real-time gamma curve data of the imaging equipment based on the environment where the imaging equipment is located; and generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data.

Wherein, the calibration data includes calibration color correction matrix data, calibration gamma curve data and a color mapping table, and the color correction matrix data and gamma curve data are associated through the color mapping table.

Among them, based on calibration data and real-time gamma curve data, generating real-time color correction matrix data includes:

The correction coefficient is calculated based on the color mapping table, the calibrated color correction matrix data, the calibrated gamma curve data and the real-time gamma curve data; the real-time color correction matrix data is calculated based on the correction coefficient and the calibrated color correction matrix data.

Calculate the correction coefficient based on the color mapping table, calibrated color correction matrix data, calibrated gamma curve data and real-time gamma curve data, including:

Obtain the matrix product of calibrated color correction matrix data and color mapping table; obtain gamma data based on matrix product and calibrated gamma curve data; obtain color correction matrix data based on real-time gamma curve data and gamma data; based on color correction matrix Multiply the data and the matrix to obtain the correction coefficient.

Among them, the gamma data is obtained based on the matrix product and the calibration gamma curve data, including:

Input the matrix product into the calibrated gamma curve data for table lookup to obtain gamma data.

Among them, based on the real-time gamma curve data and gamma data, the color correction matrix data is obtained, including:

Obtain an inverse array of the real-time gamma curve data based on the real-time gamma curve data; input the gamma data into the inverse array to perform table lookup to obtain color correction matrix data.

Among them, the real-time color correction matrix data is calculated based on the correction coefficient and the calibration color correction matrix data, including:

Calculate the uncorrected matrix data of the real-time color correction matrix data based on the correction coefficient and the calibrated color correction matrix data; perform a normalization operation on the uncorrected matrix data to obtain the real-time color correction matrix data.

Wherein, the real-time gamma curve data of the camera device is obtained based on the environment where the camera device is located, including:

Adjust the dynamic range of the camera device in real time based on the environment; obtain the first image data and the second image data before and after the dynamic range adjustment of the camera device; based on the first image data and the second image data, obtain real-time gamma curve data through curve fitting .

Wherein, the color correction matrix adjustment method further includes:

Acquiring the current image; generating current color correction matrix data based on the environment information of the current image; performing color correction on the current image based on the current color correction matrix data.

In order to solve the above technical problems, another technical solution adopted by the present application is to provide an imaging device, which includes a data acquisition module, a calculation module and a storage module.

The data acquisition module is used to obtain the calibration data of the camera equipment, and obtain the real-time gamma curve data of the camera equipment based on the environment where the camera equipment is located; the calculation module is used to generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data; The storage module is used to store calibration data.

In order to solve the above technical problems, a technical solution adopted by the present application is to provide an electronic device, the electronic device includes a processor and a memory connected to the processor, the memory stores program data, and the processor executes the program data stored in the memory , to implement: obtain the calibration data of the camera device; obtain the real-time gamma curve data of the camera device based on the environment where the camera device is located; generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data.

In order to solve the above technical problems, another technical solution adopted by this application is to provide a computer-readable storage medium, which stores program instructions inside, and the program instructions are executed to realize: obtaining the calibration data of the imaging equipment; Acquire real-time gamma curve data of camera equipment in an environment; generate real-time color correction matrix data based on calibration data and real-time gamma curve data.

The beneficial effect of the present application is: different from the situation of the prior art, the present application obtains the calibration data of the camera equipment, and then obtains the real-time gamma curve data based on the environment where the camera equipment is located, so that based on the calibration data and the real-time gamma curve data to generate real-time color correction matrix data, which improves the current situation that the traditional color correction matrix adjustment and gamma curve data adjustment are out of touch. The color correction efficiency of the device is higher, and the color style of the image output is better.

Description of drawings

Figure 1 is a schematic diagram of the sensor's response to the RGB spectrum;

Figure 2 is a schematic diagram of the response of the human eye to the RGB spectrum;

Fig. 3 is the formula of color matrix correction;

FIG. 4 is a schematic flowchart of the first embodiment of the color correction matrix adjustment method of the present application;

FIG. 5 is a schematic flow chart of a first embodiment of step S103 in FIG. 4;

FIG. 6 is a schematic flow chart of a specific embodiment of step S201 in FIG. 5;

FIG. 7 is a schematic flow chart of a specific embodiment of step S303 in FIG. 6;

FIG. 8 is a schematic flowchart of a second embodiment of step S103 in FIG. 4;

FIG. 9 is a schematic flow chart of a specific embodiment of step S102 in FIG. 4;

Fig. 10 is a schematic diagram of the control flow of a specific solution of the color correction matrix adjustment method of the present application;

FIG. 11 is a schematic flowchart of the second embodiment of the color correction matrix adjustment method of the present application;

Fig. 12 is a schematic structural diagram of an embodiment of the imaging device of the present application;

FIG. 13 is a schematic structural diagram of an embodiment of the electronic device of the present application;

Fig. 14 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

With the development of security technology, the status quo pays more and more attention to the improvement of image quality. As the most important part of image quality, color is the first indicator for users to evaluate the video. At present, the most commonly used method for color processing is the color correction matrix. The color correction matrix is a color correction method to correct the difference in spectral response between the sensor and the human eye, and to make the video image closer to the physical world seen by the human eye. Please refer to Figure 1 and Figure 2, as shown in Figure 1, Figure 1 is a schematic diagram of the response of the sensor to the RGB spectrum, and Figure 2 is a schematic diagram of the response of the human eye to the RGB spectrum, it can be seen that the RGB response curves of the two are inconsistent , and after the image is processed by white balance, there will definitely be a color cast, so it is necessary to correct the color matrix. Please refer to Figure 3, Figure 3 is the formula for color matrix correction. The color correction matrix is a 3*3 matrix, which is multiplied by the statistical value of the RGB channel of the original image to obtain the RGB channel value of the target image.

Existing color correction matrices generally calibrate a set of target color correction matrices at high, medium and low color temperatures in advance and use them in real time in camera equipment. The equipment estimates the color temperature value of the current monitoring screen through an algorithm, and then goes to The interpolation finds the corresponding color correction matrix for use in the camera device.

In actual use, the action of color correction CCM is calibrated in advance to realize the unification of the camera equipment being debugged and the target color style. During the debugging process, the human eyes are often used to subjectively judge the color style difference between the debugged camera device and the target camera device, and fine-tune each element in the 3*3 matrix of the color correction matrix according to the debugging experience of the actual debugger. After that, it will be validated to the camera equipment side for comparison of color style differences. The intermediate process is time-consuming and labor-intensive, which is very inefficient. At the same time, in the actual use of camera equipment, the dynamic range module will be adjusted in real time according to the current scene, and different gamma curves will be used at this time.

Dynamic range refers to the richness of dark and bright details recorded by a digital camera at the same time. The higher the dynamic range, the richer the picture details can be recorded. In camera equipment, due to graphics cards or monitors, there may be deviations in the brightness of the actual output image, and the gamma curve is used to correct the brightness deviation of the image.

After the dynamic range module of the camera equipment changes, it will affect the color reproduction of the image. In theory, in order to maintain the color reproduction, it can be consistent with the laboratory calibration stage in any scene. It is best to need to be in the laboratory calibration stage. All possible gamma curves will be used for calibration, and all the calibrated color correction matrix values will be saved in the camera device, and the corresponding color correction will be selected according to the currently used gamma curve in actual use matrix values. However, in actual use, the gamma curve is not fixed, nor is it a limited number of lines, but is dynamically adjusted and generated by the camera equipment in real time according to the current scene. Therefore, it is impossible to estimate the gamma curve that will be used in advance, and it is impossible to calibrate the color correction matrix in advance.

In order to solve the above problems, the present application first proposes a color correction matrix adjustment method, please refer to FIG. 4 , as shown in FIG. 4 , and FIG. 4 is a schematic flowchart of the first embodiment of the color correction matrix adjustment method of the present application. The method specifically includes steps S101 to S103:

Step S101: Obtain calibration data of an imaging device.

The camera equipment obtains its own calibration data, which includes color mapping table, calibration color correction matrix data, and calibration gamma curve data. The color mapping table is a 3*n table, which belongs to a 3D display lookup table; calibration color The correction matrix data is the color correction matrix in the laboratory calibration stage, which is a 3*3 matrix as shown in Figure 3; the calibration gamma curve data is the gamma curve corresponding to the calibration color correction matrix data, so that the image output by the camera equipment After calibrating the color correction matrix data and calibrating the gamma curve data, the output quality is better. The camera device stores its own calibration data, including the color mapping table, in its own device to provide calculation basis for subsequent adjustment of real-time color correction matrix data.

Step S102: Obtain real-time gamma curve data of the imaging device based on the environment where the imaging device is located.

In the actual use of camera equipment, the gamma curve data will be adjusted in real time according to the environment. At this time, the gamma curve will be transformed to generate real-time gamma curve data corresponding to the environment. At this time, the camera equipment can Obtain its own real-time gamma curve data.

Step S103: Generate real-time color correction matrix data based on the calibration data and real-time gamma curve data.

The camera equipment is based on its own calibration color correction matrix data and calibration gamma curve data, and then introduces the color mapping table, combines the color correction matrix data and gamma curve data through arithmetic operations, and calculates and generates video by obtaining real-time gamma curve data. The real-time color correction matrix data of the device can realize the dynamic adjustment of the gamma curve data and the synchronous adjustment of the color correction matrix data during the actual operation of the camera device to generate real-time color correction matrix data.

Different from the prior art, the present application obtains the calibration data of the imaging equipment, and then acquires the real-time gamma curve data based on the environment where the imaging equipment is located, thereby generating real-time color correction matrix data based on the calibration data and the real-time gamma curve data , improving the current situation that the traditional color correction matrix adjustment is out of touch with the gamma curve data adjustment. This application can enable the dynamic linkage adjustment of the color correction matrix data and the gamma curve data, so that the efficiency of the color correction of the camera equipment is higher. The color style of the image output is unified.

Optionally, please refer to FIG. 5 for the method of generating real-time color correction matrix data. As shown in FIG. 5 , FIG. 5 is a schematic flowchart of a first embodiment of step S103 in FIG. 4 . Wherein, the calibration data includes calibration color correction matrix data, calibration gamma curve data and color mapping. The color correction matrix data and the gamma curve data are associated through a color mapping table. In this embodiment, step S103 can be realized through the method shown in FIG. 4 , and the specific implementation steps include steps S201 to S202:

Step S201: Calculating correction coefficients based on the color mapping table, calibration color correction matrix data, calibration gamma curve data and real-time gamma curve data.

Among them, the color mapping table is a 3*n table, belonging to a 3D display lookup table, which divides the three channels of RGB into several parts to form a three-dimensional table. The color mapping table calibrated in this application can be a linear table. In this embodiment, it can be specifically a 3*4096 table. In other embodiments, this application can freely choose other display lookup tables. This is not limited.

The imaging device first obtains its own calibrated color correction matrix data and calibrated gamma curve data, and stores the color mapping table lut_original, calibrated color correction matrix data ccm_original, and calibrated gamma curve data gamma_original in its own device. The imaging device then obtains its own real-time gamma curve data, and calculates the correction coefficient of the color correction data by introducing the color mapping table lut_original to associate the color correction matrix data with the gamma curve data. In this implementation, the color mapping table lut_original is 3 * A matrix data of 4096.

Optionally, FIG. 6 is a schematic flowchart of a specific embodiment of step S201 in FIG. 5 . In this embodiment, step S201 can be realized through the method shown in FIG. 6, and the specific implementation steps include step S301 to step S304:

Step S301: Obtain the matrix product of the calibration color correction matrix data and the color mapping table.

The imaging device obtains the matrix product ccmMat_original of the calibration color correction matrix data ccm_original and the color mapping table lut_original. Wherein, the matrix product ccmMat_original=ccm_original*lut_original.

Step S302: Obtain gamma data based on matrix product and calibration gamma curve data.

The imaging device can obtain gamma data gammaMat_orginal based on the matrix product ccmMat_original and the calibration gamma curve data gamma_original.

Specifically, this embodiment can realize step S302 through the following method, and the method is as follows:

Input the matrix product into the calibrated gamma curve data for table lookup to obtain gamma data.

The imaging device can use the matrix product ccmMat_original as an input to perform a table lookup in the calibration gamma curve data gamma_original, so as to obtain the gamma data gammaMat_orginal.

In other embodiments, the imaging device may also obtain the gamma data gammaMat_orginal in other ways, which is not limited herein.

Step S303: Obtain color correction matrix data based on the real-time gamma curve data and the gamma data.

Based on the real-time gamma curve data gamma_curr and the gamma data gammaMat_orginal, the imaging device can obtain the color correction matrix data ccmMat_curr through a correlation calculation method.

Optionally, FIG. 7 is a schematic flowchart of a specific embodiment of step S303 in FIG. 6 . In this embodiment, step S303 can be realized through the method shown in FIG. 7, and the specific implementation steps include step S401 to step S402:

Step S401: Obtain an inverse array of the real-time gamma curve data based on the real-time gamma curve data.

The imaging device acquires real-time gamma curve data gamma_curr, and obtains an inverse array invgamma_curr of real-time gamma curve data by inverting the real-time gamma curve data gamma_curr.

Step S402: Input the gamma data into the inverse array for table lookup to obtain color correction matrix data.

The imaging device then uses the gamma data gammaMat_orginal as an input to perform table lookup in the inverse array invgamma_curr to obtain the color correction matrix data ccmMat_curr.

Step S304: Obtain correction coefficients based on the color correction matrix data and the matrix product.

The imaging device obtains the correction coefficient coef based on the color correction matrix data ccmMat_curr and the matrix product ccmMat_original.

Among them, the correction coefficient coef=ccmMat_original\ccmMat_curr, "\" is a matrix calculation method, the matrix product ccmMat_original and the color correction matrix data ccmMat_curr are both 3*4096 arrays, and the calculation result is a 3*3 array. The "\" used in it is a usage of inverse division, which is equivalent to multiplying the inverse of cmMat_original by ccmMat_curr.

Step S202: Calculate real-time color correction matrix data based on the correction coefficients and the calibration color correction matrix data.

After the camera equipment calculates the correction coefficient coef, the real-time color correction matrix data CCM can be calculated and obtained by multiplying the correction coefficient coef by the calibration color correction matrix data ccm_original. Wherein, CCM=coef*ccm_original.

Different from the prior art, the color correction matrix adjustment method of the present application is combined with the use of the color mapping table lut_original, so that the color correction matrix data and the gamma curve data can be dynamically adjusted in conjunction, so that the image output efficiency of the camera device is higher, and the image The color uniform style is better;

In fact, this application is based on the color mapping table lut_original, the calibration color correction matrix data ccm_original and the calibration gamma curve data gamma_original, combined with the real-time gamma wireless data of the camera equipment in actual use, to realize the dynamics of the final real-time color correction matrix data Adjustment can realize the quantization operation of color correction matrix data adjustment; and this application aims at the problem of unequal physical equivalent between color correction matrix data and gamma curve data, and proposes to use the color mapping table lut_original as a bridge between the two physical quantities to The color correction matrix data and the gamma curve data can be mixed and calculated.

In the calculation process of this application, it is also proposed to use the table lookup method to calculate the intermediate gamma data gammaMat_orginal and the color correction matrix data ccmMat_curr, which can effectively improve the calculation efficiency, and realize the dynamics of the color correction matrix data and the gamma curve data Adjustment and blending calculations.

Optionally, please refer to FIG. 8 for the method of generating real-time color correction matrix data. As shown in FIG. 8 , FIG. 8 is a schematic flowchart of a second embodiment of step S103 in FIG. 4 . Wherein, the calibration data includes the color mapping table lut_original, the calibration color correction matrix data ccm_original and the calibration gamma curve data gamma_original. In this embodiment, step S103 can be realized through the method shown in FIG. 8, and the specific implementation steps include step S501 to step S503:

Step S501: Calculating correction coefficients based on the color mapping table, calibration color correction matrix data, calibration gamma curve data and real-time gamma curve data.

Step S501 is the same as step S201 and will not be repeated here.

Step S502: Calculate uncorrected matrix data of the real-time color correction matrix data based on the correction coefficients and the calibration color correction matrix data.

The camera equipment calculates the uncorrected matrix data of the real-time color correction matrix data CCM based on the correction coefficient coef and the calibration color correction matrix data ccm_original, because the calculated correction coefficient coef only reflects the difference between the color correction matrix data ccmMat_curr and the matrix product ccmMat_original At this time, it cannot represent the effective color correction matrix value that needs to be superimposed on the camera device at this time, so the final effective color correction matrix value needs to be calculated with the original color correction matrix value for final correction.

Step S503: Perform a normalization operation on the uncorrected matrix data to obtain real-time color correction matrix data.

The camera equipment performs a normalization operation on the uncorrected matrix data to obtain the final real-time color correction matrix data. The specific method is: calculate the result of CCM of the real-time color correction matrix data, assuming that the CCM result of the color correction matrix data is [c00 c01 c02; c10 c11 c12; c20 c21 c22], then in order to ensure the standardization of the matrix, c02 can be constrained =1-c00-c01; c12=1-c10-c11; c22=1-c20-c21. In other embodiments, there are many specific constraint manners, and are not limited to this one constraint manner in this embodiment.

In this application, by performing a normalization operation on the final calculated real-time color correction matrix data CCM, it can ensure that the final real-time color correction matrix data can take effect in the camera device, and the stability of the color correction matrix adjustment method is improved.

Optionally, please refer to FIG. 9 for the method of acquiring real-time gamma curve data. As shown in FIG. 9 , FIG. 9 is a schematic flowchart of a specific embodiment of step S102 in FIG. 4 . In this embodiment, step S102 can be realized through the method shown in FIG. 9 , and the specific implementation steps include step S601 to step S603:

Step S601: Adjust the dynamic range of the real-time camera device based on the environment.

The camera adjusts its own dynamic range in real time based on the environment. The dynamic range is adjusted in real time based on the current environment. Due to the difference in the dynamic range, the gamma curve data used by the camera equipment is different. When the camera equipment adjusts the dynamic range, there are many ways to adjust the dynamic range, and there are many ways to adjust the dynamic range. It is more common to adjust the dynamic range by adjusting the gamma curve data.

Step S602: Obtain the first image data and the second image data before and after the dynamic range adjustment of the imaging device.

However, the adjustment method adopted by the camera device to adjust the dynamic range is not to adjust the gamma curve data. At this time, the camera device needs to obtain the first image data and the second image data before and after the dynamic range adjustment of the camera device. For example, the first image brightness data A of the imaging device before the adjustment and the second image brightness data B of the imaging device after adjustment are acquired.

Step S603: Obtain real-time gamma curve data by curve fitting based on the first image data and the second image data.

The imaging device can acquire real-time gamma curve data gamma_curr by means of curve fitting between the second image brightness data B and the first image brightness data A.

This application obtains the first image data and the second image data before and after the adjustment of the dynamic module, and performs fitting to obtain real-time gamma curve data. No matter when the camera equipment adjusts the dynamic range in any way, the kinetic energy can obtain equivalent real-time The gamma curve data is beneficial to improve the efficiency of the color correction matrix adjustment method.

In an application scenario, please refer to FIG. 10 . FIG. 10 is a schematic control flow diagram of a specific solution of the color correction matrix adjustment method of the present application. The control flow of a specific solution of the color correction matrix adjustment method of this embodiment specifically includes steps S701 to S709:

Step S701: Store the color mapping table lut_original, the calibration color correction matrix data ccm_original and the calibration gamma curve data gamma_original in the imaging device.

Step S702: Calculate the matrix product ccmMat_original=ccm_original*lut_original.

Step S703: use the matrix product ccmMat_original as input to perform a table lookup in the calibration gamma curve data gamma_original to obtain the gamma data gammaMat_orginal.

Step S704: The dynamic range of the camera device is automatically adjusted according to the real-time scene, and real-time gamma curve data gamma_curr is obtained by calculation.

Step S705: Calculate the inverse array invgamma_curr of the real-time gamma curve data gamma_curr.

Step S706: Take the gamma data gammaMat_orginal as an input and perform table lookup in the inverse array invgamma_curr to obtain the color correction matrix data ccmMat_curr.

Step S707: Calculate the correction coefficient coef=ccmMat_original\ccmMat_curr.

Step S708: Calculate the final CCM=coef*ccm_original.

Step S709: Perform a normalization operation on the obtained preliminary CCM value to obtain a final CCM value.

Optionally, the present application further proposes a color correction matrix adjustment method, please refer to FIG. 11 , as shown in FIG. 11 , which is a schematic flowchart of a second embodiment of the color correction matrix adjustment method of the present application. The method for adjusting the color correction matrix further includes steps S801 to S803:

Step S801: Acquire the current image.

The camera device acquires the current image.

Step S802: Generate current color correction matrix data based on the environment information of the current image.

The imaging device obtains the real-time gamma curve data gamma_curr based on the current environment information, and generates the current color correction matrix data through the above calculation method through the stored color mapping table lut_original, the calibration color correction matrix data ccm_original and the calibration gamma curve data gamma_original.

Step S803: Perform color correction on the current image based on the current color correction matrix data.

The camera device performs color correction on the current image based on the current color correction matrix data, so that the efficiency of the color correction of the camera device is higher, and the color style of the image output is unified.

Different from the existing technology, this application is based on the fact that the image color style of the device to be debugged needs to be adjusted to be consistent with the style of the target device based on the actual working conditions of the camera device. However, the method of the prior art relies on manual subjective correction of the color correction matrix data The debugging of the evaluation is time-consuming and labor-intensive, and after the gamma curve data changes dynamically due to scene changes, the corresponding color correction matrix data cannot be adjusted in time, resulting in the deviation of the color style. This application obtains the calibration data of the camera equipment, and then obtains real-time gamma curve data based on the environment where the camera equipment is located, thereby generating real-time color correction matrix data based on the calibration data and real-time gamma curve data, which improves the traditional color correction method. It corrects the current situation that the matrix adjustment is out of touch with the gamma curve data adjustment, and proposes to make full use of the influence of the real-time gamma curve data on the color space on the premise of judging the current real scene color without the aid of an intelligent detection algorithm to extract the Among them, the regular characteristics of quantitative expression, with the help of the color mapping table, the color correction matrix data and the gamma curve data are skillfully combined, and the automatic and dynamic adjustment of the color correction matrix data is realized in the process of changing the gamma curve data of the camera equipment. The method of application does not require human intervention, and can be converted in real time in the camera equipment.

Optionally, the present application further proposes an imaging device, please refer to FIG. 12 , which is a schematic structural diagram of an embodiment of the imaging device of the present application. The imaging device 100 of this embodiment includes a data acquisition module 101 , a calculation module 102 and a storage module 103 .

The data acquisition module 101 is respectively connected with the calculation module 102 and the storage module.

The data acquisition module 101 is used to acquire the calibration data of the imaging device 100, and acquire the real-time gamma curve data of the imaging device 100 based on the environment where the imaging device 100 is located. Wherein, the calibration data is a color mapping table, calibration color correction matrix data, calibration gamma curve data.

The calculation module 102 is used for generating real-time color correction matrix data based on the calibration data and real-time gamma curve data. The storage module 103 is used for storing calibration data.

Optionally, the present application further proposes an electronic device. Please refer to FIG. 13 . FIG. 13 is a schematic structural diagram of an embodiment of the electronic device of the present application. The electronic device 200 includes a processor 201 and a memory 202 connected to the processor 201 .

The processor 201 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 201 may be an integrated circuit chip with signal processing capability. The processor 201 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components . A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The memory 202 is used to store program data required by the processor 201 to run.

The processor 201 is also configured to execute the program data stored in the memory 202 to implement the above method for adjusting the color correction matrix.

Optionally, the present application further provides a computer-readable storage medium. Please refer to FIG. 14 . FIG. 14 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.

The computer-readable storage medium 300 of the embodiment of the present application stores program instructions 310 inside, and the program instructions 310 are executed to implement the above-mentioned color correction matrix adjustment method.

Wherein, the program instruction 310 can form a program file and be stored in the above-mentioned storage medium in the form of a software product, so that an electronic device (which can be a personal computer, server, or network device, etc.) or a processor (processor) executes various All or part of the steps of the method of implementation. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. , or terminal devices such as computers, servers, mobile phones, and tablets.

The computer-readable storage medium 300 of this embodiment may be, but not limited to, a USB flash drive, an SD card, a PD optical drive, a mobile hard disk, a large-capacity floppy drive, a flash memory, a multimedia memory card, a server, and the like.

In one embodiment there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the electronic device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the electronic device executes the steps in the foregoing method embodiments.

In addition, if the above-mentioned functions are implemented in the form of software functions and sold or used as independent products, they can be stored in a storage medium that can be read by a mobile terminal, that is, the application also provides a storage device that stores program data, so The above program data can be executed to implement the methods of the above embodiments, and the storage device can be, for example, a U disk, an optical disk, a server, and the like. That is to say, the present application may be embodied in the form of a software product, which includes several instructions for enabling an intelligent terminal to execute all or part of the steps of the method described in each embodiment.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present application, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Any process or method descriptions described in flowcharts or otherwise herein may be understood to represent a mechanism, segment or portion of code comprising one or more executable instructions for implementing specific logical functions or steps of a process , and the scope of preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in substantially simultaneous fashion or in reverse order depending on the functions involved, which shall It should be understood by those skilled in the art to which the embodiments of the present application belong.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium, For the use of instruction execution systems, devices or equipment (which may be personal computers, servers, network equipment or other systems that can fetch instructions from instruction execution systems, devices or devices and execute instructions), or in combination with these instruction execution systems, devices or devices And use. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. The program is processed electronically and stored in computer memory.

The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process conversion made by using the specification and drawings of the application, or directly or indirectly used in other related technologies fields, are all included in the scope of patent protection of this application in the same way.

Claims (11)

1. A method for adjusting a color correction matrix, comprising: Obtain the calibration data of the camera equipment; Acquiring real-time gamma curve data of the imaging device based on the environment where the imaging device is located; Generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data. 2. The method for adjusting the color correction matrix according to claim 1, wherein the calibration data includes calibration color correction matrix data, calibration gamma curve data and a color mapping table, wherein the color correction matrix data and the gamma curve Data is associated through said color map; The generating real-time color correction matrix data based on the calibration data and the real-time gamma curve data includes: calculating a correction coefficient based on the color mapping table, the calibration color correction matrix data, the calibration gamma curve data and the real-time gamma curve data; The real-time color correction matrix data is calculated based on the correction coefficients and the calibration color correction matrix data. 3. The color correction matrix adjustment method according to claim 2, wherein said color correction matrix data based on said color mapping table, said calibration color correction matrix data, said calibration gamma curve data and said real-time gamma curve Correction coefficients are calculated from the data, including: Obtaining the matrix product of the calibration color correction matrix data and the color mapping table; Obtaining gamma data based on the matrix product and the calibration gamma curve data; Obtaining color correction matrix data based on the real-time gamma curve data and the gamma data; The correction coefficient is obtained based on the color correction matrix data and the matrix product. 4. The color correction matrix adjustment method according to claim 3, wherein said obtaining gamma data based on said matrix product and said calibration gamma curve data comprises: inputting said matrix product into said Perform a table lookup on the calibration gamma curve data to obtain the gamma data. 5. The method for adjusting a color correction matrix according to claim 3, wherein said obtaining color correction matrix data based on said real-time gamma curve data and said gamma data comprises: Obtaining an inverse array of the real-time gamma curve data based on the real-time gamma curve data; Inputting the gamma data into the inverse array to perform table lookup to obtain the color correction matrix data. 6. The method for adjusting the color correction matrix according to claim 2, wherein the calculation of the real-time color correction matrix data based on the correction coefficient and the calibration color correction matrix data comprises: calculating uncorrected matrix data of the real-time color correction matrix data based on the correction coefficients and the calibration color correction matrix data; A normalization operation is performed on the uncorrected matrix data to obtain the real-time color correction matrix data. 7. The method for adjusting the color correction matrix according to claim 1, wherein the acquiring the real-time gamma curve data of the imaging device based on the environment where the imaging device is located comprises: adjusting the dynamic range of the imaging device in real time based on the environment; Acquiring the first image data and the second image data before and after the adjustment of the dynamic range of the imaging device; Based on the first image data and the second image data, the real-time gamma curve data is obtained by curve fitting. 8. The method for adjusting the color correction matrix according to any one of claims 1 to 7, wherein the method for adjusting the color correction matrix further comprises: Get the current image; Generate current color correction matrix data based on the environment information of the current image; Perform color correction on the current image based on the current color correction matrix data. 9. An imaging device, characterized in that it comprises: The data acquisition module is used to obtain the calibration data of the imaging equipment, and obtain the real-time gamma curve data of the imaging equipment based on the environment where the imaging equipment is located; A calculation module, configured to generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data; A storage module, configured to store the calibration data. 10. An electronic device, characterized in that the electronic device comprises a processor and a memory connected to the processor, wherein program data is stored in the memory, and the processor executes the stored program in the memory. The program data described above to implement implementation: Obtain the calibration data of the camera equipment; Acquiring real-time gamma curve data of the imaging device based on the environment where the imaging device is located; Generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data. 11. A computer-readable storage medium, characterized in that it stores program instructions inside, and the program instructions are executed to realize: Obtain the calibration data of the camera equipment; Acquiring real-time gamma curve data of the imaging device based on the environment where the imaging device is located; Generate real-time color correction matrix data based on the calibration data and the real-time gamma curve data.

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