WO2021018001A1

WO2021018001A1 - Method and device for adjusting white balance value of television, and computer readable storage medium

Info

Publication number: WO2021018001A1
Application number: PCT/CN2020/103837
Authority: WO
Inventors: 蒋明珠
Original assignee: 惠州视维新技术有限公司
Priority date: 2019-07-26
Filing date: 2020-07-23
Publication date: 2021-02-04
Also published as: CN110267024A

Abstract

Disclosed in the present application is a method for adjusting a white balance value of a television. The method comprises: obtaining a display image of a television to be adjusted; performing calculation on the display image according to a pre-trained optimal parameter network model to obtain a corresponding white balance value; and sending the white balance value to the corresponding television to be adjusted so that the television to be adjusted performs white balance value adjustment according to the white balance value. Also disclosed in the present application is a device for adjusting a white balance value of a television, and a computer-readable storage medium.

Description

Method, device and computer readable storage medium for adjusting TV white balance value

This application claims the priority of the Chinese patent application filed on July 26, 2019, the application number is 201910686516.9, and the title is "Method, device and computer-readable storage medium for adjusting TV white balance value", the entire content of which is incorporated by reference Incorporated in this application.

Technical field

This application relates to the field of television images, and in particular, to a method, device and computer-readable storage medium for adjusting a television white balance value.

Background technique

In the production process of LCD TVs, in order to adapt to the subjective perception of colors of different people, each TV needs to be adjusted for white balance before leaving the factory. The adjustment of the white balance value is a kind of adjustment of the color of the TV picture, so that the TV can reflect the true color of the picture. With the popularity of digital TV,

The market has put forward higher requirements for the color effect of digital TV, so the adjustment of the TV white balance value is an important part of the TV production process.

At present, the method of adjusting the white balance value of the TV is to manually adjust the white balance value manually by using the white balance adjustment station. This method takes a lot of time to adjust the white balance value of each TV, and wastes huge manpower costs, which makes the economic efficiency of TV production low and cannot meet the current demand for high-efficiency and fast-paced TV production.

Technical solutions

The main purpose of this application is to provide a method, a device and a computer-readable storage medium for adjusting a TV white balance value, and aim to provide a method for adjusting a TV white balance value with a faster adjustment speed.

In order to achieve the above objective, the present application provides a method for adjusting a TV white balance value. The method for adjusting a TV white balance value includes the following steps:

Obtain the display image of the TV to be adjusted;

Calculating the display image according to the optimal parameter network model obtained by pre-training to obtain a corresponding white balance value;

The white balance value is sent to the corresponding TV to be adjusted, so that the TV to be adjusted adjusts the white balance value according to the white balance value.

In an embodiment, the step of calculating the display image according to the optimal parameter network model obtained in advance to obtain the corresponding white balance value includes:

Processing the display image to obtain the RGB value of each pixel of the display image;

The RGB value is calculated according to the pre-trained network model to obtain the white balance value corresponding to the display image.

In an embodiment, before the step of calculating the display image according to the optimal parameter network model obtained by pre-training, and obtaining the corresponding white balance value, the method further includes:

Obtaining a first preset number of training image sample data, where the training image sample data includes a first original image and corresponding first white balance data;

Training according to the training image sample data and a preset convolutional neural network model to obtain a training network model;

The accuracy of the training network model is verified, and the optimal parameter network model is obtained according to the verification result.

In an embodiment, the training is performed according to the training image sample data and a preset convolutional neural network model, and the step of obtaining a training network model includes:

Extracting features from the training image sample data through a convolutional layer;

Pooling the features extracted by the convolutional layer to obtain pooling features;

Perform iterative calculations according to the pooling characteristics to obtain a training network model.

In an embodiment, the step of verifying the accuracy of the training network model, and obtaining the optimal parameter network model according to the verification result includes:

Obtaining a second preset number of verification image sample data, where the verification image sample data includes a second original image and corresponding second white balance data;

Substituting the second original television image into a training network model for calculation to obtain a third white balance value of the second original television image;

Comparing the third white balance value with the second white balance data to determine the accuracy of the training network model;

If the accuracy is less than the preset accuracy, re-execute the step: train according to the training image sample data and the preset convolutional neural network model to obtain a training network model until the accuracy is greater than the preset accuracy;

If the accuracy is greater than the preset accuracy, the trained network model is used as the optimal parameter network model.

In an embodiment, the step of comparing the third white balance value with the second white balance data, and determining the accuracy of the training network model includes:

Counting the proportions of the same number of the third white balance value and the second white balance data;

The ratio is compared with a preset threshold to determine the accuracy of the training network model.

In an embodiment, the step of obtaining the display image of the TV to be adjusted includes:

Sending a TV picture to the TV to be adjusted, so that the TV displays the TV picture on the TV display screen;

Sending a shooting instruction to the camera, so that the camera shoots the TV display screen to obtain the captured TV image;

Receiving the television image sent by the camera, and using the television image as the display image of the television to be adjusted.

In one embodiment, the television picture is an 80% gray field picture.

In addition, in order to achieve the above object, the present application also provides a device for adjusting a TV white balance value. The device for adjusting a TV white balance value includes: a memory, a processor, and a The program for adjusting the TV white balance value is executed on the computer, and the steps of the method for adjusting the TV white balance value as described above are realized when the program for adjusting the TV white balance value is executed by the processor.

In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a program for adjusting the TV white balance value, and the program for adjusting the TV white balance value is executed by the processor When realizing the steps of the method for adjusting the TV white balance value.

This application provides a method, device and computer storage medium for adjusting the white balance value of a TV. In this method, the display image of the TV to be adjusted is obtained; the display image is calculated according to the optimal parameter network model obtained in advance to obtain the corresponding white balance value; and the white balance value is sent to the corresponding to be adjusted A television, so that the television to be adjusted performs white balance value adjustment according to the white balance value. Through the above method, the present application can intelligently calculate the TV image that needs to be adjusted through the pre-trained network model of optimal parameters based on deep learning, and quickly obtain the white balance data of the TV that needs to be adjusted, thereby enabling the TV Adjusting according to the white balance data and replacing the original manual method of repeatedly adjusting the white balance data by software calculation can significantly speed up the adjustment of the TV white balance value and improve the efficiency of the TV production process.

Description of the drawings

FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;

2 is a schematic flowchart of a first embodiment of a method for adjusting a TV white balance value according to this application;

3 is a schematic flowchart of a second embodiment of a method for adjusting a TV white balance value according to this application;

4 is a schematic flowchart of a third embodiment of a method for adjusting a TV white balance value according to this application;

5 is a schematic flowchart of a fourth embodiment of a method for adjusting a TV white balance value according to this application;

6 is a schematic flowchart of a fifth embodiment of a method for adjusting a TV white balance value according to this application;

7 is a schematic flowchart of a sixth embodiment of a method for adjusting a TV white balance value according to this application;

FIG. 8 is a schematic flowchart of a seventh embodiment of a method for adjusting a TV white balance value according to this application.

The realization, functional characteristics, and advantages of the purpose of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Embodiments of the invention

It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

As shown in FIG. 1, FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application.

The terminal in the embodiment of the present application may be a PC, or a terminal device with data processing functions, such as a smart phone, a tablet computer, and a portable computer.

As shown in FIG. 1, the terminal may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to implement connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a magnetic disk memory. Optionally, the memory 1005 may also be a storage device independent of the foregoing processor 1001.

In an embodiment, the terminal may also include a camera, an RF (Radio Frequency, radio frequency) circuit, a sensor, an audio circuit, a Wi-Fi module, and so on. Among them, sensors such as light sensors, motion sensors and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display screen according to the brightness of the ambient light. The proximity sensor can turn off the display screen and/or when the mobile terminal is moved to the ear. Backlight. As a kind of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in various directions (usually three-axis), and can detect the magnitude and direction of gravity when it is stationary. It can be used for applications that recognize the posture of the mobile terminal (such as horizontal and vertical screen switching, Related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, percussion), etc.; of course, the mobile terminal can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. No longer.

Those skilled in the art can understand that the terminal structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or fewer components than shown in the figure, or combine some components, or arrange different components.

As shown in FIG. 1, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a program for adjusting the white balance value of a TV.

In the terminal shown in FIG. 1, the network interface 1004 is mainly used to connect to a background server and perform data communication with the background server; the user interface 1003 is mainly used to connect to a client (user side) and perform data communication with the client; and the processor 1001 can be used to call the program for adjusting the TV white balance value stored in the memory 1005 and perform the following operations:

Obtain the display image of the TV to be adjusted;

Further, the processor 1001 may call a program for adjusting the TV white balance value stored in the memory 1005, and also perform the following operations:

The TV picture is an 80% gray field picture.

The specific embodiments of the device for adjusting the TV white balance value of the present application are basically the same as the following embodiments of the method for adjusting the TV white balance value, and will not be repeated here.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of a method for adjusting a TV white balance value according to the present application. The method for adjusting a TV white balance value includes:

Step S100, obtaining a display image of the TV to be adjusted;

This embodiment is a method for adjusting the white balance value of a television. In the TV production process, due to the subtle differences in the screen of each TV produced, and because of the influence of the TV backlight, it is necessary to adjust the white balance value of the TV. That is, continuously adjust the RGB value of the TV image for each TV, so that the color temperature of the TV image corresponding to the RGB is the standard color temperature. The process of adjusting the TV RGB value to make the color temperature of the TV image the standard color temperature is what we call adjusting the TV white balance value. the process of. In this embodiment, the displayed image can be an image sent directly from the TV’s external camera to shoot the screen of the TV to be adjusted, or it can be an image sent by the camera indirectly through other devices, or it can be an image displayed on the TV’s own screen. For the images sent later, the embodiment of the application does not limit the way of acquiring the displayed image.

Step S200, calculating the display image according to the optimal parameter network model obtained by pre-training to obtain a corresponding white balance value;

The network model in this embodiment is a convolutional neural network model, which is based on a certain number of initial values of the original TV image and the corresponding adjusted white balance data, which are trained by putting them into the convolutional neural network. The resulting network model with a large number of specific parameters. Because the million-level parameters in the network model are obtained through training corresponding to the original TV image and the corresponding white balance data, the network model is a unique one that corresponds to the original TV image and the corresponding white balance data. Model, the model obtained through training can obtain the feature law of the original TV image and the corresponding white balance data through processes such as deep learning to extract features. Through this law, the model can calculate the RGB values that need to be adjusted for other original TV images. Therefore, after the display image of the TV that needs to be adjusted is obtained, the display image is calculated according to the model, and the calculated RGB value corresponding to the display image, that is, the white balance value, can be obtained.

Step S300: Send the white balance value to the corresponding TV to be adjusted, so that the TV to be adjusted adjusts the white balance value according to the white balance value.

After calculating the white balance value that the TV needs to adjust through the network model, the white balance value is sent to the corresponding TV that needs to be adjusted, so that the TV adjusts the white balance value. So as to achieve the purpose of adjusting the TV white balance value. This application can be sent to the TV through a WIFI connection to the TV, and can be sent to the TV through a wired data cable connection to the TV. This embodiment does not limit the sending method of the white balance value.

Please refer to FIG. 3, which is a schematic flowchart of a second embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, step S200 includes:

Step S210, processing the display image to obtain the RGB value of each pixel of the display image;

In the embodiment of the present application, image processing is first performed on the display image to obtain the RGB value of each pixel in the display image. The RGB color model is a color standard. RGB represents the colors of the three channels of red, green, and blue. Each pixel corresponds to its own RGB value. Therefore, multiple RGB value data sets are obtained.

Step S220: Calculate the RGB value according to the pre-trained network model to obtain the white balance value corresponding to the display image.

According to the pre-trained network model, the RGB value of the displayed image is extracted using the convolution kernel to obtain the feature data. After the feature data is pooled, the number of feature data is reduced and the number of feature data is reduced and compared with other predictions in the network model. Set the parameters for calculation, and obtain the calculated white balance value corresponding to the displayed image.

Please refer to FIG. 4, which is a schematic flowchart of a third embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, before step S200, the method further includes:

Step S010: Obtain a first preset number of training image sample data, where the training image sample data includes a first original image and corresponding first white balance data respectively;

In this embodiment, a first preset number of training image sample data is obtained, and the training image sample data includes the number of original images and the corresponding number of white balance data. The white balance data is manually adjusted white balance data corresponding to the original image, that is, correct white balance data obtained by a traditional method, and the number of training image sample data is used to train the network model.

Step S020, training according to the training image sample data and the preset convolutional neural network model to obtain a training network model;

After the training image sample data is obtained, the training image sample data is put into the preset original convolutional neural network model for training, that is, convolution extracts features through multiple convolution kernels, and pooling reduces convolutional layer extraction Repeated operations such as the number of features and so on to obtain a training network model with specific parameters.

The concept of deep learning comes from the research of artificial neural networks. The multilayer perceptron with multiple hidden layers is a kind of deep learning structure. Deep learning forms a more abstract high-level representation attribute category or feature by combining low-level features to discover distributed feature representations of data. Convolutional neural network is a deep feedforward artificial neural network, which has been successfully applied in other fields.

Step S030, verify the accuracy of the training network model, and obtain the optimal parameter network model according to the verification result.

After the training network model is obtained by training a certain number of training image sample data, the accuracy of the training network model needs to be checked to determine the accuracy of the model. If the accuracy of the model meets the requirements, we will obtain the optimal parameter network model we need to train. If the accuracy of the model does not meet the requirements, we need to go back and continue to adjust the parameters for training until the accuracy of the network model meets the requirements. So far.

Please refer to FIG. 5, which is a schematic flowchart of a fourth embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, step S020 includes:

Step S021, extract features from the training image sample data through a convolutional layer;

In this embodiment, feature extraction is performed on the training image sample data through a convolutional layer, that is, feature extraction is performed on the RGB values of each pixel in the training image sample data to obtain feature data of the training image sample data.

Step S022, performing pooling processing on the features extracted by the convolutional layer to obtain pooling features;

After the feature data of the training image sample data is obtained, the feature data of the training image sample data is pooled to obtain the pooled feature after pooling.

Step S023: Perform iterative calculation according to the pooling feature to obtain a training network model.

Through iterative calculation of pooling features, that is, repeated training calculations, when the trained network model can meet certain requirements for the training image sample data, the training network model is obtained.

Please refer to FIG. 6, which is a schematic flowchart of a fifth embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, step S030 includes:

Step S031: Obtain a second preset number of verification image sample data, where the verification image sample data includes a second original image and corresponding second white balance data respectively;

In this embodiment, a second preset number of verification image sample data is obtained, and the verification image sample data includes a certain number of original images and the corresponding amount of white balance data. The white balance data is the white balance data corresponding to the original image that has been manually adjusted, that is, the correct white balance data obtained by traditional methods. The number of verification image sample data is used to verify the accuracy of the training network model degree.

Step S032, substituting the second original TV image into a training network model for calculation to obtain a third white balance value of the second original TV image;

After obtaining the verification image sample data, input the verification image sample data into the training network model for calculation, and obtain the calculated white balance value calculated by the training network model, and the calculated white balance value is the third white balance value.

Step S033, comparing the third white balance value with the second white balance data, and judging the accuracy of the training network model;

The calculated third white balance value is compared with the second white balance data manually adjusted by the traditional method, and the accuracy of the calculation result of the training network model is judged.

If the accuracy is less than the preset accuracy, re-execute step S020: train according to the training image sample data and the preset convolutional neural network model to obtain a training network model until the accuracy is greater than the preset accuracy;

If the accuracy is greater than the preset accuracy, step S034 is executed to use the trained network model as the optimal parameter network model.

If the accuracy is less than the preset accuracy, the parameters are modified to continue training to obtain the training network model until the accuracy of the training network model is greater than the preset accuracy. If the accuracy is greater than the preset accuracy, the training of the training network model is completed, and the training network model is used as the trained optimal parameter network model.

Please refer to FIG. 7, which is a schematic flowchart of a sixth embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, step S033 includes:

Step S035: Count the proportion of the same number of the third white balance value and the second white balance data;

The embodiment of the present application is a detailed embodiment for comparing the third white balance value with the second white balance data to determine the accuracy of the training network model. In this embodiment, the proportion of the same number of the third white balance value and the second white balance data obtained by manual adjustment is calculated. If there are 2000 calibration image sample data, 2000 third white balance values are obtained by calculation, and 2000 second white balance data obtained through manual adjustment in the calibration image sample data are obtained, and the statistics between the two The same number, and calculate the proportion of the same number in 2000 data to obtain the ratio value.

Step S036: Compare the ratio with a preset threshold to determine the accuracy of the training network model.

After obtaining the ratio value, compare the ratio value with the preset threshold value to judge the accuracy of the training network model. For example, the preset threshold value is 80%. If 1800 data out of 2000 data are the same, that is, the ratio If the value is 90%, it is compared with the preset threshold of 80%. If the requirement is met, the accuracy of the training network model meets the requirement.

Please refer to FIG. 8, which is a schematic flowchart of a seventh embodiment of a method for adjusting a TV white balance value according to the present application.

Based on the foregoing embodiment, in this embodiment, step S100 includes:

Step S110: Send a TV picture to the TV to be adjusted, so that the TV displays the TV picture on the TV display screen;

This embodiment is a detailed embodiment for obtaining the display image of the TV to be adjusted. In this embodiment, the TV picture is sent to the TV to be adjusted, and the TV picture is an 80% gray field picture. Make the TV display the TV picture on the TV screen. Experiments have proved that the color temperature of 80% gray field pictures is more accurate than other pictures such as 100% gray field pictures.

Step S120, sending a shooting instruction to the camera, so that the camera shoots the TV display screen to obtain the captured TV image;

After the TV picture is displayed on the TV screen, an external camera is used to aim at the center of the TV screen and shoot a 512×512 image. The white balance data of this station is obtained as the original TV image under the initial value.

Step S130: Receive the TV image sent by the camera, and use the TV image as the display image of the TV to be adjusted.

Receive the original TV image under the initial value sent by the TV, and use the TV image as the display image of the TV to be adjusted.

In addition, the embodiment of the present application also proposes a computer-readable storage medium.

The computer-readable storage medium of the present application stores a program for adjusting the TV white balance value. The program for adjusting the TV white balance value is executed by the processor to implement the steps of the method for adjusting the TV white balance value as described above.

The method implemented when the program for adjusting the TV white balance value running on the processor is executed can refer to the various embodiments of the method for adjusting the TV white balance value of the present application, which will not be repeated here.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, It also includes other elements not explicitly listed, or elements inherent to the process, method, article, or system. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article or system that includes the element.

The serial numbers of the foregoing embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , Magnetic disk, optical disk), including several instructions to make a terminal device (can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the method described in each embodiment of the present application.

The above are only preferred embodiments of this application, and do not limit the scope of this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

A method for adjusting a TV white balance value, wherein the method for adjusting a TV white balance value includes the following steps:

Obtain the display image of the TV to be adjusted;

Calculating the display image according to the optimal parameter network model obtained by pre-training to obtain a corresponding white balance value;

The white balance value is sent to the corresponding TV to be adjusted, so that the TV to be adjusted adjusts the white balance value according to the white balance value.
The method for adjusting the white balance value of a TV according to claim 1, wherein the step of calculating the display image according to the optimal parameter network model obtained by pre-training to obtain the corresponding white balance value comprises:

Processing the display image to obtain the RGB value of each pixel of the display image;

The RGB value is calculated according to the pre-trained network model to obtain the white balance value corresponding to the display image.
The method for adjusting the white balance value of a TV according to claim 1, wherein the step of calculating the display image according to the optimal parameter network model obtained by pre-training and obtaining the corresponding white balance value further comprises:

Obtaining a first preset number of training image sample data, where the training image sample data includes a first original image and corresponding first white balance data;

Training according to the training image sample data and a preset convolutional neural network model to obtain a training network model;

The accuracy of the training network model is verified, and the optimal parameter network model is obtained according to the verification result.
8. The method for adjusting TV white balance value according to claim 3, wherein said training according to said training image sample data and a preset convolutional neural network model, and the step of obtaining a training network model comprises:

Extracting features from the training image sample data through a convolutional layer;

Pooling the features extracted by the convolutional layer to obtain pooling features;

Perform iterative calculations according to the pooling characteristics to obtain a training network model.
3. The method for adjusting the TV white balance value of claim 3, wherein the step of verifying the accuracy of the training network model, and obtaining the optimal parameter network model according to the verification result comprises:

Obtaining a second preset number of verification image sample data, where the verification image sample data includes a second original image and corresponding second white balance data;

Substituting the second original television image into a training network model for calculation to obtain a third white balance value of the second original television image;

Comparing the third white balance value with the second white balance data to determine the accuracy of the training network model;

If the accuracy is less than the preset accuracy, re-execute the step: train according to the training image sample data and the preset convolutional neural network model to obtain a training network model until the accuracy is greater than the preset accuracy;

If the accuracy is greater than the preset accuracy, the trained network model is used as the optimal parameter network model.
8. The method for adjusting the TV white balance value of claim 5, wherein the step of comparing the third white balance value with the second white balance data to determine the accuracy of the training network model comprises :

Counting the proportions of the same number of the third white balance value and the second white balance data;

The ratio is compared with a preset threshold to determine the accuracy of the training network model.
8. The method for adjusting the white balance value of a TV according to claim 1, wherein the step of obtaining the display image of the TV to be adjusted comprises:

Sending a TV picture to the TV to be adjusted, so that the TV displays the TV picture on the TV display screen;

Sending a shooting instruction to the camera, so that the camera shoots the TV display screen to obtain the captured TV image;

Receiving the television image sent by the camera, and using the television image as the display image of the television to be adjusted.
8. The method for adjusting a TV white balance value of claim 7, wherein the TV picture is an 80% gray field picture.
A device for adjusting a TV white balance value, wherein the device for adjusting a TV white balance value includes: a memory, a processor, and a device for adjusting the TV white balance value stored in the memory and running on the processor A program for implementing the steps of the method for adjusting the TV white balance value according to any one of claims 1 to 8 when the program for adjusting the TV white balance value is executed by the processor.
A computer-readable storage medium, wherein a program for adjusting the TV white balance value is stored on the computer-readable storage medium, and the program for adjusting the TV white balance value is implemented by a processor as in claims 1 to 8. Any of the steps of the method for adjusting the TV white balance value.