Method, device, display device and storage medium for converting image data

Abstract

This article discloses a method, device, display device, and storage medium for converting image data. The method includes: establishing a neural network model for image data conversion, training the model using a preset input and output image data set to obtain a trained model; the neural network model inputs the RGB three-primary color component data of the input image and the input backlight voltage, and outputs the RGBW four-primary color component data of the output image and the output backlight voltage; optimizing the trained neural network model based on the display mode to obtain an optimized neural network model corresponding to at least one display mode; receiving an input image, selecting a matching optimized neural network model based on the current display mode, and inputting the three-primary color component data of the input image and the input backlight voltage into the optimized neural network model to convert the image data. This article optimizes the display effect and backlight power consumption of the output image based on the characteristics of different display modes.

Description

Technical Field

[0001] This article relates to, but is not limited to, the field of display technology, and in particular to a method, device, display device, and storage medium for converting image data. Background Art

[0002] With the development of display technology, display technologies with diversified structures that are conducive to high resolution have been developed one after another. Figure 1 As shown, RGBW technology adds a white sub-pixel to the original three primary colors of red, green, and blue, creating a four-color pixel design. When displaying images of the same brightness, the RGBW pixel architecture consumes less power than the RGB pixel architecture; and at the same power consumption, the RGBW pixel architecture significantly increases brightness compared to the RGB pixel architecture.

[0003] Therefore, the RGBW pixel architecture significantly improves image brightness compared to the traditional RGB pixel architecture, but the addition of the W sub-pixel can also lead to problems such as faded colors. For projectors with an RGBW pixel architecture, the input image is RGB data. After the projector motherboard converts the RGB data into RGBW data, the addition of the W sub-pixel often causes problems such as loss of image saturation, affecting the user's viewing experience. Summary of the Invention

[0004] In a first aspect, an embodiment of the present disclosure provides a method for converting image data, comprising:

[0005] Establishing a neural network model for image data conversion, and training the neural network model using a preset input image data set and an output image data set to obtain a trained neural network model; wherein the input of the neural network model is the red, green, and blue (RGB) three-primary color component data of the input image and the input backlight voltage, and the output of the neural network model is the red, green, blue, and white (RGBW) four-primary color component data of the output image and the output backlight voltage;

[0006] Optimizing the trained neural network model based on the display mode to obtain an optimized neural network model corresponding to at least one display mode;

[0007] An input image is received, a matching optimized neural network model is selected according to the current display mode, and the RGB three primary color component data of the input image and the input backlight voltage are input into the optimized neural network model to convert the image data.

[0008] In a second aspect, an embodiment of the present disclosure provides an apparatus for converting image data, comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned method for converting image data are implemented.

[0009] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the steps of the above-mentioned method for converting image data when executed by a processor.

[0010] In a fourth aspect, an embodiment of the present disclosure provides a display device, comprising: the above-mentioned device for converting image data.

[0011] The methods, devices, display devices, and storage media for converting image data provided by the embodiments of the present disclosure enable the display device to select a matching neural network optimization model based on the current display mode after receiving an input image. Because the neural network model compensates for the effects of the W sub-pixel on the display based on the characteristics of the display mode during optimization training, the display quality of the output image can be significantly improved after converting the image data using the optimized neural network model. Furthermore, backlight power consumption accounts for a significant proportion of the overall power consumption of a display device. Using the image data conversion methods of the embodiments of the present disclosure, backlight voltage is used as input to the neural network model. The model can fully utilize the beneficial effects of the W primary color component on display brightness to dynamically adjust the backlight voltage, thereby reducing backlight power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide an understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation to the technical solution of the present disclosure.

[0013] Figure 1 A schematic diagram of converting RGB image data into RGBW image data in the related art;

[0014] Figure 2 A flowchart of a method for converting image data provided by an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram of the structure of a convolutional neural network model provided in an embodiment of the present disclosure;

[0016] Figure 4 A schematic diagram of the structure of an apparatus for converting image data provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the embodiments can be implemented in a variety of different forms. A person of ordinary skill in the art can easily understand the fact that the methods and contents can be transformed into various forms without departing from the purpose and scope of the present disclosure. Therefore, the present disclosure should not be interpreted as being limited to the contents described in the following embodiments. In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be arbitrarily combined with each other.

[0018] In this specification, ordinal numbers such as “first”, “second” and “third” are provided to avoid confusion among constituent elements, and are not intended to limit the number.

[0019] like Figure 2 As shown, the embodiment of the present disclosure provides a method for converting image data, including:

[0020] Step S10, establishing a neural network model for image data conversion, and training the neural network model using a preset input image data set and an output image data set to obtain a trained neural network model; wherein the input of the neural network model is the red, green, and blue (RGB) three-primary color component data of the input image and the input backlight voltage, and the output of the neural network model is the red, green, blue, and white (RGBW) four-primary color component data of the output image and the output backlight voltage;

[0021] Step S20, optimizing the trained neural network model based on the display mode to obtain an optimized neural network model corresponding to at least one display mode;

[0022] Step S30: receiving an input image, selecting a matching optimized neural network model according to the current display mode, and inputting the RGB three primary color component data of the input image and the input backlight voltage into the optimized neural network model to convert the image data.

[0023] The method for converting image data provided in the above embodiment establishes a neural network model for image data conversion, trains the neural network model using a preset input image data set and an output image data set to obtain a trained neural network model; wherein the input of the neural network model is the red, green, and blue (RGB) three primary color component data of the input image and the input backlight voltage, and the output of the neural network model is the red, green, blue, and white (RGBW) four primary color component data of the output image and the output backlight voltage; optimizes the trained neural network model based on the display mode to obtain an optimized neural network model corresponding to at least one display mode; receives an input image, selects a matching optimized neural network model according to the current display mode, and inputs the RGB three primary color component data of the input image and the input backlight voltage into the optimized neural network model to convert the image data. The method provided in the above embodiment can optimize the display effect and backlight power consumption of the output image according to the characteristics of different display modes.

[0024] In an exemplary embodiment, the red, green, and blue (RGB) primary color component data of the input image includes: the RGB primary color component data of all pixels of the input image; the input backlight voltage of the input image includes: the input backlight voltage corresponding to a frame of the input image (the input backlight voltage value corresponding to all pixels on the input image is the same); the red, green, blue, and white (RGBW) four-primary color component data of the output image includes: the RGBW four-primary color component data of all pixels on the output image; and the output backlight voltage includes: the output backlight voltage corresponding to a frame of the output image (the output backlight voltage value corresponding to all pixels on the output image is the same). The input backlight voltage and the output backlight voltage are the same voltage value for the entire image.

[0025] In an exemplary embodiment, the neural network model includes a convolutional neural network (CNN) model. In other embodiments, the neural network model may also be other types of neural network models, such as a deep neural network (DNN) model.

[0026] In an exemplary embodiment, Figure 3 As shown, the convolutional neural network model includes the following structural layers connected in series: input layer, first convolution layer, first pooling layer, second convolution layer, second pooling layer, third convolution layer, third pooling layer, fully connected layer and output layer.

[0027] The input layer is used for data input. The convolutional layers (the first, second, and third convolutional layers) use convolution kernels to extract and map features. The pooling layers (the first, second, and third pooling layers) downsample and sparsify the feature maps, reducing the amount of data computation. The fully connected layer performs refitting to minimize feature information loss. The output layer outputs the results.

[0028] In an exemplary embodiment, the first convolution layer, the second convolution layer, and the third convolution layer include: a convolution kernel layer, a batch normalization layer, and an excitation layer.

[0029] In an exemplary embodiment, the number and size of convolution kernels in the first, second, and third convolution layers can be set based on the display resolution. For example, for a resolution of 1920*1080, the first convolution layer can use 32 5*5 convolution kernels; the second convolution layer can use 64 3*3 convolution kernels; and the third convolution layer can use 128 3*3 convolution kernels.

[0030] In an exemplary embodiment, the excitation layer uses a ReLU activation function.

[0031] In an exemplary embodiment, the first pooling layer and the second pooling layer are maximum pooling layers; and the third pooling layer is a global average pooling layer.

[0032] In an exemplary embodiment, a plurality of residual blocks are further included between the first convolutional layer and the first pooling layer, and between the second convolutional layer and the second pooling layer, each residual block including two convolutional layers and a skip connection. The skip connection is to connect the input directly to the output, bypassing the two convolutional layers, so that information can be directly transmitted across the layers, avoiding the problems of gradient vanishing and gradient exploding. The use of the residual block structure can help the convolutional neural network model better capture the features of the input image. Batch normalization is also used to accelerate training and improve accuracy. In addition, a global average pooling layer is used to reduce the amount of computation while improving the stability and reliability of the network.

[0033] The convolutional neural network model algorithm can achieve accurate output values ​​through training of key parameters, resulting in superior display performance. Larger convolution kernels can be used in the input layer and the first convolution layer to handle high-resolution input images. Batch normalization can accelerate training and improve accuracy. The use of the first and second pooling layers can further reduce the dimensionality of the convolutional layer output, improving the computational efficiency and speed of the convolutional neural network model. Using a global average pooling layer can reduce computational effort while improving the stability and reliability of the convolutional neural network model.

[0034] In one exemplary embodiment, the preset input and output image datasets include a set of pure-color images in the DCI-P3 color gamut. DCI-P3 is a color gamut standard used in digital cinemas and is based on human visual experience. By training a convolutional neural network model using pure-color images in the DCI-P3 color gamut, dimensionality reduction is achieved, significantly reducing computational effort, as every pixel in the input image has the same RGB data.

[0035] In an exemplary embodiment, the input image includes: a static image or a dynamic image; the dynamic image includes: a video or an animation.

[0036] In an exemplary embodiment, the number of nodes in each layer of the convolutional neural network model can be determined based on the requirements of the input image. For example, when the input image (such as a video) needs to be processed quickly, the number of nodes in each layer of the convolutional neural network model can be reduced. When the image quality of the input image (such as a picture) needs to be improved, the number of nodes in each layer of the convolutional neural network model can be increased.

[0037] In one exemplary embodiment, the input backlight voltage of any input image frame in a preset input image dataset is the same as the output backlight voltage of the corresponding output image. When training a convolutional neural network model using the preset input and output image datasets, setting the backlight voltage so that the input value and the output value are the same can reduce the complexity of the convolutional neural network model and save computation time.

[0038] In an exemplary embodiment, optimizing the trained neural network model based on the display mode includes:

[0039] According to different display modes, the trained neural network model is optimized using a policy gradient-based reinforcement learning method; wherein the policy gradient-based reinforcement learning method includes determining a reward function according to the display mode.

[0040] In an exemplary embodiment, the optimizing the trained neural network model using a policy gradient-based reinforcement learning method further includes:

[0041] Define a state space, an action space, and a policy network; wherein the state of the state space is the input data of the trained neural network model; the action of the action space is the output data of the trained neural network model; and the policy network is the trained neural network model;

[0042] The policy network is trained using the RF (REINFORCE) algorithm based on Monte Carlo sampling, and a gradient-based optimization method is used during the training process to accelerate the training of the policy network, such as the Adam algorithm.

[0043] In an exemplary embodiment, the display mode can be set by a user.

[0044] In an exemplary embodiment, the display mode includes at least one of the following: a default mode, an eye protection mode, a cinema mode, and a custom mode. The display mode is a mode setting related to the display effect of the picture. Different display modes differ in display indicators (such as the saturation, brightness, contrast, etc. of the picture). Among them, the default mode is the default setting of the system. The eye protection mode mainly reduces the output of blue light. The cinema mode is a mode that simulates the viewing scene setting of a cinema. The custom mode is a mode in which the user can set the display indicators independently.

[0045] In an exemplary embodiment, the custom mode may provide at least one of the following display indicators for the user to set: image saturation, image brightness, and image contrast.

[0046] In an exemplary embodiment, the reward function may include a penalty term of at least one of the following control indicators: saturation of the output image, brightness of the output image, color gamut of the output image, and output backlight voltage of the output image.

[0047] In an exemplary embodiment, the reward and punishment rules of the reward function include: when the actual value of the control index of the output image is greater than the target value of the control index, the parameters of the trained neural network model are updated for punishment; when the actual value of the control index of the output image is less than the target value of the control index, the parameters of the trained neural network model are updated for reward.

[0048] In an exemplary embodiment, when the display mode is the default mode, determining the reward function according to the display mode includes performing at least one of the following processes:

[0049] A saturation penalty term is set in the reward function, where the control indicator of the saturation penalty term is the saturation of the output image, and the reward and punishment rule of the saturation penalty term is as follows: if the saturation of the output image is greater than a first saturation threshold, the parameters of the trained neural network model are updated to perform a penalty; if the saturation of the output image is less than the first saturation threshold, the parameters of the trained neural network model are updated to perform a reward;

[0050] A brightness penalty term is set in the reward function, where the control indicator of the brightness penalty term is the brightness of the output image, and the reward and punishment rule of the brightness penalty term is as follows: if the brightness of the output image is greater than a first brightness threshold, the parameters of the trained neural network model are updated for penalty; if the brightness of the output image is less than the first brightness threshold, the parameters of the trained neural network model are updated for reward;

[0051] A color gamut penalty term is set in the reward function, where the control indicator of the color gamut penalty term is the color gamut of the output image, and the reward and punishment rule of the color gamut penalty term is as follows: if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is greater than a first color gamut threshold, then the parameters of the trained neural network model are updated for penalty; if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is less than the first color gamut threshold, then the parameters of the trained neural network model are updated for reward;

[0052] A backlight power consumption penalty item is set in the reward function, and the control indicator of the backlight power consumption penalty item is the output backlight voltage of the output image. The reward and punishment rules of the backlight power consumption penalty item are: if the output backlight voltage of the output image is greater than the first voltage threshold, the parameters of the trained neural network model are updated for penalty; if the output backlight voltage of the output image is less than the first voltage threshold, the parameters of the trained neural network model are updated for reward.

[0053] In an exemplary embodiment, when the display mode is the cinema mode, determining the reward function according to the display mode includes performing at least one of the following processes:

[0054] A saturation penalty term is set in the reward function, where the control indicator of the saturation penalty term is the saturation of the output image, and the reward and punishment rule of the saturation penalty term is as follows: if the saturation of the output image is greater than a second saturation threshold, the parameters of the trained neural network model are updated to perform a penalty; if the saturation of the output image is less than the second saturation threshold, the parameters of the trained neural network model are updated to perform a reward;

[0055] A brightness penalty term is set in the reward function, where the control indicator of the brightness penalty term is the brightness of the output image, and the reward and punishment rule of the brightness penalty term is as follows: if the brightness of the output image is greater than a second brightness threshold, the parameters of the trained neural network model are updated to perform a penalty; if the brightness of the output image is less than the second brightness threshold, the parameters of the trained neural network model are updated to perform a reward;

[0056] A color gamut penalty term is set in the reward function, where the control indicator of the color gamut penalty term is the color gamut of the output image, and the reward and punishment rule of the color gamut penalty term is as follows: if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is greater than a second color gamut threshold, the parameters of the trained neural network model are updated for penalty; if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is less than the second color gamut threshold, the parameters of the trained neural network model are updated for reward;

[0057] A backlight power consumption penalty item is set in the reward function, and the control indicator of the backlight power consumption penalty item is the output backlight voltage of the output image. The reward and punishment rules of the backlight power consumption penalty item are: if the output backlight voltage of the output image is greater than the second voltage threshold, the parameters of the trained neural network model are updated for penalty; if the output backlight voltage of the output image is less than the second voltage threshold, the parameters of the trained neural network model are updated for reward.

[0058] In an exemplary embodiment, when the display mode is eye protection mode, determining a reward function based on the display mode includes: setting a blue light penalty term in the reward function, where the control indicator of the blue light penalty term is the intensity of the blue light component in the output image, and the reward and punishment rules of the blue light penalty term are as follows: if the intensity of the blue light component in the output image is greater than an intensity threshold, then the parameters of the trained neural network model are updated to impose a penalty; if the intensity of the blue light component in the output image is less than the intensity threshold, then the parameters of the trained neural network model are updated to impose a reward. Introducing a blue light penalty term in eye protection mode can reduce blue light output and protect the viewer's eyesight.

[0059] In an exemplary embodiment, the method further includes: detecting the intensity of the blue light component of the output image by a sensor.

[0060] Introducing saturation, brightness, and color gamut penalty terms into the reward function can optimize the proportion of the white primary color component W in the output image, thereby avoiding or mitigating the adverse effects of the white primary color component on the display effect (for example, fading the color W).

[0061] Introducing the backlight power consumption penalty term into the reward function can fully utilize the beneficial effect of the white primary color component W on the display brightness, further reduce the backlight voltage, and reduce the backlight power consumption.

[0062] In an exemplary embodiment, the first saturation threshold is less than the second saturation threshold; the first brightness threshold is greater than the second brightness threshold, and the first color gamut threshold is greater than the second color gamut threshold. Compared with the default mode, the target saturation of the cinema mode is set to be greater than the target saturation of the default mode, which can improve the contrast and clarity of the picture in the cinema mode. Setting the target brightness of the cinema mode to be less than the target brightness of the default mode can match the viewing environment of the cinema mode (dim light) and reduce power consumption. Setting the target color gamut width requirement of the cinema mode to be greater than the target color gamut width requirement of the default mode can enrich the picture color of the cinema mode.

[0063] On an RGBW screen, the R, G, and B subpixels are responsible for displaying color, while the W subpixel is used to increase brightness and white representation. Assume that in cinema mode, the reward function sets the target saturation value (the second saturation threshold) to 50 and the target brightness value (the second brightness threshold) to 110 nits. Without changing the hardware, because pixel voltage and grayscale are related, projecting RGBW into the HSV (hue (H), saturation (S), brightness (V)) color space can obtain the average saturation (image saturation) and average brightness (image brightness) of all pixels in the entire screen. In cinema mode, if the saturation of the output image is greater than 50, the parameters of the trained neural network model are updated to apply a penalty. If the saturation of the output image is less than 50, the parameters of the trained neural network model are updated to apply a reward. If the brightness of the output image is greater than 110 nits, the parameters of the trained neural network model are updated to apply a penalty. If the brightness of the output image is less than 110 nits, the parameters of the trained neural network model are updated to apply a reward.

[0064] like Figure 4 As shown, an embodiment of the present disclosure provides an apparatus for converting image data, comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned method for converting image data are implemented.

[0065] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which implements the steps of the above-mentioned method for converting image data when executed by a processor.

[0066] An embodiment of the present disclosure further provides a display device, comprising the above-mentioned device for converting image data.

[0067] In an exemplary embodiment, the display device includes: a projector.

[0068] For projector products with RGBW pixel architecture, the input image is RGB data. After the projector converts the RGB data into RGBW data, the related technology will cause problems such as the loss of saturation of the projected image due to the addition of W sub-pixels, affecting the user's viewing experience.

[0069] Using the image data conversion method of the disclosed embodiment, after receiving an input image, the projector can select a matching neural network model optimization model based on the current display mode. Because the neural network model compensates for the impact of the W sub-pixel on the display according to the characteristics of the display mode during the optimization training process, the display quality of the projected image can be significantly improved after converting the image data using the optimized neural network model. Furthermore, backlight power consumption accounts for a large proportion of the projector's overall power consumption. Using the image data conversion method of the disclosed embodiment, the backlight voltage serves as the input of the neural network model. The model can fully utilize the beneficial effect of the W primary color component on display brightness to achieve dynamic adjustment of the backlight voltage, thereby reducing backlight power consumption.

[0070] The display device may also include a liquid crystal display device or an OLED (Organic Light-Emitting Diode) display device. The display device may also be any product or component with a display function, such as a mobile phone, a tablet computer, a television, a monitor, a laptop computer, a digital photo frame, or a navigation system. Other essential components of the display device are well understood by those skilled in the art and are not detailed here, nor should they be construed as limitations of the present invention.

[0071] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0072] It should be noted that the above-described embodiments or implementations are merely illustrative and not restrictive. Therefore, the present disclosure is not limited to what is specifically shown and described herein. Various modifications, substitutions, or omissions may be made to the forms and details of the implementations without departing from the scope of the present disclosure.

Claims

1. A method for converting image data, comprising: Establishing a neural network model for image data conversion, and training the neural network model using a preset input image data set and an output image data set to obtain a trained neural network model; wherein the input of the neural network model is the red, green, and blue (RGB) three-primary color component data of the input image and the input backlight voltage, and the output of the neural network model is the red, green, blue, and white (RGBW) four-primary color component data of the output image and the output backlight voltage; Optimizing the trained neural network model based on the display mode to obtain an optimized neural network model corresponding to at least one display mode; wherein the display mode is a mode setting related to the display effect of the image, and different display modes have different display indicators; the display indicators include at least one of the following: saturation, brightness, and contrast of the image; An input image is received, a matching optimized neural network model is selected according to the current display mode, and the RGB three primary color component data of the input image and the input backlight voltage are input into the optimized neural network model to convert the image data.

2. The method according to claim 1, wherein: The optimizing the trained neural network model based on the display mode includes: According to different display modes, the trained neural network model is optimized using a policy gradient-based reinforcement learning method; wherein the policy gradient-based reinforcement learning method includes determining a reward function according to the display mode.

3. The method according to claim 2, wherein: The method of optimizing the trained neural network model by using a policy gradient-based reinforcement learning method further includes: Define a state space, an action space, and a policy network; wherein the state of the state space is the input data of the trained neural network model; the action of the action space is the output data of the trained neural network model; and the policy network is the trained neural network model; The policy network is trained using an RF algorithm based on Monte Carlo sampling, and a gradient-based optimization method is used during the training process to accelerate the training of the policy network.

4. The method according to claim 2, wherein: The reward function includes a penalty term of at least one of the following control indicators: saturation of the output image, brightness of the output image, color gamut of the output image, and output backlight voltage of the output image.

5. The method according to claim 4, wherein: The reward and punishment rules of the reward function include: when the actual value of the control index of the output image is greater than the target value of the control index, the parameters of the trained neural network model are updated for punishment; when the actual value of the control index of the output image is less than the target value of the control index, the parameters of the trained neural network model are updated for reward.

6. The method according to claim 1, wherein: The display mode includes at least one of the following: a default mode, an eye protection mode, a cinema mode, and a custom mode.

7. The method according to claim 6, wherein: The custom mode provides at least one of the following display indicators for the user to set: image saturation, image brightness, and image contrast.

8. The method according to claim 2, wherein: When the display mode is the default mode, determining the reward function according to the display mode includes performing at least one of the following processes: A saturation penalty term is set in the reward function, where the control indicator of the saturation penalty term is the saturation of the output image, and the reward and punishment rule of the saturation penalty term is as follows: if the saturation of the output image is greater than a first saturation threshold, the parameters of the trained neural network model are updated to perform a penalty; if the saturation of the output image is less than the first saturation threshold, the parameters of the trained neural network model are updated to perform a reward; A brightness penalty term is set in the reward function, where the control indicator of the brightness penalty term is the brightness of the output image, and the reward and punishment rule of the brightness penalty term is as follows: if the brightness of the output image is greater than a first brightness threshold, the parameters of the trained neural network model are updated for penalty; if the brightness of the output image is less than the first brightness threshold, the parameters of the trained neural network model are updated for reward; A color gamut penalty term is set in the reward function, where the control indicator of the color gamut penalty term is the color gamut of the output image, and the reward and punishment rule of the color gamut penalty term is as follows: if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is greater than a first color gamut threshold, then the parameters of the trained neural network model are updated for penalty; if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is less than the first color gamut threshold, then the parameters of the trained neural network model are updated for reward; A backlight power consumption penalty item is set in the reward function, and the control indicator of the backlight power consumption penalty item is the output backlight voltage of the output image. The reward and punishment rules of the backlight power consumption penalty item are: if the output backlight voltage of the output image is greater than the first voltage threshold, the parameters of the trained neural network model are updated for penalty; if the output backlight voltage of the output image is less than the first voltage threshold, the parameters of the trained neural network model are updated for reward.

9. The method according to claim 2, wherein: When the display mode is the cinema mode, determining the reward function according to the display mode includes performing at least one of the following processes: A saturation penalty term is set in the reward function, where the control indicator of the saturation penalty term is the saturation of the output image, and the reward and punishment rule of the saturation penalty term is as follows: if the saturation of the output image is greater than a second saturation threshold, the parameters of the trained neural network model are updated to perform a penalty; if the saturation of the output image is less than the second saturation threshold, the parameters of the trained neural network model are updated to perform a reward; A brightness penalty term is set in the reward function, where the control indicator of the brightness penalty term is the brightness of the output image, and the reward and punishment rule of the brightness penalty term is as follows: if the brightness of the output image is greater than a second brightness threshold, the parameters of the trained neural network model are updated to perform a penalty; if the brightness of the output image is less than the second brightness threshold, the parameters of the trained neural network model are updated to perform a reward; A color gamut penalty term is set in the reward function, where the control indicator of the color gamut penalty term is the color gamut of the output image, and the reward and punishment rule of the color gamut penalty term is as follows: if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is greater than a second color gamut threshold, the parameters of the trained neural network model are updated for penalty; if the proportion of pixels in the color gamut of the output image that fall outside the target color gamut is less than the second color gamut threshold, the parameters of the trained neural network model are updated for reward; A backlight power consumption penalty item is set in the reward function, and the control indicator of the backlight power consumption penalty item is the output backlight voltage of the output image. The reward and punishment rules of the backlight power consumption penalty item are: if the output backlight voltage of the output image is greater than the second voltage threshold, the parameters of the trained neural network model are updated for penalty; if the output backlight voltage of the output image is less than the second voltage threshold, the parameters of the trained neural network model are updated for reward.

10. The method according to claim 2, wherein: When the display mode is the eye protection mode, the reward function is determined according to the display mode, including: setting a blue light penalty item in the reward function, the control indicator of the blue light penalty item is the intensity of the blue light component in the output image, and the reward and punishment rules of the blue light penalty item are: if the intensity of the blue light component in the output image is greater than the intensity threshold, the parameters of the trained neural network model are updated for punishment; if the intensity of the blue light component in the output image is less than the intensity threshold, the parameters of the trained neural network model are updated for reward.

11. The method according to claim 1, wherein: The input image includes: a static image or a dynamic image; the dynamic image includes: a video or an animation.

12. The method according to claim 1, wherein: The neural network model includes: a convolutional neural network model.

13. A device for converting image data, comprising: A memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the method for converting image data according to any one of claims 1 to 12 are implemented.

14. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of the method for converting image data according to any one of claims 1 to 12.

15. A display device comprising: The device for converting image data as claimed in claim 13.

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