Computer display screen color gamut processing system

Abstract

The invention discloses a computer display screen color gamut processing system, and particularly relates to the technical field of display color gamut processing, which comprises an input signal intelligent module used for analyzing and optimizing input signals in real time, and a color gamut intelligent conversion and adjustment module used for automatically adjusting colors to realize consistency between devices and optimize visual experience. The color gamut processing capability of the display screen is remarkably improved through the intelligent processing module, the input signal intelligent processing module adopts an artificial intelligence algorithm, automatically identifies and analyzes a signal format, dynamically adjusts an analysis strategy, realizes optimal conversion of a color space, dynamically adjusts a gamma curve in combination with ambient light and image content, and improves the color gamut processing capability of the display screen. And deep learning is utilized to remove noise and ensure that the image is clear, and the intelligent color gamut conversion and adjustment module optimizes color gamut mapping and emotion color adjustment through deep learning and emotion calculation technologies, and adaptively adjusts white balance to ensure that the color is accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of display color gamut processing, and in particular to a computer display color gamut processing system. Background Art

[0002] In modern computers and multimedia devices, the color processing capability of the display screen directly affects the user's visual experience and work efficiency. Especially in the fields of image processing, video editing, professional design, etc., the requirements for the color gamut of the display screen are more stringent. The main function of the color gamut processing system is to optimize the color performance of the display screen by processing the input signal, so that it can accurately restore the true color of the image and improve the display effect. However, the existing display color gamut processing technology still has many shortcomings in terms of color accuracy, dynamic adjustment capability and adaptability.

[0003] Most of the existing display color gamut processing systems use fixed color adjustment algorithms, which are difficult to dynamically adjust according to changes in ambient light and different image content, resulting in less than ideal color performance in different usage scenarios. In addition, traditional color gamut processing systems have limitations in noise removal and image detail enhancement, which can easily lead to reduced image clarity and contrast. Existing systems also lack the ability to learn and adaptively adjust user preferences, and are unable to optimize colors according to user visual habits, thus affecting the overall user experience. Therefore, we provide a computer display color gamut processing system. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a computer display screen color gamut processing system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A computer display screen color gamut processing system, comprising an input signal intelligent module for real-time analysis and optimization of input signals, a color gamut intelligent conversion and adjustment module for automatically adjusting colors to achieve consistency between devices and optimize visual experience, an image intelligent enhancement and optimization module for using advanced algorithms to enhance image details, contrast and color performance, and an output signal intelligent processing module for ensuring that processed image signals are output to a display device with high quality and stability;

[0007] The input signal intelligent processing module includes an adaptive signal parsing module for automatically identifying and parsing various input video signal formats through artificial intelligence algorithms to dynamically adjust the parsing strategy, a dynamic color space conversion module for autonomously selecting the optimal color space conversion method based on input signal characteristics and historical data using a machine learning model, a gamma curve adaptive adjustment module for dynamically adjusting the gamma curve based on ambient light and image content, and an intelligent denoising module for removing image noise in real time using a deep learning algorithm;

[0008] The color gamut intelligent conversion and adjustment module includes an adaptive color gamut mapping module for automatically adjusting the color gamut mapping strategy by analyzing the input image content in combination with a deep learning model, a color emotion recognition and adjustment module for identifying the color conveying emotional information in the image using emotional computing technology, an intelligent white balance adjustment module for adaptively adjusting the white balance using an ambient light sensor and a machine learning algorithm, and a color health optimization module for optimizing the image color based on biofeedback and visual health research to reduce harmful light such as blue light.

[0009] The present invention is further configured as follows: the image intelligent enhancement optimization module includes a deep learning sharpening module for performing image sharpening processing using a convolutional neural network to enhance details and edges, an intelligent contrast enhancement module for dynamically adjusting the contrast of different regions of an image based on a content-aware contrast enhancement technology, a color prediction and enhancement module for intelligently enhancing the color of an image through a prediction algorithm and a deep learning model, and a local intelligent color enhancement module for performing intelligent color enhancement on a specific region in an image using a fine-grained segmentation technology;

[0010] The output signal intelligent processing module includes an output signal intelligent synthesis module for optimizing the synthesis process of processed image data using an AI algorithm, an adaptive gamma correction module for adaptively adjusting the output gamma according to display device characteristics and ambient light conditions based on a machine learning model, an intelligent encoding module for intelligently encoding processed image data using a deep learning algorithm, and an intelligent signal transmission module for combining a network optimization algorithm and a transmission protocol.

[0011] The present invention is further configured as follows: the gamma curve adaptive adjustment module includes an ambient light detection module for using an advanced light sensor and a machine learning algorithm to detect the ambient light intensity and color temperature in real time, an image content analysis module for image content analysis with deep learning capability to analyze scenes, subjects and brightness distribution in real time in an image, a human eye vision model module for simulating human eye visual characteristics and optimizing gamma adjustment strategies according to human eye perception of different brightness and contrast, a gamma curve generation module for dynamically generating a gamma curve suitable for current display conditions according to ambient light and image content using an adaptive algorithm, an image quality determination module for real-time evaluation of the adjusted image quality to ensure that the gamma-adjusted image effect is better than the original image, a user preference learning module for learning user preferences for image brightness and contrast through user behavior and feedback data, a real-time feedback adjustment module for adjusting the gamma curve in real time according to feedback from the image quality determination module and the user preference learning module, a scene recognition module for identifying the specific scene of the current image using deep learning technology, and an intelligent gamma library module for maintaining a gamma curve library containing a variety of preset and learned gamma curves to select the most suitable gamma curve application according to different needs and scenes.

[0012] The present invention is further configured as follows: the real-time feedback adjustment module includes an image brightness evaluation module for real-time evaluation of the overall brightness level of the image to determine whether the gamma curve needs to be adjusted to optimize the brightness, an image contrast evaluation module for analyzing the contrast distribution of the image to detect whether there are areas with too high or too low contrast, a user feedback collection module for obtaining direct user feedback on the current image quality through a user interface, a user behavior analysis module for analyzing the user's operating behavior under different image settings to infer the user's preference, and a real-time feedback comprehensive analysis module for summarizing and analyzing the evaluation results from the two branches of image quality feedback and user preference feedback to make a comprehensive judgment.

[0013] The present invention is further configured as follows: the ambient light detection module detects and transmits the ambient light intensity and color temperature information to the image content analysis module in real time, so that these data can more accurately analyze the image scene, subject and brightness distribution, and ensure that the image analysis result takes into account the ambient light conditions; the image content analysis module analyzes and obtains the scene, subject and brightness distribution information of the image, and then transmits these data to the human eye vision model module, so that it can use this information to simulate the human eye's perception of different brightness and contrast to optimize the gamma adjustment strategy; the human eye vision model module provides an optimization strategy based on the simulated visual perception results, and these strategies are transmitted to the gamma curve generation module, so that it can dynamically generate the most suitable gamma curve under the current display conditions according to the strategy and the real-time ambient light image content; the gamma curve generation module generates a gamma curve and applies it to the image, and then the image quality judgment module evaluates the adjusted image quality in real time to ensure that the image effect after gamma adjustment is better than the original image.

[0014] The present invention is further configured as follows: the image quality determination module evaluates the adjusted image quality and transmits the evaluation result and user feedback data to the user preference learning module so that the module can learn the user's preference for image brightness and contrast in combination with these data to provide personalized adjustment suggestions; the user preference learning module provides the user's preferred brightness and contrast information and transmits it to the real-time feedback adjustment module so that the module can adjust the gamma curve in real time in combination with these preferences and the data from the image quality determination module to ensure that the output image meets the user's expectations; the scene recognition module identifies the specific scene of the current image and transmits the scene information to the intelligent gamma library module so that the module can select the most suitable gamma curve for application according to different scenes to ensure that the best display effect can be obtained in different scenes.

[0015] The present invention is further configured as follows: the adaptive signal analysis module automatically identifies and analyzes various input video signal formats through an artificial intelligence algorithm, dynamically adjusts the analysis strategy and passes it to the dynamic color space conversion module so that it can autonomously select the optimal color space conversion method according to the input signal characteristics and historical data to ensure that the color space conversion effect after signal analysis is the best; the dynamic color space conversion module optimizes the input signal according to the selected color space conversion method and then passes the optimized image to the gamma curve adaptive adjustment module so that it can dynamically adjust the gamma curve based on ambient light and image content; the gamma curve adaptive adjustment module dynamically adjusts the gamma curve and then passes the image to the intelligent denoising module so that it can use a deep learning algorithm to remove image noise in real time to ensure the clarity and detail retention of the final image.

[0016] The present invention is further configured as follows: the adaptive color gamut mapping module automatically adjusts the color gamut mapping strategy by analyzing the input image content through a deep learning model and transmits the adjusted color gamut information to the color emotion recognition and adjustment module so that it can use the emotion computing technology to recognize and adjust the emotion information conveyed in the image to enhance the visual experience; the color emotion recognition and adjustment module recognizes and adjusts the image color emotion information and then transmits the result to the intelligent white balance adjustment module so that it can use the ambient light sensor and the machine learning algorithm to adaptively adjust the white balance to ensure the accuracy and consistency of the image color; the intelligent white balance adjustment module transmits the image to the color health optimization module after adjusting the white balance so that it can optimize the image color based on biofeedback and visual health research, reduce harmful light such as blue light, and protect the user's vision health.

[0017] The present invention is further configured as follows: the deep learning sharpening module uses a convolutional neural network to perform image sharpening processing to enhance details and edges, and then passes the image to an intelligent contrast enhancement module so that it dynamically adjusts the contrast of different areas of the image based on content-aware contrast enhancement technology; the intelligent contrast enhancement module adjusts the image contrast and then passes the image to a color prediction and enhancement module so that it intelligently enhances the image color through a prediction algorithm and a deep learning model; the color prediction and enhancement module enhances the image color and then passes the image to a local intelligent color enhancement module so that it uses fine-grained segmentation technology to perform intelligent color enhancement on specific areas in the image to ensure that the colors of the local areas are accurate and rich.

[0018] The present invention is further configured as follows: the output signal intelligent synthesis module uses the AI ​​algorithm to optimize the synthesis process of the processed image data and then passes the synthesized image to the adaptive gamma correction module so that it can adaptively adjust the output gamma based on the machine learning model according to the characteristics of the display device and the ambient light conditions to ensure the consistency of the image on different devices; the adaptive gamma correction module adjusts the output gamma and then passes the image to the intelligent encoding module so that it can use the deep learning algorithm to intelligently encode the processed image data to ensure high-quality transmission of the image signal; the intelligent encoding module intelligently encodes the image data and then passes the encoded data to the intelligent signal transmission module so that it can combine the network optimization algorithm and the transmission protocol to ensure that the image signal is efficiently and stably transmitted to the display device.

[0019] The beneficial effects of the present invention are:

[0020] 1. The present invention significantly improves the color gamut processing capability of the display screen through the intelligent processing module. The input signal intelligent processing module adopts an artificial intelligence algorithm to automatically identify and analyze the signal format, dynamically adjust the analysis strategy, and achieve the optimal conversion of the color space. It combines the ambient light and image content to dynamically adjust the gamma curve, and uses deep learning to remove noise to ensure image clarity. The color gamut intelligent conversion and adjustment module optimizes the color gamut mapping and emotional color adjustment through deep learning and emotional computing technology, and adaptively adjusts the white balance to ensure color accuracy. The image intelligent enhancement optimization module improves image details, contrast and color performance through convolutional neural networks and content perception technology. Finally, the output signal intelligent processing module ensures high-quality and stable image output, and achieves color consistency and optimization of visual experience between devices.

[0021] 2. The present invention obtains light and color temperature information in real time through the ambient light detection module, combines the scene analysis of the image content analysis module, optimizes the gamma adjustment strategy, and uses the human eye vision model to simulate visual perception. The gamma curve generation module dynamically generates the most suitable gamma curve, and the image quality judgment module performs quality evaluation to ensure that the image effect is better than the original. The user preference learning module learns user preferences through feedback and behavior, and the real-time feedback adjustment module optimizes the gamma curve based on this. The scene recognition module selects the best gamma curve through the intelligent gamma library to ensure the best display effect in different scenarios. This system responds quickly and has strong adaptability, which greatly improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the system flow in the present invention.

[0023] Figure 2 It is a partial system flow diagram of the gamma curve adaptive adjustment module in the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] Example 1

[0026] like Figure 1 As shown, a color gamut processing system for a computer display screen includes an input signal intelligent module for real-time analysis and optimization of input signals, a color gamut intelligent conversion and adjustment module for automatically adjusting colors to achieve consistency between devices and optimize visual experience, an image intelligent enhancement and optimization module for using advanced algorithms to enhance image details, contrast and color performance, and an output signal intelligent processing module for ensuring that the processed image signal is output to a display device with high quality and stability;

[0027] The input signal intelligent processing module includes an adaptive signal parsing module for automatically identifying and parsing various input video signal formats through artificial intelligence algorithms to dynamically adjust the parsing strategy, a dynamic color space conversion module for autonomously selecting the optimal color space conversion method based on input signal characteristics and historical data using a machine learning model, a gamma curve adaptive adjustment module for dynamically adjusting the gamma curve based on ambient light and image content, and an intelligent denoising module for removing image noise in real time using a deep learning algorithm;

[0028] The color gamut intelligent conversion and adjustment module includes an adaptive color gamut mapping module for automatically adjusting the color gamut mapping strategy by analyzing the input image content in combination with a deep learning model, a color emotion recognition and adjustment module for identifying the color conveying emotional information in the image using emotional computing technology, an intelligent white balance adjustment module for adaptively adjusting the white balance using an ambient light sensor and a machine learning algorithm, and a color health optimization module for optimizing the image color based on biofeedback and visual health research to reduce harmful light such as blue light;

[0029] The image intelligent enhancement and optimization module includes a deep learning sharpening module for performing image sharpening processing using a convolutional neural network to enhance details and edges, an intelligent contrast enhancement module for dynamically adjusting the contrast of different regions of an image based on content-aware contrast enhancement technology, a color prediction and enhancement module for intelligently enhancing the color of an image through a prediction algorithm and a deep learning model, and a local intelligent color enhancement module for performing intelligent color enhancement on specific regions in an image using fine-grained segmentation technology;

[0030] The output signal intelligent processing module includes an output signal intelligent synthesis module for optimizing the synthesis process of processed image data using an AI algorithm, an adaptive gamma correction module for adaptively adjusting the output gamma according to display device characteristics and ambient light conditions based on a machine learning model, an intelligent encoding module for intelligently encoding the processed image data using a deep learning algorithm, and an intelligent signal transmission module for combining a network optimization algorithm and a transmission protocol;

[0031] The adaptive signal analysis module automatically identifies and analyzes various input video signal formats through artificial intelligence algorithms, dynamically adjusts the analysis strategy and passes it to the dynamic color space conversion module so that it can autonomously select the optimal color space conversion method according to the input signal characteristics and historical data to ensure the best color space conversion effect after signal analysis; the dynamic color space conversion module optimizes the input signal according to the selected color space conversion method and then passes the optimized image to the gamma curve adaptive adjustment module so that it can dynamically adjust the gamma curve based on ambient light and image content; the gamma curve adaptive adjustment module dynamically adjusts the gamma curve and then passes the image to the intelligent denoising module so that it can use the deep learning algorithm to remove image noise in real time to ensure the clarity and detail retention of the final image;

[0032] The adaptive color gamut mapping module automatically adjusts the color gamut mapping strategy by analyzing the input image content through a deep learning model and passes the adjusted color gamut information to the color emotion recognition and adjustment module so that it can use emotional computing technology to recognize and adjust the emotional information conveyed in the image to enhance the visual experience; the color emotion recognition and adjustment module recognizes and adjusts the image color emotional information and then passes the result to the intelligent white balance adjustment module so that it can use the ambient light sensor and machine learning algorithm to adaptively adjust the white balance to ensure the accuracy and consistency of the image color; after adjusting the white balance, the intelligent white balance adjustment module passes the image to the color health optimization module so that it can optimize the image color based on biofeedback and visual health research to reduce harmful light such as blue light and protect the user's vision health;

[0033] The deep learning sharpening module uses a convolutional neural network to perform image sharpening processing to enhance details and edges, and then passes the image to the intelligent contrast enhancement module to dynamically adjust the contrast of different areas of the image based on content-aware contrast enhancement technology; the intelligent contrast enhancement module adjusts the image contrast and then passes the image to the color prediction and enhancement module to intelligently enhance the image color through a prediction algorithm and a deep learning model; the color prediction and enhancement module enhances the image color and then passes the image to the local intelligent color enhancement module to use fine-grained segmentation technology to perform intelligent color enhancement on specific areas in the image to ensure that the color of the local area is accurate and rich;

[0034] The output signal intelligent synthesis module uses the AI ​​algorithm to optimize the synthesis process of the processed image data and then passes the synthesized image to the adaptive gamma correction module, which allows it to adaptively adjust the output gamma based on the machine learning model according to the characteristics of the display device and the ambient light conditions to ensure the consistency of the image on different devices; after adjusting the output gamma, the adaptive gamma correction module passes the image to the intelligent encoding module, which allows it to use the deep learning algorithm to intelligently encode the processed image data to ensure high-quality transmission of the image signal; after intelligently encoding the image data, the intelligent encoding module passes the encoded data to the intelligent signal transmission module, which combines the network optimization algorithm and the transmission protocol to ensure efficient and stable transmission of the image signal to the display device.

[0035] In the above embodiments, the input signal intelligent processing module identifies and analyzes various input video signals through artificial intelligence algorithms, and dynamically adjusts the gamma curve according to the ambient light and image content, while using a deep learning algorithm to remove image noise in real time. The color gamut intelligent conversion adjustment module combines the deep learning model to analyze the image content to automatically adjust the color gamut mapping strategy, and uses emotional computing technology and ambient light sensors to adaptively adjust the white balance and color health optimization. The image intelligent enhancement optimization module uses convolutional neural networks and content-aware technology to dynamically adjust the sharpness, contrast and color of the image to ensure the improvement of detail and color expression. The output signal intelligent processing module optimizes the image data synthesis process through AI algorithms, uses machine learning models and deep learning algorithms to adaptively adjust the output gamma and perform intelligent encoding and transmission to ensure high-quality and stable image signals are output to the display device. The entire system achieves real-time optimization, color consistency and visual experience through multi-level intelligent processing.

[0036] Example 2

[0037] like Figure 1-2 As shown, a color gamut processing system for a computer display screen, the gamma curve adaptive adjustment module includes an ambient light detection module for using an advanced light sensor and a machine learning algorithm to detect the ambient light intensity and color temperature in real time, an image content analysis module for image content analysis with deep learning capability to analyze scenes, subjects and brightness distribution in real time in an image, a human eye vision model module for simulating human eye visual characteristics to optimize the gamma adjustment strategy according to the human eye's perception of different brightness and contrast, a gamma curve generation module for using an adaptive algorithm to dynamically generate a gamma curve suitable for current display conditions according to ambient light and image content, an image quality determination module for real-time evaluation of the adjusted image quality to ensure that the gamma-adjusted image effect is better than the original image, a user preference learning module for learning the user's preference for image brightness and contrast through user behavior and feedback data, a real-time feedback adjustment module for adjusting the gamma curve in real time according to the feedback of the image quality determination module and the user preference learning module, a scene recognition module for using deep learning technology to identify the specific scene of the current image, and an intelligent gamma library module for maintaining a library of gamma curves containing a variety of presets and generated by learning to select the most suitable gamma curve application according to different needs and scenes;

[0038] The real-time feedback adjustment module includes an image brightness evaluation module for evaluating the overall brightness level of the image in real time to determine whether the gamma curve needs to be adjusted to optimize the brightness, an image contrast evaluation module for analyzing the contrast distribution of the image to detect whether there is an area with too high or too low contrast, a user feedback collection module for obtaining direct feedback from the user on the current image quality through the user interface, a user behavior analysis module for analyzing the user's operating behavior under different image settings to infer the user's preference, and a real-time feedback comprehensive analysis module for summarizing and analyzing the evaluation results from the two branches of image quality feedback and user preference feedback to make a comprehensive judgment;

[0039] The ambient light detection module detects and transmits the ambient light intensity and color temperature information to the image content analysis module in real time, so that these data can more accurately analyze the image scene, subject and brightness distribution, and ensure that the image analysis results take into account the ambient light conditions; after the image content analysis module analyzes the scene, subject and brightness distribution information of the image, it transmits these data to the human eye vision model module, so that it can use this information to simulate the human eye's perception of different brightness and contrast to optimize the gamma adjustment strategy; the human eye vision model module provides optimization strategies based on the simulated visual perception results, and these strategies are transmitted to the gamma curve generation module, so that it can dynamically generate the most suitable gamma curve under the current display conditions according to the strategy and the real-time ambient light image content; after the gamma curve generation module generates the gamma curve and applies it to the image, the image quality judgment module evaluates the adjusted image quality in real time to ensure that the image effect after gamma adjustment is better than the original image;

[0040] The image quality determination module evaluates the adjusted image quality and passes the evaluation result and user feedback data to the user preference learning module so that it can combine these data to learn the user's preference for image brightness and contrast and provide personalized adjustment suggestions; the user preference learning module provides the user's preferred brightness and contrast information and passes it to the real-time feedback adjustment module so that it can adjust the gamma curve in real time based on these preferences and the data from the image quality determination module to ensure that the output image meets the user's expectations; the scene recognition module identifies the specific scene of the current image and passes the scene information to the intelligent gamma library module so that it can select the most suitable gamma curve according to different scenes for application to ensure that the best display effect can be obtained in different scenes.

[0041] In the above embodiment, first, the ambient light detection module uses a light sensor and a machine learning algorithm to capture the ambient light intensity and color temperature in real time, and passes the data to the image content analysis module. The image content analysis module uses deep learning to perform a detailed analysis of the scene, subject, and brightness distribution in the image, and collaborates with the human eye vision model module to simulate the human eye's perception of brightness and contrast, and optimize the gamma adjustment strategy. The gamma curve generation module dynamically generates a suitable gamma curve based on these strategies and environmental conditions, and evaluates the adjusted image quality in real time through the image quality determination module to ensure that it is better than the original effect. In addition, the user preference learning module continuously optimizes the system's adjustment of brightness and contrast through user feedback, and the real-time feedback adjustment module finely adjusts the gamma curve based on user preferences and quality determination data to ensure that the image display meets user expectations. Finally, the scene recognition module and the intelligent gamma library module are used to select the best gamma curve application for different scenes.

[0042] Among them, the image brightness assessment module assesses the overall brightness level of the image and passes the brightness assessment result to the image contrast assessment module, allowing the latter to further analyze the contrast distribution of the image and comprehensively evaluate whether gamma adjustment is needed; the user feedback collection module obtains the user's direct feedback on the current image quality and passes the feedback data to the user behavior analysis module, allowing the latter to analyze user preferences based on user operation behavior history data and provide adjustment suggestions based on real user behavior.

[0043] Among them, the evaluation results of the image brightness evaluation module and the image contrast evaluation module are summarized in the real-time feedback comprehensive analysis module, so that the latter analyzes these data together with the feedback of user preferences to make a comprehensive judgment and adjust the gamma curve in real time.

[0044] Working principle: When the present invention is used, first, the input signal intelligent processing module automatically identifies and analyzes various input video signal formats through artificial intelligence algorithms, dynamically adjusts the analysis strategy, and uses machine learning models to autonomously select the optimal color space conversion method based on input signal characteristics and historical data. The module also dynamically adjusts the gamma curve based on ambient light and image content, and removes image noise in real time through deep learning algorithms to ensure color space conversion and image clarity after signal analysis.

[0045] Next, the color gamut intelligent conversion adjustment module combines the deep learning model to analyze the input image content, automatically adjusts the color gamut mapping strategy, and uses emotional computing technology to identify the emotional information conveyed in the image to optimize the color. The intelligent white balance adjustment module uses the ambient light sensor and machine learning algorithm to adaptively adjust the white balance to ensure the accuracy and consistency of the image color. The color health optimization module optimizes the image color to reduce harmful light and protect the user's vision health based on biofeedback and visual health research.

[0046] Finally, the image intelligent enhancement optimization module uses a convolutional neural network to sharpen the image, enhance details and edges, dynamically adjust the contrast of different areas of the image through content-aware contrast enhancement technology, and use a deep learning model to intelligently enhance the image color. The output signal intelligent processing module optimizes the image data synthesis process through AI algorithms, uses machine learning models and deep learning algorithms to adaptively adjust the output gamma, and performs intelligent encoding and transmission to ensure high-quality and stable image signals are output to the display device. The entire system achieves adaptive adjustment of the gamma curve through steps such as ambient light detection, image content analysis, human eye vision simulation, and user feedback learning to ensure the best display effect in different scenarios.

[0047] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A computer display screen color gamut processing system, characterized in that: It includes an input signal intelligence module for real-time analysis and optimization of input signals, a color gamut intelligent conversion and adjustment module for automatically adjusting colors to achieve consistency between devices and optimize the visual experience, an image intelligent enhancement and optimization module for using advanced algorithms to improve image details, contrast and color performance, and an output signal intelligent processing module for ensuring that the processed image signal is output to the display device with high quality and stability; The input signal intelligent processing module includes an adaptive signal parsing module for automatically identifying and parsing various input video signal formats through artificial intelligence algorithms to dynamically adjust the parsing strategy, a dynamic color space conversion module for autonomously selecting the optimal color space conversion method based on input signal characteristics and historical data using a machine learning model, a gamma curve adaptive adjustment module for dynamically adjusting the gamma curve based on ambient light and image content, and an intelligent denoising module for removing image noise in real time using a deep learning algorithm; The color gamut intelligent conversion and adjustment module includes an adaptive color gamut mapping module for automatically adjusting the color gamut mapping strategy by analyzing the input image content in combination with a deep learning model, a color emotion recognition and adjustment module for identifying the color conveying emotional information in the image using emotional computing technology, an intelligent white balance adjustment module for adaptively adjusting the white balance using an ambient light sensor and a machine learning algorithm, and a color health optimization module for optimizing the image color based on biofeedback and visual health research to reduce harmful light such as blue light.

2. A computer display screen color gamut processing system according to claim 1, characterized in that: The image intelligent enhancement and optimization module includes a deep learning sharpening module for performing image sharpening processing using a convolutional neural network to enhance details and edges, an intelligent contrast enhancement module for dynamically adjusting the contrast of different regions of an image based on content-aware contrast enhancement technology, a color prediction and enhancement module for intelligently enhancing the color of an image through a prediction algorithm and a deep learning model, and a local intelligent color enhancement module for performing intelligent color enhancement on specific regions in an image using fine-grained segmentation technology; The output signal intelligent processing module includes an output signal intelligent synthesis module for optimizing the synthesis process of processed image data using an AI algorithm, an adaptive gamma correction module for adaptively adjusting the output gamma according to display device characteristics and ambient light conditions based on a machine learning model, an intelligent encoding module for intelligently encoding processed image data using a deep learning algorithm, and an intelligent signal transmission module for combining a network optimization algorithm and a transmission protocol.

3. A computer display screen color gamut processing system according to claim 1, characterized in that: The gamma curve adaptive adjustment module includes an ambient light detection module for using advanced light sensors and machine learning algorithms to detect the intensity and color temperature of ambient light in real time, an image content analysis module for image content analysis with deep learning capabilities to analyze scenes, subjects and brightness distribution in real time in images, a human eye vision model module for simulating human eye visual characteristics to optimize gamma adjustment strategies based on human eye perception of different brightness and contrast, a gamma curve generation module for using an adaptive algorithm to dynamically generate a gamma curve suitable for current display conditions based on ambient light and image content, an image quality determination module for real-time evaluation of the adjusted image quality to ensure that the gamma-adjusted image effect is better than the original image, a user preference learning module for learning user preferences for image brightness and contrast through user behavior and feedback data, a real-time feedback adjustment module for adjusting the gamma curve in real time based on feedback from the image quality determination module and the user preference learning module, a scene recognition module for using deep learning technology to identify the specific scene of the current image, and an intelligent gamma library module for maintaining a library of gamma curves containing a variety of presets and learned gamma curves to select the most suitable gamma curve application according to different needs and scenarios.

4. A computer display screen color gamut processing system according to claim 3, characterized in that: The real-time feedback adjustment module includes an image brightness evaluation module for evaluating the overall brightness level of the image in real time to determine whether the gamma curve needs to be adjusted to optimize the brightness, an image contrast evaluation module for analyzing the contrast distribution of the image to detect whether there are areas with too high or too low contrast, a user feedback collection module for obtaining direct feedback from users on the current image quality through a user interface, a user behavior analysis module for analyzing the user's operating behavior under different image settings to infer user preferences, and a real-time feedback comprehensive analysis module for summarizing and analyzing the evaluation results from the two branches of image quality feedback and user preference feedback to make a comprehensive judgment.

5. A computer display screen color gamut processing system according to claim 4, characterized in that: The ambient light detection module detects and transmits the ambient light intensity and color temperature information to the image content analysis module in real time, so that these data can more accurately analyze the image scene, subject and brightness distribution, and ensure that the image analysis results take into account the ambient light conditions; after the image content analysis module analyzes the scene, subject and brightness distribution information of the image, it transmits these data to the human eye vision model module so that it can use this information to simulate the human eye's perception of different brightness and contrast to optimize the gamma adjustment strategy; The human eye vision model module provides optimization strategies based on the simulated visual perception results. These strategies are passed to the gamma curve generation module, which dynamically generates the most suitable gamma curve under the current display conditions based on the strategies and the real-time ambient light image content. The gamma curve generation module generates a gamma curve and applies it to the image. The image quality judgment module evaluates the adjusted image quality in real time to ensure that the image effect after gamma adjustment is better than the original image.

6. A computer display screen color gamut processing system according to claim 5, characterized in that: The image quality determination module evaluates the adjusted image quality and transmits the evaluation result and user feedback data to the user preference learning module so that the module can combine these data to learn the user's preference for image brightness and contrast and provide personalized adjustment suggestions; The user preference learning module provides the user's preferred brightness and contrast information to the real-time feedback adjustment module, allowing it to adjust the gamma curve in real time based on these preferences and the data from the image quality determination module to ensure that the output image meets the user's expectations; the scene recognition module identifies the specific scene of the current image and transmits the scene information to the intelligent gamma library module, allowing it to select the most suitable gamma curve for application according to different scenes to ensure the best display effect in different scenes.

7. A computer display screen color gamut processing system according to claim 1, characterized in that: The adaptive signal analysis module automatically identifies and analyzes various input video signal formats through artificial intelligence algorithms, dynamically adjusts the analysis strategy and passes it to the dynamic color space conversion module, allowing it to autonomously select the optimal color space conversion method according to the input signal characteristics and historical data to ensure the best color space conversion effect after signal analysis; the dynamic color space conversion module optimizes the input signal according to the selected color space conversion method and then passes the optimized image to the gamma curve adaptive adjustment module, allowing it to dynamically adjust the gamma curve based on ambient light and image content; the gamma curve adaptive adjustment module dynamically adjusts the gamma curve and then passes the image to the intelligent denoising module, allowing it to use a deep learning algorithm to remove image noise in real time to ensure the clarity and detail retention of the final image.

8. A computer display screen color gamut processing system according to claim 1, characterized in that: The adaptive color gamut mapping module automatically adjusts the color gamut mapping strategy by analyzing the input image content through a deep learning model and passes the adjusted color gamut information to the color emotion recognition and adjustment module, allowing it to use emotional computing technology to recognize and adjust the emotional information conveyed in the image to enhance the visual experience; the color emotion recognition and adjustment module recognizes and adjusts the image color emotional information and then passes the result to the intelligent white balance adjustment module, allowing it to use the ambient light sensor and machine learning algorithm to adaptively adjust the white balance to ensure the accuracy and consistency of the image color; after adjusting the white balance, the intelligent white balance adjustment module passes the image to the color health optimization module, allowing it to optimize the image color based on biofeedback and visual health research, reduce harmful light such as blue light, and protect the user's vision health.

9. A computer display screen color gamut processing system according to claim 2, characterized in that: The deep learning sharpening module uses a convolutional neural network to perform image sharpening processing to enhance details and edges, and then passes the image to the intelligent contrast enhancement module, which dynamically adjusts the contrast of different areas of the image based on content-aware contrast enhancement technology; the intelligent contrast enhancement module adjusts the image contrast and then passes the image to the color prediction and enhancement module, which intelligently enhances the image color through a prediction algorithm and a deep learning model; the color prediction and enhancement module enhances the image color and then passes the image to the local intelligent color enhancement module, which uses fine-grained segmentation technology to perform intelligent color enhancement on specific areas in the image to ensure that the colors of the local areas are accurate and rich.

10. A computer display screen color gamut processing system according to claim 2, characterized in that: The output signal intelligent synthesis module uses the AI ​​algorithm to optimize the synthesis process of the processed image data and then passes the synthesized image to the adaptive gamma correction module, which allows it to adaptively adjust the output gamma based on the machine learning model according to the characteristics of the display device and the ambient light conditions to ensure the consistency of the image on different devices; after adjusting the output gamma, the adaptive gamma correction module passes the image to the intelligent encoding module, which allows it to use the deep learning algorithm to intelligently encode the processed image data to ensure high-quality transmission of the image signal; after intelligently encoding the image data, the intelligent encoding module passes the encoded data to the intelligent signal transmission module, which combines the network optimization algorithm and the transmission protocol to ensure efficient and stable transmission of the image signal to the display device.

Images