Adaptive image adjustment method and device for liquid crystal display screen based on ambient light sensing

Abstract

This application provides a method and device for adaptive image adjustment of liquid crystal displays based on ambient light sensing, which relates to the field of display technology. The method includes: performing ambient light field perception and establishing light field emotion perception using 3D light field distribution; extracting the display content of the liquid crystal display and constructing an adjustment tolerance constraint based on the display content; activating a linkage acquisition device, performing multi-dimensional data perception of the user based on the linkage acquisition device, and establishing a user emotion vector using the multi-dimensional data perception results; after establishing the adjustment decision vector, sending the light field emotion perception, adjustment tolerance constraint, and user emotion vector as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization; and rendering the display image of the liquid crystal display based on the adjustment decision vector optimization results. This application can achieve the technical goal of multi-dimensional intelligent collaborative optimization of ambient light, user emotion, and display content, and achieve the technical effect of improving scene adaptability.

Description

Technical Field

[0001] The present application relates to the technical field of display screens, and in particular to a method and device for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing. Background Art

[0002] With the rapid development of display technology and the increasing demand for user visual experience, the adaptive adjustment of ambient light for liquid crystal displays (LCDs) has become a research hotspot. However, existing technologies still have many shortcomings and are unable to meet the intelligent and personalized needs in complex scenarios.

[0003] Currently, existing solutions mostly rely on single light sensors or simple RGB cameras, which can only capture the average brightness and color temperature of ambient light and are unable to accurately reconstruct three-dimensional light field distributions (such as the superposition of multiple light sources and dynamic light and shadow changes). For example, in scenes with dappled shade or flickering neon lights, existing systems often fail to accurately capture key parameters such as light direction and intensity gradients, leading to screen adjustment lag or overshoot (such as the sudden brightness change of in-vehicle displays when entering or exiting tunnels). Secondly, they lack emotional coordination mechanisms. Current technologies only perform mechanical adjustments based on physical lighting parameters and fail to dynamically adapt to the user's emotional state (such as relaxation, focus, and excitement). For example, the low color temperature environment on a rainy day may require a soothing display tone to match the user's mood, but existing systems still use fixed color temperature mapping, resulting in a fragmented visual experience. Furthermore, multi-objective optimization capabilities are weak. Existing technologies lack an intelligent trade-off mechanism between display content fidelity (such as color accuracy requirements for medical images), user emotional needs (such as high contrast preferences for entertainment scenes), and ambient light integration (such as the coordination of natural tones at sunset), which often leads to parameter conflicts (such as oversaturation or loss of details when reading outdoors).

[0004] In summary, the existing technology has technical problems such as the lack of emotional coordination mechanism, which leads to the disconnection between screen adjustment strategy and real needs of users, further affecting visual comfort, emotional interactive experience and scene adaptability. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive image adjustment method and device for a liquid crystal display screen based on ambient light sensing, so as to solve the technical problem in the prior art that the screen adjustment strategy is out of touch with the real needs of users due to the lack of an emotional coordination mechanism, further affecting visual comfort, emotional interactive experience and scene adaptability.

[0006] In view of the above problems, the present application provides a method and device for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing.

[0007] In the first aspect, the present application provides an adaptive image adjustment method for a liquid crystal display screen based on ambient light sensing, which is implemented by an adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing, including: calling an RGB camera and an ambient light sensor to perform ambient light field perception, reconstructing the ambient 3D light field distribution, and using the 3D light field distribution to establish light field emotion perception; extracting the display content of the liquid crystal display screen, and constructing an adjustment tolerance constraint based on the display content; activating a linkage acquisition device, performing multi-dimensional data perception of the user based on the linkage acquisition device, and establishing a user emotion vector using the multi-dimensional data perception result; after establishing the adjustment decision vector, the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector are sent as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization, the collaborative objectives of the multi-objective collaborative adjustment controller including a user emotion resonance target, an adjustment tolerance violation target, and a light field emotion fusion matching target; and rendering the display image of the liquid crystal display screen according to the adjustment decision vector optimization result.

[0008] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing also includes: executing historical environmental data calls to obtain a historical environmental data set; after performing weather similarity clustering on the historical environmental data set, predicting the mutation time point based on the time identifiers of the similarity clusters, and establishing a mutation node prediction result; obtaining the positioning data of the liquid crystal display screen, performing networked weather reading based on the positioning data, and establishing a networked weather reading result; and using the mutation node prediction result and the networked weather reading result to compensate for light field emotional perception.

[0009] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing also includes: performing time series extraction on ambient light field perception to establish a time series distribution data set of ambient 3D light field distribution; performing time series prediction using the time series distribution data set to establish light field emotion perception; performing conflict identification of time series prediction based on the mutation node prediction result, and establishing a first compensation feedback using the conflict identification result; performing an impact analysis of time series prediction using the networked weather reading result to establish a second compensation feedback; and compensating for the light field emotion perception using the first compensation feedback and the second compensation feedback.

[0010] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing further includes: the adjustment decision vector includes a hue offset, a color temperature adjustment amount, a contrast adjustment factor, a clarity adjustment factor, and an emotional style transfer factor.

[0011] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing also includes: performing scene recognition based on the display content, multi-dimensional data perception results, and ambient 3D light field distribution to establish a scene recognition result; using the scene recognition result to crop the search space, and performing optimization management of the adjustment decision vector based on the cropped search space.

[0012] Preferably, the method for adaptive image adjustment of a liquid crystal display based on ambient light sensing also includes: in each round of iteration, after initializing the current adjustment decision vector, performing iterative optimization through a lightweight gradient descent device; performing multiple rounds of iterative record evaluation to generate continuous iterative evaluation results; if the continuous iterative evaluation results cannot meet the preset convergence threshold, generating auxiliary optimization instructions; and using the auxiliary optimization instructions to call the historical context adjustment template for enhanced optimization.

[0013] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing also includes: using the 3D light field distribution to extract features and establish a feature set, the extracted features including illumination color distribution, brightness gradient, saturation distribution, incident azimuth and altitude angles, degree of light spot mottle, and color temperature estimation; establishing an emotion label mapping based on an existing emotional environment image data set; performing sliding time window aggregation matching of the feature set according to the emotion label mapping to establish light field emotion perception.

[0014] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing also includes: establishing a user's emotional data set and configuring an energy-saving tolerance factor for each emotional state; using the energy-saving tolerance factor to construct an energy-saving optimization function, and performing energy-saving optimization of the adjustment decision vector optimization result based on the energy-saving optimization function; and rendering the display image of the liquid crystal display screen using the energy-saving optimization result.

[0015] Preferably, the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing further includes: the linkage acquisition device includes an acquisition camera, a microphone, and a wearable physiological data acquisition device.

[0016] In the second aspect, the present application also provides an adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing, which is used to execute the adaptive image adjustment method for a liquid crystal display screen based on ambient light sensing as described in the first aspect, including: an ambient light field perception module, which is used to call an RGB camera and an ambient light sensor to perform ambient light field perception, reconstruct the ambient 3D light field distribution, and use the 3D light field distribution to establish light field emotion perception; a constraint construction module, which is used to extract the display content of the liquid crystal display screen and construct an adjustment tolerance constraint based on the display content; a multi-dimensional data perception module, which is used to activate a linkage acquisition device, perform multi-dimensional data perception of the user based on the linkage acquisition device, and establish a user emotion vector using the multi-dimensional data perception result; an adjustment decision vector optimization module, which is used to establish an adjustment decision vector, and then send the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization, where the collaborative objectives of the multi-objective collaborative adjustment controller include a user emotion resonance target, an adjustment tolerance violation degree target, and a light field emotion fusion matching target; a display image rendering module, which is used to render the display image of the liquid crystal display screen according to the adjustment decision vector optimization result.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of multi-dimensional intelligent collaborative optimization of ambient light-user emotions-display content, the technical effects of improving visual comfort, enhancing emotional resonance, and adapting to dynamic scenes are achieved.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0020] Figure 1 This is a flow chart of the method for adaptive image adjustment of a liquid crystal display based on ambient light sensing in this application;

[0021] Figure 2This is a schematic diagram of the structure of the liquid crystal display adaptive image adjustment device based on ambient light sensing in this application.

[0022] Explanation of the accompanying symbols: ambient light field perception module 11, constraint construction module 12, multi-dimensional data perception module 13, adjustment decision vector optimization module 14, display image rendering module 15. DETAILED DESCRIPTION

[0023] This application provides a method and device for adaptive image adjustment of liquid crystal displays based on ambient light sensing. This solves the technical problem in the prior art where the lack of an emotional coordination mechanism causes screen adjustment strategies to be disconnected from actual user needs, further affecting visual comfort, emotional interaction experience, and scene adaptability. This method achieves the technical goal of multi-dimensional intelligent collaborative optimization of ambient light, user emotion, and display content, achieving the technical effects of improving visual comfort, enhancing emotional resonance, and adapting to dynamic scenes.

[0024] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0025] For example, see the attached Figure 1 The present application provides a method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing, which is applied to an adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing, and specifically includes the following steps:

[0026] S1: Call the RGB camera and ambient light sensor to perceive the ambient light field, reconstruct the ambient 3D light field distribution, and use the 3D light field distribution to establish light field emotion perception.

[0027] Specifically, by invoking the RGB camera and ambient light sensor, it is possible to obtain lighting information of the current surrounding environment. The RGB camera is used to capture color information, while the ambient light sensor is responsible for measuring the overall light intensity. After fusing these two types of perception data, a three-dimensional light field distribution with spatial depth information can be restored, namely the ambient 3D light field distribution. This can show the direction and intensity of the light source, as well as information such as color distribution, laying the foundation for subsequent emotional perception and image adjustment.

[0028] Utilizing 3D light field distribution to establish light field emotion perception, the system extracts multiple characteristic parameters of light and compares them with an existing emotion labeling system to identify the user's emotional state that may be stimulated by the current light environment and establish light field emotion perception. 3D light field distribution is a complete representation of information such as color, brightness, direction, and changing trends of light at different locations in space.

[0029] S2: extracting display content of the liquid crystal display screen, and constructing an adjustment tolerance constraint based on the display content.

[0030] Specifically, the system extracts the display content of the LCD screen, actively reads and analyzes the image or video information currently displayed on the screen, and understands the content type and its visual characteristics. Display content can include static images, dynamic images, text interfaces, game screens, or user interfaces. Each type of content has different sensitivity and adjustment requirements for image parameters such as brightness, color, and contrast. Based on the properties of the displayed content, an adjustment tolerance constraint is constructed. The adjustment tolerance constraint defines the acceptable range of variation of image parameters and is used to adjust the parameters without affecting the viewing experience.

[0031] S3: activating the linkage acquisition device, performing multi-dimensional data perception of the user based on the linkage acquisition device, and establishing a user emotion vector using the multi-dimensional data perception result.

[0032] Specifically, the linked data collection devices are activated, starting external or embedded sensor devices that work in conjunction with the LCD screen. These may include cameras, microphones, and wearable physiological monitoring devices. Each device performs different data collection tasks. For example, cameras can analyze facial expressions and eye movements, microphones can monitor voice inflections, and wearable devices such as smart bracelets can collect physiological parameters such as heart rate, galvanic skin response, and body temperature.

[0033] Based on linked data collection devices, multi-dimensional user data perception is performed, simultaneously perceiving the user's current psychological and physiological state from multiple dimensions. Multi-dimensional data perception means that the user data acquired is not limited to a single type, but rather integrates visual, auditory, tactile, and even physiological signals. For example, the user's voice pitch may rise, their heart rate may increase, and their facial expression may become tense, resulting in a more comprehensive and accurate understanding of the user's state. The multi-dimensional data perception results are then used to construct a user emotion vector, converting the raw data into a quantifiable, inputtable structured representation. The user emotion vector is a multidimensional vector, with each dimension representing an emotional characteristic or state indicator, such as pleasure, concentration, tension, and anxiety. Emotion vectors can be derived through model mapping and data training. Based on learning from previous data, input signals such as facial features, voice frequency, and heart rate are combined and mapped to specific emotional states.

[0034] S4: After establishing the adjustment decision vector, the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector are sent as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization. The collaborative objectives of the multi-objective collaborative adjustment controller include the user emotion resonance objective, the adjustment tolerance violation degree objective, and the light field emotion fusion matching objective.

[0035] Specifically, after establishing the adjustment decision vector, the system simultaneously collects the light field emotional state of the current environment, the allowed adjustment range of the displayed content, and the user's current multi-dimensional emotional state. The light field emotional perception, adjustment tolerance constraints, and user emotion vector are then sent as input to a multi-objective collaborative adjustment controller to perform an optimization search for the adjustment decision vector. The multi-objective collaborative adjustment controller then fine-tunes the adjustment vector, using methods such as weighted linear programming or evolutionary optimization to find the optimal solution across multiple objectives. The collaborative objectives of the multi-objective collaborative adjustment controller include a user emotional resonance objective, an adjustment tolerance violation objective, and a light field emotional fusion match objective. The user emotional resonance objective ensures that the adjustment result positively resonates with the user's current emotional state. For example, when a user is feeling down, soft, warm tones should be prioritized to evoke a sense of comfort. The adjustment tolerance violation objective limits the optimization process to within the constraints imposed by the displayed content on image parameters, for example, not allowing sharpness to decrease by more than 20%. The light field emotional fusion match objective emphasizes that the adjustment result naturally blends with the ambient light mood to avoid any sense of dissonance. For example, a highly saturated warm image should not be output in an environment with strong cold light fluctuations.

[0036] S5: Rendering the display image of the LCD screen according to the optimization result of the adjustment decision vector.

[0037] Specifically, the LCD display's display image rendering is performed based on the optimized adjustment decision vector results. Based on the optimal combination of adjustment parameters, the final image content displayed on the LCD screen is reprocessed and presented. The optimized adjustment decision vector results are a set of parameter values derived through multiple rounds of optimization algorithms. These values are used to control multiple aspects of the image, such as hue, brightness, contrast, and sharpness, to ensure that the screen display is consistent with the current ambient light field and the user's emotional state. Display image rendering involves applying these parameters to the image processing engine, making real-time adjustments to the original image to create an image that is more visually pleasing or more in line with the target emotional atmosphere. For example, if the color temperature adjustment in the optimized results is reduced by 300 Kelvin, the overall image will have a warmer tone; while a contrast adjustment factor of 1.2 will create a clearer boundary between light and dark, enhancing visual tension.

[0038] Furthermore, the present application also includes: executing historical environmental data calls to obtain historical environmental data sets; after performing weather similarity clustering on the historical environmental data sets, predicting mutation time points based on the time identifiers of the similarity clusters, and establishing mutation node prediction results; obtaining positioning data of the LCD display screen, performing networked weather reading based on the positioning data, and establishing networked weather reading results; and using the mutation node prediction results and the networked weather reading results to compensate for light field emotion perception.

[0039] Specifically, we retrieve ambient light data from past periods to generate historical environmental data, which includes lighting patterns and characteristics at different times and under different weather conditions. By performing cluster analysis on this historical environmental data, we can identify data segments with similar weather conditions, such as cloudy skies, high humidity, and low brightness, thereby constructing a grouping structure based on weather similarity.

[0040] Subsequently, we analyze the changing trends of illumination patterns based on the time stamps of weather-like clusters, and predict potential mutation nodes. Mutation nodes are points in time when ambient light characteristics undergo a dramatic change, such as sudden rain, sunset, or a light switch. By establishing mutation node predictions, we can foresee dramatic changes in the ambient light field and prepare early warnings for image adjustment strategies. Table 1 shows a partial record of the most recent mutation node prediction results.

[0041] Table 1: Partial record of the latest mutation node prediction results

[0042] ;

[0043] ;

[0044] Collect real-time positioning data of the LCD screen's location, and rely on the network to obtain the latest weather information corresponding to the positioning data, which is then used to predict and provide real-world environmental references.

[0045] Finally, the predicted mutation node prediction results are combined with the networked weather reading results obtained through the Internet to compensate and optimize the light field emotion perception originally established based on real-time perception, thereby improving the stability and emotion matching of the overall image adjustment.

[0046] Furthermore, the present application also includes: performing time series extraction on ambient light field perception to establish a time series distribution data set of ambient 3D light field distribution; using the time series distribution data set to perform time series prediction to establish light field emotion perception; performing conflict identification of time series prediction based on the mutation node prediction results, and establishing a first compensation feedback using the conflict identification results; using the networked weather reading results to perform impact analysis of time series prediction to establish a second compensation feedback; and using the first compensation feedback and the second compensation feedback to compensate for the light field emotion perception.

[0047] Specifically, the process of temporal extraction of ambient light field perception organizes the ambient light data continuously acquired over a period of time in chronological order to form a series of time-stamped light field snapshots, thereby constructing a temporal distribution dataset of the ambient 3D light field distribution. This can obtain the dynamic trend of the light field over time, providing a basis for subsequent analysis.

[0048] Next, time series prediction is performed using the time series distribution dataset. This involves using a time series analysis algorithm to predict the evolution of ambient light over a period of time, thereby establishing light field emotional perception. For example, when the ambient light field exhibits a combination of high brightness, high color temperature, and dramatic changes, light field characteristics with a strong sense of stimulation and dynamics are identified. This may trigger more visual and physiological activation signals in the user, evoking positive and focused emotions. When the light field exhibits low brightness, moderate humidity, and rapid lighting changes, this is identified as a potential trigger for anxiety or restlessness. This discomfort may be alleviated by increasing contrast and adding smooth transitions. When the ambient light is predominantly warm, with steady changes in light source direction and consistent direction, it is identified as an emotional pattern that evokes feelings of relaxation and security. When the ambient light is cooler, with multiple incident directions and accompanied by high-frequency, small fluctuations, it is labeled as conveying feelings of stress or apathy.

[0049] Furthermore, combined with the mutation node prediction results, we can identify whether the current time series prediction is inconsistent with the mutation warning, that is, perform conflict identification, which can reveal the deviation between the model prediction and the sudden environmental changes. For example, if the time series prediction believes that the light will gradually become brighter, but the mutation node prediction shows that it is about to rain, causing the environment to darken, then the first compensation feedback is established as the basis for correcting the emotional perception of the light field.

[0050] At the same time, the networked weather reading results are used to analyze their potential impact on the emotional perception of the light field. For example, if the network weather data shows that there will be strong winds and cooling within 30 minutes, the external real data may have an impact on the local light field changes. Based on this, a second compensation feedback is formed to correct the time series prediction to make it closer to the actual environmental changes.

[0051] Finally, the first compensation feedback and the second compensation feedback are combined to perform final compensation optimization on the established light field emotion perception, making the light field emotion judgment more accurate and the displayed content more adapted to the environment.

[0052] Furthermore, the present application also includes: the adjustment decision vector includes a hue offset, a color temperature adjustment amount, a contrast adjustment factor, a clarity adjustment factor, and an emotional style transfer factor.

[0053] Specifically, the adjustment decision vector is a multidimensional set of parameters used to guide LCD screen image adjustments. Each dimension represents the direction and magnitude of adjustment for a specific image attribute. The hue shift refers to the degree of shift in the primary color of the image. For example, a bluish image can be adjusted to green or red, adapting to different lighting environments or emotional needs. The color temperature adjustment refers to the degree of adjustment between warm and cool tones. A higher color temperature makes the image appear cooler, while a lower color temperature makes it appear warmer, adjusting the image's atmosphere. The contrast adjustment factor determines the difference between light and dark in the image. A higher value makes brighter areas brighter and darker areas darker, enhancing the visual impact and clearer boundaries. The clarity adjustment factor enhances edge sharpness and details. The emotional style transfer factor reflects the degree to which the overall visual style of the image matches the user's current emotional state. It combines light field emotion perception with the user's emotional vector analysis results to imbue images with specific emotional tendencies, such as tranquility, warmth, or tension.

[0054] Furthermore, the present application also includes: performing scene recognition based on the display content, multi-dimensional data perception results, and environmental 3D light field distribution to establish scene recognition results; using the scene recognition results to crop the search space, and adjusting the decision vector optimization management based on the cropped search space.

[0055] Specifically, scene recognition is performed based on the displayed content, multi-dimensional data perception results, and the ambient 3D light field distribution. The system analyzes the image information currently displayed on the LCD screen, the user's physiological and emotional state, and the three-dimensional lighting characteristics of the surrounding environment to determine the current application scenario type. Display content includes the image's subject category, color composition, and dynamic characteristics, such as whether it is a video, static text, or a graphical interface. Multi-dimensional data perception results include psychological state indicators such as the user's emotional vector and attention level. The ambient 3D light field distribution is a three-dimensional model of light propagation in space, including parameters such as light intensity, color, and direction. This allows the system to identify different scenarios, such as office work, leisure time, gaming, or nighttime reading, and further establish scene recognition results to provide a basis for subsequent decision-making.

[0056] The search space is cropped using the scene recognition results, and the range of feasible image adjustment parameters is restricted based on the scene, thereby reducing the computational space required for parameter optimization. The search space refers to the set of all possible parameter combinations when performing adjustment decision optimization. Cropping the search space means only retaining the adjustment parameter range that is most relevant to the current scene and most likely to be adopted, thereby improving computational efficiency and adjustment accuracy. For example, in reading mode, parameter combinations with low color temperature, soft brightness, and moderate contrast are prioritized, and candidates that are too bright or overly bright are excluded.

[0057] Based on the cropped search space, the optimization management of the adjustment decision vector is performed, searching for the optimal image adjustment strategy within a limited but precise parameter range. The adjustment decision vector is composed of multiple dimensions, including hue shift, color temperature adjustment, contrast, clarity, and emotional style. Each dimension has specific candidate values or variation ranges. Through multiple rounds of calculation, the parameter combination that best matches the current scene and user emotion is found.

[0058] Furthermore, the present application also includes: in each round of iteration, after initializing the current adjustment decision vector, iterative optimization is performed through a lightweight gradient descent device; multiple rounds of iterative record evaluation are performed to generate continuous iterative evaluation results; if the continuous iterative evaluation results cannot meet the preset convergence threshold, an auxiliary optimization instruction is generated; and the auxiliary optimization instruction is used to call the historical context adjustment template for enhanced optimization.

[0059] Specifically, during each iteration, after initializing the current adjustment decision vector, an initial combination of adjustment parameters is set. This combination covers adjustment factors such as hue, color temperature, contrast, and clarity. This combination is then iteratively optimized using a lightweight gradient descent method. The lightweight gradient descent method is a simplified gradient descent method that offers high computational efficiency and minimal resource usage, making it suitable for deployment on devices with high real-time requirements. In each iteration, the lightweight gradient descent method calculates the effect of the current adjustment parameter combination based on the objective evaluation function and continuously fine-tunes the parameters to gradually approach the optimal result.

[0060] Next, multiple rounds of iterative recording and evaluation are performed, and the corresponding effect score is recorded each time, such as the image comfort score, user emotional feedback value or visual contrast index, to generate continuous iterative evaluation results. For example, the continuous evaluation values are combined into an iterative optimization curve for real-time judgment of the current adjustment and to provide a trend reference for subsequent optimization.

[0061] If the results of the continuous iterative evaluations fail to meet the preset convergence threshold, the current parameter path is deemed unable to converge to a satisfactory result, and auxiliary optimization instructions are generated to guide the subsequent adjustment process. The convergence threshold can be customized by those skilled in the art based on actual conditions.

[0062] Finally, auxiliary optimization instructions are used to call historical context adjustment templates for enhanced optimization. This combines the currently recognized scene information with previously stored adjustment templates from similar scenarios, directly borrowing parameter combinations known to work well, breaking out of the current local optimum and retrying optimization. Historical context adjustment templates are derived from past user interaction records or expert system recommendations. For example, for the "night reading" scene, there may be an adjustment combination of 3500 Kelvin, 60% brightness, and 0.5 contrast that has been proven to work well.

[0063] Furthermore, the present application also includes: using the 3D light field distribution to extract features and establish a feature set, the extracted features including illumination color distribution, brightness gradient, saturation distribution, incident azimuth and altitude angles, degree of light spot mottle, and color temperature estimation; establishing an emotion label mapping based on an existing emotional environment image dataset; performing sliding time window aggregation matching of the feature set according to the emotion label mapping to establish light field emotion perception.

[0064] Specifically, 3D light field distribution is used for feature extraction to extract key attributes that reflect emotional characteristics, and a feature set is established for subsequent analysis. The illumination color distribution describes the proportion and spatial variation of red, green, and blue colors in the ambient light. The brightness gradient indicates the degree of change in light intensity from one area to another. The saturation distribution measures the purity and vividness of the color. The incident azimuth and altitude angles refer to the horizontal and vertical angles of the light direction. The degree of light spot reflects the uniformity or speckle of the light on the surface. The color temperature estimation is the ambient light color temperature calculated by physical calculation or sensor measurement, which is used to determine whether the light is warm or cool.

[0065] Next, an emotion label mapping is established based on an existing emotional environment image dataset. Utilizing a large amount of environmental image data with emotion labels, specific optical features are mapped one-to-one with emotional states, such as sample images of emotional states such as relaxation, concentration, anxiety, and pleasure. Each image is pre-labeled with the emotion type. A model is trained through statistical or machine learning algorithms so that, given a set of light field features, the most likely corresponding emotion label can be mapped.

[0066] Then, based on the emotion label mapping, a sliding time window aggregation and matching is performed on the feature set. In the temporal dimension, continuous light field features are sliced and gradually aggregated for analysis. This establishes light field emotion perception, detects the changing trends of emotional states over time, and avoids biases in emotional judgment caused by occasional changes in illumination. This sliding window technique groups continuously changing illumination features within a time period of, for example, 5 seconds, and matches them to emotion labels, ultimately forming a stable light field emotion judgment for a specific period.

[0067] Furthermore, the present application also includes: establishing a user's emotional data set and configuring an energy-saving tolerance factor for each emotional state; using the energy-saving tolerance factor to construct an energy-saving optimization function, and performing energy-saving optimization of the adjustment decision vector optimization result based on the energy-saving optimization function; and using the energy-saving optimization result to render the display image on the LCD screen.

[0068] Specifically, multi-dimensional sensing devices are used to collect physiological or behavioral characteristics of users in different emotional states, such as heart rate, facial expressions, and voice tones, and aggregate them into an emotional sample library for algorithm training. An energy-saving tolerance factor is assigned to each emotional state to indicate the user's acceptance of the screen energy-saving strategy in that emotional state. For example, when a user is in a relaxed state, they may be more tolerant of a display effect with slightly lower brightness or slightly biased colors. In this case, the tolerance factor can be set to a higher value, such as 0.8. When the user is in a focused state, their sensitivity to image details increases, and their tolerance for the reduction in image quality caused by energy saving is lower. The tolerance factor can be set to 0.3.

[0069] Subsequently, an energy-saving optimization function was constructed using the energy-saving tolerance factor to evaluate the balance between energy efficiency and user experience under different display adjustment schemes. This function comprehensively considers the trade-off between reduced power consumption and decreased emotional quality of experience, maximizing energy savings without significantly disrupting the user experience. If a certain adjustment scheme reduces energy consumption by 30% but is virtually imperceptible to a relaxed user, the energy-saving optimization function will assign a higher score to this scheme.

[0070] Next, energy-saving optimization is performed on the adjustment decision vector optimization result based on the energy-saving optimization function, and its energy efficiency performance is further analyzed. If the energy-saving expectations are not met, the adjustment vector is refined in combination with the emotional tolerance factor, such as appropriately reducing the brightness or reducing the distribution of high-power color to ensure further optimization of overall energy consumption.

[0071] Finally, the energy-saving optimization results are used to render the display image on the LCD screen. That is, the optimized adjustment decisions are applied to the actual image output, controlling the color temperature, contrast, clarity and other parameters of the display screen. The final rendered image has an acceptable visual effect while significantly reducing power consumption. For example, the average brightness is reduced by 20%, but the change is not obvious in human eye perception.

[0072] Furthermore, the present application also includes: the linkage acquisition device includes an acquisition camera, a microphone, and a wearable physiological data acquisition device.

[0073] Specifically, the linked acquisition equipment includes a camera, a visual sensing device that works in coordination with other devices to capture real-time image information such as a user's facial expressions, gestures, and gaze direction. The camera's resolution, frame rate, and light sensitivity determine its sensitivity to subtle changes in detail. For example, a camera with a resolution of 1920 x 1080 pixels can clearly discern subtle changes in facial expressions, while a frame rate of 30 frames per second ensures that continuous expression transitions can be captured.

[0074] Next, the microphone takes on the task of collecting sound, capturing characteristics of the user's voice, tone, breathing rate, and other acoustic features, further inferring their emotional state. For example, a microphone detecting a faster, louder voice signal may indicate excitement or nervousness, while a slower, lower volume may indicate fatigue or calmness. Microphones at different sampling rates reproduce sound details to varying degrees, ensuring accurate capture of emotional nuance.

[0075] Wearable physiological data collection devices, which use sensors worn on the user's body to collect physiological parameters such as heart rate, galvanic skin response, body temperature, and physical activity, include smart bracelets, watches, and body patches, enabling continuous, high-frequency data collection. For example, the rate of change in heart rate can reflect the user's level of stress. If the heart rate increases by more than 20 beats per minute within 10 seconds, it may indicate elevated emotions or anxiety.

[0076] To sum up, the adaptive image adjustment method for liquid crystal display screens based on ambient light sensing provided in this application has the following technical effects: by achieving the technical goal of multi-dimensional intelligent collaborative optimization of ambient light-user emotions-display content, the technical effects of improving visual comfort, enhancing emotional resonance, and adapting to dynamic scenes are achieved.

[0077] In the second embodiment, based on the same inventive concept as the method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing in the aforementioned embodiment, the present application also provides an adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing, as shown in the attached drawings. Figure 2 , including: an ambient light field perception module 11, used to call the RGB camera and the ambient light sensor to perform ambient light field perception, reconstruct the ambient 3D light field distribution, and use the 3D light field distribution to establish light field emotion perception; a constraint construction module 12, used to extract the display content of the liquid crystal display screen, and construct an adjustment tolerance constraint based on the display content; a multi-dimensional data perception module 13, used to activate the linkage acquisition device, perform multi-dimensional data perception of the user based on the linkage acquisition device, and establish a user emotion vector using the multi-dimensional data perception result; an adjustment decision vector optimization module 14, used to establish the adjustment decision vector, and send the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization. The collaborative objectives of the multi-objective collaborative adjustment controller include user emotion resonance objectives, adjustment tolerance violation degree objectives, and light field emotion fusion matching objectives; a display image rendering module 15, used to perform display image rendering of the liquid crystal display screen according to the adjustment decision vector optimization result.

[0078] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: execute historical environmental data calls to obtain a historical environmental data set; after performing weather similarity clustering on the historical environmental data set, perform mutation time point prediction based on the time identifiers of the similarity clusters to establish a mutation node prediction result; obtain positioning data of the liquid crystal display screen, perform networked weather reading based on the positioning data, and establish a networked weather reading result; and use the mutation node prediction result and the networked weather reading result to compensate for light field emotional perception.

[0079] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: perform time series extraction on ambient light field perception to establish a time series distribution data set of ambient 3D light field distribution; use the time series distribution data set to perform time series prediction to establish light field emotion perception; perform conflict identification of time series prediction based on the mutation node prediction result, and establish a first compensation feedback using the conflict identification result; use the networked weather reading result to perform an impact analysis of time series prediction to establish a second compensation feedback; and use the first compensation feedback and the second compensation feedback to compensate for the light field emotion perception.

[0080] Furthermore, the liquid crystal display adaptive image adjustment device based on ambient light sensing is also used for: the adjustment decision vector includes a hue offset, a color temperature adjustment amount, a contrast adjustment factor, a clarity adjustment factor, and an emotional style transfer factor.

[0081] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: perform scene recognition based on the display content, multi-dimensional data perception results, and ambient 3D light field distribution to establish a scene recognition result; use the scene recognition result to crop the search space, and perform optimization management of the adjustment decision vector based on the cropped search space.

[0082] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: in each round of iteration, after initializing the current adjustment decision vector, perform iterative optimization through a lightweight gradient descent device; perform multiple rounds of iterative record evaluation to generate continuous iterative evaluation results; if the continuous iterative evaluation results cannot meet the preset convergence threshold, generate auxiliary optimization instructions; and use the auxiliary optimization instructions to call the historical context adjustment template for enhanced optimization.

[0083] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: use the 3D light field distribution to extract features and establish a feature set, the extracted features including light color distribution, brightness gradient, saturation distribution, incident azimuth and altitude angles, degree of light spot mottle, and color temperature estimation; establish an emotion label mapping based on an existing emotional environment image data set; perform sliding time window aggregation matching of the feature set according to the emotion label mapping to establish light field emotion perception.

[0084] Furthermore, the adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing is also used to: establish a user's emotional data set and configure an energy-saving tolerance factor for each emotional state; use the energy-saving tolerance factor to construct an energy-saving optimization function, and perform energy-saving optimization of the adjustment decision vector optimization result based on the energy-saving optimization function; and use the energy-saving optimization result to render the display image of the liquid crystal display screen.

[0085] Furthermore, the liquid crystal display adaptive image adjustment device based on ambient light sensing is also used for: the linkage acquisition device includes an acquisition camera, a microphone, and a wearable physiological data acquisition device.

[0086] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The liquid crystal display adaptive image adjustment method based on ambient light sensing and the specific examples in the aforementioned embodiment 1 are also applicable to the liquid crystal display adaptive image adjustment device based on ambient light sensing in this embodiment. Through the aforementioned detailed description of the liquid crystal display adaptive image adjustment method based on ambient light sensing, those skilled in the art can clearly understand the liquid crystal display adaptive image adjustment device based on ambient light sensing in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0087] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0088] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing, characterized in that: The method comprises: Invoke the RGB camera and ambient light sensor to perceive the ambient light field, reconstruct the ambient 3D light field distribution, and use the 3D light field distribution to establish light field emotion perception; Extracting display content of the liquid crystal display screen, and constructing an adjustment tolerance constraint based on the display content; activating a linkage collection device, performing multi-dimensional data perception of the user based on the linkage collection device, and establishing a user emotion vector using the multi-dimensional data perception result; After establishing the adjustment decision vector, the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector are sent as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization. The collaborative objectives of the multi-objective collaborative adjustment controller include the user emotion resonance objective, the adjustment tolerance violation degree objective, and the light field emotion fusion matching objective. Rendering the display image of the LCD screen according to the optimization result of the adjustment decision vector; The method of establishing light field emotion perception by utilizing the 3D light field distribution includes: Execute historical environment data call to obtain historical environment data set; After performing weather similarity clustering on the historical environmental data set, a mutation time point prediction is performed based on the time identifiers of the similarity clusters to establish a mutation node prediction result; Acquire positioning data of the liquid crystal display screen, perform network weather reading based on the positioning data, and establish a network weather reading result; Compensating light field emotion perception using the mutation node prediction result and the networked weather reading result; The method of compensating light field emotion perception by using the mutation node prediction result and the networked weather reading result includes: Perform time series extraction on ambient light field perception and establish a time series distribution dataset of ambient 3D light field distribution; Using the time series distribution data set to perform time series prediction and establish light field emotion perception; Performing conflict identification of timing prediction based on the mutation node prediction result, and establishing a first compensation feedback using the conflict identification result; Using the networked weather reading results to perform an impact analysis on time series prediction and establish a second compensation feedback; The light field emotion perception is compensated using the first compensation feedback and the second compensation feedback.

2. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 1, wherein: The adjustment decision vector includes a hue shift amount, a color temperature adjustment amount, a contrast adjustment factor, a clarity adjustment factor, and an emotional style transfer factor.

3. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 1, wherein: The sending of the vector to the multi-objective coordinated regulation controller to perform regulation decision vector optimization includes: Perform scene recognition based on the display content, the multi-dimensional data perception results, and the ambient 3D light field distribution, and establish a scene recognition result; The scene recognition result is used to trim the search space, and the decision vector optimization management is performed based on the trimmed search space.

4. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 3, wherein: The optimizing management of the adjustment decision vector based on the pruned search space includes: In each round of iteration, after initializing the current adjustment decision vector, iterative optimization is performed through a lightweight gradient descent; Perform multiple rounds of iterative record evaluation to generate continuous iterative evaluation results; If the continuous iterative evaluation results cannot meet the preset convergence threshold, an auxiliary optimization instruction is generated; The auxiliary optimization instruction is used to call the historical context adjustment template for enhanced optimization.

5. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 1, wherein: The method of establishing light field emotion perception by utilizing the 3D light field distribution includes: Using the 3D light field distribution to extract features and establish a feature set, the extracted features include light color distribution, brightness gradient, saturation distribution, incident azimuth and altitude angles, light spot mottled degree, and color temperature estimation; Establish an emotion label map based on the existing emotional environment image dataset; A sliding time window aggregation and matching of a feature set is performed according to the emotion label mapping to establish light field emotion perception.

6. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 1, wherein: The rendering of the display image on the liquid crystal display screen according to the optimization result of the adjustment decision vector includes: Establish a user's emotional data set and configure the energy-saving tolerance factor for each emotional state; constructing an energy-saving optimization function using the energy-saving tolerance factor, and performing energy-saving optimization of the adjustment decision vector optimization result based on the energy-saving optimization function; Rendering of display images for LCD screens is performed with energy-saving optimization results.

7. The method for adaptive image adjustment of a liquid crystal display screen based on ambient light sensing according to claim 1, wherein: The linkage acquisition device includes an acquisition camera, a microphone, and a wearable physiological data acquisition device.

8. An adaptive image adjustment device for a liquid crystal display screen based on ambient light sensing, characterized in that: The steps for executing the method for adaptive image adjustment of a liquid crystal display based on ambient light sensing according to any one of claims 1 to 7 include: The ambient light field perception module is used to call the RGB camera and the ambient light sensor to perceive the ambient light field, reconstruct the ambient 3D light field distribution, and establish light field emotion perception using the 3D light field distribution; A constraint construction module, configured to extract display content of the liquid crystal display screen and construct an adjustment tolerance constraint based on the display content; A multi-dimensional data perception module, configured to activate a linkage acquisition device, perform multi-dimensional data perception of the user based on the linkage acquisition device, and establish a user emotion vector using the multi-dimensional data perception result; An adjustment decision vector optimization module is configured to, after establishing an adjustment decision vector, send the light field emotion perception, the adjustment tolerance constraint, and the user emotion vector as input data to a multi-objective collaborative adjustment controller to perform adjustment decision vector optimization, wherein the collaborative objectives of the multi-objective collaborative adjustment controller include a user emotion resonance objective, an adjustment tolerance violation objective, and a light field emotion fusion matching objective; The display image rendering module is used to render the display image of the liquid crystal display screen according to the optimization result of the adjustment decision vector.

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