Transparent screen ambient light bidirectional light correction method, system and device and storage medium

Abstract

The invention relates to a transparent screen ambient light bidirectional light calibration method, system and device and a storage medium, and the method comprises the steps: obtaining panel photosensitive sensing data of a transparent screen, and carrying out the calibration processing, and obtaining a sensing response parameter; obtaining bidirectional illumination data of the transparent screen, and performing bidirectional differential analysis on the bidirectional illumination data and the sensor response parameters to obtain bidirectional light influence parameters; obtaining display state data of the transparent screen, and performing display quality correction in combination with the bidirectional light influence parameter to obtain an initial correction parameter; performing pixel-level optimization on the display state data based on the bidirectional light influence parameter to obtain display compensation information; and performing display correction on the transparent screen according to the initial correction parameter and the display compensation information to obtain a global display parameter. The method can comprehensively evaluate the influence characteristics of the ambient light on the display effect, and effectively solves the limitation of a conventional one-way light correction method.

Description

Technical Field

[0001] The present invention relates to the technical field of transparent screens, and in particular to a method, system, device and storage medium for bidirectional optical calibration of ambient light for a transparent screen. Background Art

[0002] With the continuous development of transparent display technology, improving the display quality of transparent screens in complex lighting environments has become a key research topic. Existing display correction methods only focus on the impact of ambient light from a single direction, such as the interference of front or back light sources on the display effect, but ignore the unique bidirectional light transmission characteristics of transparent screens and the comprehensive impact of different display content on the display effect. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method, system, device and storage medium for bidirectional light calibration of transparent screen ambient light, which can comprehensively evaluate the impact characteristics of front and rear ambient light on the display effect and effectively solve the limitations of traditional unidirectional light correction methods.

[0004] To achieve the above objectives, the present invention provides a method for bidirectional optical calibration of a transparent screen under ambient light, comprising: Obtaining the panel light-sensitive sensor data of the transparent screen, performing calibration processing, and obtaining the sensor response parameters; Obtaining bidirectional illumination data of the transparent screen, performing bidirectional differential analysis on the data with the sensor response parameters, and obtaining bidirectional light impact parameters; Acquiring display status data of the transparent screen, performing display quality correction based on the bidirectional light impact parameter, and obtaining initial correction parameters; Performing pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; Display correction is performed on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters.

[0005] Furthermore, the acquisition of the transparent screen panel light-sensitive sensor data and the calibration process to obtain the sensor response parameters include: Collecting original electrical signals of photosensors from multiple areas of the transparent screen, performing panel filtering, and obtaining panel electrical signal data; Performing sensor response data conversion according to the panel electrical signal data to obtain the panel light-sensitive sensor data; Performing multi-channel sensitivity collaborative processing on the panel light sensing data to obtain channel-consistent data; Thermal drift correction and response curve reshaping are performed on the channel uniformity data according to preset standard light source data to obtain the sensing response parameters.

[0006] Furthermore, the acquiring of the bidirectional illumination data of the transparent screen and performing bidirectional differential analysis on the sensor response parameters to obtain bidirectional light impact parameters include: Performing feature demodulation on the bidirectional illumination data to obtain forward light sensing data and backward light sensing data; Performing ambient light incident angle analysis on the forward light sensing data and the backward light data to obtain an ambient light incident coefficient; Performing phase compensation and attenuation coupling on the ambient light incidence coefficient to obtain an ambient light dynamic attenuation parameter; The ambient light dynamic attenuation parameter and the ambient light incidence coefficient are subjected to bidirectional optical path coupling reconstruction to obtain the bidirectional light impact parameter.

[0007] Furthermore, the acquiring of the display status data of the transparent screen and performing display quality correction in combination with the bidirectional light impact parameter to obtain initial correction parameters include: Performing grayscale distribution analysis and multi-scale visual perception decomposition on the display state data to obtain display grayscale information; Dividing the display area of ​​the transparent screen into blocks according to the display grayscale information to obtain multiple display sub-areas and regional light intensity distribution data; Performing dynamic gamma mapping on the multiple display sub-areas according to the bidirectional light impact parameter to obtain a gamma mapping parameter; Performing color gamut space conversion processing according to the regional light intensity distribution data and the bidirectional light impact parameter to obtain an ambient light compensation color gamut coefficient; Luminance and chromaticity coupling is performed on the gamma mapping parameters and the ambient light compensation color gamut to obtain the initial correction parameters.

[0008] Furthermore, the performing pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information includes: Performing sub-pixel segmentation and pixel partitioning on the display state data to generate a pixel unit mapping data set and a regional light intensity data set; Performing light attenuation parameter calculation on the pixel unit mapping data set based on the bidirectional light impact parameter to obtain a light attenuation factor coefficient; Perform bidirectional transmission compensation according to the light attenuation factor coefficient and the regional light intensity data set to obtain a transmission gain component and a reflection suppression component; Performing optical rebalancing and interference quantization processing based on the transmission gain component and the reflection suppression component to obtain a pixel-level brightness compensation coefficient and a chromaticity offset correction coefficient; Multi-channel compensation is performed on the display state data based on the brightness compensation coefficient and the chromaticity offset correction coefficient to obtain the display compensation information.

[0009] Furthermore, performing bidirectional transmission compensation according to the light attenuation factor coefficient and the regional light intensity dataset to obtain a transmission gain component and a reflection suppression component includes: Separate the transmission characteristics and reflection characteristics of the light attenuation factor coefficient to obtain the transmission attenuation characteristics and reflection enhancement characteristics; Performing penetration path compensation on the regional light intensity dataset according to the transmission attenuation characteristics to generate a penetration compensation coefficient set; Performing surface reflection compensation on the regional light intensity dataset based on the reflection enhancement feature to generate a reflection compensation coefficient set; Performing sub-pixel gain allocation on the penetration compensation coefficient set to obtain the transmission gain component; Performing regional reflection suppression allocation on the reflection compensation coefficient set to obtain the reflection suppression component.

[0010] Furthermore, performing display correction on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters includes: Decouple the initial correction parameters from the correction parameter domain to obtain global correction parameters and local refined correction parameters; Performing dynamic error compensation on the global correction parameter according to the display compensation information to obtain error optimization correction data; Performing tensor fusion on the error optimization correction data and the local refinement correction parameters to obtain collaborative correction data; Performing spatial mapping and photoelectric conversion based on the collaborative correction data and the bidirectional light impact parameter to obtain a drive adjustment mapping table; The transparent screen is refreshed frame by frame in real time according to the drive adjustment mapping table to obtain the global display parameters.

[0011] The present invention further provides a transparent screen ambient light bidirectional optical calibration system, which is applied to any of the above-mentioned transparent screen ambient light bidirectional optical calibration methods, comprising: An acquisition module is used to acquire the light-sensitive sensor data of the transparent screen panel, perform calibration processing, and obtain sensor response parameters; An analysis module, configured to obtain bidirectional illumination data of the transparent screen, perform bidirectional differential analysis on the data with the sensor response parameters, and obtain bidirectional light impact parameters; an association module, the association module being configured to obtain display status data of the transparent screen, perform display quality correction in combination with the bidirectional light impact parameter, and obtain initial correction parameters; a processing module, configured to perform pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; A control module is used to perform display correction on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters.

[0012] The present invention also provides a transparent screen ambient light bidirectional optical calibration device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for bidirectional optical calibration of transparent screen ambient light.

[0013] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The present invention provides a method, system, device, and storage medium for bidirectional calibration of ambient light for a transparent screen, which has the following beneficial effects: By calibrating the transparent screen panel's photosensitive sensor data, accurate sensor response parameters are obtained, providing a reliable data foundation for subsequent display correction. By combining bidirectional illumination data with sensor response parameters for differential analysis, it is possible to comprehensively evaluate the impact of front and rear ambient light on the display effect, effectively addressing the limitations of traditional one-way light correction methods. By combining display status data with bidirectional light impact parameters for display quality correction, adaptive adjustment of different display content is achieved, improving the overall performance of the display screen. Pixel-level optimization of display status data based on bidirectional light impact parameters enables more refined display compensation, effectively improving the contrast and color reproduction of the display screen. Through the combined application of initial correction parameters and display compensation information, the global display parameters of the transparent screen in complex lighting environments are achieved, significantly improving the user's viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for bidirectional optical calibration of a transparent screen under ambient light provided by the present invention; Figure 2 This is a structural diagram of a transparent screen ambient light bidirectional optical calibration system provided by the present invention; Figure 3 This is a structural diagram of a transparent screen ambient light bidirectional optical calibration device provided by the present invention.

[0016] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0019] Reference Figure 1 As shown, the present invention provides a method for bidirectional optical calibration of a transparent screen under ambient light, comprising: Step S1: Acquire the panel light-sensitive sensor data of the transparent screen, perform calibration processing, and obtain sensor response parameters; Step S2: Obtain bidirectional illumination data of the transparent screen, perform bidirectional differential analysis on the data with the sensor response parameters, and obtain bidirectional light impact parameters; Step S3: Obtaining display status data of the transparent screen, performing display quality correction based on bidirectional light impact parameters, and obtaining initial correction parameters; Step S4: performing pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; Step S5: performing display calibration on the transparent screen according to the initial calibration parameters and the display compensation information to obtain global display parameters.

[0020] Based on the above steps, the detailed process is as follows: Step S1: The transparent screen panel is equipped with multiple light sensors located at the four corners and edges. These sensors simultaneously capture light information from both the front (viewer side) and the back (background side). Acquiring light sensor data is the starting point of the entire calibration process. This step first activates all sensors and collects their raw output values ​​in the current environment.

[0021] The calibration process begins in a controlled environment, using a standard light source in a darkroom. The standard light source is set to varying intensities (e.g., from 100 lux to 10,000 lux, in 500 lux increments) and illuminates the transparent screen from both the front and back. The output of each sensor is recorded at each light intensity. A professional photometer is used to measure the actual light intensity as a reference standard.

[0022] The collected data is processed to establish a mapping relationship between the sensor output and the actual light intensity. This process uses a polynomial fitting method to generate a characteristic curve for each sensor. The temperature response characteristics of the sensor are also taken into account. The above measurement process is repeated at different temperature conditions (such as 18°C, 25°C, 30°C, and 35°C) to generate a temperature compensation coefficient.

[0023] The sensor response parameters ultimately form a dataset containing each sensor's sensitivity coefficient, response curve coefficient, temperature compensation factor, and noise threshold. These parameters are stored in the system and used to convert the sensor's raw output into accurate light intensity values ​​in real time. The calibration process also includes sensor aging analysis to predict how sensor performance changes over time, ensuring accuracy during long-term use. After calibration, the system can accurately and in real time obtain information about the lighting environment surrounding the transparent screen, laying the foundation for subsequent bidirectional light analysis.

[0024] Step S2: Based on the sensors calibrated in Step S1, the system begins continuously collecting bidirectional lighting data from the transparent screen environment. The data collection frequency is dynamically adjusted based on the rate of environmental change, starting with once per minute in stable lighting environments and increasing to multiple times per second in rapidly changing environments. The collected data includes parameters such as forward light intensity, backward light intensity, color temperature, and estimated light source direction.

[0025] Bidirectional differential analysis first separates forward and backward light data. The system uses spatial distribution information from multiple sensors and an interpolation algorithm to generate a light intensity distribution map across the entire screen surface. These models then construct forward and backward light distribution models, respectively. These models reflect the non-uniform distribution of light across the screen surface.

[0026] Differential analysis calculates the intensity ratio, distribution differences, and temporal variation characteristics of forward and backward light. The system identifies lighting modes such as direct, diffuse, and reflected light and evaluates their varying impacts on the display. It specifically focuses on the unique mixing of light transmission and reflection found in transparent screens, quantifying the effect of backward light passing through the screen and the combined effect of forward light.

[0027] The bidirectional light impact parameters are a set of parameters that describe the impact of ambient light on display quality. These parameters include a contrast impact coefficient matrix, a brightness attenuation map, a color distortion vector, and a spatial distribution nonuniformity index. The system establishes a mathematical model to predict the extent to which visual performance in different areas of the screen will be affected under the current bidirectional light environment. These characteristic parameters take into account the material properties of the transparent screen, such as transmittance, reflectivity, and scattering characteristics, as well as the nonlinear characteristics of human visual perception. The resulting bidirectional light impact parameters will guide the subsequent display quality correction process.

[0028] Step S3: Display Status Data Acquisition: The system directly reads pixel information of the current display content from the transparent screen's image processing unit. The system accesses the display buffer and extracts the complete frame data, including each pixel's RGB value, transparency value (if applicable), and display control parameters such as the current brightness and contrast settings.

[0029] Image processing algorithms extract and classify displayed content features. They identify areas of high light intensity, low light intensity, high-contrast edges, text, and solid color within the image. Each type of content has different sensitivities to ambient light and requires differentiated processing. The algorithm calculates the image's histogram, frequency domain features, and local contrast map to quantify the complexity of the displayed content and its sensitivity to lighting interference.

[0030] Display status data is combined with bidirectional light impact parameters to construct a display quality assessment model. This model simulates the visual effects of displayed content under current lighting conditions and predicts potential issues such as insufficient contrast, color distortion, or loss of detail. The assessment results are expressed as numerical indicators such as regional clarity score, contrast preservation, and color accuracy.

[0031] Based on the evaluation results, the display quality correction algorithm calculates optimal global adjustment parameters. These initial correction parameters include a global brightness gain coefficient, independent gain matrices for the RGB channels, gamma curve adjustment parameters, and a color balance matrix. These parameters are applied to the display rendering pipeline, enabling real-time adjustments to the displayed content. The correction algorithm considers the hardware limitations of transparent displays, ensuring that all parameters are within achievable ranges to avoid display anomalies or excessive power consumption caused by overcompensation.

[0032] Step S4: Pixel-Level Optimization further refines the global correction from step S3 down to the individual pixel level. The system creates a compensation matrix that matches the screen resolution and calculates independent adjustment parameters for each pixel. This process begins with a light distribution map, mapping the bidirectional light impact parameters from step S2 onto the pixel grid and calculating the actual lighting conditions at each pixel location.

[0033] The optimization algorithm incorporates the local characteristics of the displayed content, analyzing the content complexity, contrast, and color characteristics of each pixel and its surrounding area. The system identifies visually important areas, such as text, icons, and high-contrast edges. These areas dominate visual perception and prioritize clarity and legibility.

[0034] For each pixel, the optimal compensation value is calculated. This compensation calculation takes into account the current pixel value, the target visual effect, and hardware limitations. For areas affected by strong backlighting, contrast and saturation are increased; for areas with severe frontal light reflections, the brightness curve is adjusted to improve the visibility of dark details. The compensation process pays special attention to smooth transitions between adjacent pixels to avoid introducing new visual artifacts.

[0035] Display compensation information is generated in the form of a pixel map, which contains the RGB gain value, contrast adjustment factor, and sharpening parameters for each pixel. This compensation information is combined with the initial correction parameters and applied to the actual rendering process through the display driver. The system continuously monitors changes in ambient light and recalculates the compensation value when significant changes are detected to ensure the best display effect in dynamic lighting environments. The optimization process calculates resource consumption and uses adaptive algorithms for devices of different performance levels. It achieves full-resolution pixel-level optimization on high-end devices and uses block-level optimization on low-end devices to balance performance and effect.

[0036] The present invention provides a method for bidirectional optical calibration of transparent screen ambient light, which obtains accurate sensor response parameters by calibrating the transparent screen panel's light-sensitive sensor data, providing a reliable data basis for subsequent display correction. By combining bidirectional illumination data with sensor response parameters for differential analysis, it is possible to comprehensively evaluate the impact characteristics of front and rear ambient light on the display effect, effectively solving the limitations of traditional one-way light correction methods. By combining display status data with bidirectional light impact parameters for display quality correction, adaptive adjustment of different display contents is achieved, improving the overall performance of the display screen. Pixel-level optimization of display status data based on bidirectional light impact parameters achieves more refined display compensation, effectively improving the contrast and color reproduction of the display screen. Through the comprehensive application of initial correction parameters and display compensation information, the global display parameters of the transparent screen in complex lighting environments are achieved, significantly improving the user's viewing experience.

[0037] In one embodiment, the photosensitive sensor data of the transparent screen panel is obtained and calibrated to obtain the sensor response parameters, including: The transparent screen is equipped with multiple light sensors, located at strategic locations along its four corners and edges. Each light sensor generates a weak current signal when exposed to light. These raw electrical signals are converted to digital signals via an analog-to-digital converter. A high-precision data acquisition module is used during the data acquisition process, with a sampling frequency set at 1kHz to ensure that rapid changes in ambient light are captured. The raw electrical signals contain ambient noise, power supply ripple, and sensor noise floor.

[0038] The panel filter uses digital filtering technology to process these raw electrical signals. The filter consists of a low-pass filter and a median filter. The low-pass filter removes high-frequency noise components, with a cutoff frequency set to 100Hz, preserving the effective signal of ambient light changes. The median filter eliminates sudden pulse interference, with a filter window length of 5 sampling points. The filtered signal is smoother, preserving the true characteristics of light changes.

[0039] The panel's electrical signal data is stored with 16-bit precision, recording the response value of each sensor. This data includes a timestamp, sensor ID, and corresponding electrical signal strength. This data reflects the distribution of light intensity in different areas of the transparent screen, providing basic data support for subsequent sensor response data conversion.

[0040] The sensor response data conversion process maps the panel's electrical signal data to actual light intensity values. This conversion process is based on the photosensor's characteristic curve, which describes the relationship between the sensor's output voltage and incident light intensity. This conversion uses a lookup table to map the digitized electrical signal values ​​to pre-calibrated light intensity values.

[0041] Light sensor data is uniformly expressed in lux, and the sensor's calibration coefficients are applied during the conversion process. These coefficients include the sensor's baseline response value, linear range, and saturation threshold. The conversion algorithm utilizes a segmented approach for nonlinear regions, using corresponding conversion functions for different light intensity ranges.

[0042] The converted panel light sensor data contains the light intensity value at each sensor location. The data structure includes spatial location information, light intensity value, and measurement time. This data reflects the actual lighting environment around the transparent screen and lays the foundation for subsequent channel consistency processing.

[0043] Multi-channel sensitivity collaborative processing addresses sensitivity differences between different sensors. This process establishes a sensor response model and normalizes the response characteristics of each sensor channel to a unified standard. Normalization is based on response data under a standard light source, calculating the sensitivity coefficient for each sensor.

[0044] Collaborative processing uses a weighted averaging approach, assigning weights to sensors based on their locational importance and reliability. The weighting factors reflect the sensor's contribution to overall light perception. The processing algorithm compensates for crosstalk between sensors and eliminates interference between adjacent sensors.

[0045] Channel-wise normalization data is presented as a uniformly dimensioned light intensity distribution graph. The data format includes normalized light intensity values, spatial distribution information, and confidence metrics. This normalization ensures comparability between sensors at different locations, providing accurate baseline data for subsequent thermal drift correction.

[0046] Thermal drift correction uses real-time data from the temperature sensor to correct for temperature-related deviations in the photosensor's response. This correction utilizes a temperature compensation model derived from calibration data obtained under various laboratory temperature conditions. The compensation range covers an operating temperature range of -20°C to 70°C.

[0047] The response curve reshaping process compares standard light source data with measured data to adjust the sensor's response characteristics. This reshaping uses piecewise linear interpolation to establish correction coefficients for different light intensity ranges. The standard light source data comes from professional integrating sphere test equipment and contains standard response values ​​under different light intensities.

[0048] The resulting sensor response parameters include a temperature compensation coefficient, a response curve correction factor, and a sensitivity calibration factor. These sensor response parameters are stored in a system configuration file and used to calibrate the sensor output in real time. This set of parameters reflects the sensor's response characteristics under various environmental conditions, ensuring the accuracy and reliability of light data collection.

[0049] This embodiment collects the original electrical signals of light-sensitive sensors from multiple areas of the transparent screen and performs panel filtering processing, which can effectively eliminate environmental noise and power supply interference, making the electrical signal data more stable and reliable. The conversion of sensor response data based on the panel electrical signal data can accurately reflect the light intensity in different areas of the transparent screen, thereby improving the accuracy and precision of the data. Multi-channel sensitivity collaborative processing of the panel light-sensitive sensor data eliminates the sensitivity differences between different sensors and ensures the consistency and comparability of the sensor data. By performing thermal drift correction and response curve reshaping based on preset standard light source data, the accuracy and reliability of the sensor under different temperature conditions are further improved, ensuring the accurate measurement of light data in various environments.

[0050] In one embodiment, bidirectional light data of the transparent screen is obtained, and bidirectional difference analysis is performed with the sensor response parameters to obtain bidirectional light impact parameters, including: The performance of a transparent display under varying ambient lighting conditions relies on accurately capturing and demodulating both forward light (toward the viewer) and backward light (toward the background). The feature demodulation step begins with raw data collected from multiple light sensors distributed across the transparent display's surface. Each sensor simultaneously records the intensity of light incident from both the front and back directions. The feature demodulation algorithm separates the forward and backward light signals to generate independent forward and backward light sensing data.

[0051] Forward light sensor data reflects the distribution of light intensity in the viewer's direction, which can directly affect the viewer's perception of the transparent screen's display. To accurately capture forward light data, the system uses the sensor's spatial distribution and angular characteristics to spatially reconstruct the sensor data through interpolation and weighted averaging, generating a forward light intensity distribution map.

[0052] Backlight data records the intensity of light incident from the background. This light is transmitted or reflected by the transparent screen, affecting the composite forward light. The acquisition and processing of backward light data is similar to that of forward light. Spatial interpolation and data fusion techniques are used to generate a backward light intensity distribution map. This data provides the foundation for subsequent ambient light analysis and compensation.

[0053] The angle of incidence of ambient light directly affects its appearance on transparent screens. The ambient light angle analysis step calculates the angular distribution of ambient light incident from different directions based on forward and backward light sensor data. Using illumination models and geometric analysis, combined with sensor position and panel characteristics, the incident angle is determined.

[0054] By analyzing illumination data at different incident angles, we calculate the effect of ambient light on the transparent screen. The ambient light incidence coefficient is a multi-dimensional parameter set that describes the intensity variation, transmittance, and reflectivity of light at different incident angles. This step also takes into account the optical properties of the transparent screen material, such as refractive index and scattering characteristics, to ensure the accuracy of the analysis results.

[0055] During the joint analysis process, the incident angle illumination data is normalized to eliminate response differences between sensors and ensure data consistency and comparability. The ambient light incidence coefficient provides a key input parameter for subsequent optical path coupling reconstruction, directly affecting the final display compensation effect.

[0056] The performance of ambient light on a transparent screen is also affected by the optical path phase and intensity attenuation. The phase compensation and attenuation coupling step performs an in-depth analysis of the ambient light incidence coefficient to calculate the phase change and intensity attenuation characteristics of light at different incident angles. Phase compensation accounts for the light propagation path and refraction effects in the transparent screen material, adjusting the light phase distribution to eliminate interference effects.

[0057] Attenuation coupling analysis calculates the dynamic attenuation curve of light under different environmental conditions based on the time-varying characteristics of light intensity. Light propagation through a transparent screen is affected by various factors, such as material absorption, scattering, and multiple reflections, which gradually weaken the light intensity. The attenuation coupling step quantifies these factors by establishing mathematical relationships to generate dynamic ambient light attenuation parameters.

[0058] The dynamic attenuation parameters of ambient light describe how light intensity changes over time and space, providing detailed dynamic parameters for bidirectional optical path coupling reconstruction. Through phase compensation and attenuation coupling, the nonlinear changes of light on the transparent screen are corrected, ensuring a stable and consistent final display effect.

[0059] The dynamic attenuation parameter for ambient light and the ambient light incidence coefficient are the core input parameters for the bidirectional optical path coupling reconstruction step. This step comprehensively analyzes both parameters to generate the optical performance of the transparent screen under the current lighting conditions. This step considers the combined effects of forward and backward light on the transparent screen, calculating the combined impact of light on the screen.

[0060] The reconstruction process simulates the path and intensity variations of light on the transparent screen. Through iterative calculations, the light path is optimized to ensure that the reconstruction closely matches the actual lighting conditions. The bidirectional light impact parameters are the final output, describing the optical performance of the transparent screen under the current lighting conditions, including brightness distribution, contrast changes, and color distortion.

[0061] These characteristic parameters provide the foundational data for display compensation algorithms, guiding the optimization and adjustment of display content. The accuracy of bidirectional light influencing parameters directly impacts the final display quality, so the reconstruction step requires high-precision calculations and simulations to ensure that every detail is fully considered. By comprehensively analyzing and reconstructing the bidirectional light path, the system is able to provide stable, high-quality display effects in complex lighting environments.

[0062] This embodiment accurately models the effects of bidirectional ambient light by performing feature demodulation, angle analysis, and optical path coupling reconstruction on the transparent screen's forward and backward light data, effectively improving the display stability and visual consistency of the transparent screen under complex lighting conditions. By introducing joint analysis of ambient light incident angles and dynamic attenuation feature modeling, the accuracy of the response to real-world lighting changes is improved while also enhancing the fidelity of displayed content details. Pixel-level optimization and compensation methods can effectively improve contrast loss and color shift caused by uneven lighting in local areas while maintaining overall brightness balance, significantly improving the visual clarity and color reproduction of the transparent screen.

[0063] In one embodiment, the display state data of the transparent screen is obtained, and the display quality correction is performed in combination with the bidirectional light impact parameters to obtain the initial correction parameters, including: Display state data contains the complete image currently displayed on the screen. This data is stored as a pixel matrix, with each pixel containing RGB channel values. Grayscale distribution analysis starts with the raw RGB data and converts the color image into a grayscale representation using a standard luminance-weighted calculation method. After grayscale conversion, a grayscale histogram is calculated for the entire screen image to quantify the distribution of pixels at each grayscale level. Histogram analysis reveals the image's light and dark distribution characteristics, identifying the proportions of highlight, midtone, and shadow areas.

[0064] Multi-scale visual perceptual decomposition leverages the characteristics of the human visual system to decompose images into different spatial frequency components. This process utilizes a Gaussian pyramid or wavelet transform to decompose the original image into 4-6 scale levels. The bottom layer contains image detail, while the top layer captures large-scale brightness variations. Perceptual weighting is applied to each level to simulate the human eye's varying sensitivity to information at different scales, with high-frequency details being more noticeable in bright areas than in dark areas.

[0065] Visual perceptual decomposition, combined with a contrast sensitivity function, quantifies the human eye's threshold for contrast perception under varying background brightness. This decomposition takes into account visual phenomena such as Mach band effects, simultaneous contrast, and spatial frequency adaptation, ensuring that the correction process is compatible with the human visual system.

[0066] The analysis results are integrated into a grayscale display, a data matrix with the same resolution as the original image. Each element contains the grayscale value, local contrast value, visual saliency score, and spatial frequency characteristic identifier for that location. The feature map uses pseudo-color coding to intuitively display the visual importance of different image regions, with high-saliency regions marked in warm colors and low-saliency regions in cool colors.

[0067] The display region segmentation process begins with the display grayscale information obtained in the previous step. The goal is to divide the entire screen into sub-regions with similar display characteristics, allowing correction parameters to be optimized for the characteristics of different regions. The segmentation algorithm uses an adaptive quadtree structure to recursively partition the screen based on the spatial homogeneity of grayscale features. The segmentation process evaluates the variance, contrast distribution, and frequency characteristics of the grayscale values ​​within each candidate region. The segmentation process stops when the characteristics within the region are sufficiently uniform or when the preset minimum segment size (64×64 pixels) is reached.

[0068] The resulting block segmentation produces a grid of irregularly sized subregions, with visually complex regions (such as text and image edges) forming smaller subregions and visually simple regions (such as solid backgrounds) forming larger subregions. For example, a 1080p resolution screen is divided into 100-400 subregions. Each subregion is assigned a unique identifier, and its representative features are calculated, including the average grayscale value, grayscale standard deviation, main frequency components, and visual saliency score.

[0069] During the acquisition of regional light intensity distribution data, the actual lighting impact on each sub-region is calculated, combined with the bidirectional lighting data from step 1. The unique bidirectional lighting characteristics of transparent screens cause forward light (viewer-side) and backward light (background-side) to have different degrees of impact on each sub-region. The regional light intensity distribution data records the forward light intensity, backward light intensity, lighting unevenness, and primary light source direction for each sub-region.

[0070] Light intensity distribution calculations use an interpolation algorithm to derive a continuous illumination distribution across the entire screen surface from data from a limited number of photosensors. For areas with sparse sensor counts, the algorithm supplements this with inference using an ambient optical model. The resulting regional light intensity distribution data is a dataset corresponding to the sub-region structure, containing each sub-region's average forward and backward light intensity, the intensity gradient within the region, and color temperature information. This data directly influences the subsequent gamma mapping and color gamut conversion processes, ensuring that the correction parameters accurately reflect the actual lighting conditions in each region.

[0071] Perform dynamic gamma mapping on multiple display sub-areas based on the bidirectional light impact parameters to obtain gamma mapping parameters Dynamic gamma mapping adjusts the image's brightness response curve to compensate for the effects of ambient light on display contrast. Conventional displays have a fixed gamma value of 2.2, but transparent displays in bidirectional lighting environments require dynamic gamma curve adjustments based on actual lighting conditions. The mapping process begins with the multiple display sub-regions and bidirectional light impact parameters obtained in the previous step, calculating the optimal gamma curve for each sub-region.

[0072] Gamma mapping calculations are based on the human visual perception model, analyzing the visibility of image content under current lighting conditions. The calculation process takes into account the laws of visual perception, appropriately lowering the gamma value in strong ambient light conditions to improve the visibility of dark details, and increasing the gamma value in weak ambient light conditions to enhance image contrast. The mapping algorithm considers the combined effects of forward and backward light. Forward light primarily affects screen reflection, reducing image contrast; backward light transmits through the transparent screen and directly superimposes with the display light, changing the image brightness baseline.

[0073] For each display sub-area, three key gamma parameters are calculated: low gray gamma, mid-gray gamma, and high gray gamma, forming a segmented gamma curve. Gamma adjustment in the low gray area (0-30%) is used to preserve shadow detail, in the mid-gray area (30-70%) to improve overall contrast, and in the high gray area (70-100%) to optimize highlight detail. Each segmented gamma curve is connected by a smooth transition function to avoid artifacts in the transition areas.

[0074] Gamma mapping parameters are stored as a lookup table, with each subregion corresponding to a set of gamma curve parameters. These parameters include segmented gamma values, brightness offsets, contrast gain factors, and curve connection point locations. For time-varying lighting environments, gamma mapping parameters are updated in real time as ambient light changes. The update frequency is related to the rate of illumination change and is approximately every 0.5-5 seconds.

[0075] The gamma mapping process is hardware-accelerated, with the lookup table loaded directly into the display processing unit to ensure real-time performance. The resulting mapping maintains image contrast and detail visibility while avoiding display saturation and loss of detail caused by over-enhancement. The calculation of gamma mapping parameters takes into account the dynamic range limitations of the display hardware, achieving optimal visual effects within the hardware's capabilities and providing a luminance foundation for subsequent color space conversion.

[0076] Color space conversion addresses the impact of ambient light on displayed color accuracy. Ambient light not only alters the human eye's perception of brightness but also affects color perception, especially in mixed light environments. The conversion process starts with a standard color space and moves to a target color space. The target color space is then compensated for ambient light to ensure that the colors perceived by the viewer match the original content.

[0077] The first stage of processing involves color adaptation analysis, based on the color constancy mechanism of the human visual system. This analysis utilizes a color appearance model to calculate the human eye's adaptation state under current lighting conditions. Regional light intensity distribution data provides light intensity and color temperature information, which is used to determine the adaptation brightness and white point. In the bidirectional lighting environment unique to transparent screens, the color temperature differences between forward and backward light lead to a complex mixing effect of color adaptation. A weighted model is used to quantify the contribution of both light sources to the final color adaptation.

[0078] The second stage of the conversion calculates the color gamut mapping matrix, transforming the original colors from the standard RGB space to the compensated display color gamut. This conversion process first converts the RGB values ​​to a device-independent color space, applies an ambient light compensation algorithm, and then converts them back to the display device's RGB space. The compensation algorithm accounts for the weakening effect of ambient light on color saturation, boosting saturation appropriately in high ambient light conditions. It also accounts for the hue shift effect of ambient light and compensates for color casts.

[0079] For each display sub-area, unique color gamut conversion parameters are calculated, including tristimulus correction coefficients, chromaticity coordinate offset vectors, and saturation gain factors. The system pays special attention to common colors such as skin tones, sky blues, and grass greens, ensuring these memory colors maintain a natural appearance under various lighting conditions. For applications requiring the highest degree of color accuracy, such as professional design and medical imaging, the system applies even stricter color fidelity standards.

[0080] The ambient light compensation color gamut coefficients are ultimately stored as a transformation matrix and an additional correction vector, and applied to the color processing stage of the display rendering pipeline. The coefficients are dynamically updated as ambient light changes, ensuring consistent color performance in varying lighting environments. The color gamut conversion process works in conjunction with the aforementioned gamma mapping, with gamma mapping optimizing brightness response and color gamut conversion optimizing color performance. Together, they enhance the display quality of transparent screens in complex lighting environments.

[0081] Luminance-chromaticity coupling is the final step in the display calibration process. It integrates the aforementioned gamma mapping parameters and the ambient light compensation color gamut coefficients to form a unified set of initial calibration parameters. This coupling process addresses the interplay between how brightness changes affect color perception and how color changes affect brightness perception. The non-independence of brightness and chromaticity in the human visual system means that different colors of the same luminance are perceived as different brightnesses. This characteristic is exacerbated in the bidirectional lighting environment of transparent displays.

[0082] The coupling algorithm draws on color science theory and incorporates experimental psychophysics data to establish a model of the mutual influence of brightness and chromaticity. This model quantifies the influence of chromaticity changes on perceived brightness, and vice versa. For each display sub-area, the optimal coupling coefficient is calculated based on its content characteristics and lighting conditions. Highly saturated color areas receive stronger brightness compensation to prevent loss of detail due to oversaturation, while dark areas receive more refined chromaticity adjustments to ensure color discernibility at low brightness levels.

[0083] The coupling process is implemented as a three-dimensional lookup table, whose three input dimensions are the original RGB values ​​and whose output is the corrected RGB values. The lookup table interpolation uses a trilinear interpolation algorithm to ensure smooth color transitions. To balance computational efficiency and accuracy, the 3D lookup table typically has a resolution of 17×17×17 or 33×33×33. Spatial filtering is applied at subregion boundaries to eliminate potential boundary artifacts.

[0084] The initial calibration parameters are a data set consisting of global and sub-region parameters. Global parameters set the overall brightness gain, contrast scaling, and color balance baseline; sub-region parameters define local adjustments, including local gamma curve parameters, color correction matrix, and luminance-chrominance coupling coefficients. The parameter format is designed for hardware efficiency and optimized for data structures directly supported by the display processing unit, reducing runtime computational overhead.

[0085] Correction parameters are applied to the image rendering pipeline, processing display content in real time. Parameter updates adhere to a smooth transition to avoid sudden changes in ambient light. The entire coupled process is designed as an adaptive closed-loop system, continuously optimizing parameters based on feedback from the light sensor to adapt to dynamically changing ambient light conditions. Initial correction parameters serve as input for pixel-level optimization, providing a foundational correction framework and laying the foundation for subsequent, more refined optimization.

[0086] This embodiment accurately captures the structural characteristics of image content by performing grayscale distribution analysis and multi-scale visual perception decomposition on display state data, ensuring that the correction process prioritizes display quality in visually important areas, significantly improving the user viewing experience. Adaptive regional segmentation technology, based on display grayscale information, enables precise optimization of correction parameters for different regional characteristics, effectively addressing the limitations of traditional fixed segmentation methods that struggle to cope with complex display content. The synergistic effect of dynamic gamma mapping and color gamut space conversion enables the system to respond to ambient light changes in real time, ensuring image contrast and detail visibility while avoiding display saturation and detail loss caused by over-enhancement. Specifically for the bidirectional lighting environment unique to transparent screens, luminance and chromaticity coupling technology addresses the interplay between luminance changes and color perception, ensuring consistent color rendering under various lighting conditions. The entire correction process is designed as an adaptive closed-loop system that continuously optimizes parameters based on feedback from photosensors to adapt to dynamically changing ambient light conditions, significantly improving the display quality of transparent screens in complex lighting environments.

[0087] In one embodiment, pixel-level optimization is performed on the display state data based on the bidirectional light impact parameter to obtain display compensation information, including: The sub-pixel segmentation process breaks down the transparent screen's display state data into RGB sub-pixel units. Each pixel on the transparent screen is composed of red, green, and blue sub-pixels, each with unique optical properties and response curves. The segmentation operation extracts the brightness, chromaticity, and transparency properties of each sub-pixel from the raw display data to create a sub-pixel data structure.

[0088] The pixel partitioning algorithm divides the entire screen into multiple functional areas based on the visual characteristics of the displayed content. Partitioning criteria include content type (text, images, video, UI elements), brightness distribution, color complexity, and edge density. Typical partitions include highlights, shadows, high-contrast edge areas, pure color areas, and transition areas. The partitioning process uses computer vision algorithms to analyze the current frame content and identify key and less important visual areas, laying the foundation for subsequent differentiation processing.

[0089] The generation of a pixel-unit mapping dataset combines sub-pixel data with partition information to form a multidimensional data structure. This dataset contains each pixel's spatial coordinates, RGB component values, region type, and surrounding pixel association information. The mapping process establishes topological relationships between pixels, describing the spatial structure of the displayed content. The dataset uses an optimized storage format to ensure real-time processing performance.

[0090] The regional light intensity dataset is generated by analyzing the sensitivity of each region to changes in ambient light. Light intensity response measurements utilize high dynamic range sampling to record changes in the visual appearance of each region under varying lighting conditions. Response data includes brightness response curves, contrast change rates, and color drift vectors. Measurements are performed under varying ambient light conditions to simulate a variety of real-world usage scenarios. These two datasets form the foundation for subsequent pixel-level optimization: pixel mapping provides spatial structure information, and regional light intensity response provides optical response characteristics.

[0091] The light attenuation calculation extracts key optical interaction parameters from bidirectional light influencing parameters, including forward light reflection coefficient, backward light transmission coefficient, material scattering properties, and angular dependence. The calculation constructs a local light path model for each pixel location, combining the actual lighting environment at that location with the screen material properties. This light path model describes the propagation path and energy attenuation of light through the various layers of the transparent screen, accounting for multiple reflections, scattering, and absorption.

[0092] Bidirectional light impact parameters are combined with a pixel mapping dataset to perform spatial correlation analysis. This analysis matches the ambient light distribution map with the spatial distribution of displayed content, identifying hotspots of interaction between areas of uneven lighting and displayed content. Particular attention is paid to the visual degradation of high-contrast content areas under uneven lighting, quantifying the correlation between the degree of degradation and lighting conditions.

[0093] Light attenuation calculations utilize a combination of physical optics and perceptual models. The physical model calculates actual light energy transmission based on the material parameters of the transparent screen, while the perceptual model converts physical light energy into brightness perceived by the human eye. The calculations take into account the nonlinear characteristics of the human visual system, such as the logarithmic relationship in brightness perception described by the Weber-Fechner law and the power law-based contrast perception. This dual-model approach ensures that the compensation effect is consistent with human visual perception.

[0094] The light attenuation coefficients are the final result of the calculation. The matrix dimensions match the screen resolution, with each element corresponding to the light attenuation parameter at a pixel location. The matrix contains three main components: brightness attenuation, contrast attenuation, and color attenuation. The brightness attenuation describes the loss of visual brightness due to ambient light, the contrast attenuation quantifies the degree of reduction in detail visibility, and the color attenuation represents the change in color saturation and accuracy.

[0095] The light attenuation factor reflects the degradation of the display effect in each area of ​​the transparent screen under the current ambient light conditions, providing accurate target parameters for subsequent compensation processing. The matrix is ​​continuously updated through real-time calculations to respond to changes in ambient light, ensuring dynamic adaptability of the compensation effect.

[0096] The bidirectional transmission compensation stage combines the light attenuation factor coefficients with the regional light intensity dataset to address both forward light reflection interference and backward light transmission interference. The compensation process calculates a transmission gain component, which enhances the displayed content to counteract the effects of backward light transmission, and a reflection suppression component, which reduces the interference of forward light reflection on the displayed content.

[0097] The calculation of the transmission gain component is based on the backlight transmission relationship, which describes the visual mixing effect of background light and displayed content after passing through the transparent screen. The calculation process analyzes the visual contrast relationship between the backlight intensity distribution and the displayed content to identify areas of visual conflict. For areas with high backlight intensity, the gain calculation increases the brightness and contrast of the corresponding displayed content, so that the content remains visible under strong background light. The gain calculation uses a nonlinear function to provide a higher gain ratio in low-brightness areas to avoid overcompensation in highlight areas. The gain value is associated with the visual importance of the displayed content, providing priority compensation for key information areas such as text and UI elements.

[0098] The reflection suppression component addresses forward light reflections. The calculation process analyzes the position, intensity, and reflection angle characteristics of the forward light source. The suppression algorithm adjusts the local contrast and color characteristics of the displayed content to reduce the visual confusion between reflected light and the displayed content. Particular attention is paid to forward light reflection hotspots located near the reflection angle formed by the screen surface normal and the light source direction. The suppression calculation incorporates the human eye's perceptual characteristics, leveraging visual masking to reduce the perceived impact of reflected light while avoiding loss of displayed content due to excessive suppression.

[0099] The bidirectional transmissive compensation process combines the optical properties of the transparent screen with the visual characteristics of the displayed content to balance the effects of transmissive gain and reflection suppression at the pixel level. The compensation algorithm considers the interplay between gain and suppression to avoid compensation conflicts. The results are represented by two independent pixel-level matrices, one for the transmissive gain component and the other for the reflection suppression component. The matrix dimensions match the screen resolution, and each element contains compensation values ​​for all three RGB channels. These two component matrices are then fed into the next stage of optical rebalancing, ultimately forming a comprehensive display compensation solution.

[0100] The optical rebalancing process integrates the transmission gain component and the reflection suppression component to resolve potential conflicts between them. The rebalancing algorithm constructs a global optimization objective function to find the optimal balance between transmission gain and reflection suppression. This objective function incorporates three primary evaluation metrics: content visibility, color accuracy, and display consistency, which are weighted and summed to form a comprehensive score. The balancing process uses an iterative optimization method to maximize the overall score by fine-tuning the weighting coefficients of the two components. Optical rebalancing takes into account the physical limitations of transparent displays, such as maximum brightness, contrast range, and color gamut boundaries, ensuring that the generated compensation parameters are within the hardware's achievable range.

[0101] Interference quantification analyzes the degree of ambient light interference on the display and establishes a numerical interference description model. The quantification process evaluates three dimensions: brightness interference, contrast interference, and color interference. Luminance interference quantification uses a signal-to-noise ratio method to calculate the ratio of display signal strength to ambient light interference. Contrast interference quantification measures the contrast loss caused by ambient light based on the local contrast change rate. Color interference quantification uses a color difference calculation method to assess the degree of color shift caused by ambient light. The interference quantification results form an interference map, which visually displays the interference distribution in different areas of the screen.

[0102] Pixel-level brightness compensation coefficients are generated by analyzing the brightness and contrast interference data in the interference map. The compensation coefficient calculation takes into account the brightness adaptation characteristics of the human eye and adjusts the compensation strength according to different ambient brightness levels. Brightness compensation uses an adaptive local enhancement method to provide stronger compensation in high-interference areas while maintaining a natural display in low-interference areas. The compensation coefficient matrix is ​​spatially smoothed to ensure a smooth transition of compensation strength between adjacent pixels and avoid visible compensation edges.

[0103] Chromaticity offset correction coefficients are calculated based on color interference data, aiming to restore accurate color reproduction despite ambient light interference. These correction coefficients, which include gain adjustment values ​​and cross-compensation coefficients for the three RGB channels, are used to correct for color shifts caused by ambient light. The correction process utilizes the principle of color constancy, simulating the human eye's ability to maintain consistent color perception under varying lighting conditions. Chromaticity correction specifically focuses on accurately reproducing key memory colors such as skin tones, sky blue, and grass green, ensuring these colors remain visually consistent across various lighting conditions. The brightness compensation coefficients and chromaticity offset correction coefficients together constitute a pixel-level compensation parameter set, providing precise control data for subsequent multi-channel compensation.

[0104] The multi-channel compensation process applies brightness compensation coefficients and chroma offset correction coefficients to the original display state data, achieving differentiated processing for the three independent RGB channels. Compensation begins with color space conversion, converting the RGB color data into YCbCr space, separating the brightness channel (Y) from the chroma channels (Cb, Cr). This separation allows brightness and chroma compensation to be performed independently, avoiding mutual interference.

[0105] Luminance channel compensation applies a brightness compensation coefficient to adjust the local brightness distribution of the displayed content. This compensation process utilizes a nonlinear mapping function, applying varying levels of compensation to different brightness areas. Dark areas receive higher gain to enhance detail visibility, midtones receive moderate compensation to maintain a smooth transition, and highlights receive controlled gain to avoid overexposure. Luminance compensation specifically focuses on preserving local contrast, ensuring clarity of texture details and text edges. The compensation algorithm employs edge-preserving filtering technology when processing high-contrast edges, enhancing edge sharpness while suppressing noise amplification.

[0106] Chroma channel compensation applies a chroma offset correction factor to correct color shifts caused by ambient light. The compensation process adjusts the gain and offset values ​​of the Cb and Cr channels to correct color saturation and hue. Chroma compensation smoothes color transitions to avoid color banding. The compensation algorithm protects the original color boundaries to prevent color gamut overflow caused by compensation. Chroma compensation pays special attention to the accurate reproduction of key memory colors, such as skin tones, by applying specific color mapping rules to these areas.

[0107] After multi-channel compensation is complete, the algorithm converts the processed YCbCr data back into the RGB color space for final integration adjustments. Gamma correction is applied during the integration phase to ensure consistent visual experience across varying brightness conditions. The integration process considers the hardware limitations of the display device and intelligently truncates values ​​outside the display range to minimize information loss.

[0108] Display compensation information is the final output, consisting of compensation values ​​for each pixel's RGB channels, brightness gain coefficients, and color correction matrices. This information is stored in an optimized data structure, ensuring efficient application by the display driver. The compensation process is performed in real time during each frame rendering, rapidly responding to changes in ambient light and ensuring the transparent screen delivers optimal display quality under various lighting conditions.

[0109] This embodiment accurately captures each pixel's performance under varying lighting conditions by performing sub-pixel processing and regional light intensity analysis on display data, providing a precise basis for compensation. Based on the light attenuation factor, it effectively quantifies the impact of ambient light on the display, helping to improve the stability and consistency of the display. Transmission gain and reflection suppression compensation enhance the visibility of displayed content and reduce interference, thereby optimizing the display. Optical rebalancing and interference quantification provide compensation coefficients for brightness and chromaticity, further enhancing display quality.

[0110] In one embodiment, bidirectional transmission compensation is performed based on the light attenuation factor coefficient and the regional light intensity dataset to obtain a transmission gain component and a reflection suppression component, including: The light attenuation factor (LAF) records the degree of light attenuation of the transparent display under different ambient lighting conditions, including the effects of both transmission and reflection. Separating the LAF into transmission and reflection characteristics allows for separate treatment of these two distinct effects. During this separation process, high and low attenuation regions within the LAF are identified and their spatial relationship to the light source is analyzed. High attenuation regions correspond to areas of high reflected light intensity, while low attenuation regions correspond to areas of high transmitted light intensity.

[0111] By calculating the attenuation differences for each pixel at different illumination angles, we can further distinguish between the effects of transmission and reflection. Transmission characteristics focus on the energy loss of light as it passes through the transparent screen, including the absorption and scattering effects of each material layer. The transmission attenuation characteristic reflects the spatial distribution of these energy losses. Reflection characteristics focus on the reflection enhancement effects of light on the transparent screen surface and internal interfaces, while the reflection enhancement characteristic describes the spatial distribution of these reflection effects.

[0112] The transmission attenuation characteristics and reflection enhancement characteristics are stored in two independent data matrices. Each matrix corresponds to the pixel location on the transparent screen and contains independent components of the RGB channels. These matrices will serve as the basis for the subsequent compensation steps, guiding the specific calculation of transmission gain and reflection suppression.

[0113] Transmission attenuation characteristics describe the energy loss of light when it passes through a transparent screen. These characteristics are used to compensate for the transmission path of the regional light intensity dataset, aiming to restore the brightness and contrast of the displayed content affected by ambient light. The compensation process begins by analyzing the spatial relationship between the transmission attenuation characteristics and the regional light intensity response data to identify areas with the most significant light intensity attenuation.

[0114] Transmission compensation calculates the required brightness gain for each pixel under current lighting conditions to compensate for energy lost during transmission. This calculation takes into account the transmittance of the transparent screen material, the angle of incidence, and the intensity distribution of the light. In areas with severe light attenuation, a higher transmission compensation factor is used to improve the brightness and contrast of the displayed content, maintaining visibility and detail clarity.

[0115] The penetration compensation coefficient set is stored as pixel-level gain values, with each pixel location containing independent gain values ​​for the RGB channels. Using an interpolation algorithm, the compensation coefficients are derived from limited sensor data to create a continuous distribution across the entire screen, ensuring a smooth spatial transition. To avoid display anomalies caused by overcompensation, the compensation coefficient set is spatially smoothed to ensure smooth changes in compensation values ​​between adjacent pixels.

[0116] The compensation results generate a set of transmission compensation coefficients, which serve as the basis for the transmission gain component and provide guidance for subsequent gain allocation. The transmission compensation coefficient set is updated in real time as the ambient light changes, ensuring consistent and stable display effects under dynamic lighting conditions.

[0117] Reflection enhancement features describe the reflection effects of light on the transparent display's surface and internal interfaces. Based on these features, surface reflection compensation is performed on the regional light intensity dataset to reduce the interference of forward light reflections on the displayed content. The compensation process begins by analyzing the spatial relationship between the reflection enhancement features and the regional light intensity response data to identify the areas with the strongest reflected light.

[0118] Surface reflection compensation calculates the amount of reflected light intensity that needs to be reduced for each pixel under current lighting conditions to minimize the impact of reflection interference on the displayed content. This calculation takes into account the reflectivity of the transparent screen surface, the angle of incidence of the light, and the intensity distribution of the light. For areas with high reflected light intensity, the reflection compensation coefficient is higher, reducing the brightness and contrast of the displayed content to minimize reflection interference.

[0119] The reflection compensation coefficient set is stored as pixel-level attenuation values, with each pixel location containing independent attenuation values ​​for the RGB channels. The compensation coefficients are interpolated from limited sensor data to create a continuous distribution across the entire screen, ensuring smooth spatial transitions. To avoid dimming caused by over-suppression, the compensation coefficient set is spatially smoothed to ensure smooth changes in compensation values ​​between adjacent pixels.

[0120] The compensation results generate a set of reflection compensation coefficients, which serve as the basis for reflection suppression components and provide guidance for subsequent suppression allocation. The reflection compensation coefficient set is updated in real time as the ambient light changes, ensuring consistent and stable display effects under dynamic lighting conditions.

[0121] The penetration compensation coefficient set provides a gain value for each pixel location. Based on these gain values, sub-pixel gain allocation is performed to obtain the transmission gain component. The gain allocation process begins by analyzing the spatial distribution of the penetration compensation coefficient set and identifying the areas with the highest and lowest gain.

[0122] Gain allocation calculates the required brightness gain for each subpixel under current lighting conditions to compensate for energy lost during transmission. This calculation also factors in the transparent screen material's transmittance, the incident angle of the light, and the intensity distribution. In areas with severe light attenuation, higher gain values ​​are applied to improve the brightness and contrast of the displayed content, maintaining visibility and detail clarity.

[0123] The transmission gain component is stored as sub-pixel gain values, with each sub-pixel location containing independent gain values ​​for the RGB channels. Gain allocation is derived from limited sensor data using an interpolation algorithm to achieve a continuous distribution across the entire screen, ensuring smooth spatial transitions. To avoid display anomalies caused by overcompensation, the gain components are spatially smoothed to ensure smooth gain transitions between adjacent pixels.

[0124] The gain component is updated in real time as ambient light changes, ensuring consistent and stable display performance under dynamic lighting conditions. The gain component is the core output of bidirectional transmission compensation and provides the basic data for the final display compensation.

[0125] The reflection compensation coefficient set provides an attenuation value for each pixel location. Based on these attenuation values, regional reflection suppression allocation is performed to obtain reflection suppression components. The suppression allocation process starts by analyzing the spatial distribution of the reflection compensation coefficient set and identifying the areas with the highest and lowest attenuation.

[0126] Suppression allocation calculates the amount of reflected light intensity that needs to be reduced for each pixel under current lighting conditions to minimize the impact of reflection interference on the displayed content. This calculation takes into account the reflectivity of the transparent screen surface, the angle of incidence of the light, and the intensity distribution of the light. Areas with high reflected light intensity receive higher attenuation values, reducing the brightness and contrast of the displayed content to minimize reflection interference.

[0127] The reflection suppression component is stored as pixel-level attenuation values, with each pixel location containing independent attenuation values ​​for the RGB channels. This suppression allocation is derived from the limited sensor data using an interpolation algorithm to create a continuous distribution across the entire screen, ensuring smooth spatial transitions. To avoid dimming caused by over-suppression, the suppression component is spatially smoothed to ensure smooth variations in suppression values ​​between adjacent pixels.

[0128] The reflection suppression component is updated in real time as ambient light changes, ensuring consistent and stable display effects under dynamic lighting conditions. The transmission gain component and reflection suppression component are the core outputs of bidirectional transmission compensation, providing the basic data for the final display compensation.

[0129] This embodiment combines the light attenuation factor coefficient with the regional light intensity data set to accurately distinguish between transmission and reflection characteristics and compensate for them separately, thereby effectively solving the interference of ambient light changes on the display effect of the transparent screen. By compensating for the transmission attenuation characteristics, the brightness and details of the displayed content are restored on the penetration path, so that the screen can still maintain a high display quality under different lighting conditions. At the same time, the compensation of the reflection enhancement feature effectively suppresses the interference of surface reflected light, improves the contrast and clarity of the displayed content, and can significantly improve the stability of the display effect, especially under strong light. By performing sub-pixel gain allocation and reflection suppression allocation on the compensation coefficient, precise local adjustment can be achieved, avoiding display anomalies caused by over-compensation or suppression, and making the display effect more natural and balanced. In one embodiment, display correction is performed on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters, including: The initial calibration parameters contain the basic data for transparent screen display calibration. These parameters describe the screen's optical characteristics and calibration requirements as a whole. Calibration parameter domain decoupling separates these initial parameters into two parameter sets at different scales: global calibration parameters and local, refined calibration parameters. Global calibration parameters describe the common characteristics of the entire screen, such as macro parameters like overall brightness, contrast, and color balance, which have a universal impact on all pixel areas. Local, refined calibration parameters focus on the specific correction requirements of different areas of the screen, such as micro parameters like local brightness unevenness, color shift, and detail distortion, which vary from area to area.

[0130] The decoupling process uses multi-scale analysis to decompose the initial parameters at different spatial scales. The decoupling operation performs frequency domain analysis on the initial correction parameters, extracting low-frequency components as global correction parameters and high-frequency components as local refined correction parameters. This decoupling approach ensures that global and local characteristics can be treated separately during the subsequent correction process, maintaining overall consistency while enabling local fine-tuning. The decoupled global correction parameters are stored in a low-dimensional matrix containing the basic correction values ​​for the RGB channels; the local refined correction parameters are stored in a high-dimensional data structure, recording the differentiated correction values ​​for each region. The global correction parameters will be used for subsequent dynamic error compensation, while the local refined correction parameters will be used in the tensor fusion process.

[0131] Display compensation information includes pixel-level brightness compensation coefficients and chromaticity offset correction coefficients, which reflect the compensation requirements for the displayed content under the current ambient light conditions. The dynamic error compensation process combines this display compensation information with global correction parameters to correct for inaccuracies and time-variability in the global parameters.

[0132] The compensation process begins by comparing the display compensation information with the global correction parameters, calculating the error between the actual requirements and the global parameters. This error analysis covers multiple dimensions, including brightness deviation, color distortion, and response nonlinearity. For brightness deviation, dynamic compensation adjusts the global brightness parameters to match the actual lighting conditions. For color distortion, the compensation process corrects the global color parameters to ensure accurate color reproduction. For response nonlinearity, the compensation algorithm optimizes the global gamma parameters to improve the depth and detail of the displayed content.

[0133] The compensation calculation utilizes a feedback regulation mechanism, dynamically adjusting the compensation intensity based on the error magnitude and updating the compensation value in real time as the ambient light changes. Error optimization correction data is stored in matrix form, containing the revised global correction parameters and the error compensation increment. This data maintains the same structure as the global correction parameters, facilitating subsequent processing. The error optimization process pays special attention to compensation stability and smooth transitions to avoid display anomalies caused by sudden changes in compensation parameters. The optimized correction data is then used in subsequent tensor fusion and combined with the locally refined correction parameters to form a complete correction solution.

[0134] Tensor fusion is the process of integrating error-optimized correction data with locally refined correction parameters into a unified correction matrix. This fusion process, based on tensor operations, ensures the coordination and consistency of global and local characteristics. Tensors are multidimensional data structures suitable for representing complex spatial distributions, effectively combining characteristics at different scales through tensor operations. Fusion begins by constructing a tensor representation suitable for global and local data. The global data is expanded to a uniform distribution across the entire screen, while the local data retains its original spatial variation characteristics.

[0135] The fusion algorithm combines the two parameters in tensor space using a weighted combination, with the weights dynamically adjusted based on regional characteristics and ambient lighting conditions. Local refinement parameters are weighted more heavily in areas with rich display content, while global parameters dominate in areas with smooth display content. The fusion process utilizes edge-preserving filtering to ensure smooth correction transitions while preserving image edge sharpness. The collaborative correction data is stored as a high-dimensional tensor at the same resolution as the screen, containing the complete correction parameters for all three RGB channels.

[0136] The matrix element values ​​reflect the optimal correction parameters for each pixel location under the current ambient light conditions, taking into account both global consistency and local fine-tuning. The dynamic nature of the collaborative matrix ensures that the correction effect is updated in real time as the ambient light changes, adapting to varying lighting conditions. The matrix is ​​used in subsequent spatial mapping and photoelectric conversion to generate the actual drive adjustment parameters.

[0137] Spatial mapping and optoelectronic conversion transform the coordinated correction data into usable driver parameters for the actual display hardware. This process incorporates bidirectional light influence parameters to ensure that the correction results are adapted to the actual lighting environment. Spatial mapping first converts the parameter space of the correction matrix into the physical space of the display device, taking into account the physical structure of the screen, pixel arrangement, and sub-pixel layout. The mapping process uses bilinear interpolation or cubic spline interpolation algorithms to ensure the continuity and accuracy of the spatial transformation.

[0138] The mapping results are combined with bidirectional light impact parameters to calculate the actual visual effect of each pixel under current lighting conditions. These parameters provide information about the behavior of light on the transparent screen, including the effects of forward light reflection and backward light transmission. Photoelectric conversion converts the spatial mapping results into drive signal parameters, taking into account the display device's response characteristics, gamma curve, and color space. This conversion process uses a lookup table approach to map the correction values ​​to drive voltages or digital drive values.

[0139] The mapping table is constructed based on the nonlinear response of the display device and uses an inverse response curve correction to ensure a linear correspondence between the input signal and the actual display. The driver adjustment mapping table is stored in a format compatible with the display driver system and contains the driver adjustment parameters for each pixel. The mapping table's update frequency is linked to the rate of change in ambient light, ensuring timely adjustment of driver parameters as lighting changes. The driver adjustment mapping table is the direct basis for the final display calibration and will be used in the real-time frame-by-frame refresh process.

[0140] Real-time frame-by-frame refresh is the process of applying a driver adjustment map to the transparent display driver, enabling dynamic ambient light adaptability. The refresh process begins by receiving raw display data and, combined with the driver adjustment map, calculating the final drive value for each pixel. This calculation takes into account the content characteristics of the raw data, such as brightness, contrast, and color distribution, to ensure that the correction effect is consistent with the displayed content. The refresh mechanism adjusts the update frequency based on the rate of change in ambient light, reducing the update frequency to conserve computing resources when the lighting is stable, and increasing the update frequency to ensure display quality when the lighting changes rapidly.

[0141] The refresh process utilizes frame buffering technology, storing corrected display data in a buffer and updating the entire screen at once when the vertical sync signal arrives, eliminating screen tearing and flickering. Real-time processing is hardware-accelerated, utilizing a graphics processing unit (GPU) or dedicated display processing chip to ensure real-time performance on high-resolution screens. The result is a globally optimized display that remains crisp, bright, and color-accurate in all ambient lighting conditions.

[0142] Optimization results include improved brightness and contrast, reduced reflection interference, enhanced color reproduction, and improved detail. Global display parameters dynamically adjust as ambient light changes, ensuring an optimal visual experience under varying lighting conditions. The entire transparent screen's bidirectional ambient light correction method forms a complete processing chain from ambient light sensing to display correction, enabling high-quality display in complex lighting environments.

[0143] This embodiment decouples the correction parameter domain of the initial correction parameters to distinguish between global and local correction requirements. This ensures the consistency of the display while making fine adjustments to the details, ensuring that the transparent screen maintains the best display effect in different usage scenarios. Dynamic error compensation is performed based on the display compensation information, so that the global correction parameters can adapt to changes in ambient light in real time, thereby improving the accuracy and stability of the display. The error optimization correction data and the local refined correction parameters are tensor fused to form collaborative correction data. Spatial mapping and photoelectric conversion are performed based on the collaborative correction data and the bidirectional light influence parameters, and the correction data is converted into actually usable driving parameters to ensure that the transparent screen provides the best visual experience under different lighting conditions. Through the real-time frame-by-frame refresh mechanism, the transparent screen can quickly respond to changes in ambient light and maintain high-quality display effects.

[0144] Reference Figure 2 As shown, the present invention also provides a transparent screen ambient light bidirectional optical calibration system, which is applied to any of the above transparent screen ambient light bidirectional optical calibration methods, comprising: The acquisition module is used to obtain the light-sensitive sensor data of the transparent screen panel, perform calibration processing, and obtain the sensor response parameters; An analysis module is used to obtain bidirectional illumination data of the transparent screen, perform bidirectional differential analysis on the data with the sensor response parameters, and obtain bidirectional light impact parameters; The correlation module is used to obtain the display status data of the transparent screen, perform display quality correction based on the bidirectional light impact parameters, and obtain initial correction parameters; A processing module, the processing module is used to perform pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; The control module is used to perform display correction on the transparent screen according to the initial correction parameters and the display compensation information to obtain the global display parameters.

[0145] The present invention provides a transparent screen ambient light bidirectional optical calibration system, which obtains accurate sensor response parameters by calibrating the transparent screen panel light-sensitive sensor data, providing a reliable data basis for subsequent display correction. By combining the bidirectional illumination data with the sensor response parameters for differential analysis, it is possible to comprehensively evaluate the impact characteristics of the front and rear ambient light on the display effect, effectively solving the limitations of the traditional one-way light correction method. By combining the display status data with the bidirectional light impact parameters for display quality correction, adaptive adjustment of different display contents is achieved, improving the overall performance of the display picture. Pixel-level optimization of the display status data based on the bidirectional light impact parameters achieves more refined display compensation, effectively improving the contrast and color reproduction of the display picture. Through the comprehensive application of the initial correction parameters and the display compensation information, the global display parameters of the transparent screen in a complex lighting environment are achieved, significantly improving the user's viewing experience.

[0146] Reference Figure 3 As shown, the present invention also provides a transparent screen ambient light bidirectional optical calibration device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for bidirectional optical calibration of transparent screen ambient light.

[0147] In this embodiment, the processor and memory may be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0148] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0149] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for bidirectional optical calibration of a transparent screen under ambient light, characterized in that: include: Obtaining the panel light-sensitive sensor data of the transparent screen, performing calibration processing, and obtaining the sensor response parameters; Obtaining bidirectional illumination data of the transparent screen, performing bidirectional differential analysis on the data with the sensor response parameters, and obtaining bidirectional light impact parameters; Acquiring display status data of the transparent screen, performing display quality correction based on the bidirectional light impact parameter, and obtaining initial correction parameters; Performing pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; Display correction is performed on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters.

2. The method for bidirectional optical calibration of a transparent screen according to claim 1, characterized in that: The step of obtaining the panel light-sensitive sensor data of the transparent screen and performing calibration processing to obtain the sensor response parameters includes: Collecting original electrical signals of photosensors from multiple areas of the transparent screen, performing panel filtering, and obtaining panel electrical signal data; Performing sensor response data conversion according to the panel electrical signal data to obtain the panel light-sensitive sensor data; Performing multi-channel sensitivity collaborative processing on the panel light sensing data to obtain channel-consistent data; Thermal drift correction and response curve reshaping are performed on the channel uniformity data according to preset standard light source data to obtain the sensing response parameters.

3. The method for bidirectional optical calibration of a transparent screen according to claim 1, characterized in that: The acquiring of the bidirectional illumination data of the transparent screen and performing bidirectional differential analysis on the sensor response parameters to obtain bidirectional light impact parameters includes: Performing feature demodulation on the bidirectional illumination data to obtain forward light sensing data and backward light sensing data; Performing ambient light incident angle analysis on the forward light sensing data and the backward light data to obtain an ambient light incident coefficient; Performing phase compensation and attenuation coupling on the ambient light incidence coefficient to obtain an ambient light dynamic attenuation parameter; The ambient light dynamic attenuation parameter and the ambient light incidence coefficient are subjected to bidirectional optical path coupling reconstruction to obtain the bidirectional light impact parameter.

4. The method for bidirectional optical calibration of a transparent screen according to claim 1, wherein: The acquiring of the display status data of the transparent screen and performing display quality correction in combination with the bidirectional light impact parameter to obtain initial correction parameters include: Performing grayscale distribution analysis and multi-scale visual perception decomposition on the display state data to obtain display grayscale information; Dividing the display area of ​​the transparent screen into blocks according to the display grayscale information to obtain multiple display sub-areas and regional light intensity distribution data; Performing dynamic gamma mapping on the multiple display sub-areas according to the bidirectional light impact parameter to obtain a gamma mapping parameter; Performing color gamut space conversion processing according to the regional light intensity distribution data and the bidirectional light impact parameter to obtain an ambient light compensation color gamut coefficient; Luminance and chromaticity coupling is performed on the gamma mapping parameters and the ambient light compensation color gamut to obtain the initial correction parameters.

5. The method for bidirectional optical calibration of a transparent screen according to claim 1, wherein: The performing pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information includes: Performing sub-pixel segmentation and pixel partitioning on the display state data to generate a pixel unit mapping data set and a regional light intensity data set; Performing light attenuation parameter calculation on the pixel unit mapping data set based on the bidirectional light impact parameter to obtain a light attenuation factor coefficient; Perform bidirectional transmission compensation according to the light attenuation factor coefficient and the regional light intensity data set to obtain a transmission gain component and a reflection suppression component; Performing optical rebalancing and interference quantization processing based on the transmission gain component and the reflection suppression component to obtain a pixel-level brightness compensation coefficient and a chromaticity offset correction coefficient; Multi-channel compensation is performed on the display state data based on the brightness compensation coefficient and the chromaticity offset correction coefficient to obtain the display compensation information.

6. The method for bidirectional optical calibration of a transparent screen according to claim 5, characterized in that: The bidirectional transmission compensation is performed according to the light attenuation factor coefficient and the regional light intensity data set to obtain a transmission gain component and a reflection suppression component, including: Separate the transmission characteristics and reflection characteristics of the light attenuation factor coefficient to obtain the transmission attenuation characteristics and reflection enhancement characteristics; Performing penetration path compensation on the regional light intensity dataset according to the transmission attenuation characteristics to generate a penetration compensation coefficient set; Performing surface reflection compensation on the regional light intensity dataset based on the reflection enhancement feature to generate a reflection compensation coefficient set; Performing sub-pixel gain allocation on the penetration compensation coefficient set to obtain the transmission gain component; Performing regional reflection suppression allocation on the reflection compensation coefficient set to obtain the reflection suppression component.

7. The method for bidirectional optical calibration of a transparent screen according to claim 1, wherein: The performing display correction on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters includes: Decouple the initial correction parameters from the correction parameter domain to obtain global correction parameters and local refined correction parameters; Performing dynamic error compensation on the global correction parameter according to the display compensation information to obtain error optimization correction data; Performing tensor fusion on the error optimization correction data and the local refinement correction parameters to obtain collaborative correction data; Performing spatial mapping and photoelectric conversion based on the collaborative correction data and the bidirectional light impact parameter to obtain a drive adjustment mapping table; The transparent screen is refreshed frame by frame in real time according to the drive adjustment mapping table to obtain the global display parameters.

8. A transparent screen ambient light bidirectional optical calibration system, characterized in that: The method for bidirectional optical calibration of a transparent screen for ambient light as described in any one of claims 1 to 7 comprises: An acquisition module is used to acquire the light-sensitive sensor data of the transparent screen panel, perform calibration processing, and obtain sensor response parameters; An analysis module, configured to obtain bidirectional illumination data of the transparent screen, perform bidirectional differential analysis on the data with the sensor response parameters, and obtain bidirectional light impact parameters; an association module, the association module being configured to obtain display status data of the transparent screen, perform display quality correction in combination with the bidirectional light impact parameter, and obtain initial correction parameters; a processing module, configured to perform pixel-level optimization on the display state data based on the bidirectional light impact parameter to obtain display compensation information; A control module is used to perform display correction on the transparent screen according to the initial correction parameters and the display compensation information to obtain global display parameters.

9. A transparent screen ambient light bidirectional optical calibration device, characterized in that: include: Memory, used to store programs; The processor is configured to execute the program to implement the steps of the method for bidirectional optical calibration of ambient light for a transparent screen as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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