HDR dynamic tone mapping method and system

Abstract

The invention discloses an HDR dynamic tone mapping method and system. The HDR dynamic tone mapping method comprises the following steps: S1, acquiring an ambient light parameter, a user interaction parameter and a display equipment state parameter which are acquired in real time; s2, performing dynamic parameter fusion on the ambient light parameters, the user interaction parameters, the display equipment state parameters and video content metadata to generate dynamic metadata; s3, generating a self-adaptive tone mapping curve based on the dynamic metadata, wherein the self-adaptive tone mapping curve adjusts a brightness mapping relation through an equipment aging compensation parameter; and S4, measuring the error between the actual display image quality and the theoretical value, and correcting the compensation parameter in the dynamic parameter fusion according to the error value. According to the method, various parameters are collected in real time, the adaptive tone mapping curve is generated through dynamic fusion, and errors are corrected in combination with closed-loop feedback, so that the problem of image quality degradation caused by environment change, equipment aging and user interaction difference in the traditional HDR technology is solved, and high-precision and adaptive dynamic display optimization is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of image processing, and relates to an HDR dynamic tone mapping method and system. BACKGROUND

[0002] Traditional high dynamic range (HDR) technology faces multiple challenges in practical applications, such as dynamic environmental changes, display device aging, and user interaction differences. Existing technologies usually use fixed metadata (such as HDR10) or dynamic adjustment methods based on AI prediction, but these solutions have significant limitations: first, fixed metadata cannot adapt to changes in environmental light intensity and color temperature in real time, for example, when the environmental light switches from indoor weak light to outdoor strong light, the high light area of the picture is prone to overexposure, and the dark details are severely lost; second, display devices (such as OLED panels) will experience brightness decay and color gamut shift as they are used over time, and traditional techniques only rely on firmware upgrades to adjust parameters statically, resulting in a compensation error > 20%, significantly shortening the device's lifespan (from a theoretical 15,000 hours to 10,000 hours); third, user viewing distance and position changes are not effectively included in the optimization logic, for example, when viewing at close range, the local contrast is insufficient, and the dark field detail recognition rate decreases by > 30%. These problems result in a significant deviation between actual display results and theoretical design. SUMMARY

[0003] The application provides an HDR dynamic tone mapping method and system, which dynamically fuses to generate an adaptive tone mapping curve by real-time acquisition of environmental light parameters, user interaction parameters, and device state parameters, and combines closed-loop feedback to correct errors, solving the picture quality degradation problem caused by environmental changes, device aging, and user interaction differences in traditional HDR technology, and achieving high-precision, adaptive dynamic display optimization.

[0004] To achieve the above purpose, the application adopts the following technical solutions: An HDR dynamic tone mapping method based on multi-modal perception and dynamic feedback, comprising the following steps: S1. Obtain the environmental light parameters, user interaction parameters, and display device state parameters collected by the multi-modal perception module in real time; S2. Dynamically fuse the environmental light parameters, user interaction parameters, and display device state parameters with video content metadata to generate dynamic metadata; S3. Generate an adaptive tone mapping curve based on the dynamic metadata, and adjust the luminance mapping relationship through a device aging compensation parameter; S4. Measure the error between the actual display quality and the theoretical value, and correct the compensation parameter in the dynamic parameter fusion according to the error value.

[0005] Further, the ambient light parameter comprises an ambient light intensity and a color temperature; The user interaction parameter comprises a user viewing distance and a user preference parameter; The display device state parameter comprises a panel brightness decay rate and a color gamut offset.

[0006] Further, the dynamic parameter fusion comprises the following steps: Adjusting a perception weight dynamically according to the ambient light intensity; Triggering a local contrast enhancement rule according to the user viewing distance; Obtaining video content metadata, and performing weighted calculation on the video content metadata, the display device state parameter and the user preference parameter.

[0007] Further, the triggering of the local contrast enhancement rule according to the user viewing distance comprises: When the user viewing distance is less than 1.5 meters, a dark portion slope is increased to 1.3 times of a basic value; When the user viewing distance is greater than or equal to 1.5 meters, a global contrast optimization mode is adopted.

[0008] Further, the triggering of the local contrast enhancement rule according to the user viewing distance further comprises, when there are multiple users, the user viewing distance is a minimum viewing distance among all users or an average viewing distance of all users.

[0009] Further, the obtaining of the video content metadata and the weighted calculation on the video content metadata, the display device state parameter and the user preference parameter comprise: The video content metadata weight allocation formula is: Coverage rate, wherein MaxCLL is a luminance value of a brightest pixel in a video frame, and is a preset coefficient, and N is a normalization constant; The display device state parameter weight allocation formula is: , wherein is a decay coefficient; The user preference parameter weight is updated by an exponential smoothing method, and an update formula is: , wherein is a smoothing factor.

[0010] Further, a calculation formula of the device aging compensation parameter is: ; , wherein MaxCLL is a luminance value of a brightest pixel in a video frame, is a panel brightness actual decay value, is a panel initial brightness, compensate for the aging of the display panel.

[0011] Further, the generation of the adaptive tone mapping curve comprises: when the ambient light intensity is greater than 500 lux, setting the Roll-off starting point of the PQ curve to 75% of the luminance value of the brightest pixel in the video frame; when the ambient light intensity is less than or equal to 500 lux, setting the Roll-off starting point to 95% of the luminance value of the brightest pixel in the video frame; wherein MaxCLL is the luminance value of the brightest pixel in the video frame.

[0012] Further, the threshold determination step of the error comprises: when the luminance error is greater than 1.5, triggering the adjustment of the luminance value compensation coefficient of the brightest pixel in the video frame; when the chrominance error is greater than 0.003, updating the color gamut compensation lookup table.

[0013] An HDR dynamic tone mapping system based on multi-modal perception and dynamic feedback, for executing the HDR dynamic tone mapping method based on multi-modal perception and dynamic feedback, comprising: a multi-modal perception module, comprising an ambient light sensor, a user distance sensor, and a device state sensor, for respectively collecting the ambient light intensity and color temperature, the user viewing distance and user preference parameters, the panel brightness attenuation rate and the color gamut offset in real time; a dynamic parameter fusion module, in communication connection with the multi-modal perception module, for dynamically fusing the ambient light parameters, the user interaction parameters, the display device state parameters, and the video content metadata to generate dynamic metadata; an adaptive mapping engine, connected with the dynamic parameter fusion module, for generating an adaptive tone mapping curve based on the dynamic metadata generated by the adaptive mapping engine, the adaptive tone mapping curve adjusting the luminance mapping relationship through the device aging compensation parameter; a closed-loop feedback control module, connected with the adaptive mapping engine, for measuring the error between the actual display quality and the theoretical value, and correcting the compensation parameters in the dynamic parameter fusion according to the error value; an execution module, for driving the tone mapping processing unit of the display device according to the corrected compensation parameters.

[0014] The beneficial effects of this invention are as follows: This invention collects ambient light parameters, user interaction parameters, and display device status parameters in real time, dynamically fuses them with video content metadata to generate dynamic metadata, and generates an adaptive tone mapping curve based on this data. Combined with error detection and closed-loop correction mechanisms, it significantly optimizes the HDR display effect. By dynamically fusing ambient light parameters and video content metadata, it adaptively suppresses highlight clipping and enhances the ability to restore details in dark scenes, ensuring clear image layers under different lighting conditions. Based on user interaction parameters, it dynamically adjusts the mapping rules to improve local contrast and detail recognition. At the same time, through closed-loop feedback, it measures display errors in real time and corrects compensation parameters to continuously offset brightness decay and color gamut shift caused by device aging, maintaining long-term display consistency, thereby achieving a stable and high-quality visual experience in complex environments and long-term usage scenarios. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0016] Figure 2 This is a schematic diagram of the dynamic parameter fusion method of the present invention. Detailed Implementation

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0018] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0020] This invention provides an appendix Figures 1-2The application provides an HDR dynamic tone mapping method and system, which dynamically generates an adaptive tone mapping curve by collecting environmental light parameters, user interaction parameters and device state parameters in real time, and combines closed-loop feedback error correction, solves the picture quality degradation problem caused by environmental changes, device aging and user interaction differences in traditional HDR technology, and realizes high-precision, adaptive dynamic display optimization. Embodiment one An HDR dynamic tone mapping method based on multi-modal perception and dynamic feedback, comprising the following steps: S1. Obtain the environmental light parameters, user interaction parameters and display device state parameters collected by the multi-modal perception module in real time; Specifically, the environmental light intensity and color temperature, user viewing distance, device panel brightness state and other data are obtained in real time through a sensor module, and video content metadata (such as maximum brightness level and color gamut information) are analyzed. All parameters are updated synchronously at a high frequency to ensure real-time performance and data consistency.

[0022] S2. Dynamically fuse the environmental light parameters, the user interaction parameters, the display device state parameters and the video content metadata to generate dynamic metadata; Specifically, the environmental light intensity and user distance are used as the basis for weight distribution to dynamically adjust the parameter fusion priority. For example, in a strong light environment, high light overflow is preferentially suppressed, and in a weak light environment, dark field detail enhancement is emphasized; when the user watches from a close distance, local contrast optimization rules are triggered to improve the picture level. The fusion result generates dynamic metadata to guide adaptive mapping rule generation.

[0023] S3. Generate an adaptive tone mapping curve based on the dynamic metadata, and the adaptive tone mapping curve adjusts the brightness mapping relationship through device aging compensation parameters; Specifically, the shape of the tone mapping curve is adjusted based on the dynamic metadata, including the high light compression starting point, the dark slope and the mid-gray area contrast. For example, when the environmental light intensity is high, the high light region is compressed in advance; when the user distance is close, the contrast gradient of the dark part and the mid-gray area is enhanced to ensure the detail distinguishability.

[0024] S4. Measure the error between the actual display quality and the theoretical value, and correct the compensation parameters in the dynamic parameter fusion according to the error value.

[0025] Specifically, the brightness and chrominance deviation between the actual display picture and the theoretical value is detected in real time by a sensor. When the error exceeds the threshold value that can be perceived by the human eye, the mapping parameters (such as compensation coefficients or color gamut mapping table) are dynamically adjusted, and iterative correction is performed until the error converges to an unperceivable range, forming a closed-loop optimization mechanism.

[0026] The embodiment optimizes the HDR display effect by multi-modal perception and dynamic parameter fusion. Real-time detection and adaptive adjustment of ambient light intensity effectively suppresses highlight overflow and enhances the restoration of dark field details, ensuring clear picture levels under different lighting conditions. Real-time monitoring of the user viewing distance triggers local contrast optimization, improving detail recognition at close range and making the transition from dark to mid-gray area more natural and rich. The dynamic compensation mechanism for device aging state continuously corrects brightness decay and color gamut shift through closed-loop feedback, maintaining long-term display consistency and delaying performance degradation. The error detection and iterative correction mechanism real-time suppresses brightness and chroma deviation, ensuring that the picture output always approaches the theoretical design, thereby achieving stable and high-quality visual experience in complex use scenarios. Embodiment Two The ambient light parameters include ambient light intensity and color temperature. The user interaction parameters include user viewing distance and user preference parameters. The display device state parameters include panel brightness decay rate and color gamut shift.

[0028] Specifically, the ambient light parameters include ambient light intensity and color temperature, which are collected in real time by a dual-channel spectral sensor (such as AMS AS7341L), with a detection range of 0.01-100k lux, a sampling rate ≥60Hz, color temperature detection based on CIE 1931 chromaticity coordinates, a range of 2000K-10000K, updated every 0.5 seconds and using Kalman filtering to eliminate transient interference. In the user interaction parameters, the user viewing distance is measured by a ToF (Time of Flight) sensor with a detection accuracy of ±3cm and an update frequency of 30Hz, and the user preference parameters are collected through an interactive interface, including a dark field enhancement coefficient (0.5-2.0 sliding bar adjustment) and a saturation preference (natural / vibrant mode switching). In the display device state parameters, the panel brightness decay rate is monitored by a photodiode array with an accuracy of ±2%, and the calculation formula is: Wherein is the initial brightness, is the current brightness; the color gamut shift is based on CIE 1976 standard to calculate the chroma difference Δu'v', with a detection period of 1 hour and a threshold for triggering compensation of chroma difference Δu'v'>0.003.

[0029] The embodiment significantly suppresses high light overflow in strong light environment and dark field detail loss in weak light scene through dynamic adaptation of ambient light intensity and color temperature, and adaptively optimizes local contrast based on accurate measurement of user viewing distance, so that the picture layer is matched with the human eye visual characteristics; through real-time monitoring and compensation of panel brightness decay rate and color gamut offset, the brightness decay and color deviation caused by display device aging are effectively offset, and the picture quality consistency during long-term use is ensured; combined with dynamic fusion of user personalized preference parameters, intelligent adaptation of human-computer interaction is realized, which significantly optimizes the user's subjective experience while improving the objective indicators of display effect. Embodiment three The dynamic parameter fusion comprises the following steps: S21. dynamically adjusting the perception weight according to the ambient light intensity; Specifically, the ambient light intensity is collected in real time by a spectrum sensor, and the detection range covers weak light to strong light environment (0.01-100k lux), and the sampling rate is ≥60Hz.

[0031] When the ambient light intensity is high, the ambient parameter weight is increased to suppress high light overflow; When the ambient light intensity is low, the ambient parameter weight is reduced to retain the content dynamic range S22. triggering a local contrast enhancement rule according to the user viewing distance; Specifically, a ranging sensor (such as ToF technology) is used to monitor the user viewing distance in real time, and the detection accuracy meets ±3cm and the update frequency is 30Hz.

[0032] According to the user viewing distance, the contrast optimization mode (local or global) is dynamically selected; The rule divides the trigger condition through a preset distance threshold, so as to ensure detail enhancement when viewing at close range.

[0033] S23. obtaining video content metadata, and weightedly calculating the video content metadata, the display device state parameter and the user preference parameter.

[0034] Specifically, the video content metadata parses the MaxCLL (Maximum Content Light Level) and color gamut coverage rate in the video stream as content weight input, and the weight distribution is dynamically balanced based on content brightness and color gamut richness; The display device state parameter monitors the panel brightness decay rate and color gamut offset in real time, calculates the device weight through an exponential decay model, and the weight reflects the influence of device aging degree on picture quality; The user preference parameter collects the dark field enhancement coefficient and saturation preference manually adjusted by the user, and generates the user weight through a smoothing algorithm (such as exponential average) to fuse real-time input and historical preference.

[0035] This embodiment dynamically adjusts environmental weights to suppress highlight clipping (in bright light environments) and preserve details in dark areas (in low light environments). The contrast optimization mode triggered by user distance improves the detail recognition when viewed at close range. It also performs weighted calculations of comprehensive content, device, and user parameters to ensure that image quality matches device status and user needs in real time. Specific Implementation Example 4 The step of triggering local contrast enhancement rules based on the user's viewing distance includes: When the user's viewing distance is less than 1.5 meters, the slope of the dark area is increased to 1.3 times the base value; When the user's viewing distance is greater than or equal to 1.5 meters, the global contrast optimization mode is used.

[0037] Specifically, when the distance is less than 1.5 meters, the local contrast enhancement mode is triggered, increasing the slope of the shadows to 1.3 times the base value. The calculation formula is as follows: The reference slope is preset according to the display device type (e.g., the reference slope is 0.8 for OLED and 0.6 for LCD). When the distance is greater than or equal to 1.5 meters, switch to global contrast optimization mode. The slope of the dark area remains at the baseline value, but the Gamma value of the mid-gray area is increased from 2.2 to 2.4 to enhance the overall contrast.

[0038] In this embodiment, the gradient levels from dark areas to mid-gray areas are significantly enhanced in close-up mode, improving detail recognition. In long-distance mode, global contrast optimization makes the distribution of light and dark in the image more in line with the human eye's wide-range observation characteristics. Through real-time filtering of distance data, image quality jumps are avoided when switching modes, ensuring a consistent visual experience. Specific Implementation Example 5 The step of triggering local contrast enhancement rules based on the user's viewing distance further includes, when there are multiple users, the user's viewing distance being either the minimum viewing distance among all users or the average viewing distance among all users.

[0040] Specifically, the first method is minimum viewing distance triggering, which takes the minimum value in the user distance set as the trigger benchmark. If the minimum value is less than 1.5 meters, a local contrast enhancement rule is triggered on the entire screen or the user's area (the slope of dark areas is increased to 1.3 times the benchmark value), prioritizing the visual sensitivity needs of the nearest user to ensure the optimal core user experience. The second method is average viewing distance triggering, which calculates the average distance of all users. ,in, The average distance, To balance viewing distance, this solution caters to the viewing needs of multiple users and is suitable for public settings such as conference rooms and exhibition halls. Specific Implementation Example Six Traditional HDR technology typically relies on fixed weight allocation or single parameter optimization, failing to dynamically integrate video content characteristics, device aging status, and user preferences, leading to the following problems: 1. Image quality deviation: It is easy to overexpose in high brightness, and the color reproduction of wide color gamut content is insufficient (ΔE>5). 2. Impact of equipment aging: Panel brightness decay and color gamut shift are not quantified and compensated, resulting in a significantly shortened lifespan; 3. Lack of user interaction: Personalized needs (such as dark field enhancement and saturation preference) cannot be integrated into the image quality optimization logic in real time.

[0042] This embodiment systematically solves the above problems through multi-parameter weighted calculation.

[0043] The step of obtaining video content metadata and weighting the calculation of video content metadata, display device status parameters, and user preference parameters includes: The video content metadata weight allocation logic is as follows: High-brightness content (such as sunlight and flames) is given a higher brightness weight to suppress overexposure; For wide color gamut content (such as natural scenery and animation), the weight of color expression is enhanced and saturation is increased; The weighting formula is: Coverage, where MaxCLL is parsed from HDR video stream metadata, is the brightness value of the brightest pixel in the video frame. and This is a preset coefficient used to balance brightness and color priority (experimental optimization value), where N is a normalization constant (e.g., 1000). ; Specifically, high-brightness content (such as sunlight and lights) is processed using MaxCLL normalization to suppress overexposure; high color gamut content (such as natural landscapes and animations) has its color performance weight increased to enhance saturation.

[0044] The display device status parameters are sourced from the following: Panel usage time: Cumulative working time (unit: hours); Brightness decay rate: The percentage deviation between the real-time monitored panel brightness and the initial value; The logic for weighting the display device status parameters is as follows: The longer the usage time and the more severe the brightness decay, the higher the weight of the device status. Dynamically increase the intensity of aging compensation to ensure consistent image quality.

[0045] The weighting formula is: ,in The attenuation coefficient is determined experimentally based on the panel type (e.g., 1 / 3000 for OLED), and m is the panel usage time. Specifically, the longer the panel is used, the more significant the impact of device aging on image quality becomes, and the higher the weighting of this impact becomes; the exponential decay model simulates the device aging curve to ensure that the compensation intensity matches the degree of aging.

[0046] The user preference parameters are sourced from the following: User input: Set preferences such as dark field enhancement and saturation through the interactive interface (slider, voice); Historical data: Records users' recent adjustment trends to smoothly transition parameter changes; The user preference parameter weight update logic is as follows: Real-time input is integrated with historical data to avoid parameter jumps; Balance instantaneous response and stability through smoothing algorithms (such as exponential averaging); The weights of the user preference parameters are updated using exponential smoothing, and the update formula is as follows: ,in A smoothing factor (e.g., 0.7) is used to control the influence of historical data on the weights. The result is a smoothed result of the user's historical preference weights. The weighted average of the most recently adjusted values ​​is stored. z is the real-time adjustment value input by the user through the interactive interface (such as sliders or voice commands), such as the dark field enhancement coefficient (0.5-2.0) or the saturation mode selection (natural / vivid).

[0047] Specifically, a smoothing factor balances historical preferences with real-time input to avoid parameter abrupt changes; user interaction data is captured in real time through an event listening module to ensure the immediacy of weight updates.

[0048] More specifically, through the dynamic weighted fusion of video content metadata, device status parameters, and user preference parameters, the following comprehensive improvements are achieved: image quality adaptability, with significantly enhanced highlight suppression for high-brightness content and color reproduction capabilities for wide color gamut content, closely aligning with the content creator's intent; aging compensation accuracy, with the impact of device aging on image quality dynamically quantified and the compensation intensity adaptively adjusted over time to maintain display consistency; and user experience consistency, with smooth integration of user preferences and real-time interactive data, reducing operation delays and image quality jumps, and improving the naturalness and satisfaction of the interaction. Specific Implementation Example 7 Display devices (such as OLED and LCD) experience brightness decay and color gamut shift over time. Traditional compensation solutions rely on periodic firmware upgrades, which cannot adapt to the aging process in real time. This embodiment dynamically calculates the compensated MaxCLL (maximum content brightness level) to offset the image quality degradation caused by panel aging in real time.

[0050] The calculation formula for the equipment aging compensation parameter is as follows: ; Where MaxCLL is the brightness value of the brightest pixel in the video frame. This is the measured brightness attenuation value of the panel. This is the initial brightness of the panel. These are aging compensation parameters.

[0051] Specifically, the initial brightness of the panel when the device leaves the factory. It is precisely measured and stored in the firmware. For example, the OLED panel's... This value cannot be altered and serves as the benchmark for aging compensation.

[0052] The current brightness of the panel is monitored in real time using a photoelectric sensor array. The sampling interval is ≤1 second, and the accuracy is ±2%. For example, after the panel has been used for 3000 hours, the measured... ; Brightness attenuation value Calculated by difference: ; This step converts the amount of physical aging into a quantifiable value, providing input for subsequent compensation.

[0053] The compensation coefficient α is preset according to the panel type, for example: OLED panel: α=0.8 (material aging rate is fast, requiring higher compensation strength); LCD panel: α=0.7 (backlight module decays more slowly); This coefficient is calibrated through accelerated aging tests (IEC 62341 standard) to ensure that the compensation strength matches the degree of aging.

[0054] The formula for calculating MaxCLL after compensation is: ; Theoretically, MaxCLL is the raw brightness value (e.g., 2000 nits) parsed from video metadata, which is the value when the panel ages ( When the brightness is reduced, MaxCLL is increased proportionally after compensation to offset the brightness reduction. Example calculation is as follows: ; This step dynamically links the theoretical brightness with the panel's actual capabilities, ensuring that the display brightness matches the intended content. Figure 1 To.

[0055] This embodiment detects panel brightness decay in real time and dynamically increases the compensated MaxCLL. This effectively counteracts brightness loss caused by device aging, significantly reducing overexposure in highlight areas and loss of detail in dark areas, while maintaining accurate color reproduction. The compensation mechanism dynamically adjusts the output brightness according to the degree of aging, avoiding display deviations caused by static parameters in traditional solutions. This extends device lifespan, improves image consistency, and ensures that the display effect at different aging stages closely matches the content creator's intent. Specific Implementation Example 8 In traditional HDR technology, the roll-off starting point (the starting position of highlight compression) of the tone mapping curve is usually set fixedly, which cannot adapt to the display needs under different ambient light intensities. For example, highlights are easily overexposed in bright light environments, while details in shadows are lost in low light environments. This embodiment achieves environmentally adaptive HDR display optimization by dynamically adjusting the roll-off starting point and combining ambient light intensity with the brightness characteristics of video content.

[0057] The generation of the adaptive tone mapping curve includes: When the ambient light intensity is greater than 500 lux, set the roll-off starting point of the PQ curve to 75% of MaxCLL; When the ambient light intensity is less than or equal to 500 lux, set the roll-off starting point to 95% of MaxCLL; MaxCLL is the brightness value of the brightest pixel in the video frame.

[0058] Specifically, the rules for dynamically adjusting the roll-off starting point are as follows: Ambient light intensity > 500 lux (strong light scene): The roll-off starting point is set to 75% of MaxCLL: Human visual characteristics: Under strong light, the human eye's sensitivity to highlights decreases, but its tolerance for overexposure is even lower. Pre-compressing highlight areas (such as sunlight and lamplight) can suppress overexposure while preserving the dynamic range from mid-gray to dark areas; experimental data shows that a 75% starting point balances highlight suppression with mid-gray contrast (Gamma=2.2), avoiding a grayish image or highlight clipping. Ambient light intensity ≤ 500 lux (low light scene): The roll-off starting point is set to 95% of MaxCLL: Human visual characteristics: Sensitivity to details in dark areas is significantly enhanced in low light, so it is necessary to preserve the dark field gradation to the maximum extent. Technical verification: Delayed highlight compression to 95% starting point ensures complete gradation from dark to mid-gray areas, with Δu'v' color shift < 0.003 (compliant with JNCD standards). Specific Implementation Example Nine Traditional HDR technology lacks a closed-loop feedback mechanism for display errors. When panel aging or sudden environmental changes cause the actual display quality to deviate from the theoretical value, the error accumulates over time. For example, when panel brightness decreases, highlights may lose detail due to insufficient brightness (ΔE > 1.5), and color gamut shift will lead to color distortion (Δu'v' > 0.003). This embodiment detects errors in real time and triggers dynamic compensation, forming a closed-loop control logic of "detection → judgment → correction," systematically solving the problem of error accumulation. The threshold determination step for the error includes: When the brightness error is greater than 1.5, the brightness compensation coefficient of the brightest pixel in the video frame is adjusted. When the chromaticity error is greater than 0.003, update the gamut compensation lookup table.

[0060] Specifically, the actual brightness and chromaticity data displayed on the screen are collected in real time using photoelectric sensors and color sensors, and compared with the theoretical values ​​of the video content to calculate two types of errors: Brightness error (ΔE): Based on the CIEDE2000 standard, the brightness deviation is quantified (e.g., theoretical brightness 2000 nits, actual display 1900 nits, ΔE=1.8). Chromaticity error (Δu'v'): Based on the CIE 1976 chromaticity space, calculate the Euclidean distance between the actual chromaticity coordinates and the theoretical values ​​(e.g., a deviation of 0.004 for the red coordinates).

[0061] The brightness error ΔE = 1.5 is the critical value for brightness difference that the human eye can perceive (JNCD standard). Exceeding this threshold requires compensation. When ΔE is greater than 1.5, the equipment aging compensation coefficient α is increased (e.g., adjusted from 0.8 to 0.85) to enhance the brightness compensation intensity. The formula is as follows: , where k is the adjustment gain coefficient (0.1-0.3); The chromaticity error Δu'v' = 0.03 is the threshold of color shift perceptible to the human eye. If this threshold is exceeded, the color mapping needs to be corrected. When Δu'v' is greater than 0.003, the gamut compensation lookup table (LUT) is updated, and the color coordinates are remapped using an interpolation algorithm. The formula is: ;in, The corrected color value corresponding to the ii-th index in the updated gamut compensation lookup table. The value is the original color value of the ii-th index in the color gamut compensation lookup table before the update, and m is the color gamut compensation gain coefficient, which ranges from 0.1 to 0.5 and is determined by the panel color gamut attenuation model.

[0062] When a brightness error (ΔE > 1.5) or a chromaticity error (Δu'v' > 0.003) is detected, the system triggers a dynamic correction mechanism: For brightness deviation, the brightness compensation intensity is enhanced by increasing the device aging compensation coefficient α, for example, adjusting α from 0.8 to 0.85, driving the MaxCLL after compensation to match the theoretical brightness; for chromaticity deviation, the color gamut compensation lookup table (LUT) is updated through an interpolation algorithm, remapping the color coordinates to the theoretical color gamut range. The corrected parameters are loaded into the display processing chip in real time to drive the screen output, while the sensor continuously monitors the actual display effect after correction. If the error still exceeds the threshold, the system iteratively adjusts the compensation parameters (such as α or LUT) until the error converges to a range imperceptible to the human eye (ΔE ≤ 1.5, Δu'v' ≤ 0.003), forming a closed-loop verification logic of "detection → correction → re-detection" to ensure long-term display stability. Specific Implementation Example 10 An HDR dynamic tone mapping system based on multimodal perception and dynamic feedback, used to execute the HDR dynamic tone mapping method based on multimodal perception and dynamic feedback, includes: The multimodal sensing module includes a spectral sensor, a user distance sensor, and a device status sensor, which are used to collect ambient light intensity and color temperature, user viewing distance and user preference parameters, panel brightness decay rate and color gamut shift in real time, respectively. The dynamic parameter fusion module is communicatively connected to the multimodal perception module and is used to dynamically fuse the ambient light parameters, user interaction parameters, display device status parameters and video content metadata to generate dynamic metadata. An adaptive mapping engine, connected to the dynamic parameter fusion module, is used to generate an adaptive tone mapping curve based on the dynamic metadata. The adaptive tone mapping curve adjusts the brightness mapping relationship through device aging compensation parameters. A closed-loop feedback control module, connected to the adaptive mapping engine, is used to measure the error between the actual displayed image quality and the theoretical value, and to correct the compensation parameters in the dynamic parameter fusion based on the error value. The execution module is used to drive the tone mapping processing unit of the display device according to the corrected compensation parameters.

[0064] This invention can be used in a wide range of general-purpose or special-purpose computer system environments or configurations.

[0065] Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0066] This invention can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules.

[0067] Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks.

[0068] In a distributed computing environment, program modules can reside on local and remote computer storage media, including storage devices.

[0069] Specifically, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0070] It should be understood that although the steps in the flowcharts in the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order in which these steps are performed, and they may be performed in other orders.

[0071] Moreover, at least some steps in the flowchart of the attached figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. Their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0072] Obviously, the embodiments described above are only some embodiments of the present invention, and not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the scope of the invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the present invention.

[0073] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, whether directly or indirectly applied to other related technical fields, are similarly within the scope of protection of this patent.

Claims

1. A method for HDR dynamic tone mapping based on multimodal perception and dynamic feedback, characterized in that, Includes the following steps: S1. Acquire real-time ambient light parameters, user interaction parameters, and display device status parameters; S2. Dynamically fuse the ambient light parameters, user interaction parameters, display device status parameters, and video content metadata to generate dynamic metadata; S3. Generate an adaptive tone mapping curve based on the dynamic metadata, wherein the adaptive tone mapping curve adjusts the brightness mapping relationship through device aging compensation parameters; S4. Measure the error between the actual displayed image quality and the theoretical value, and correct the compensation parameters in the dynamic parameter fusion based on the error value.

2. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 1, characterized in that, The ambient light parameters include ambient light intensity and color temperature; The user interaction parameters include user viewing distance and user preference parameters; The display device status parameters include panel brightness attenuation rate and color gamut offset.

3. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 2, characterized in that, The dynamic parameter fusion includes the following steps: The sensing weights are dynamically adjusted based on the ambient light intensity. Local contrast enhancement rules are triggered based on the user's viewing distance; Obtain video content metadata, and perform weighted calculations of the video content metadata, the display device status parameters, and the user preference parameters.

4. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 3, characterized in that, The step of triggering local contrast enhancement rules based on the user's viewing distance includes: When the user's viewing distance is less than 1.5 meters, the slope of the dark area is increased to 1.3 times the base value; When the user's viewing distance is greater than or equal to 1.5 meters, the global contrast optimization mode is used.

5. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 4, characterized in that, The step of triggering local contrast enhancement rules based on the user's viewing distance further includes, when there are multiple users, the user's viewing distance being either the minimum viewing distance among all users or the average viewing distance among all users.

6. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 3, characterized in that, The step of obtaining video content metadata and weighting the calculation of video content metadata, display device status parameters, and user preference parameters includes: The formula for weighting video content metadata is: Coverage, where MaxCLL is the brightness value of the brightest pixel in the video frame. and Here, N is the preset coefficient, and N is the normalization constant. The weighting formula for the display device status parameters is as follows: ,in The attenuation coefficient; The weights of the user preference parameters are updated using exponential smoothing, and the update formula is as follows: ,in This is a smoothing factor.

7. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 1, characterized in that, The calculation formula for the equipment aging compensation parameters is as follows: ； Where MaxCLL is the brightness value of the brightest pixel in the video frame. This is the measured brightness attenuation value of the panel. This is the initial brightness of the panel. These are aging compensation parameters.

8. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 1, characterized in that, The generation of the adaptive tone mapping curve includes: When the ambient light intensity is greater than 500 lux, set the roll-off starting point of the PQ curve to 75% of the brightness value of the brightest pixel in the video frame; When the ambient light intensity is less than or equal to 500 lux, set the roll-off starting point to 95% of the brightness value of the brightest pixel in the video frame; MaxCLL is the brightness value of the brightest pixel in the video frame.

9. The HDR dynamic tone mapping method based on multimodal perception and dynamic feedback according to claim 1, characterized in that, The threshold determination step for the error includes: When the brightness error is greater than 1.5, the brightness compensation coefficient of the brightest pixel in the video frame is adjusted. When the chromaticity error is greater than 0.003, update the gamut compensation lookup table.

10. An HDR dynamic tone mapping system based on multimodal perception and dynamic feedback, used to execute the HDR dynamic tone mapping method based on multimodal perception and dynamic feedback as described in claims 1 to 8, characterized in that, include: The multimodal sensing module includes an ambient light sensor, a user distance sensor, and a device status sensor, which are used to collect ambient light intensity and color temperature, user viewing distance and user preference parameters, panel brightness decay rate and color gamut shift in real time, respectively. The dynamic parameter fusion module is communicatively connected to the multimodal perception module and is used to dynamically fuse the ambient light parameters, user interaction parameters, display device status parameters and video content metadata to generate dynamic metadata. An adaptive mapping engine, connected to the dynamic parameter fusion module, is used to generate an adaptive tone mapping curve based on the dynamic metadata. The adaptive tone mapping curve adjusts the brightness mapping relationship through device aging compensation parameters. A closed-loop feedback control module, connected to the adaptive mapping engine, is used to measure the error between the actual displayed image quality and the theoretical value, and to correct the compensation parameters in the dynamic parameter fusion based on the error value. The execution module is used to drive the tone mapping processing unit of the display device according to the corrected compensation parameters.

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