Naked-eye 3D screen and 3D image display method

Abstract

This invention relates to the field of 3D image display technology, and more particularly to a naked-eye 3D screen and a 3D image display method. The method includes the following steps: constructing a 3D spatial geometric model of the background-foreground subject in a multimedia video playback image to obtain the background-foreground subject 3D spatial geometric model; quantifying the illusion of light change due to viewpoint movement in the background-foreground subject 3D spatial geometric model to obtain quantized data of the illusion of light change due to viewpoint movement; evaluating the parallax focusing gradient loss based on the quantized data of the illusion of light change due to viewpoint movement to obtain parallax focusing gradient loss data; designing a variable light control mechanism based on the parallax focusing gradient loss data to obtain the variable light control mechanism; and performing logical memory learning on the variable light control mechanism based on a policy gradient algorithm to obtain logical memory data of the variable light control. This invention improves 3D image display technology through optimization.

Description

Technical Field

[0001] This invention relates to the field of 3D image display technology, and in particular to a naked-eye 3D screen and a 3D image display method. Background Technology

[0002] 3D image display technology is an interdisciplinary product of information technology, visual science, and graphics. It aims to transform two-dimensional image data into display content with spatial depth and a three-dimensional feel by simulating how the human eye perceives the real world. This technology stems from humanity's pursuit of a more realistic representation of the natural world. From the invention of perspective in art to the development of modern computer-generated virtual reality (VR) and augmented reality (AR), the emphasis on three-dimensional spatial representation is evident. 3D image display methods not only occupy an important position in the entertainment field (such as film, games, and animation) but are also widely used in medical imaging, industrial design, aerospace, and military simulation, becoming a significant driving force for technological and social development. However, traditional 3D image display methods suffer from low accuracy in analyzing visual fatigue and large errors in control precision. Summary of the Invention

[0003] Therefore, it is necessary to provide a naked-eye 3D screen and a 3D image display method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objective, a 3D image display method is provided, the method comprising the following steps:

[0005] Step S1: Obtain the multimedia video playback image; construct a 3D spatial geometric model of the background-foreground subject from the multimedia video playback image to obtain the 3D spatial geometric model of the background-foreground subject.

[0006] Step S2: Obtain user viewpoint movement data; quantify the illusion of light change based on viewpoint movement in the 3D spatial geometric model of background-foreground subject based on the user viewpoint movement data to obtain quantified data of light change illusion based on viewpoint movement; evaluate the parallax focusing gradient loss based on the quantified data of light change illusion based on viewpoint movement to obtain parallax focusing gradient loss data.

[0007] Step S3: Based on the parallax focusing gradient loss data, visual perception fatigue is assessed to obtain visual perception fatigue data; based on the visual perception fatigue data, a variable light control mechanism is designed for the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain the variable light control mechanism.

[0008] Step S4: Based on the policy gradient algorithm, perform logical memory learning on the optical variable control mechanism to obtain optical variable control logical memory data; design automated control firmware based on the optical variable control logical memory data to obtain optical variable control logical firmware; embed the optical variable control logical firmware into the naked-eye 3D screen image control processor to execute the 3D image display method.

[0009] This invention acquires images from multimedia video playback, utilizes computer vision and deep learning algorithms to extract spatial information of the background and foreground subjects (i.e., main elements, characters, etc.) and performs 3D geometric modeling. This process transforms the image content in the video into a 3D data structure capable of depth analysis and processing, providing a basic geometric framework for subsequent operations such as perspective adjustment and lighting changes, resulting in more accurate and natural visual effects. The system acquires user perspective movement data and analyzes the perspective changes in the constructed 3D spatial geometric model based on this data. By quantifying the illusion of lighting changes caused by perspective movement, the system evaluates the gradients of parallax and focus changes under different perspectives. This data helps the system understand the impact of different perspective switching on user visual perception, especially changes in parallax and focus, and thus assess the resulting discomfort and visual fatigue. Based on parallax and focus gradient loss data, the system further assesses visual perception fatigue by analyzing user visual feedback under different perspectives and detecting discomfort symptoms such as dizziness or eye fatigue. Based on the evaluation results, a variable light control mechanism was designed to dynamically adjust changes in light to reduce visual discomfort caused by parallax, optimize the user's visual experience, and improve the comfort of viewing 3D images. A policy gradient algorithm was used to perform logical memory learning on the variable light control mechanism. By continuously optimizing the control strategy, the system can intelligently adjust image display parameters under different user viewing angles to ensure optimal visual effects. Simultaneously, this control logic was transformed into automated control firmware and embedded in the image control processor of the naked-eye 3D screen. This allows for real-time automatic adjustment of 3D image display parameters without user intervention, thereby improving the stability of visual effects and the comfort of the user experience. Therefore, this invention is an optimization of a traditional 3D image display method, solving the problems of low accuracy in visual fatigue analysis and large control precision errors in traditional 3D image display methods, improving the accuracy of visual fatigue analysis and reducing control precision errors.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire multimedia video playback image;

[0012] Step S12: Perform image edge optimization processing on the multimedia video playback image to obtain video playback image edge optimization data;

[0013] Step S13: Mark the background reference object-foreground subject plane coordinates on the edge optimization data of the video playback image to obtain the background reference object-foreground subject plane coordinates;

[0014] Step S14: Construct a 3D spatial geometric model of the background-foreground subject based on the background reference object-foreground subject plane coordinates of the video playback image edge optimization data, and obtain the 3D spatial geometric model of the background-foreground subject.

[0015] This invention first extracts image data from the video playback source. The core of this step is capturing high-quality image frames to provide raw visual information for subsequent processing. By ensuring image clarity and accuracy, the system can provide precise input data for 3D spatial modeling. This step lays the foundation for subsequent image optimization, spatial modeling, and visual effect improvement, ensuring the system can acquire accurate scene information to handle complex visual tasks. Edge optimization processing is performed on the video playback images. This operation enhances the image's edge details, improving resolution and clarity, especially foreground and background in complex scenes. Image edge optimization helps improve the overall expressiveness and depth of the image, making it more accurate in subsequent spatial modeling and providing higher-quality data support for constructing clear 3D geometric models. The optimized image facilitates subsequent visual effect analysis and adjustment. By analyzing the optimized image and marking the planar coordinates between background references and foreground subjects (i.e., the main elements in the image, characters, etc.), this operation can accurately extract various elements in the scene, especially the key boundaries and references separating the background and foreground. By marking planar coordinates, the system can clearly define the spatial layout of the image, determine the relative positions of various objects, and provide an accurate reference frame for 3D spatial modeling. This process improves the accuracy of subsequent geometric model construction. Based on the marked planar coordinates of the background reference objects and the foreground subject, a 3D spatial geometric model of the background and foreground subject of the video playback image is constructed. By combining the image's depth information and coordinate markings, the system can accurately reconstruct the three-dimensional structure of the scene. This process extends the image's expressiveness beyond a two-dimensional perspective to three-dimensional space, thus providing ample spatial data support for subsequent perspective adjustments, image enhancement, and visual effect optimization.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Acquire user-perspective movement data;

[0018] Step S22: Based on the user's perspective movement data, identify the focus area of ​​the background-foreground subject 3D spatial geometric model from different perspectives to obtain the background-foreground subject perspective focus area data;

[0019] Step S23: Based on the user's perspective movement data, perform perspective movement light change illusion quantification on the background-foreground subject perspective focus area data to obtain perspective movement light change illusion quantification data;

[0020] Step S24: Based on the quantification data of the illusion of light change due to viewpoint movement, evaluate the parallax focusing gradient loss of the background-foreground subject viewpoint focusing area data to obtain the parallax focusing gradient loss data.

[0021] This invention acquires real-time user perspective movement data via sensors, cameras, or user input devices. This data reflects the trajectory and direction changes of the user's eyes or head while watching videos or using a 3D system. Acquiring perspective data is crucial for subsequent steps because it provides dynamic information about the user's interaction with the image, helping the system adapt to the user's viewing habits and precisely adjust the image display to provide a personalized visual experience. Based on the acquired user perspective movement data, the system analyzes and identifies focal areas in the background and foreground subjects. These areas are where the user's visual focus is located and typically include detailed or eye-catching elements. Through this identification, the system can optimize the focal areas under different viewing angles, ensuring these areas remain clear as the user's perspective moves and present an appropriate sense of depth in 3D space. This process helps improve the accuracy of the visual focus, thereby enhancing the user's viewing experience. Based on the user perspective movement data and the focal area data, the illusions caused by changes in light in the image are quantified. As the user's perspective changes, the projection path of light and the lighting effects under that perspective also change, thus affecting visual perception. By quantifying this illusion of changing light, the system can assess and identify visual discomfort under different viewing angles, providing a quantitative basis for subsequent optimization and adjustment. This step ensures that the system can accurately grasp the user's visual feedback and make corresponding adjustments. Based on the quantified data of the illusion of changing light caused by viewing angle movement, the parallax focusing gradient loss in the viewing angle focus area is evaluated. The parallax focusing gradient loss is a key parameter that measures the depth difference and focus effect changes between the background and foreground subject during the user's viewing angle movement. Through this evaluation, the system can detect visual discomfort or focusing problems caused by changes in viewing angle and provide a scientific basis for adjusting image display. The evaluation results help the system optimize the depth perception of the focus area, making the user's visual experience more natural and comfortable under different viewing angles.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: Based on the user's perspective movement data, perform a simulation calculation of the perspective movement offset of the background-foreground subject perspective focus area data to obtain the perspective movement offset of the focus area.

[0024] Step S232: Calculate the horizontal section offset angle of the focal area view movement offset to obtain the horizontal section offset angle data;

[0025] Step S233: Based on the focal area viewpoint movement offset and horizontal section offset angle data, perform light scattering geometric color wheel illusion evaluation on the background-foreground subject viewpoint focal area data to obtain geometric color wheel illusion data;

[0026] Step S234: Based on the geometric color wheel illusion data, perform quantification of the illusion of light change with viewpoint movement on the background-foreground subject viewpoint focus area data to obtain quantified data of the illusion of light change with viewpoint movement.

[0027] This invention simulates and calculates the offset of the focal area within the background-foreground subject based on user perspective movement data. This calculation, through precise analysis of the dynamic changes in the user's perspective, determines the positional offset of the focal area relative to the initial perspective, ensuring the system can track changes in the user's perspective in real time. This process is crucial for subsequent lighting changes and visual effect optimization, as it provides fundamental data for evaluating and correcting image performance under different perspectives. Further analysis of the calculated focal area perspective movement offset yields a horizontal cross-sectional offset angle, which describes the displacement of the focal area along the horizontal plane during user perspective movement. This calculation allows the system to more accurately understand the spatial changes of the focal area, thereby evaluating the image presentation effect under different perspectives. This step ensures the spatial sense and realism of the image, enabling smooth transitions when the user's perspective changes and reducing visual illusions. Based on the focal area perspective movement offset and the horizontal cross-sectional offset angle data, a geometric color wheel illusion assessment is performed on the light scattering effect in the image. The movement of the perspective not only changes the image's focus but also affects the propagation path of light and color representation, creating a visual illusion that impacts the user's perceived depth and detail. By evaluating the geometric color wheel illusion, the system can analyze its impact on visual effects and optimize accordingly, ensuring consistent color and brightness across different viewing angles and enhancing the naturalness of the visual experience. Based on the geometric color wheel illusion data, the system further quantifies the illusion of light changes during viewpoint movement in the background-foreground subject focus area. This quantification process measures the degree of illusion in light, color, and depth perception during viewpoint changes using numerical methods. The system can then fine-tune the image based on this quantified data, optimizing the user's visual experience, ensuring smooth transitions between different viewing angles, and reducing visual fatigue and illusions. Ultimately, this step effectively enhances the immersiveness and realism of the image, providing users with a higher quality visual experience.

[0028] Preferably, step S233 includes the following steps:

[0029] Perform background-foreground subject geometric morphology analysis on the background-foreground subject perspective focus area data to obtain background geometric morphology data and foreground subject geometric morphology data respectively;

[0030] The geometric surface normal vectors of the background geometric shape data and the foreground subject geometric shape data are interleaved to obtain the background geometric shape normal vector interleaving data and the foreground subject geometric shape normal vector interleaving data, respectively.

[0031] The background-foreground-subject light scattering beam interference is calculated by intersecting the background geometric normal vector data and the foreground subject geometric normal vector data to obtain global light scattering beam interference data.

[0032] Spatial dispersion distribution data are obtained by performing spatial dispersion distribution analysis based on global light scattering beam interferometry data;

[0033] Based on the focal region viewpoint movement offset and horizontal section offset angle data, viewpoint offset dispersion anisotropy analysis is performed on the spatial dispersion distribution data to obtain viewpoint offset dispersion anisotropy data.

[0034] Viewpoint shift dispersion overlap analysis was performed on the viewpoint shift dispersion anisotropy data to obtain viewpoint shift dispersion overlap data;

[0035] The geometric color wheel illusion is evaluated based on the viewpoint shift dispersion anisotropy data and viewpoint shift dispersion overlap data, and the geometric color wheel illusion data is obtained.

[0036] This invention first performs geometric morphological analysis on the focal area data of the background and foreground subject, extracting background and foreground geometric morphological data separately. This analysis provides the foundation for subsequent light scattering and illusion calculations, as different geometric shapes have different effects on the reflection, refraction, and scattering of light. By processing the background and foreground geometric shapes separately, the system can precisely capture their respective structural features and provide more accurate geometric basis data for the calculation of light interaction. This step ensures the accurate representation of the image under different viewpoints, laying a solid foundation for the evaluation of visual illusions. The geometric surface normal vectors of the background and foreground subject geometric morphological data are then interleaved, yielding background geometric normal vector interleaving data and foreground subject geometric normal vector interleaving data, respectively. Geometric surface normal vectors are key data used to describe the direction and normal direction of each point on a three-dimensional surface, affecting the reflection and refraction behavior of light. The interleaving calculation simulates the light interaction between the background and foreground under different viewpoints, reflecting the impact of viewpoint changes on visual effects. This step, by precisely calculating the intersection of normal vectors on different object surfaces, provides accurate geometric information for subsequent light scattering and geometric illusion evaluation, ensuring high-quality visual presentation. Light scattering and beam splitting interference calculations are performed on the background geometric normal vector intersection data and the foreground subject geometric normal vector intersection data to obtain global light scattering and beam splitting interference data. Light undergoes scattering and interference effects when passing through different surfaces, angles, and media, which affect visual color distribution, brightness changes, and depth perception. Global light scattering and beam splitting interference calculations not only consider the light changes of a single object but also simulate the complex light interaction process between the background and foreground, enabling a more realistic reproduction of visual perception from the user's perspective, reducing illusions and visual discomfort. This step provides data support for optimizing light scattering effects, making image color and brightness transitions more natural and smooth under dynamic viewing angles. Based on the global light scattering and beam splitting interference data, spatial dispersion distribution analysis is performed to obtain spatial dispersion distribution data. Dispersion is the color separation phenomenon caused by the wavelength and frequency of light when passing through different media. In 3D images and virtual reality, dispersion affects image clarity, sharpness, and depth perception. By analyzing the spatial distribution of global light scattering and interference effects, the system can identify chromatic aberration characteristics under different viewing angles and lighting conditions, thereby adjusting the color distribution in the image to make the colors more balanced and natural. This step effectively improves the accuracy and immersion of the visual effect and reduces visual fatigue. Based on the viewing angle shift and horizontal cross-section offset data of the focused area, viewing angle shift dispersion anisotropy analysis is performed on the spatial dispersion distribution data to obtain viewing angle shift dispersion anisotropy data. As the viewing angle changes, the dispersion phenomenon in the image will change. Different angles cause color shifts, brightness changes, and other problems, affecting the naturalness of the visual experience.By analyzing dispersion anisotropy, the system can identify the different effects of dispersion on visual effects from different viewing angles, and then take corresponding adjustment measures to ensure that the color and brightness of the image remain consistent when the viewing angle changes. This step provides a precise basis for subsequent image optimization and viewing angle adjustment. Viewing angle displacement dispersion overlap analysis is performed on the viewing angle displacement dispersion anisotropy data to obtain viewing angle displacement dispersion overlap data. This analysis further optimizes the visual effect of the image by evaluating the overlap of different color and brightness ranges when the viewing angle changes. Through this analysis, the system can identify visual errors caused by dispersion overlap and take measures to adjust the transition of color and brightness, so that the dispersion effect does not produce abrupt visual effects when the user's viewing angle changes, ensuring that the user can smoothly and naturally transition to the new viewing angle. This step improves visual quality while helping to reduce light distortion or color deviation. Combining the viewing angle displacement dispersion anisotropy data and the viewing angle displacement dispersion overlap data, a geometric color wheel illusion assessment of light scattering is performed to obtain geometric color wheel illusion data. Geometric color wheel illusion refers to visual illusions of color and geometric structure caused by light scattering, dispersion, and changes in viewing angle. This assessment, by comprehensively analyzing the impact of viewing angle changes on color, light, and depth, helps the system identify which areas produce unrealistic visual effects and correct these areas. Through this assessment, the system can provide more consistent and realistic visual effects under different viewing angles, avoiding visual distortion or discomfort, and ultimately providing users with a more immersive and natural visual experience.

[0037] Preferably, step S24 includes the following steps:

[0038] Step S241: Perform parallax depth level analysis on the background-foreground subject viewpoint focal area data based on the quantification data of the illusion of light change with viewpoint movement, and obtain the parallax depth level data of the focal area.

[0039] Step S242: Calculate the average depth difference between layers of the parallax depth layer data of the focused area to obtain the average parallax depth difference of the region layer.

[0040] Step S243: Based on the regional hierarchical parallax depth difference, the quantification data of the illusion of light change due to viewpoint movement is used to evaluate the sharpness loss of the focal point imaging, and the sharpness loss data of the focal point imaging is obtained.

[0041] Step S244: Based on the focal point imaging sharpness loss data and the regional hierarchical parallax depth average difference, perform parallax focusing gradient loss assessment to obtain parallax focusing gradient loss data.

[0042] This invention analyzes the parallax depth hierarchy of the focal region from both the background and foreground perspectives. This allows for precise quantification of the optical illusion effect of viewpoint movement on light changes, providing more accurate depth hierarchy data for subsequent image quality analysis. This analysis effectively reveals the parallax differences between the background and foreground at different viewpoints, thus providing a scientific basis for focal point adjustment and image sharpness optimization. By calculating the average depth difference of the parallax depth hierarchy data in the focal region, the differences between different depth levels within the region can be quantified, thereby assessing the overall parallax uniformity. This calculation helps identify the non-uniformity of depth hierarchy changes during imaging, thus supporting the optimization of image focal length and visual effects, and reducing visual discomfort caused by parallax non-uniformity. After obtaining the average parallax depth difference at the regional level, further assessment of focal point image sharpness loss based on the optical illusion of light changes due to viewpoint movement is performed. This quantifies the loss of image sharpness at the focal point. This process enables the technology to accurately assess the degree of image sharpness loss under different focal length conditions, providing a quantitative basis for improving focal point sharpness and visual effects, thereby enhancing image quality. Based on focal point imaging sharpness loss data and regional hierarchical parallax depth difference, parallax focusing gradient loss assessment can be performed to more comprehensively evaluate the imaging quality loss under complex perspectives. This assessment helps to further optimize image focal length focusing and depth adjustment, improve the accuracy of the imaging system, and especially when it is necessary to process complex background and foreground layers, it can effectively reduce visual distortion and blurring in imaging, thereby improving the overall visual experience.

[0043] Preferably, step S3 includes the following steps:

[0044] Step S31: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, perform spatial deformation analysis of the focusing region on the background-foreground subject 3D spatial geometric model to obtain the spatial deformation data of the focusing region;

[0045] Step S32: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, the visual perception fatigue data is evaluated on the spatial deformation data of the focusing area to obtain the visual perception fatigue data.

[0046] Step S33: Based on visual perception fatigue data, design a variable light control mechanism by quantifying the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data, and obtain the variable light control mechanism.

[0047] This invention first performs a detailed analysis of the 3D spatial geometry models of the background and foreground using quantified data on the illusion of light changes due to viewpoint movement and parallax focusing gradient loss data. This analysis identifies the spatial deformation of the focusing area, helping to accurately depict the spatial changes of objects and scenes within the focusing area, thus providing data support for subsequent visual effect optimization. This step effectively assesses the degree of distortion and misalignment of spatial geometry under different viewpoints, providing an important basis for optimizing display effects and visual perception. Further analysis of the spatial deformation data in the focusing area is conducted by combining the quantified data on the illusion of light changes due to viewpoint movement and parallax focusing gradient loss data to assess the resulting visual fatigue. By quantifying the degree of visual fatigue, it is possible to identify fatigue problems experienced by users under specific viewpoints or display settings. This assessment provides important feedback for subsequent control mechanisms, ensuring that the technical solution can proactively adjust to reduce visual fatigue, thereby improving visual comfort and user experience. Based on the visual fatigue data obtained in the first two steps, a variable light control mechanism is designed in this step to adjust light changes and viewing angle settings. This mechanism can automatically adjust the display light source during the visual experience, optimize visual perception, reduce visual fatigue, and achieve a more comfortable visual effect by controlling viewing angle and light changes. This design can automatically adjust parameters according to different fatigue feedback, thereby providing optimal visual comfort in different usage scenarios and ensuring that users do not feel uncomfortable during prolonged use.

[0048] Preferably, step S33 includes the following steps:

[0049] Step S331: Based on visual perception fatigue data, perform focus drift effect analysis on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain focus drift effect data;

[0050] Step S332: Perform color temperature-brightness coupling adjustment analysis on the focus drift effect data to obtain color temperature-brightness coupling adjustment data;

[0051] Step S333: Based on the color temperature-brightness coupling adjustment data, perform local light field rendering deviation correction on the quantization data of the illusion of light change with viewpoint movement to obtain local light field rendering deviation correction data;

[0052] Step S334: Perform focus spatial light correction on the focus drift effect data based on the local light field rendering deviation correction data to obtain focus spatial light correction data;

[0053] Step S335: Design a variable light control mechanism based on the local light field rendering deviation correction data and the focal space light correction data to obtain the variable light control mechanism.

[0054] This invention quantifies the focus drift effect based on visual perception fatigue data by analyzing data on the illusion of light changes with viewing angle movement and parallax focusing gradient loss data. Focus drift refers to the shift in the focal position under different viewing angles and lighting conditions, leading to image blurring or distortion. Precise analysis of the focus drift effect identifies the conditions under which focus drift occurs, providing data support for subsequent adjustment and optimization, helping to reduce visual fatigue and improve image clarity and stability. The analysis of focus drift effect data leads to a coupling adjustment stage between color temperature and brightness. In this step, considering the characteristics of the focus drift effect, color temperature and brightness are optimized to reduce visual discomfort and fatigue by adjusting the relationship between the color temperature and brightness of the light source. Reasonable coupling of color temperature and brightness improves overall image comfort, ensuring a balance of visual effects under different display conditions, allowing users to maintain high visual comfort during prolonged use of display devices and reducing fatigue caused by light changes. Based on the color temperature-brightness coupling adjustment data, local light field rendering deviations are corrected for the quantified data on the illusion of light changes with viewing angle movement. Local light field rendering deviation refers to the phenomenon of inaccurate or inconsistent light field rendering in local areas under different lighting conditions and viewing angles. Precise correction can effectively avoid problems such as image blurring and color inconsistency caused by light field deviation. This step optimizes light field rendering, enhances the visual consistency and clarity of the image, and improves the user's visual experience. Further spatial light correction is performed on the focus drift effect data using local light field rendering deviation correction data. Focus spatial light correction mainly adjusts the direction and focal point of light to ensure accurate and clear focus in space. This correction step aims to eliminate visual discomfort and image distortion caused by focus drift or light field deviation, enabling the display device to maintain clear and stable imaging effects under different viewing angles and environments, further improving visual comfort and image quality. Finally, combining local light field rendering deviation correction data and focus spatial light correction data, this step designs a variable light control mechanism. This mechanism can adjust light and display parameters in real time under dynamic environments to cope with problems such as different viewing angles, light changes, and focus drift. By intelligently controlling light and focus, it ensures that the display device maintains optimal visual effects during prolonged use, thereby reducing visual fatigue and improving the overall visual experience. This mechanism provides users with a more comfortable and personalized audio-visual experience while optimizing the device's display performance.

[0055] Preferably, step S334 includes the following steps:

[0056] Based on the local light field rendering deviation correction data, the difference in light field intensity distribution between different focus drift directions is evaluated to obtain light field intensity distribution difference data.

[0057] The light field intensity distribution difference data is decomposed into light field offset vectors between different focus drift directions to obtain the light field offset vectors;

[0058] Based on the optical field offset vector, the focus drift effect data is reconstructed by performing focus error vector reconstruction to obtain focus error vector reconstructed data;

[0059] Spatial light scattering interferometry analysis was performed based on focal error vector reconstruction data and local light field rendering deviation correction data to obtain focal light scattering interferometry data;

[0060] Based on the focal light scattering interferometry data and the focal error vector reconstruction data, the focal drift effect data is corrected by focal spatial light correction to obtain focal spatial light correction data.

[0061] This invention, based on local light field rendering deviation correction data, evaluates the differences in light field intensity distribution between different focus drift directions in focus drift effect data. This evaluation aims to quantify the changes in light field intensity caused by focus drift in different directions, thereby revealing the non-uniformity of light field distribution due to viewing angle changes or focus drift. By evaluating the differences in light field intensity, key information can be provided for subsequent optical correction, ensuring the stability of the focus position and the consistency of image quality, and reducing visual discomfort and blurring effects. The light field intensity distribution difference data is decomposed to obtain the light field offset vector between different focus drift directions. The light field offset vector represents the direction and magnitude of light ray displacement in the light field due to focus drift or viewing angle changes. Through precise decomposition of the offset vector, the specific situation of light field displacement in different directions can be fully understood, providing necessary numerical basis for the next step of error vector reconstruction and spatial light correction. This analysis can help optimize the optical performance of display devices in various environments and improve the stability of visual effects. Based on the light field offset vector, focus error vector reconstruction is performed to obtain focus error vector reconstruction data. The purpose of focus error vector reconstruction is to more accurately restore the error sources in the focus drift effect, obtaining more refined focus position data. This data reveals optical errors caused by focus drift or light field shift, providing a basis for subsequent fine-tuning. Through this process, the focus position can be adjusted in a targeted manner to ensure the accuracy and stability of the display effect. Based on the focus error vector reconstruction data and local light field rendering deviation correction data, spatial light scattering interferometry analysis is performed. This analysis simulates and evaluates the interference effect in the light field by considering the impact of focus error on light scattering. Interference phenomena can cause optical distortion in the image, affecting visual quality. Through spatial light scattering interferometry analysis, the control of the light field can be further refined, accurately identifying and correcting image problems caused by light scattering or interference, laying the foundation for focus spatial light correction and reducing visual interference. Finally, based on the focus light scattering interferometry data and focus error vector reconstruction data, focus spatial light correction is performed to obtain focus spatial light correction data. Through this correction process, the focus position and light field distribution are precisely adjusted to ensure that the focus is always in the optimal position under different viewing angles and display conditions, avoiding blurring or visual fatigue. Spatial light correction effectively eliminates inconsistencies and errors in the light field, providing a clear and stable image display. Ultimately, this step optimizes the entire display process, enhancing user visual comfort and viewing experience.

[0062] Preferably, a glasses-free 3D screen is also provided, including a glasses-free 3D screen image control processor for executing the 3D image display method described above, the glasses-free 3D screen comprising:

[0063] The 3D spatial geometry model construction module is used to acquire multimedia video playback images; it constructs a background-foreground subject 3D spatial geometry model from the multimedia video playback images to obtain the background-foreground subject 3D spatial geometry model.

[0064] The light change illusion analysis module is used to acquire user viewpoint movement data; based on the user viewpoint movement data, the background-foreground subject 3D spatial geometric model is subjected to viewpoint movement light change illusion quantification to obtain viewpoint movement light change illusion quantification data; based on the viewpoint movement light change illusion quantification data, the parallax focusing gradient loss is evaluated to obtain parallax focusing gradient loss data.

[0065] The optical variable control mechanism design module is used to evaluate visual perception fatigue based on parallax focusing gradient loss data to obtain visual perception fatigue data; based on the visual perception fatigue data, the optical variable control mechanism is designed to quantify the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data to obtain the optical variable control mechanism.

[0066] The firmware design module is used to learn the logical memory of the optical variable control mechanism based on the policy gradient algorithm to obtain the optical variable control logical memory data; based on the optical variable control logical memory data, the automatic control firmware is designed to obtain the optical variable control logic firmware; the optical variable control logic firmware is embedded into the naked-eye 3D screen image control processor to execute the 3D image display method.

[0067] The beneficial effects of this invention are:

[0068] By acquiring images from multimedia video playback, the system uses computer vision and deep learning algorithms to extract spatial information of the background and foreground subjects (i.e., the main elements, characters, etc.) and performs 3D geometric modeling. This process transforms the image content in the video into a 3D data structure that can be deeply analyzed and processed, providing a basic geometric framework for subsequent operations such as perspective adjustment and lighting changes, resulting in more accurate and natural visual effects. The system acquires user perspective movement data and analyzes the perspective changes in the constructed 3D spatial geometric model based on this data. By quantifying the illusion of lighting changes caused by perspective movement, the system evaluates the gradients of parallax and focus changes under different perspectives. This data helps the system understand the impact of different perspective switching on user visual perception, especially changes in parallax and focus, and thus assess the resulting discomfort and visual fatigue. Based on parallax and focus gradient loss data, the system further assesses visual perception fatigue by analyzing user visual feedback under different perspectives and detecting discomfort symptoms such as dizziness or eye fatigue. Based on the evaluation results, a variable light control mechanism was designed to dynamically adjust changes in light to reduce visual discomfort caused by parallax, optimize the user's visual experience, and improve the comfort of viewing 3D images. A policy gradient algorithm was used to perform logical memory learning on the variable light control mechanism. By continuously optimizing the control strategy, the system can intelligently adjust image display parameters under different user viewing angles to ensure optimal visual effects. Simultaneously, this control logic was transformed into automated control firmware and embedded in the image control processor of the naked-eye 3D screen. This allows for real-time automatic adjustment of 3D image display parameters without user intervention, thereby improving the stability of visual effects and the comfort of the user experience. Therefore, this invention is an optimization of a traditional 3D image display method, solving the problems of low accuracy in visual fatigue analysis and large control precision errors in traditional 3D image display methods, improving the accuracy of visual fatigue analysis and reducing control precision errors. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the steps of a 3D image display method.

[0070] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0071] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0072] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0074] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0075] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0076] To achieve the above objectives, please refer to Figures 1 to 3 A 3D image display method, the method comprising the following steps:

[0077] Step S1: Obtain the multimedia video playback image; construct a 3D spatial geometric model of the background-foreground subject from the multimedia video playback image to obtain the 3D spatial geometric model of the background-foreground subject.

[0078] Step S2: Obtain user viewpoint movement data; quantify the illusion of light change based on viewpoint movement in the 3D spatial geometric model of background-foreground subject based on the user viewpoint movement data to obtain quantified data of light change illusion based on viewpoint movement; evaluate the parallax focusing gradient loss based on the quantified data of light change illusion based on viewpoint movement to obtain parallax focusing gradient loss data.

[0079] Step S3: Based on the parallax focusing gradient loss data, visual perception fatigue is assessed to obtain visual perception fatigue data; based on the visual perception fatigue data, a variable light control mechanism is designed for the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain the variable light control mechanism.

[0080] Step S4: Based on the policy gradient algorithm, perform logical memory learning on the optical variable control mechanism to obtain optical variable control logical memory data; design automated control firmware based on the optical variable control logical memory data to obtain optical variable control logical firmware; embed the optical variable control logical firmware into the naked-eye 3D screen image control processor to execute the 3D image display method.

[0081] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of a 3D image display method according to the present invention. In this example, the 3D image display method includes the following steps:

[0082] Step S1: Obtain the multimedia video playback image; construct a 3D spatial geometric model of the background-foreground subject from the multimedia video playback image to obtain the 3D spatial geometric model of the background-foreground subject.

[0083] In this embodiment of the invention, when extracting frame data from the video stream, a physical interface is first used to connect to the video signal processing module to obtain high-frame-rate frame-by-frame image information from the video source, ensuring the temporal integrity and resolution accuracy of the extracted images. Image segmentation algorithms, such as active contour-based segmentation methods, are used to perform pixel-level analysis of the video frames, separating dynamic objects from static background regions. The segmentation results are corrected for discontinuous boundaries using morphological operations to ensure segmentation quality. Using point cloud reconstruction technology and multi-view projection, multiple frames are combined with an intrinsically corrected camera model, and a spatial depth map is generated using a triangulation algorithm. Utilizing the depth information, 3D geometric models are constructed for the segmented foreground and background regions using voxel modeling, refining the spatial shape. During model optimization, Laplacian smoothing is used to eliminate surface noise during reconstruction, and normal vector resampling enhances surface smoothness and realism, generating a 3D spatial geometric model of the background-foreground subject.

[0084] Step S2: Obtain user viewpoint movement data; quantify the illusion of light change based on viewpoint movement in the 3D spatial geometric model of background-foreground subject based on the user viewpoint movement data to obtain quantified data of light change illusion based on viewpoint movement; evaluate the parallax focusing gradient loss based on the quantified data of light change illusion based on viewpoint movement to obtain parallax focusing gradient loss data.

[0085] In this embodiment of the invention, embedded sensors (such as IMU units or eye trackers) are used to capture the motion data of the user's head or eyes in real time. The inertial parameters recorded by the sensors include angular velocity and linear acceleration. These parameters are combined with attitude calculation algorithms (such as quaternion attitude calculation) to dynamically reconstruct the viewpoint movement trajectory, generating three-dimensional viewpoint movement data. Based on the user's viewpoint data, changes in light are modeled as dynamic inputs to a ray tracing algorithm. This algorithm, based on the light source and the surface normal vector of the object, simulates the refraction, reflection, and shadow distribution of light, calculating changes in light distribution in real time. Furthermore, an illusion assessment quantification model is used to mathematically model the spatial illusions caused by changes in light. Illusion parameters include variables such as the light source offset angle, light intensity gradient, and gaze focus error. The impact of changes in light on viewpoint perception is comprehensively evaluated using the error propagation method, resulting in quantified data of viewpoint movement light change illusions. Subsequently, based on the above quantified data, a parallax assessment function is used to analyze the depth misalignment of the user's gaze focus. The gradient descent method is used to calculate the cumulative focus error at different parallax positions, generating parallax focus gradient loss data.

[0086] Step S3: Based on the parallax focusing gradient loss data, visual perception fatigue is assessed to obtain visual perception fatigue data; based on the visual perception fatigue data, a variable light control mechanism is designed for the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain the variable light control mechanism.

[0087] In this embodiment of the invention, based on parallax focusing gradient loss data, a visual fatigue assessment framework is adopted. The ratio of cumulative parallax error to the number of frequent focusing adjustments is calculated to assess changes in the user's visual load. Fourier transform analysis is used to analyze the spectral distribution of parallax fluctuations, extracting high-frequency parallax components and calculating a cumulative fatigue index. Combining the visual fatigue assessment results, a variable light control mechanism is designed using a parameter selection method based on optimization algorithms (such as multi-objective particle swarm optimization). This mechanism comprises two parts: first, adjusting the brightness gradient of the display light source; and second, adjusting the directional distribution of reflected light. Numerical integration techniques are used to simulate the light source intensity distribution, generating a dynamic light source adjustment function. The direction field of reflected light is calculated using geometric optics principles, and a light distribution control function is generated by combining a gradient change strategy, thus completing the design of the variable light control mechanism.

[0088] Step S4: Based on the policy gradient algorithm, perform logical memory learning on the optical variable control mechanism to obtain optical variable control logical memory data; design automated control firmware based on the optical variable control logical memory data to obtain optical variable control logical firmware; embed the optical variable control logical firmware into the naked-eye 3D screen image control processor to execute the 3D image display method.

[0089] In this embodiment of the invention, the parameter optimization of the light-variable control mechanism is trained using a policy gradient algorithm. This algorithm takes light source control parameters and user feedback data (such as visual fatigue change indicators) as input, and optimizes the performance of the control strategy through policy iteration and gradient ascent. Specifically, light source brightness and reflection direction are the main optimization variables. The policy parameters are iteratively updated until convergence to the optimal state, generating light-variable control logic memory data. This logic memory data is integrated into a hardware description language (HDL) for developing embedded control firmware. The firmware development process includes: generating dynamic control logic based on the control strategy; implementing the logic circuit in the naked-eye 3D screen image control processor using FPGA programming technology; embedding the firmware into the controller through a physical interface; and performing circuit timing simulation tests to verify the firmware's response speed and stability. Finally, the design and embedding of the light-variable control logic firmware are completed, achieving automated control of 3D image display.

[0090] Preferably, step S1 includes the following steps:

[0091] Step S11: Acquire multimedia video playback image;

[0092] Step S12: Perform image edge optimization processing on the multimedia video playback image to obtain video playback image edge optimization data;

[0093] Step S13: Mark the background reference object-foreground subject plane coordinates on the edge optimization data of the video playback image to obtain the background reference object-foreground subject plane coordinates;

[0094] Step S14: Construct a 3D spatial geometric model of the background-foreground subject based on the background reference object-foreground subject plane coordinates of the video playback image edge optimization data, and obtain the 3D spatial geometric model of the background-foreground subject.

[0095] In this embodiment of the invention, the operation of extracting video playback images from a multimedia data source requires first connecting to a physical interface, such as an HDMI input signal channel or a high-bandwidth digital video interface. When acquiring the video stream, decoder hardware is used to parse the input video signal and convert it into a frame sequence. The extraction of the frame sequence should maintain the integrity of the timeline order, and the image resolution should be verified to ensure it meets the requirements of subsequent processing. A video compression format based on DCT (Discrete Cosine Transform) is used, and high-fidelity video frame images are restored using inverse vector quantization. After extraction, the frame sequence is stored in high-performance memory for subsequent processing. For the extracted video playback images, edge optimization processing is applied to enhance the clarity of the image contours. First, the Canny edge detection algorithm is used to extract preliminary edge information of the image, obtaining contour points by calculating the pixel gradient of the image. Then, morphological dilation and erosion operations are used to repair the breaks in the edge lines, making the edge information more continuous. To reduce noise interference, bilateral filtering is performed on the image to simultaneously preserve edge information and remove noise from flat areas. Furthermore, to enhance edge details, the Laplacian of Gauss (LoG) operator can be used for secondary edge detection. The processed edge optimization data of the video playback image is stored in a buffer as input for subsequent labeling operations. Using the edge optimization data of the video playback image, an image segmentation algorithm is used to initially separate the background reference object from the foreground subject. Image segmentation employs the region growing method, starting from selected seed pixels and recursively labeling the region range based on the gray-level similarity of adjacent pixels. After segmentation, a centroid calculation algorithm is used to obtain the geometric center coordinates of the foreground subject and the background reference object. Then, for the background region, a point cloud registration algorithm (such as the ICP algorithm) is used to generate its two-dimensional planar coordinate description; for the foreground region, pixel projection technology is used to determine its coordinate boundaries on the video image plane. To ensure the accuracy of the labeling results, a shape matching algorithm is introduced during the labeling process to compare and calibrate the actual boundary contour with a standard contour template, ultimately generating the background reference object-foreground subject planar coordinates, where the foreground subject refers to the visual focus displayed by the dynamic figure or image; based on the background reference object-foreground subject planar coordinates, a triangulation algorithm is used to convert the planar coordinates into three-dimensional coordinates. The specific operation involves performing stereo matching on corresponding points of each pair of images, calculating the depth difference between point pairs using multi-view geometry principles, and generating preliminary 3D point cloud data. After generating the point cloud, a 3D surface mesh structure is constructed using the Delaunay triangulation algorithm. After the mesh is constructed, the model is refined using voxel segmentation to eliminate outliers caused by uneven depth. Furthermore, surface smoothness and model integrity are optimized through normal vector resampling and surface smoothing, ultimately obtaining a 3D spatial geometric model of the background-foreground subject, which is then stored in a spatial geometry database for subsequent use.

[0096] Preferably, step S2 includes the following steps:

[0097] Step S21: Acquire user-perspective movement data;

[0098] Step S22: Based on the user's perspective movement data, identify the focus area of ​​the background-foreground subject 3D spatial geometric model from different perspectives to obtain the background-foreground subject perspective focus area data;

[0099] Step S23: Based on the user's perspective movement data, perform perspective movement light change illusion quantification on the background-foreground subject perspective focus area data to obtain perspective movement light change illusion quantification data;

[0100] Step S24: Based on the quantification data of the illusion of light change due to viewpoint movement, evaluate the parallax focusing gradient loss of the background-foreground subject viewpoint focusing area data to obtain the parallax focusing gradient loss data.

[0101] As an example of the present invention, reference is made to... Figure 2 As shown, in this example, step S2 includes:

[0102] Step S21: Acquire user-perspective movement data;

[0103] In this embodiment of the invention, the acquisition of user viewpoint movement data requires a combination of sensor hardware and data acquisition algorithms. First, an infrared eye-tracking sensor and a high-precision accelerometer are embedded in the naked-eye 3D display device to capture the user's gaze point position in real time using eye-tracking technology. A Kalman filter algorithm is used to dynamically predict and suppress noise in the captured data, ensuring the accuracy of the gaze trajectory. Second, the tilt angle and rotation direction of the user's head are obtained by combining the accelerometer data, and these data are converted into a rotation matrix representation in three-dimensional space using the Euler angle formula. By combining the gaze point and head rotation angle, dynamic viewpoint matrix data of the user is generated. To ensure data temporal synchronization, timestamps are introduced to uniformly mark all data, and linear interpolation is used to compensate for viewpoint movement data in interval frames, ultimately generating complete user viewpoint movement data.

[0104] Step S22: Based on the user's perspective movement data, identify the focus area of ​​the background-foreground subject 3D spatial geometric model from different perspectives to obtain the background-foreground subject perspective focus area data;

[0105] In this embodiment of the invention, user perspective movement data is used to dynamically analyze the 3D spatial geometric model of the background-foreground subject, and a multi-view ray projection method is employed to identify the focal region under different perspectives. Specifically, the viewpoint position is first determined based on the user's perspective matrix, and the 3D spatial geometric model of the background-foreground subject is projected onto the user's viewing plane to generate a real-time two-dimensional projection image. Next, a focus detection algorithm is used to segment the image region on the viewing plane. During this process, a focus calculation method based on Gaussian blur weights is used to identify the main areas of interest in the user's line of sight. After identifying the focal region, combined with depth data from the 3D geometric model, a Z-buffer depth test algorithm is used to filter the 3D coordinate range of the focal region in the field of view, ultimately generating the background-foreground subject perspective focal region data.

[0106] Step S23: Based on the user's perspective movement data, perform perspective movement light change illusion quantification on the background-foreground subject perspective focus area data to obtain perspective movement light change illusion quantification data;

[0107] In this embodiment of the invention, based on user viewpoint movement data and background-foreground subject viewpoint focusing area data, a ray tracing algorithm is used to simulate and analyze the changes in light caused by viewpoint movement. First, a light refraction and reflection model based on Fresnel's equations is constructed to simulate the propagation path of light from the background-foreground subject surface to the user's viewpoint. During the simulation, the interference effect of different surface materials on the light propagation path is calculated, and the path deviation is quantified using Monte Carlo random sampling. An illusion quantification index is introduced, calculating the illusion intensity by the ratio of the line-of-sight movement speed to the light path offset angle. Combining the frequency analysis data of user viewpoint changes, the frequency domain characteristics of the light illusion changes are further decomposed and reconstructed using Discrete Fourier Transform (DFT), ultimately generating quantified data of the illusion of light changes due to viewpoint movement.

[0108] Step S24: Based on the quantification data of the illusion of light change due to viewpoint movement, evaluate the parallax focusing gradient loss of the background-foreground subject viewpoint focusing area data to obtain the parallax focusing gradient loss data.

[0109] In this embodiment of the invention, disparity focusing gradient loss is evaluated using disparity gradient analysis, which utilizes quantified data of optical illusion changes during viewpoint movement and background-foreground subject viewpoint focusing region data. First, the disparity value change of each focusing region during viewpoint movement is obtained using the binocular disparity calculation formula, and the three-dimensional coordinate difference between the two viewpoint focusing points is recorded. Then, gradient direction and amplitude analysis is used to evaluate the spatial distribution gradient of the disparity values. The rate of change and fluctuation amplitude of disparity between different focusing regions are calculated using a gradient loss function (such as Laplacian gradient loss). To improve the stability of the evaluation, a Bayesian optimization algorithm is introduced to dynamically adjust the parameters during the evaluation process, eliminating the influence of outliers on the loss calculation. Finally, all disparity evaluation results are integrated to generate disparity focusing gradient loss data, providing data support for the subsequent design of optical control mechanisms.

[0110] Preferably, step S23 includes the following steps:

[0111] Step S231: Based on the user's perspective movement data, perform a simulation calculation of the perspective movement offset of the background-foreground subject perspective focus area data to obtain the perspective movement offset of the focus area.

[0112] Step S232: Calculate the horizontal section offset angle of the focal area view movement offset to obtain the horizontal section offset angle data;

[0113] Step S233: Based on the focal area viewpoint movement offset and horizontal section offset angle data, perform light scattering geometric color wheel illusion evaluation on the background-foreground subject viewpoint focal area data to obtain geometric color wheel illusion data;

[0114] Step S234: Based on the geometric color wheel illusion data, perform quantification of the illusion of light change with viewpoint movement on the background-foreground subject viewpoint focus area data to obtain quantified data of the illusion of light change with viewpoint movement.

[0115] In this embodiment of the invention, the offset of the focal area caused by the viewpoint movement is calculated by combining user viewpoint movement data and background-foreground subject viewpoint focus area data. First, by extracting the time-series information of the user viewpoint matrix, the dynamic changes in the viewpoint are analyzed as a combination of translation vectors and rotation matrices. Then, the background-foreground subject viewpoint focus area data is mapped to a new viewpoint coordinate system using a homogeneous coordinate transformation formula, thereby calculating the three-dimensional coordinate difference of each focal point before and after the viewpoint change. During the calculation process, an interpolation algorithm is introduced to smooth the viewpoint movement trajectory, eliminating discrete jumps in the data and ensuring the continuity and accuracy of the calculation results. By integrating the offset vector of the viewpoint change in each frame over time, the cumulative effect of the overall viewpoint offset is simulated, generating the viewpoint movement offset of the focal area. Using the viewpoint movement offset data of the focal area, the offset angle on the horizontal section is calculated. Specifically, the viewpoint offset is first decomposed into horizontal and vertical components, and all three-dimensional coordinate data are mapped onto the horizontal plane using a projection method. The angle between the offset vector and the reference vector (such as the initial direction vector of the viewpoint) is calculated using the law of cosines. By traversing the data at each time point, the temporal changes of the angle are recorded. Data smoothing algorithms, such as the Savitzky-Golay filter, are used to denoise and optimize the angle calculation results, ultimately generating horizontal cross-sectional offset angle data. Combining the viewpoint movement offset of the focused area and the horizontal cross-sectional offset angle data, a geometric color wheel illusion of light scattering is evaluated in the background-foreground subject viewpoint focused area. First, based on geometric optics theory, the changes in the light propagation path caused by changes in viewpoint are simulated. A reverse ray tracing algorithm is used to dynamically locate the starting point, refraction point, reflection point, and ending point of each ray, obtaining the scattering trajectory of each ray. Then, the light scattering results are mapped onto the CIE color wheel model. Based on the light path offset and angle changes, the mixing effect of different wavelengths of light is calculated, deriving the color distribution change law of visual perception. The periodic characteristics of color changes are analyzed using Discrete Fourier Transform (DFT), quantifying the intensity of the geometric color wheel illusion. During the analysis, a weighting function was introduced to normalize the scattering path length and light intensity, ensuring the uniformity and objectivity of the evaluation results. Finally, geometric color wheel illusion data was output. Using the geometric color wheel illusion data and background-foreground subject viewpoint focusing area data, a quantitative analysis of the illusion of light changes with viewpoint movement was performed. First, by establishing a correlation matrix between light changes and user viewpoint movement, the intensity of the geometric color wheel illusion was mapped to the viewpoint change frequency of different focusing areas. A least-squares fitting algorithm was used to fit the data, extracting the dominant patterns and trends of light changes. Next, through polarization angle change analysis and optical distortion coefficient calculation, the light changes were quantified into specific illusion indicators. Time-domain signal analysis tools (such as wavelet transform) were introduced to perform time-frequency decomposition of the light change signal, capturing the illusion peak regions during rapid viewpoint movement.Based on these analytical results, quantitative data on the illusion of light changes with perspective movement are generated to provide a quantitative basis for subsequent control design.

[0116] Preferably, step S233 includes the following steps:

[0117] Perform background-foreground subject geometric morphology analysis on the background-foreground subject perspective focus area data to obtain background geometric morphology data and foreground subject geometric morphology data respectively;

[0118] The geometric surface normal vectors of the background geometric shape data and the foreground subject geometric shape data are interleaved to obtain the background geometric shape normal vector interleaving data and the foreground subject geometric shape normal vector interleaving data, respectively.

[0119] The background-foreground-subject light scattering beam interference is calculated by intersecting the background geometric normal vector data and the foreground subject geometric normal vector data to obtain global light scattering beam interference data.

[0120] Spatial dispersion distribution data are obtained by performing spatial dispersion distribution analysis based on global light scattering beam interferometry data;

[0121] Based on the focal region viewpoint movement offset and horizontal section offset angle data, viewpoint offset dispersion anisotropy analysis is performed on the spatial dispersion distribution data to obtain viewpoint offset dispersion anisotropy data.

[0122] Viewpoint shift dispersion overlap analysis was performed on the viewpoint shift dispersion anisotropy data to obtain viewpoint shift dispersion overlap data;

[0123] The geometric color wheel illusion is evaluated based on the viewpoint shift dispersion anisotropy data and viewpoint shift dispersion overlap data, and the geometric color wheel illusion data is obtained.

[0124] In this embodiment of the invention, firstly, 3D geometric point cloud data is extracted from each focused area by combining background-foreground subject viewpoint focusing region data. Neighborhood search is performed on the point cloud data, and the Delaunay triangulation algorithm is used to construct surface mesh models for the background and foreground subjects. Background geometric morphology analysis focuses on extracting geometric features from large static objects (such as the ground or buildings), while foreground subject geometric morphology analysis focuses on the detailed features of dynamic or moving objects (such as people or vehicles). Noise in the point cloud is smoothed using a surface fitting method to eliminate geometric distortion caused by measurement errors. Simultaneously, a Gaussian curvature calculation method is used to classify the morphological features of the geometric surfaces of the background and foreground subjects. Regions with positive Gaussian curvature are defined as convex structures, regions with negative Gaussian curvature are defined as concave structures, and flat regions have Gaussian curvature close to zero. Finally, the processed geometric morphology is segmented into background geometric morphology data and foreground subject geometric morphology data, which are stored separately and used as input for subsequent calculations. For each triangular mesh facet in the background and foreground subject geometric morphology data, its normal vector is calculated. Specifically, the normal vector direction is obtained by solving the cross product between the coordinates of the three vertices of each triangle. Subsequently, a normal vector field is constructed using these normal vectors to globally map the surface normal vectors of the entire geometry. During the interleaving calculation, the intersection region of the background and foreground geometric normal vector fields is selected as the focus of analysis. The vector inner product method is used to calculate the angle between each pair of normal vectors. Furthermore, the distribution of angles in the interleaving region is used to statistically summarize the degree of normal vector interleaving, yielding background geometric normal vector interleaving data and foreground subject geometric normal vector interleaving data respectively. Based on the background and foreground geometric normal vector interleaving data, the scattering path of light and its interaction with the geometric surface are calculated. First, the propagation path of light is simulated using geometric optics theory, with the background and foreground subject geometric surfaces as reflection boundaries, tracing the scattering behavior of each incident ray. The scattering path calculation is based on a ray tracing algorithm, simulating the behavior of light in the interleaving region using the laws of reflection and refraction. Then, the scattering path of light is projected into the spectral space, and the intensity change of light in the spectral dispersion effect is analyzed using Fourier transform. Based on this, combined with the theory of beam splitting interference, the interference intensity is accurately calculated using the multilayer film interference formula. By calculating the global interference intensity point by point and integrating the light scattering effects between the background and the foreground subject, global light scattering beam splitting interference data is obtained as the final output.

[0125] Preferably, step S24 includes the following steps:

[0126] Step S241: Perform parallax depth level analysis on the background-foreground subject viewpoint focal area data based on the quantification data of the illusion of light change with viewpoint movement, and obtain the parallax depth level data of the focal area.

[0127] Step S242: Calculate the average depth difference between layers of the parallax depth layer data of the focused area to obtain the average parallax depth difference of the region layer.

[0128] Step S243: Based on the regional hierarchical parallax depth difference, the quantification data of the illusion of light change due to viewpoint movement is used to evaluate the sharpness loss of the focal point imaging, and the sharpness loss data of the focal point imaging is obtained.

[0129] Step S244: Based on the focal point imaging sharpness loss data and the regional hierarchical parallax depth average difference, perform parallax focusing gradient loss assessment to obtain parallax focusing gradient loss data.

[0130] In this embodiment of the invention, based on global light scattering spectroscopic interferometry data, the angular offset and wavelength distribution of each light scattering path are first precisely analyzed. Utilizing spectral distribution characteristics, the light is segmented by wavelength, and the variation trend of its scattering intensity on the geometric surface is calculated. The degree of dispersion of light at different wavelengths is determined through the gradient distribution of scattering intensity. To obtain the spatial distribution of dispersion, a three-dimensional interpolation algorithm is used to map the spectral data to a three-dimensional coordinate space, forming a spatial dispersion map. The dispersion density in different regions is calculated using spatial frequency distribution analysis techniques. Combined with local variations in scattered light intensity, the periodic structure in the dispersion map is extracted using Fast Fourier Transform (FFT) to obtain the characteristic patterns of the spatial dispersion distribution. Finally, the data is organized and summarized into spatial dispersion distribution data, providing input for the next step of analysis. Using the focal region viewpoint movement offset data, the trajectory of the viewpoint movement in space is simulated, and the changes in the light scattering dispersion distribution at different positions during the viewpoint movement are extracted. Combined with the horizontal cross-section offset angle data, the relationship between the viewpoint offset and the spatial dispersion distribution is determined. By using a vector rotation matrix, the offset direction of viewpoint movement is mathematically coupled with the local characteristics of light dispersion to analyze the dispersion trend at each viewpoint position. Spatial anisotropy analysis is employed to quantify the directional changes in dispersion intensity. The dispersion distribution in each direction is decomposed using the principal direction vector calculation method, and the degree of difference in different directions is calculated, forming a complete viewpoint offset dispersion anisotropy feature matrix. The results are organized into viewpoint offset dispersion anisotropy data to evaluate the impact of viewpoint offset on the overall dispersion distribution. Based on the viewpoint offset dispersion anisotropy data, the dispersion characteristics of overlapping regions during viewpoint movement are analyzed. By constructing a multi-viewpoint overlap matrix, the degree of dispersion overlap in different viewpoint regions during viewpoint movement is calculated. The convolution integral method is used to simulate multiple coverages of the same region by viewpoint movement, and the superposition effect of dispersion intensity in overlapping regions is analyzed. Two-dimensional histogram statistics are performed on the dispersion overlapping regions to extract the relationship data between dispersion intensity and the number of overlaps. The density estimation method is used to quantify the changes in dispersion degree in overlapping regions, forming a complete overlap analysis model. Finally, the viewpoint shift dispersion overlap data were calculated and used as a key input for subsequent evaluation of the geometric color wheel illusion of light scattering. Combining the viewpoint shift dispersion anisotropy data and the viewpoint shift dispersion overlap data, the global dispersion effect of different viewpoint shifts on light scattering was first analyzed. Using geometric optics principles and color theory, a geometric color wheel mapping of light scattering was constructed. Through multivariate nonlinear function fitting, the effects of dispersion anisotropy and dispersion overlap were combined into a comprehensive illusion index. The calculated illusion index distribution was interpolated and fitted to generate a geometric color wheel illusion distribution map. Furthermore, the hue ring shift trend of the color wheel was analyzed to quantify the visual impact of the illusion, ultimately obtaining geometric color wheel illusion data, which serves as a key basis for subsequent 3D image display optimization.

[0131] Preferably, step S3 includes the following steps:

[0132] Step S31: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, perform spatial deformation analysis of the focusing region on the background-foreground subject 3D spatial geometric model to obtain the spatial deformation data of the focusing region;

[0133] Step S32: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, the visual perception fatigue data is evaluated on the spatial deformation data of the focusing area to obtain the visual perception fatigue data.

[0134] Step S33: Based on visual perception fatigue data, design a variable light control mechanism by quantifying the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data, and obtain the variable light control mechanism.

[0135] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes:

[0136] Step S31: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, perform spatial deformation analysis of the focusing region on the background-foreground subject 3D spatial geometric model to obtain the spatial deformation data of the focusing region;

[0137] In this embodiment of the invention, point-by-point mapping analysis is performed on the 3D spatial geometric model of the background-foreground subject by inputting quantified data of light illusion changes due to viewpoint movement and parallax focusing gradient loss data. First, using the quantified data of light illusion changes due to viewpoint movement, a parallax-focus relationship mapping matrix is ​​established for each pixel in three-dimensional space. Combined with the parallax focusing gradient loss data, curvature extraction is performed on the gradient changes in the focused area of ​​the mapping matrix to identify potential deformation regions. For the identified focused areas, a three-dimensional Lagrange interpolation method is applied to refine the geometric morphology of the local area, calculating the change in surface tension during deformation. Simultaneously, a stepwise approximation algorithm is used to determine the spatial curvature change trend of the deformed area under the influence of parallax focusing. Finally, based on the deformation analysis results, the deformation data of the focused area is saved as focused area spatial deformation data, providing accurate input for subsequent visual perception fatigue assessment.

[0138] Step S32: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, the visual perception fatigue data is evaluated on the spatial deformation data of the focusing area to obtain the visual perception fatigue data.

[0139] In this embodiment of the invention, spatial deformation data of the focal region is input into the visual perception fatigue assessment framework. A correlation model is established by combining quantified data of optical illusion caused by viewpoint movement and parallax focusing gradient loss data among the three. Using visual physiology theory, the correlation between visual fixation time and deformation change frequency in the focal region is calculated. Local sensitivity analysis is employed to quantify the stimulus intensity of the deformed region on the visual system. Specifically, the visual perception fatigue assessment uses a frequency domain analysis method based on Fourier transform to convert the deformation data into frequency domain characteristics and extract the influence of high-frequency components on visual fatigue. Simultaneously, time-domain statistics are performed on the parallax focusing gradient loss data to analyze the impact of parallax changes on eye movement trajectories. Finally, the frequency and time domain characteristics are comprehensively converted into a visual fatigue index, outputting visual perception fatigue data for subsequent design of optically variable modulation mechanisms.

[0140] Step S33: Based on visual perception fatigue data, design a variable light control mechanism by quantifying the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data, and obtain the variable light control mechanism.

[0141] In this embodiment of the invention, a variable light control mechanism is designed based on visual perception fatigue data to optimize 3D image display effects and reduce visual fatigue. First, data on the illusion of light changes are used to analyze the impact of current light changes on visual perception. Then, based on parallax focusing gradient loss data, the main interference directions of optical parallax on the focusing area are identified, and key optical parameters requiring adjustment are determined. The specific design of the variable light control mechanism includes three core steps: based on adjustment depth theory, a discrete ray tracing algorithm is used to adjust the light reflection paths from different viewpoints in real time, optimizing the scattering angle distribution of light in space; using a layered optical adjustment method, regional brightness compensation is performed on the local light intensity of the display screen to reduce the burden of high-frequency optical stimulation on the eyes; and using dynamic spectral adjustment technology, the wavelength distribution of the color spectrum is analyzed and its local energy distribution is adjusted to make the light in the focusing area closer to the visual comfort zone.

[0142] Preferably, step S33 includes the following steps:

[0143] Step S331: Based on visual perception fatigue data, perform focus drift effect analysis on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain focus drift effect data;

[0144] Step S332: Perform color temperature-brightness coupling adjustment analysis on the focus drift effect data to obtain color temperature-brightness coupling adjustment data;

[0145] Step S333: Based on the color temperature-brightness coupling adjustment data, perform local light field rendering deviation correction on the quantization data of the illusion of light change with viewpoint movement to obtain local light field rendering deviation correction data;

[0146] Step S334: Perform focus spatial light correction on the focus drift effect data based on the local light field rendering deviation correction data to obtain focus spatial light correction data;

[0147] Step S335: Design a variable light control mechanism based on the local light field rendering deviation correction data and the focal space light correction data to obtain the variable light control mechanism.

[0148] In this embodiment of the invention, the dynamic drift effect of optical focus is analyzed by inputting visual fatigue perception data, quantified data of optical illusion caused by changes in light intensity during viewpoint movement, and parallax focusing gradient loss data. First, using the quantified illusion data, the trajectory of the focal point formed by light during viewpoint changes is extracted. Then, combined with the parallax focusing gradient loss data, a multidimensional analysis of the temporal and spatial variation patterns of focus drift is performed, calculating the offset and velocity of each sampling point. In specific implementation, the discrete Fourier transform method is used to project the dynamic changes of the light focus into the frequency domain, identifying the main frequency components in the drift pattern. Combined with visual fatigue perception data, the affected areas of focus drift are segmented and labeled, and the contribution of each area to visual fatigue is analyzed. Focus drift effect data is generated through interpolation fitting and deviation statistics, providing basic data support for subsequent color temperature-brightness coupling adjustment. Using the focus drift effect data as input, the coupling relationship between color temperature and brightness in the optical system is studied. First, based on the light trajectory in the focus drift effect data, color temperature characteristics at each point are extracted using color light separation technology based on spectral distribution. The dynamic trend of brightness variation is calculated by normalizing the brightness data point by point. A coupling analysis algorithm is used to pair and fit the color temperature and brightness variation patterns, analyzing their synergistic relationship in the focus-drift region. Specifically, wavelength-weighted averaging is used to quantify the color temperature, converting brightness changes into radiant flux in the corresponding region, and establishing a nonlinear coupling equation system between the two. Finally, color temperature-brightness coupling adjustment data is generated through numerical integration, providing input for light field rendering deviation correction. The color temperature-brightness coupling adjustment data is used to correct rendering deviations in the quantified data of light field illusions caused by viewpoint movement. First, based on the coupling adjustment data, the light field distribution in areas with inconsistent color temperature and brightness is located, and the brightness peaks and color temperature mismatch points in these areas are marked. Then, combined with the illusion quantification data, the sources of deviation in these light field areas are analyzed, including perspective angle and surface reflection characteristics. In the implementation, a rendering correction algorithm based on the law of optical reflection is used to reverse-track the light paths in the marked areas, reconstructing the true light field distribution. For nonlinear deviations, a bilinear interpolation algorithm is applied to smooth the deviation distribution, generating a uniform rendering result. Finally, local light field rendering deviation correction data is output, providing an accurate optical basis for focal space light correction. The local light field rendering deviation correction data and focal drift effect data are jointly analyzed to correct and optimize the optical distribution in the focal space. First, based on the light field error distribution in the deviation correction data, the light intensity and direction distribution in the focal drift region are adjusted to form a preliminary correction scheme. Then, based on the focal drift effect data, the accurate position of each focal point in space is calculated using the spherical wavefront tracing method, and the preliminary correction scheme is iteratively adjusted through optimization algorithms to ensure that the optical focal position matches the true viewing angle.Finally, the correction effect was verified through ray tracing simulation, and focal spatial light correction data was output, providing precise correction parameters for the design of a variable light control mechanism. Combining local light field rendering deviation correction data and focal spatial light correction data, a variable light control mechanism was designed to dynamically adjust the optical characteristics of 3D image displays. First, the relationship between light field distribution and focal position was extracted from the correction data to establish a dynamic control requirement model. Then, based on optical control theory, the light field region was partitioned, distinguishing between high-intensity control areas and low-frequency control areas. In the specific design process, optical polarization control technology was used to adjust the direction of the polarizer to achieve changes in the intensity of light in specific areas. Combined with dynamic light diffraction technology, the wavefront shape of the light was controlled to adapt to the dynamic requirements of different viewing angles and focal distributions. Through a real-time feedback mechanism, the control effect was verified and fine-tuned, ultimately generating a variable light control mechanism that provides optimized dynamic optical support for 3D image displays.

[0149] Preferably, step S334 includes the following steps:

[0150] Based on the local light field rendering deviation correction data, the difference in light field intensity distribution between different focus drift directions is evaluated to obtain light field intensity distribution difference data.

[0151] The light field intensity distribution difference data is decomposed into light field offset vectors between different focus drift directions to obtain the light field offset vectors;

[0152] Based on the optical field offset vector, the focus drift effect data is reconstructed by performing focus error vector reconstruction to obtain focus error vector reconstructed data;

[0153] Spatial light scattering interferometry analysis was performed based on focal error vector reconstruction data and local light field rendering deviation correction data to obtain focal light scattering interferometry data;

[0154] Based on the focal light scattering interferometry data and the focal error vector reconstruction data, the focal drift effect data is corrected by focal spatial light correction to obtain focal spatial light correction data.

[0155] In this embodiment of the invention, local light field rendering deviation correction data and focus drift effect data are used as inputs to evaluate the differences in light field intensity distribution in different directions of focus drift. First, the light field intensity in each direction is calculated using the optical path integral method, and the change in light energy density within a specific angular range during focus drift is extracted. Then, the light field intensity distribution is decomposed into directional components using a difference analysis algorithm, and the average value and standard deviation of the distribution in each direction are compared to obtain the differences in light field intensity distribution. Specifically, Fourier transform is used to convert spatial intensity data into frequency domain information, and feature parameters of high-difference directions are extracted by spectral comparison, ultimately generating light field intensity distribution difference data, providing accurate input for subsequent light field offset vector analysis. Using the light field intensity distribution difference data as input, the light field offset vector in different focus drift directions is calculated using a vector decomposition algorithm. First, the light field intensity difference is converted into a spatial vector field using vector field interpolation technology, separating the offset components along different directions. Then, the vector field is analyzed piecewise based on the piecewise analytical method to calculate the offset intensity and displacement vector in each direction. The data is then double-checked using a standard offset model to correct the vector direction deviation caused by sampling errors. Finally, an optical field offset vector is generated, providing data support for focal error vector reconstruction. Focal error vector reconstruction uses the optical field offset vector and focal drift effect data as input, employing a multidimensional vector fitting method to reconstruct the focal error. First, the focal drift effect data is projected into the direction space of the optical field offset vector, and the relationship between the offset vector and the actual focal position is fitted using the least squares method. Then, for each directional offset, the distribution of the error vector in space is calculated using interpolation, and outlier corrections are applied to outliers. Finally, the offsets in each direction are combined using vector superposition to obtain the complete focal error vector distribution, forming the focal error vector reconstruction data, providing the basic input for spatial light scattering interferometry analysis. By introducing the focal error vector reconstruction data and local optical field rendering deviation correction data, a detailed analysis of spatial light scattering interferometry effects is conducted. First, the scattering intensity of light in different media is calculated using optical scattering theory, and high scattering regions are located based on the reconstructed data. Then, the coherence and phase difference between scattered light are evaluated using an optical interferometry analysis algorithm, and key characteristic parameters of the interference fringes are extracted. A fast Fourier transform is used to convert the interferometric data into spectral information to evaluate the consistency of light scattering in different regions. Finally, focal light scattering interferometric data are generated by combining spatial distribution characteristics, laying the optical foundation for focal spatial light correction. Using the focal light scattering interferometric data and focal error vector reconstruction data, spatial light correction is performed on the focal drift effect data. First, the scattering interferometric data and error vector data are combined using the vector field superposition method to calculate the global correction value of the spatial light field. Then, the propagation path at each point in the light field is adjusted based on optical path tracing technology to ensure that the position and direction of the light focus are consistent with the theoretical value. During this process, a point-by-point intensity tuning method is used to fine-tune the light field distribution to correct any remaining optical deviations.Finally, the focal spatial light correction data is output, providing accurate correction parameters to support subsequent 3D image display optimization.

[0156] Preferably, the present invention also provides a glasses-free 3D screen, including a glasses-free 3D screen image control processor for executing the 3D image display method described above, the glasses-free 3D screen comprising:

[0157] The 3D spatial geometry model construction module is used to acquire multimedia video playback images; it constructs a background-foreground subject 3D spatial geometry model from the multimedia video playback images to obtain the background-foreground subject 3D spatial geometry model.

[0158] The light change illusion analysis module is used to acquire user viewpoint movement data; based on the user viewpoint movement data, the background-foreground subject 3D spatial geometric model is subjected to viewpoint movement light change illusion quantification to obtain viewpoint movement light change illusion quantification data; based on the viewpoint movement light change illusion quantification data, the parallax focusing gradient loss is evaluated to obtain parallax focusing gradient loss data.

[0159] The optical variable control mechanism design module is used to evaluate visual perception fatigue based on parallax focusing gradient loss data to obtain visual perception fatigue data; based on the visual perception fatigue data, the optical variable control mechanism is designed to quantify the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data to obtain the optical variable control mechanism.

[0160] The firmware design module is used to learn the logical memory of the optical variable control mechanism based on the policy gradient algorithm to obtain the optical variable control logical memory data; based on the optical variable control logical memory data, the automatic control firmware is designed to obtain the optical variable control logic firmware; the optical variable control logic firmware is embedded into the naked-eye 3D screen image control processor to execute the 3D image display method.

[0161] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0162] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A 3D image display method, characterized in that, Includes the following steps: Step S1: Acquire multimedia video playback images; A 3D spatial geometric model of background-foreground subject is constructed for the multimedia video playback image to obtain the 3D spatial geometric model of background-foreground subject. Step S2: Obtain user viewpoint movement data; quantify the illusion of light change during viewpoint movement on the background-foreground subject 3D spatial geometric model based on the user viewpoint movement data to obtain quantified data of the illusion of light change during viewpoint movement. Based on the quantification data of the illusion of light change due to viewpoint movement, the disparity focusing gradient loss is evaluated to obtain the disparity focusing gradient loss data; wherein, step S2 includes the following steps: Step S21: Acquire user-perspective movement data; Step S22: Based on the user's perspective movement data, identify the focus area of ​​the background-foreground subject 3D spatial geometric model from different perspectives to obtain the background-foreground subject perspective focus area data; Step S23: Based on the user's perspective movement data, perform perspective movement light change illusion quantification on the background-foreground subject perspective focus area data to obtain perspective movement light change illusion quantification data; Step S24: Based on the quantification data of the illusion of light change due to viewpoint movement, evaluate the parallax focusing gradient loss of the background-foreground subject viewpoint focusing area data to obtain the parallax focusing gradient loss data; wherein, step S23 includes the following steps: Step S231: Based on the user's perspective movement data, perform a simulation calculation of the perspective movement offset of the background-foreground subject perspective focus area data to obtain the perspective movement offset of the focus area. Step S232: Calculate the horizontal section offset angle of the focal area view movement offset to obtain the horizontal section offset angle data; Step S233: Based on the focal area viewpoint movement offset and horizontal section offset angle data, perform light scattering geometric color wheel illusion evaluation on the background-foreground subject viewpoint focal area data to obtain geometric color wheel illusion data; Step S234: Based on the geometric color wheel illusion data, perform quantification of the illusion of light change with viewpoint movement on the background-foreground subject viewpoint focus area data to obtain quantified data of the illusion of light change with viewpoint movement; Step S3: Based on the parallax focusing gradient loss data, visual perception fatigue is assessed to obtain visual perception fatigue data; based on the visual perception fatigue data, a variable light control mechanism is designed for the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain the variable light control mechanism. Step S4: Based on the policy gradient algorithm, perform logical memory learning on the optical variable control mechanism to obtain optical variable control logical memory data; design automated control firmware based on the optical variable control logical memory data to obtain optical variable control logical firmware; embed the optical variable control logical firmware into the naked-eye 3D screen image control processor to execute the 3D image display method.

2. The 3D image display method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Acquire multimedia video playback image; Step S12: Perform image edge optimization processing on the multimedia video playback image to obtain video playback image edge optimization data; Step S13: Mark the background reference object-foreground subject plane coordinates on the edge optimization data of the video playback image to obtain the background reference object-foreground subject plane coordinates; Step S14: Construct a 3D spatial geometric model of the background-foreground subject based on the background reference object-foreground subject plane coordinates of the video playback image edge optimization data, and obtain the 3D spatial geometric model of the background-foreground subject.

3. The 3D image display method according to claim 1, characterized in that, Step S233 includes the following steps: Geometric morphology analysis of the background-foreground subject focus area data was performed on the background-foreground subject perspective focus area data to obtain background geometric morphology data and foreground subject geometric morphology data respectively; The geometric surface normal vectors of the background geometric shape data and the foreground subject geometric shape data are interleaved to obtain the background geometric shape normal vector interleaving data and the foreground subject geometric shape normal vector interleaving data, respectively. The background-foreground-subject light scattering beam interference is calculated by intersecting the background geometric normal vector data and the foreground subject geometric normal vector data to obtain global light scattering beam interference data. Spatial dispersion distribution data are obtained by performing spatial dispersion distribution analysis based on global light scattering beam interferometry data; Based on the focal region viewpoint movement offset and horizontal section offset angle data, viewpoint offset dispersion anisotropy analysis is performed on the spatial dispersion distribution data to obtain viewpoint offset dispersion anisotropy data. Viewpoint shift dispersion overlap analysis was performed on the viewpoint shift dispersion anisotropy data to obtain viewpoint shift dispersion overlap data; The geometric color wheel illusion is evaluated based on the viewpoint shift dispersion anisotropy data and viewpoint shift dispersion overlap data, and the geometric color wheel illusion data is obtained.

4. The 3D image display method according to claim 1, characterized in that, Step S24 includes the following steps: Step S241: Perform parallax depth level analysis on the background-foreground subject viewpoint focal area data based on the quantification data of the illusion of light change with viewpoint movement, and obtain the parallax depth level data of the focal area. Step S242: Calculate the average depth difference between layers of the parallax depth layer data of the focused area to obtain the average parallax depth difference of the region layer. Step S243: Based on the regional hierarchical parallax depth difference, the quantification data of the illusion of light change due to viewpoint movement is used to evaluate the sharpness loss of the focal point imaging, and the sharpness loss data of the focal point imaging is obtained. Step S244: Based on the focal point imaging sharpness loss data and the regional hierarchical parallax depth average difference, perform parallax focusing gradient loss assessment to obtain parallax focusing gradient loss data.

5. The 3D image display method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, perform spatial deformation analysis of the focusing region on the background-foreground subject 3D spatial geometric model to obtain the spatial deformation data of the focusing region; Step S32: Based on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data, the visual perception fatigue data is evaluated on the spatial deformation data of the focusing area to obtain the visual perception fatigue data. Step S33: Based on visual perception fatigue data, design a light-variable control mechanism by quantifying the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data, and obtain the light-variable control mechanism.

6. The 3D image display method according to claim 5, characterized in that, Step S33 includes the following steps: Step S331: Based on visual perception fatigue data, perform focus drift effect analysis on the quantification data of the illusion of light change due to viewpoint movement and the parallax focusing gradient loss data to obtain focus drift effect data; Step S332: Perform color temperature-brightness coupling adjustment analysis on the focus drift effect data to obtain color temperature-brightness coupling adjustment data; Step S333: Based on the color temperature-brightness coupling adjustment data, perform local light field rendering deviation correction on the quantization data of the illusion of light change with viewpoint movement to obtain local light field rendering deviation correction data; Step S334: Perform focus spatial light correction on the focus drift effect data based on the local light field rendering deviation correction data to obtain focus spatial light correction data; Step S335: Design a variable light control mechanism based on the local light field rendering deviation correction data and the focal space light correction data to obtain the variable light control mechanism.

7. The 3D image display method according to claim 6, characterized in that, Step S334 includes the following steps: Based on the local light field rendering deviation correction data, the difference in light field intensity distribution between different focus drift directions is evaluated to obtain light field intensity distribution difference data. The light field intensity distribution difference data is decomposed into light field offset vectors between different focus drift directions to obtain the light field offset vectors; Based on the optical field offset vector, the focus drift effect data is reconstructed by performing focus error vector reconstruction to obtain focus error vector reconstructed data; Spatial light scattering interferometry analysis was performed based on focal error vector reconstruction data and local light field rendering deviation correction data to obtain focal light scattering interferometry data; Based on the focal light scattering interferometry data and the focal error vector reconstruction data, the focal drift effect data is corrected by focal spatial light correction to obtain focal spatial light correction data.

8. A glasses-free 3D screen, characterized in that, Includes a glasses-free 3D screen image control processor for executing the 3D image display method as described in claim 1, wherein the glasses-free 3D screen includes: The 3D spatial geometry model construction module is used to acquire multimedia video playback images; it constructs a background-foreground subject 3D spatial geometry model from the multimedia video playback images to obtain the background-foreground subject 3D spatial geometry model. The light change illusion analysis module is used to acquire user viewpoint movement data; based on the user viewpoint movement data, the background-foreground subject 3D spatial geometric model is subjected to viewpoint movement light change illusion quantification to obtain viewpoint movement light change illusion quantification data; based on the viewpoint movement light change illusion quantification data, the parallax focusing gradient loss is evaluated to obtain parallax focusing gradient loss data. The optical variable control mechanism design module is used to evaluate visual perception fatigue based on parallax focusing gradient loss data to obtain visual perception fatigue data; based on the visual perception fatigue data, the optical variable control mechanism is designed to quantify the illusion data of light change with viewpoint movement and the parallax focusing gradient loss data to obtain the optical variable control mechanism. The firmware design module is used to learn the logical memory of the optical variable control mechanism based on the policy gradient algorithm to obtain the optical variable control logical memory data; based on the optical variable control logical memory data, the automatic control firmware is designed to obtain the optical variable control logic firmware; the optical variable control logic firmware is embedded into the naked-eye 3D screen image control processor to execute the 3D image display method.

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