Naked eye 3D display control system and method for real-time data adjustment

Abstract

The invention relates to the technical field of naked eye 3D display control, in particular to a naked eye 3D display control system and method for real-time data adjustment. The method comprises the following steps: acquiring naked eye 3D display real-time operation data, and combining surrounding environment information to construct an environment space matrix; through calculation of naked eye 3D visual difference data, a parallax image distortion problem is identified and detected. Analyzing and estimating visual fatigue of the user; according to the fatigue data, naked eye 3D display abnormity analysis is carried out, and potential problems in image display are identified; optimization of naked-eye 3D display is achieved through optical characteristic control, and the naked-eye 3D display effect is clearer and more stable through optimization of naked-eye 3D display.

Description

Technical Field

[0001] The present invention relates to the technical field of naked-eye 3D display control, and in particular to a naked-eye 3D display control system and method for real-time data adjustment. Background Art

[0002] The naked eye 3D display can present a three-dimensional effect without wearing special equipment, which greatly improves the user experience. However, due to the complexity of naked eye 3D display technology, it still faces many technical difficulties in practical applications, especially in maintaining image stability and visual comfort in dynamic environments. Traditional naked eye 3D display technology mainly relies on principles such as parallax barriers, cylindrical lenses or multi-viewpoint projection. Once the user deviates from the optimal viewing area, it is very easy to cause visual artifacts, ghosting, color deviation, image segmentation and other problems. This will not only reduce the clarity and three-dimensional sense of the image, but also cause visual fatigue and even physiological discomfort to the user. The interference of ambient light also poses a challenge to naked eye 3D display. Under strong light reflection or complex lighting conditions, the screen brightness, contrast and color accuracy will be significantly affected, resulting in distortion of the depth perception of the image and attenuation of visual consistency. In addition, since the parallax perception of the human eye is individual, naked eye 3D display also needs to dynamically adapt to the visual characteristics of different users to achieve better display effects. However, a conventional naked-eye 3D display control method with real-time data adjustment has the problem of inaccurate analysis of naked-eye 3D display visual differences and inaccurate analysis of naked-eye 3D image abnormalities. Summary of the invention

[0003] Based on this, it is necessary to provide a naked-eye 3D display control system and method with real-time data adjustment to solve at least one of the above technical problems.

[0004] To achieve the above object, a naked-eye 3D display control method with real-time data adjustment comprises the following steps:

[0005] Step S1: acquiring naked-eye 3D display real-time operation data; collecting surrounding environment data based on the naked-eye 3D display real-time operation data to obtain naked-eye 3D display surrounding environment data; constructing an environment space matrix based on the naked-eye 3D display surrounding environment data and the naked-eye 3D display real-time operation data to obtain naked-eye 3D display environment space matrix data;

[0006] Step S2: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data; performing naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain naked eye 3D parallax image distortion data;

[0007] Step S3: estimating user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; performing naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data;

[0008] Step S4: controlling the image optical characteristics of the naked-eye 3D visual difference data according to the naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

[0009] The present invention obtains the real-time operation data of naked-eye 3D display and combines it with the surrounding environment data to construct an environmental space matrix, thereby providing an accurate data basis for the dynamic optimization of naked-eye 3D display. The environmental space matrix can comprehensively reflect multiple parameters such as the user's viewing angle, viewing distance, and ambient light intensity, thereby ensuring that the display effect matches the actual environment. By calculating the visual difference data, the potential deviation in the user's visual perception is accurately captured, providing an important basis for the subsequent parallax image distortion detection. The parallax image distortion detection can effectively identify the problems of splitting, ghosting, and artifacts in the image, thereby improving the overall consistency and clarity of the naked-eye 3D image. Based on the distortion data, the user's visual fatigue is estimated, which helps to dynamically monitor the user's visual burden and reduce the discomfort caused by long-term viewing. By further analyzing the visual fatigue data, the specific reasons for display abnormalities, such as light interference, color overflow, etc., can be identified, and targeted adjustment directions can be provided for optimizing the display effect. Through the control of the image optical characteristics, the dynamic optimization of the brightness, contrast, color accuracy and other dimensions of the image is realized, which significantly improves the layering and depth of the naked-eye 3D display, so that users can obtain high-quality visual experience regardless of the viewing angle or distance. Therefore, the present invention is an optimization process for the traditional naked-eye 3D display control method with real-time data adjustment, which solves the problem of inaccurate analysis of naked-eye 3D display visual differences and inaccurate analysis of naked-eye 3D image anomalies in the traditional naked-eye 3D display control method with real-time data adjustment, and improves the accuracy of naked-eye 3D display visual difference analysis and the accuracy of naked-eye 3D image anomaly analysis.

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

[0011] Step S11: obtaining real-time operation data of naked-eye 3D display;

[0012] Step S12: collecting surrounding environment data based on the real-time operation data of the naked-eye 3D display to obtain the surrounding environment data of the naked-eye 3D display;

[0013] Step S13: collecting naked-eye 3D display space environment data according to the naked-eye 3D display surrounding environment data to obtain naked-eye 3D display space environment data;

[0014] Step S14: measuring the light intensity of the naked-eye 3D display surrounding environment according to the naked-eye 3D display surrounding environment data to obtain the display surrounding environment light intensity data;

[0015] Step S15: constructing an environment space matrix based on the naked-eye 3D display space environment data, the display surrounding environment light intensity data and the naked-eye 3D display real-time operation data to obtain the naked-eye 3D display environment space matrix data.

[0016] The present invention obtains the real-time operation data of naked-eye 3D display, and the system can capture the working state of the display device in real time to ensure the timeliness and accuracy of data collection. Based on these data, the surrounding environment is collected, which further expands the perception range of the display environment and comprehensively evaluates the physical environment where the naked-eye 3D display is located. Next, after collecting the surrounding environment data, the system can collect the spatial environment, which helps to establish accurate spatial perception of the naked-eye 3D display area and provides basic data for subsequent optimization operations. Combined with the surrounding environment data, the light intensity is measured to effectively evaluate the lighting conditions in the environment, especially in the use scenario of the display device, the light intensity directly affects the display effect of the 3D image. By fusing and analyzing these data, the environmental space matrix is ​​constructed, so that the system can comprehensively consider the spatial environment, light intensity and the operating state of the display device, providing a comprehensive scientific basis for the precise adjustment of the naked-eye 3D display. After the integration of these data, it can help the system to optimize the display effect in real time, reduce problems such as light interference and spatial dislocation, and improve the realism and user experience of the naked-eye 3D display.

[0017] Preferably, step S15 comprises the following steps:

[0018] Step S151: acquiring the user position of the naked-eye 3D display real-time operation data based on the sensor to obtain the naked-eye 3D display user position data;

[0019] Step S152: collecting the position of the 3D display screen according to the real-time operation data of the naked-eye 3D display to obtain the position data of the naked-eye 3D display screen;

[0020] Step S153: measuring the user distance according to the naked-eye 3D display user position data and the naked-eye 3D display screen position data to obtain the naked-eye 3D user viewing distance data;

[0021] Step S154: calculating the user viewing angle according to the naked-eye 3D display user position data and the naked-eye 3D user viewing distance data to obtain the user viewing angle data;

[0022] Step S155: constructing a rectangular coordinate system of the naked-eye 3D display space according to the naked-eye 3D display space environment data to obtain the rectangular coordinate system data of the naked-eye 3D environment space;

[0023] Step S156: constructing an environment space matrix based on the naked eye 3D space rectangular coordinate system data, the display surrounding environment illumination intensity data and the user viewing visual angle data to obtain the naked eye 3D display environment space matrix data.

[0024] The present invention obtains user position data based on sensors, can monitor the user's position change in real time, and provide data support for subsequent personalized adjustments. Combined with the acquisition of the position of the 3D display screen, the relative position of the user and the display screen is accurately determined, providing key data for evaluating the distance between the user and the display screen. By measuring the distance between the user and the display screen, the naked-eye 3D user viewing distance data is obtained. This data is crucial for optimizing the depth perception of image display and ensuring the clarity and realism of the display effect. Further, the viewing visual angle is calculated according to the user's viewing distance data and position data, which helps to accurately grasp the user's visual needs, and then adjust the visual effect of the displayed content to avoid image distortion or distortion caused by improper visual angles. In addition, through the collection and analysis of spatial environment data, a naked-eye 3D display space rectangular coordinate system is constructed, which not only provides a basis for the spatial positioning of naked-eye 3D images, but also makes subsequent image adjustments more accurate. Based on the above data, the environmental space matrix is ​​constructed based on the rectangular coordinate system, light intensity and viewing angle to achieve a comprehensive understanding and optimization of the naked-eye 3D display environment, thereby dynamically adjusting the display effect, improving the user's viewing experience, and ensuring the optimization of the display effect under different environments.

[0025] Preferably, step S2 comprises the following steps:

[0026] Step S21: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data;

[0027] Step S22: performing parallax image spatial dislocation analysis according to the naked eye 3D visual difference data to obtain naked eye 3D visual spatial dislocation data;

[0028] Step S23: estimating the ghosting effect of the visual image based on the naked eye 3D visual space dislocation data to obtain the ghosting data of the naked eye 3D visual image;

[0029] Step S24: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D visual image ghosting data and the naked-eye 3D visual space misalignment data to obtain naked-eye 3D parallax image distortion data.

[0030] The present invention performs visual difference calculation based on the spatial matrix data of the naked-eye 3D display environment, and can accurately evaluate the naked-eye 3D display effect under different viewing angles and environmental conditions, ensuring that users can obtain an ideal visual experience under different viewing angles. By further analyzing the visual difference data and performing the parallax image spatial dislocation analysis, the spatial dislocation problem in the image can be identified and quantified, which is crucial to improving the spatial perception accuracy of the naked-eye 3D image and helps to reduce the visual error caused by inconsistent viewing angles. Next, based on the spatial dislocation data, the visual image ghosting effect is estimated, and the image ghosting phenomenon can be predicted and suppressed to ensure the clarity of the display and the visual comfort. Finally, by combining the parallax image distortion detection, the distortion caused by spatial dislocation in the image can be further verified and corrected to ensure the quality and accuracy of the image. This series of operations enables the naked-eye 3D display system to adjust the visual effect in real time according to environmental changes and user needs, reduce visual fatigue and discomfort, and improve the overall performance and user experience of the naked-eye 3D display.

[0031] Preferably, step S21 includes the following steps:

[0032] Step S211: calculating the intensity of reflected light in the naked eye 3D display environment according to the naked eye 3D display environment space matrix data to obtain the intensity data of reflected light in the naked eye 3D display environment;

[0033] Step S212: performing halo effect analysis according to the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D halo effect data;

[0034] Step S213: estimating naked-eye 3D image detail loss according to naked-eye 3D halo effect data to obtain naked-eye 3D image detail loss data;

[0035] Step S214: estimating display light interference based on the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D display light interference data;

[0036] Step S215: performing naked-eye 3D image abnormality analysis based on the naked-eye 3D display environment space matrix data to obtain naked-eye 3D image abnormality data;

[0037] Step S216: performing naked-eye 3D visual difference calculation according to the naked-eye 3D display light interference data, the naked-eye 3D image abnormality data and the naked-eye 3D image detail loss data to obtain the naked-eye 3D visual difference data.

[0038] The present invention calculates the intensity of reflected light through the spatial matrix data of the naked-eye 3D display environment, and evaluates the lighting conditions in the display environment, especially the intensity of reflected light, which is crucial for understanding and adjusting the brightness and clarity of the image. Next, halo effect analysis is performed based on the reflected light intensity data, which helps to predict and suppress the halo effect caused by strong light reflection or refraction of light on the display panel, avoiding its impact on the display quality, especially damage to image details. By analyzing the halo effect data, the problem of image detail loss is further estimated to ensure that the displayed image still maintains sufficient details and depth under complex lighting conditions. In addition, the light interference data of the display environment is combined to make an estimate, predict and reduce the display effect attenuation caused by ambient light interference, which is crucial for optimizing the naked-eye 3D display effect under different environmental conditions. At the same time, image anomaly analysis can promptly identify any anomalies that occur during the display process, such as color distortion or uneven brightness, and make corrections through data feedback. By combining the data on light interference, image anomalies and detail loss, visual difference calculations can be performed to accurately adjust the display effect of the naked-eye 3D image, reduce visual deviations caused by environmental factors and display device limitations, and improve the overall visual quality and user experience of the naked-eye 3D display.

[0039] Preferably, step S215 includes the following steps:

[0040] Based on the spatial matrix data of the naked eye 3D display environment, the user viewing angle deviation is collected to obtain the user viewing angle deviation data;

[0041] Performing naked eye 3D parallax mismatch analysis according to the user viewing angle deviation data, thereby obtaining naked eye 3D parallax mismatch data;

[0042] The left-eye and right-eye image cross probability is estimated according to the naked-eye 3D parallax mismatch data, thereby obtaining the left-eye and right-eye image cross probability data;

[0043] Perform image depth perception anomaly analysis based on left-eye and right-eye image cross probability data and naked-eye 3D parallax mismatch data to obtain 3D image depth perception anomaly data;

[0044] Perform 3D image dynamic depth discontinuity detection based on 3D image depth perception abnormal data to obtain 3D image dynamic depth discontinuity data;

[0045] According to the 3D image dynamic depth discontinuity data and the 3D image depth perception abnormality data, the naked eye 3D stereoscopic effect attenuation is estimated to obtain the naked eye 3D stereoscopic effect attenuation data;

[0046] According to the naked-eye 3D stereoscopic effect attenuation data and the 3D image dynamic depth discontinuity data, the naked-eye 3D image abnormality analysis is performed to obtain the naked-eye 3D image abnormality data.

[0047] The present invention collects the user's viewing angle deviation based on the environmental space matrix data, accurately understands the deviation of the user when watching the naked-eye 3D image, and ensures the consistency of the display device and the user's viewing angle. Next, by performing parallax mismatch analysis on the viewing angle deviation data, the wrong matching of the parallax can be detected to avoid the image inaccuracy caused by the parallax misalignment, thereby ensuring the visual consistency and authenticity of the image. Further, the left and right eye image crossing probability estimation based on the parallax mismatch data helps to determine whether the images seen by the left and right eyes are crossed, which is crucial for the normal presentation of the naked-eye 3D effect. By combining the crossing probability data and the parallax mismatch data to perform image depth perception anomaly analysis, the anomaly of the 3D image depth perception can be identified and analyzed, thereby improving the spatial depth performance of the naked-eye 3D image and preventing the problem of unclear depth perception. In addition, dynamic depth discontinuity detection based on depth perception anomaly data can effectively check the depth continuity problem in the image and avoid the depth jump or interruption phenomenon that occurs during the display process, which plays an important role in improving the immersion and authenticity of naked-eye 3D. Through further estimation of stereoscopic effect attenuation, quantification and adjustment of stereoscopic effect attenuation caused by dynamic depth discontinuity can be made to ensure that the stereoscopic sense of naked-eye 3D display is not disturbed and the stability of visual effect is maintained. Through comprehensive analysis of these data, naked-eye 3D image abnormality analysis can be performed, which can detect and repair abnormal problems in the image in real time, ensuring that naked-eye 3D display can present the best image effect in any environment and conditions, improving user experience and reducing the risk of visual fatigue.

[0048] Preferably, step S24 includes the following steps:

[0049] Step S241: performing naked-eye 3D image segmentation detection on the naked-eye 3D visual space dislocation data to obtain naked-eye 3D image segmentation data;

[0050] Step S242: performing image consistency attenuation estimation based on the naked eye 3D image segmentation data to obtain image consistency attenuation data;

[0051] Step S243: performing ghost contrast reduction detection based on the naked eye 3D visual image ghost data to obtain naked eye 3D ghost contrast reduction data;

[0052] Step S244: performing image layering reduction estimation according to naked eye 3D ghosting contrast reduction data to obtain image layering reduction data;

[0053] Step S245: performing image blur analysis according to the naked eye 3D ghosting contrast reduction data and the image consistency attenuation data to obtain naked eye 3D image blur data;

[0054] Step S246: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D image blur data and the image layering reduction data to obtain naked-eye 3D parallax image distortion data.

[0055] The present invention performs image splitting detection through naked eye 3D visual spatial dislocation data, which helps to discover image discontinuity problems caused by visual dislocation and ensure the overall visual consistency of the displayed image. Next, the image consistency attenuation is estimated by analyzing the image splitting data, effectively quantifying the degree of image consistency decline, and making adjustments in time to avoid reducing the audience's immersion and experience quality due to image incoherence. At the same time, based on the naked eye 3D visual image ghosting data, ghosting contrast reduction detection is performed to detect ghosting caused by display technology or viewing angle problems, avoid visual interference caused by contrast reduction, and thus ensure the clarity and visibility of the image. Further, based on the ghosting contrast reduction data, the image layering reduction is estimated, which can effectively warn of the image layering attenuation caused by visual ghosting, help optimize the depth perception of the image, and enable users to obtain a more realistic 3D effect. Through the analysis of image blur, combined with the ghosting contrast reduction and image consistency attenuation data, the image blur problem caused by display failure or visual dislocation can be effectively identified and repaired, ensuring that the image is clear and sharp, and enhancing the performance of stereoscopic vision. Finally, parallax image distortion detection based on blur and layer reduction data can accurately capture the distortion phenomenon in the image, ensuring that the naked-eye 3D image can present a stable, distortion-free effect at multiple angles and distances, improving the audience's visual experience and comfort. Through the optimization of these steps, visual interference and image quality degradation are minimized to the greatest extent, ensuring the accuracy and visibility of naked-eye 3D display.

[0056] Preferably, step S3 comprises the following steps:

[0057] Step S31: performing edge color overflow estimation based on naked eye 3D parallax image distortion data to obtain image edge color overflow data;

[0058] Step S32: performing local visual artifact detection according to the image edge color overflow data and the naked eye 3D visual image ghosting data to obtain naked eye 3D local visual artifact data;

[0059] Step S33: estimating user visual fatigue according to the image edge color overflow data and the naked eye 3D local visual artifact data to obtain naked eye 3D user visual fatigue data;

[0060] Step S34: performing naked-eye 3D display abnormality analysis according to naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data.

[0061] The present invention can detect the color overflow at the edge of the image by estimating the edge color overflow based on the naked eye 3D parallax image distortion data, and then help adjust the display content to avoid visual discomfort or interference caused by color overflow. Then, local visual artifact detection is performed in combination with the image edge color overflow data and the naked eye 3D visual image ghosting data, which helps to find the artifact phenomenon caused by parallax mismatch or display technology limitation, thereby optimizing these problems, improving image quality and reducing visual discomfort. Then, the user's visual fatigue is estimated based on the image edge color overflow data and the local visual artifact data, effectively predicting and avoiding the user's visual fatigue during the viewing process, ensuring that the user can still maintain a comfortable experience when watching for a long time, reducing the burden on the eyes, and improving the healthy usability of the display device. Finally, based on the naked eye 3D visual fatigue data, the naked eye 3D display abnormality analysis can identify the display failure or environmental factors that cause visual discomfort, provide a basis for further optimizing the system and improving the display effect, and ensure the comfort after long-term use. Through the optimization of these steps, not only can the image quality of the naked eye 3D display be improved, but also the user's visual experience can be significantly improved, visual fatigue can be reduced, and the overall performance of the system can be enhanced.

[0062] Preferably, step S4 comprises the following steps:

[0063] Step S41: performing image layering rendering on the naked eye 3D visual difference data according to the naked eye 3D display abnormality data to obtain naked eye 3D visual image layering rendering data;

[0064] Step S42: performing environment adaptation compensation processing on the naked eye 3D visual difference data based on the naked eye 3D display abnormality data to obtain naked eye 3D environment adaptation compensation data;

[0065] Step S43: controlling the optical characteristics of the image based on the naked-eye 3D visual image layered rendering data and the naked-eye 3D environment adaptation compensation data to obtain naked-eye 3D image optical characteristics adjustment data;

[0066] Step S44: optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristic adjustment data to obtain naked-eye 3D display optimization data.

[0067] The present invention performs image layering rendering on naked eye 3D visual difference data according to naked eye 3D display abnormal data, effectively distinguishes and processes different levels of the image, so that the visual effects of different depths are clearer and more natural, and visual discomfort is reduced. Secondly, the naked eye 3D visual difference data is subjected to environmental adaptation compensation processing based on the naked eye 3D display abnormal data, which helps to dynamically adjust the image according to factors such as different ambient lighting and observation angles, ensuring that the image can still maintain the best display effect in various environments, and avoiding display effect distortion caused by external factors. Next, the image optical characteristics are controlled based on the naked eye 3D visual image layering rendering data and the naked eye 3D environmental adaptation compensation data, and the optical parameters of the display, such as brightness, contrast, color temperature, etc., are accurately adjusted, so that the image can achieve the ideal visual effect under different viewing conditions. These adjustments help to enhance the realism and depth of the image and improve the effect of naked eye 3D display. Finally, the naked eye 3D display optimization is performed according to the naked eye 3D image optical characteristics adjustment data, and the overall performance of the display system is further improved to ensure that no eye discomfort or visual fatigue will occur during long-term viewing, and the reliability and user satisfaction of the system are improved. In general, these steps significantly improve the visual effect and comfort of naked-eye 3D display through fine image adjustment and optimization, providing users with a more immersive and comfortable viewing experience.

[0068] The present invention also provides a naked-eye 3D display control system with real-time data adjustment, which is used to execute the naked-eye 3D display control method with real-time data adjustment as described above. The naked-eye 3D display control system with real-time data adjustment includes:

[0069] Environmental space matrix construction module: used to obtain real-time operation data of naked-eye 3D display; collect surrounding environment based on real-time operation data of naked-eye 3D display to obtain surrounding environment data of naked-eye 3D display; construct environmental space matrix based on surrounding environment data of naked-eye 3D display and real-time operation data of naked-eye 3D display to obtain environmental space matrix data of naked-eye 3D display;

[0070] The parallax image distortion detection module is used to calculate the naked eye 3D visual difference according to the naked eye 3D display environment space matrix data to obtain the naked eye 3D visual difference data; perform the naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain the naked eye 3D parallax image distortion data;

[0071] Display abnormality analysis module: used to estimate user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; perform naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data;

[0072] Naked-eye 3D display optimization module: used to control the image optical characteristics of naked-eye 3D visual difference data according to naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimize the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

[0073] The present invention is to obtain the real-time operation data of naked-eye 3D display and combine it with the surrounding environment data to construct an environmental space matrix, thereby providing an accurate data basis for the dynamic optimization of naked-eye 3D display. The environmental space matrix can comprehensively reflect multiple parameters such as the user's viewing angle, viewing distance, ambient light intensity, etc., so as to ensure that the display effect matches the actual environment. By calculating the visual difference data, the potential deviation in the user's visual perception is accurately captured, which provides an important basis for the subsequent parallax image distortion detection. Parallax image distortion detection can effectively identify problems such as splitting, ghosting, and artifacts in the image, thereby improving the overall consistency and clarity of the naked-eye 3D image. Based on the distortion data, the user's visual fatigue is estimated to help dynamically monitor the user's visual burden and reduce the discomfort caused by long-term viewing. By further analyzing the visual fatigue data, the specific causes of display abnormalities, such as light interference, color overflow, etc., can be identified, and targeted adjustment directions can be provided for optimizing the display effect. By controlling the image optical characteristics, dynamic optimization of multiple dimensions such as image brightness, contrast, and color accuracy is achieved, significantly improving the layering and depth of naked-eye 3D display, so that users can obtain high-quality visual experience regardless of the viewing angle or distance. Therefore, the present invention is an optimization process made on the traditional naked-eye 3D display control method with real-time data adjustment, solving the problem of inaccurate analysis of naked-eye 3D display visual differences and inaccurate analysis of naked-eye 3D image abnormalities in the traditional naked-eye 3D display control method with real-time data adjustment, and improving the accuracy of naked-eye 3D display visual difference analysis and the accuracy of naked-eye 3D image abnormality analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic diagram of the steps of a naked-eye 3D display control method for real-time data adjustment;

[0075] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0076] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0077] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0078] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0079] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0080] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0081] To achieve this, please refer to Figures 1 to 3 , a naked-eye 3D display control method with real-time data adjustment, comprising the following steps:

[0082] Step S1: acquiring naked-eye 3D display real-time operation data; collecting surrounding environment data based on the naked-eye 3D display real-time operation data to obtain naked-eye 3D display surrounding environment data; constructing an environment space matrix based on the naked-eye 3D display surrounding environment data and the naked-eye 3D display real-time operation data to obtain naked-eye 3D display environment space matrix data;

[0083] Step S2: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data; performing naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain naked eye 3D parallax image distortion data;

[0084] Step S3: estimating user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; performing naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data;

[0085] Step S4: controlling the image optical characteristics of the naked-eye 3D visual difference data according to the naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

[0086] In the embodiment of the present invention, reference Figure 1 As shown, in this example, the real-time data adjustment naked eye 3D display control method comprises the following steps:

[0087] Step S1: acquiring naked-eye 3D display real-time operation data; collecting surrounding environment data based on the naked-eye 3D display real-time operation data to obtain naked-eye 3D display surrounding environment data; constructing an environment space matrix based on the naked-eye 3D display surrounding environment data and the naked-eye 3D display real-time operation data to obtain naked-eye 3D display environment space matrix data;

[0088] In an embodiment of the present invention, the naked-eye 3D display system collects the operation data of the display device in real time through built-in sensors (such as light sensors, temperature sensors, accelerometers, gyroscopes, etc.). The real-time operation data includes the working status of the device, light intensity, user's viewing angle, and power usage of the display screen. The data obtained by these sensors reflects the current working performance and display status of the device. In addition, external sensors (such as ambient light sensors, temperature and humidity sensors, etc.) are used to collect data of the surrounding environment. Specifically, the surrounding environment data obtained by the sensor includes information such as the light intensity, temperature and humidity of the environment. These data, together with the real-time operation data of the naked-eye 3D display device, enter the next step of environmental space matrix construction. During the construction process, the algorithm combines the surrounding environment data with the display device data to form an environmental space matrix, which represents the working status of the naked-eye 3D display device under a specific environment. All data collection and construction steps are synchronized in real time through wired or wireless transmission technology to ensure that the data obtained by the system is the latest state. Through these steps, the environment and working status of the naked-eye 3D display system can be effectively tracked.

[0089] Step S2: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data; performing naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain naked eye 3D parallax image distortion data;

[0090] In an embodiment of the present invention, calculation of naked-eye 3D visual difference is performed. This calculation step involves analyzing the visual difference of the image displayed by the display device under different environmental conditions. Through the environmental space matrix, information such as the viewing angle difference of the display device at a specific position, the depth of the image display, and the color change is obtained. Through these data, the visual difference can be quantified, and the naked-eye 3D visual difference data can be calculated through a mathematical model. Next, based on this visual difference data, naked-eye 3D parallax image distortion detection is performed. Parallax image distortion is usually manifested as asymmetry or blurring of the left and right eye images. Through a distortion detection algorithm (such as by comparing the depth information difference of the image under different viewing angles), the parallax distortion data in the image can be accurately calculated. These distortion data can reflect the image error of the display device in different environments and settings, especially the distortion of depth information.

[0091] Step S3: estimating user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; performing naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data;

[0092] In an embodiment of the present invention, user visual fatigue is estimated based on naked-eye 3D parallax image distortion data. The generation of visual fatigue is closely related to the quality of image display, especially the visual discomfort caused by parallax image distortion. In order to make an estimate, the system calculates the visual fatigue data of naked-eye 3D users through a preset fatigue estimation model based on the degree of distortion, combined with the user's viewing time, screen brightness, viewing distance and other data. The model evaluates the image features that cause fatigue by analyzing the relationship between parallax distortion and user viewing conditions (such as viewing distance and angle, etc.). Subsequently, using these visual fatigue data, the system will perform naked-eye 3D display abnormality analysis. This analysis determines whether there is an abnormality in the display device by comparing historical data with real-time data, such as parallax distortion exceeding a threshold, unclear display effect, etc., and outputs naked-eye 3D display abnormality data.

[0093] Step S4: controlling the image optical characteristics of the naked-eye 3D visual difference data according to the naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

[0094] In an embodiment of the present invention, the image optical characteristics of the naked eye 3D visual difference data are controlled according to the naked eye 3D display abnormal data. In this step, the system uses the control algorithm of the display screen to adjust the displayed optical parameters (such as contrast, brightness, color temperature, color balance, etc.). The specific values ​​of these adjustments are jointly determined by the display abnormal data and the visual difference data, with the purpose of restoring the clarity of the image and the accuracy of the color. For example, if the displayed contrast is too high and causes visual discomfort, the system will correct the visual effect by reducing the contrast or adjusting the color temperature. Based on these adjustment data, the system further performs naked eye 3D display optimization. In the display optimization stage, the system not only adjusts a single display parameter, but also comprehensively considers multiple factors, such as viewing angle differences, ambient light changes, etc., and performs comprehensive optimization. This optimization ensures that each detail performs best under the naked eye 3D effect by correcting each level of the image, avoiding the user experience affected by display distortion, color deviation and other problems, and outputs naked eye 3D display optimization data.

[0095] Preferably, step S1 comprises the following steps:

[0096] Step S11: obtaining real-time operation data of naked-eye 3D display;

[0097] Step S12: collecting surrounding environment data based on the real-time operation data of the naked-eye 3D display to obtain the surrounding environment data of the naked-eye 3D display;

[0098] Step S13: collecting naked-eye 3D display space environment data according to the naked-eye 3D display surrounding environment data to obtain naked-eye 3D display space environment data;

[0099] Step S14: measuring the light intensity of the naked-eye 3D display surrounding environment according to the naked-eye 3D display surrounding environment data to obtain the display surrounding environment light intensity data;

[0100] Step S15: constructing an environment space matrix based on the naked-eye 3D display space environment data, the display surrounding environment light intensity data and the naked-eye 3D display real-time operation data to obtain the naked-eye 3D display environment space matrix data.

[0101] In an embodiment of the present invention, the naked-eye 3D display device obtains device operation data in real time through built-in sensors. The sensors include light sensors, temperature sensors, accelerometers, gyroscopes, display screen voltage and current sensors, etc. These sensors work together to collect various data about the current working state of the device. For example, the light sensor can measure the current brightness change of the display screen, the temperature sensor monitors the device temperature, and the current and voltage sensor records the energy usage of the device. These data will be transmitted to the central processing unit in real time for analyzing the state of the display device. These real-time operation data include any fluctuations, abnormalities or failure data that occur during the operation of the device. Through the external environment acquisition system of the naked-eye 3D display device, based on the obtained naked-eye 3D display real-time operation data, the surrounding environment data acquisition is started. The acquisition system includes an ambient light sensor, a humidity sensor, a temperature sensor, a noise sensor, etc. The sensor array will monitor the changes in the surrounding environment of the display device in real time, including information such as light intensity, ambient temperature, humidity, and noise level in the environment. These data are transmitted to the central processing unit and processed in real time by an algorithm to identify the impact of environmental changes on the naked-eye 3D display effect. For example, changes in ambient light intensity affect the clarity and contrast of the display effect, while changes in temperature and humidity affect the hardware performance of the display device. The collected surrounding environment data provides a comprehensive perspective of the external environment. After obtaining the surrounding environment data of the naked-eye 3D display, the system further collects the naked-eye 3D display space environment through dedicated sensors. Specifically, the spatial environment data includes the specific configuration of the space where the display device is located, the physical layout in the space (such as the distance between the wall, the desktop and the display screen, the permeability of the space, etc.), and the dynamic changes in the space (such as air flow, object movement, etc.). Through devices such as lidar sensors or 3D depth cameras, the system perceives the spatial structure around the display device in real time, measures the position and height of obstacles and objects in the space, and thus constructs an accurate three-dimensional spatial environment map. The naked-eye 3D display device further measures the light intensity based on the surrounding environment data to obtain the light intensity data of the surrounding environment of the display device. In specific operations, the system's built-in light sensor or external light measurement device collects the intensity of the ambient light in real time, paying special attention to the relative intensity of natural light and artificial light sources. During the light intensity measurement process, the system accurately measures the light direction, intensity, wavelength, etc. based on the data fed back by the sensor. This data will affect the image brightness, contrast and color performance of the naked-eye 3D display device in real time, helping the system understand the impact of ambient light on the display effect. For example, too strong or too weak ambient light will cause the contrast and details of the image display to be unclear, thus affecting the user's viewing experience. Based on the naked-eye 3D display space environment data and the display surrounding ambient light intensity data, combined with the real-time operation data of the naked-eye 3D display, the system constructs the naked-eye 3D display environment space matrix.This matrix contains the specific environmental information of the three-dimensional space where the display device is located, the light distribution in the space, and the operating status of the device. Specifically, the system processes the data of each sensor through an algorithm, performs data fusion, and obtains the geometric configuration of the display device in the space, the distribution of ambient light, and the real-time status of the display device. The construction of the environmental space matrix not only considers the spatial position and light intensity, but also factors such as the power consumption, temperature and humidity of the device. By constructing this matrix, the system can accurately capture the interaction between the environment and the device.

[0102] Preferably, step S15 comprises the following steps:

[0103] Step S151: acquiring the user position of the naked-eye 3D display real-time operation data based on the sensor to obtain the naked-eye 3D display user position data;

[0104] Step S152: collecting the position of the 3D display screen according to the real-time operation data of the naked-eye 3D display to obtain the position data of the naked-eye 3D display screen;

[0105] Step S153: measuring the user distance according to the naked-eye 3D display user position data and the naked-eye 3D display screen position data to obtain the naked-eye 3D user viewing distance data;

[0106] Step S154: calculating the user viewing angle according to the naked-eye 3D display user position data and the naked-eye 3D user viewing distance data to obtain the user viewing angle data;

[0107] Step S155: constructing a rectangular coordinate system of the naked-eye 3D display space according to the naked-eye 3D display space environment data to obtain the rectangular coordinate system data of the naked-eye 3D environment space;

[0108] Step S156: constructing an environment space matrix based on the naked eye 3D space rectangular coordinate system data, the display surrounding environment illumination intensity data and the user viewing visual angle data to obtain the naked eye 3D display environment space matrix data.

[0109] In the embodiment of the present invention, it is implemented by multiple sensors. These sensors include infrared sensors, ultrasonic sensors and visual sensors. The infrared sensor calculates the relative position between the user and the display device by emitting infrared rays and receiving reflected signals. The ultrasonic sensor can accurately obtain the distance between the user and the display device by measuring the time difference of sound wave propagation. The visual sensor uses a camera to track the user's position in real time, especially in dynamic scenes, to accurately identify the user's position change. By fusing the data of these sensors, the naked-eye 3D display system can calculate the user's precise position data in real time. The display screen position of the naked-eye 3D display device is collected by sensors, and the posture and spatial position of the display screen are usually measured by built-in sensors such as accelerometers, gyroscopes, and magnetometers. The accelerometer senses the motion state of the display screen, the gyroscope calculates its angle change by measuring the angular velocity of the display screen, and the magnetometer helps calibrate the orientation of the device in space. These sensors work together to transmit the position and angle information of the display screen to the central control system, ensuring that the system can accurately obtain the spatial coordinates of the display screen in real time. The distance between the user and the display screen is measured by the spatial distance calculation formula. In specific operation, the system calculates the actual spatial distance between the user and the display screen based on the obtained user position data and the obtained display screen position data. The calculation method is based on the distance formula in three-dimensional space. The position data of the user and the display screen are converted into specific spatial coordinates through the three-dimensional coordinate system, and then the distance between the two is calculated using the Euclidean distance formula. The naked-eye 3D user viewing distance data obtained by this calculation result is an important basis for adjusting the display content, because the change in distance directly affects the depth perception and effect of the user viewing the 3D image. According to the naked-eye 3D display user position data and the viewing distance data between the user and the display screen, the system calculates the user's viewing visual angle through a geometric method. This angle mainly involves the spatial relationship between the user's eyes and the display screen. The system accurately measures the user's eye position, combines the display screen position data, and uses trigonometric functions (such as the tangent rule) to calculate the user's viewing angle when viewing the display screen. The user's viewing visual angle data not only affects the user's viewing comfort, but is also closely related to the accuracy of the 3D display effect. By real-time monitoring and calculating the visual angle, the system can dynamically adjust the parallax, brightness and contrast of the display content, thereby ensuring that the user can experience the best naked-eye 3D effect at different positions and angles. After acquiring the spatial environment data of the naked-eye 3D display device, the system uses this data to construct the naked-eye 3D display space rectangular coordinate system. Using the spatial data of the display device and the spatial environment information collected by the sensor, the system converts the surrounding environment and the device position into a three-dimensional rectangular coordinate system. In this coordinate system, the spatial relationship between the display device and objects in the surrounding space (such as walls, desktops, etc.) will be accurately described.Through the construction of the coordinate system, the system can accurately locate the specific position of the naked-eye 3D display device, and adjust the display content according to the spatial layout to ensure the accurate presentation of the display effect. Based on the naked-eye 3D space rectangular coordinate system data, the display surrounding environment light intensity data, and the user's viewing visual angle data, the system constructs an environmental space matrix. This process will involve the comprehensive processing of multi-source data. The system will fuse the various data in the naked-eye 3D space rectangular coordinate system with the surrounding environment light intensity data to analyze the impact of light on different spatial areas. Then, combined with the user's viewing visual angle data, the system calculates how the light in the environment affects the user's viewing effect. By mathematically modeling all this data, the system can establish a three-dimensional environmental space matrix, which contains multi-dimensional data such as spatial position, light intensity, and viewing angle. The matrix will reflect the changes in the naked-eye 3D display environment in real time and provide accurate feedback for real-time adjustment of the display effect.

[0110] Preferably, step S2 comprises the following steps:

[0111] Step S21: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data;

[0112] Step S22: performing parallax image spatial dislocation analysis according to the naked eye 3D visual difference data to obtain naked eye 3D visual spatial dislocation data;

[0113] Step S23: estimating the ghosting effect of the visual image based on the naked eye 3D visual space dislocation data to obtain the ghosting data of the naked eye 3D visual image;

[0114] Step S24: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D visual image ghosting data and the naked-eye 3D visual space misalignment data to obtain naked-eye 3D parallax image distortion data.

[0115] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0116] Step S21: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data;

[0117] In an embodiment of the present invention, the spatial matrix data of the naked-eye 3D display environment is used to perform visual difference calculations. By inputting the spatial matrix data into a preset calculation framework, the system will gradually analyze the differences in illumination, angle, and position of different spatial regions in the data, and the system will extract key factors such as the display device position, user viewing angle, and ambient light intensity from the environmental matrix. These data will be combined with the characteristics of the naked-eye 3D display content to evaluate the user's visual difference perception through geometric calculations. The specific calculation logic includes calculating the difference between the left and right eye images through parallax measurement and angle changes, and then obtaining visual difference data.

[0118] Step S22: performing parallax image spatial dislocation analysis according to the naked eye 3D visual difference data to obtain naked eye 3D visual spatial dislocation data;

[0119] In an embodiment of the present invention, a parallax image spatial misalignment analysis is performed based on the naked-eye 3D visual difference data. The system analyzes the images presented at different viewing angles and measures the image misalignment corresponding to different parallax values. Each frame in the parallax image will be decomposed into independent pixel blocks, and the binocular parallax is calculated by comparison to evaluate the degree of deviation of the image between the left and right eyes. In specific implementation, the spatial misalignment caused by changes in viewing angle and viewing distance is measured by acquiring the parallax data in real time and comparing it with the preset reference value. By calculating the parallax error of each pixel point, the system can generate naked-eye 3D visual spatial misalignment data.

[0120] Step S23: estimating the ghosting effect of the visual image based on the naked eye 3D visual space dislocation data to obtain the ghosting data of the naked eye 3D visual image;

[0121] In an embodiment of the present invention, based on the naked eye 3D visual spatial dislocation data, the system estimates the ghosting effect of the visual image. The ghosting effect is often caused by factors such as parallax mismatch and angle asymmetry. The system uses the spatial dislocation data to analyze the degree of image ghosting of each pixel block, superimposes each parallax image with the frame data, and compares the ghosting phenomenon. The system analyzes these mismatched images through a mathematical model to evaluate the multiple images generated during the superposition process. In specific operations, the ghosting effect will be weighted according to different lighting conditions, viewing angles, and spatial transformations to calculate the visual ghosting effect data.

[0122] Step S24: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D visual image ghosting data and the naked-eye 3D visual space misalignment data to obtain naked-eye 3D parallax image distortion data.

[0123] In an embodiment of the present invention, naked-eye 3D parallax image distortion detection is achieved by analyzing naked-eye 3D visual image ghosting data and visual space misalignment data. The system identifies the mismatching areas between the left and right eye images by analyzing the distortion phenomenon in each frame of the image. Image distortion is usually manifested as inaccurate parallax, resulting in deformation, offset or blurring of the image. By combining the parallax image space misalignment data, the system can determine the degree of visual distortion of each part of the image. In actual operation, the system uses image processing algorithms, such as edge detection and morphological analysis, to check the distorted areas in the image. For the distorted area, the system will further evaluate its severity and generate specific parallax image distortion data.

[0124] Preferably, step S21 includes the following steps:

[0125] Step S211: calculating the intensity of reflected light in the naked eye 3D display environment according to the naked eye 3D display environment space matrix data to obtain the intensity data of reflected light in the naked eye 3D display environment;

[0126] Step S212: performing halo effect analysis according to the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D halo effect data;

[0127] Step S213: estimating naked-eye 3D image detail loss according to naked-eye 3D halo effect data to obtain naked-eye 3D image detail loss data;

[0128] Step S214: estimating display light interference based on the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D display light interference data;

[0129] Step S215: performing naked-eye 3D image abnormality analysis based on the naked-eye 3D display environment space matrix data to obtain naked-eye 3D image abnormality data;

[0130] Step S216: performing naked-eye 3D visual difference calculation according to the naked-eye 3D display light interference data, the naked-eye 3D image abnormality data and the naked-eye 3D image detail loss data to obtain the naked-eye 3D visual difference data.

[0131] In an embodiment of the present invention, key variables in the environmental space matrix data are extracted, including the physical position of the display screen, the viewing angle, and the intensity of the ambient light source. Then, an optical sensor is used to detect the lighting conditions around the display device to evaluate the impact of the reflected light intensity on the display effect. The calculation process involves the light reflected back from the surface of the display screen, and the refraction, reflection and other characteristics of the light are analyzed in combination with the sensor data to accurately measure the reflected light intensity in each direction. These measurement data are adjusted according to different lighting conditions and updated in real time according to environmental parameters to obtain the naked eye 3D display environment reflected light intensity data. Based on the naked eye 3D display environment reflected light intensity data, halo effect analysis is performed. By comparing the reflected light intensity data with the illumination intensity of the display area, the system can identify the halo effect generated by the light in different areas. These halo effects are usually caused by the presence of strong light sources or reflective objects, resulting in blurring and distortion of the image edges. In specific operations, the system calculates the light refraction path of the light source in the display area through an optical analysis model, and then simulates the reflection behavior of light on different material surfaces. The system compares the reflected light intensity with the brightness of the ambient light source, predicts the halo effect area where the image appears, and generates naked eye 3D halo effect data. Based on the naked-eye 3D halo effect data, the loss of naked-eye 3D image details is estimated. The halo effect usually causes the loss of image details, especially in the edge area, which affects the user's viewing experience. The system evaluates the degree of detail loss by analyzing the blurred area in the halo effect data. In specific operation, the system identifies the image area where the halo effect occurs, and then uses image processing algorithms (such as edge detection, contrast analysis, etc.) to calculate the amount of image detail loss in the affected area. The system estimates the degree of image detail loss by comparing the images before and after the halo effect, and generates naked-eye 3D image detail loss data. Based on the naked-eye 3D display environment reflected light intensity data, the display light interference is estimated. Light interference mainly comes from the scattering and irregular refraction of reflected light, resulting in unclear or distorted image display. The system identifies the interfering light sources around the display screen by analyzing the reflected light intensity data, especially in low-light or highly reflective environments. The system uses optical sensors combined with environmental data to simulate the light propagation path and analyze the impact of interference sources on the display area. According to the relative position of the display screen and objects in the environment, the system evaluates the degree of light interference and generates naked-eye 3D display light interference data. Based on the spatial matrix data of the naked-eye 3D display environment, the naked-eye 3D image anomaly analysis is performed. This step aims to detect image anomalies caused by environmental changes or hardware limitations, such as image distortion, blur or misalignment. The system analyzes the viewing angle relationship between the display device and the user and the influence of the ambient light source based on the environmental spatial matrix data. By combining the image sensor data, the system can monitor the image display effect in real time and compare it with the preset normal image standard. Through image processing algorithms (such as pattern recognition, defect detection, etc.), the system can identify and locate abnormal parts in the image and generate naked-eye 3D image anomaly data.Based on the naked-eye 3D display light interference data, naked-eye 3D image abnormality data, and naked-eye 3D image detail loss data, the naked-eye 3D visual difference calculation is performed. The system will comprehensively evaluate the visual difference from multiple dimensions (such as ambient light, image quality, display light source, etc.), and the system analyzes the light interference and image abnormality data to identify the key factors affecting the visual effect. Then, combined with the image detail loss data, the system evaluates the differences in the user's viewing experience and calculates the visual differences produced under different display conditions. By combining image processing and optical models, the system accurately calculates the degree of visual difference under different viewing angles and environments, and generates naked-eye 3D visual difference data.

[0132] Preferably, step S215 includes the following steps:

[0133] Based on the spatial matrix data of the naked eye 3D display environment, the user viewing angle deviation is collected to obtain the user viewing angle deviation data;

[0134] Performing naked eye 3D parallax mismatch analysis according to the user viewing angle deviation data, thereby obtaining naked eye 3D parallax mismatch data;

[0135] The left-eye and right-eye image cross probability is estimated according to the naked-eye 3D parallax mismatch data, thereby obtaining the left-eye and right-eye image cross probability data;

[0136] Perform image depth perception anomaly analysis based on left-eye and right-eye image cross probability data and naked-eye 3D parallax mismatch data to obtain 3D image depth perception anomaly data;

[0137] Perform 3D image dynamic depth discontinuity detection based on 3D image depth perception abnormal data to obtain 3D image dynamic depth discontinuity data;

[0138] According to the 3D image dynamic depth discontinuity data and the 3D image depth perception abnormality data, the naked eye 3D stereoscopic effect attenuation is estimated to obtain the naked eye 3D stereoscopic effect attenuation data;

[0139] According to the naked-eye 3D stereoscopic effect attenuation data and the 3D image dynamic depth discontinuity data, the naked-eye 3D image abnormality analysis is performed to obtain the naked-eye 3D image abnormality data.

[0140] In an embodiment of the present invention, based on the spatial matrix data of the naked eye 3D display environment, the relative position and viewing angle between the user and the display device are accurately measured. The user's position data is collected in real time through an environmental perception sensor (such as an infrared sensor, a laser radar or an ultrasonic sensor) to determine its viewing angle. These data are compared with the spatial coordinates of the display device to obtain the actual viewing angle deviation data of the user. The deviation data is generated by calculating the difference between the user's viewing point and the theoretical optimal viewing angle. According to the user's viewing angle deviation data, a naked eye 3D parallax mismatch analysis is performed. This step determines whether there is a mismatch in the parallax image by comparing the user's angle deviation with the parallax data of the naked eye 3D image. The dislocation between the left and right eye images is analyzed by an algorithm, and the disparity value deviation at each image position is calculated. The disparity mismatch data reflects the degree of 3D display dislocation caused by the user's viewing angle deviation. The system automatically adjusts the disparity parameters by analyzing the user's position at different angles, thereby avoiding the visual mismatch phenomenon. In specific implementation, the left and right eye parallax of the image is dynamically adjusted to ensure that the viewing angle deviation does not cause image dislocation or incoordination. According to the naked eye 3D parallax mismatch data, the probability of left and right eye images crossing is estimated. This step calculates the probability of left and right eye images crossing by analyzing the parallax mismatch. The crossing probability reflects the probability of image overlap when the images received by the left and right eyes are not accurately aligned. This analysis calculates the probability of each parallax point and dynamically adjusts according to the probability of image crossing. According to different user viewing angles and parallax deviations, the system estimates the probability of left and right eye images crossing in real time through algorithms to ensure the accuracy of parallax images under the user's perspective. According to the left and right eye image crossing probability data and the naked eye 3D parallax mismatch data, image depth perception abnormality analysis is performed. In this step, the crossing probability data is used to determine the degree of deviation in image depth. The generation of image depth perception abnormalities is usually related to factors such as parallax mismatch and left and right eye image crossing. By analyzing the depth information of the image pixel by pixel, the system detects whether there is a deviation in depth perception in the image. For example, the system will detect the depth confusion of distant and near objects due to perspective deviation, or the image depth is too blurred to clearly present a three-dimensional sense. At this time, the system can adjust the depth information in real time to avoid visual perception errors. Based on the 3D image depth perception anomaly data, 3D image dynamic depth discontinuity detection is performed. This step detects whether there is depth discontinuity by dynamically analyzing the changes in depth in the image, especially when the user changes the viewing angle or the screen display conditions change. The system monitors the changes in depth data in real time and detects the discontinuity that occurs, that is, the sudden jump in the depth level in the image, resulting in the discontinuity of the stereoscopic effect. Dynamic depth discontinuity detection is achieved by comparing the depth data of consecutive frames and calculating the rate of change of depth to identify areas of depth jump or abnormal change. Based on the 3D image dynamic depth discontinuity data and the 3D image depth perception anomaly data, the naked eye 3D stereoscopic effect attenuation is estimated.The system estimates the degree of stereoscopic effect attenuation caused by depth discontinuity and perception anomalies by analyzing dynamic depth changes and depth perception deviations. Stereoscopic effect attenuation is usually manifested as insufficient depth difference between near and far objects in the image, or too blurred stereoscopic effect. By calculating the depth perception of the image frame by frame, the system evaluates the stereoscopic effect attenuation under different viewing angles and display environments, and generates naked-eye 3D stereoscopic effect attenuation data. Based on the naked-eye 3D stereoscopic effect attenuation data and the dynamic depth discontinuity data of the 3D image, the naked-eye 3D image anomaly analysis is performed. In this step, the system combines the depth discontinuity detection and stereoscopic effect attenuation evaluation in the previous step to detect anomalies in the image, such as image distortion, inconsistent depth, and blurred stereoscopic effect. The system gradually optimizes the image display effect by accurately calculating the visual errors in each area of ​​the image.

[0141] Preferably, step S24 includes the following steps:

[0142] Step S241: performing naked-eye 3D image segmentation detection on the naked-eye 3D visual space dislocation data to obtain naked-eye 3D image segmentation data;

[0143] Step S242: performing image consistency attenuation estimation based on the naked eye 3D image segmentation data to obtain image consistency attenuation data;

[0144] Step S243: performing ghost contrast reduction detection based on the naked eye 3D visual image ghost data to obtain naked eye 3D ghost contrast reduction data;

[0145] Step S244: performing image layering reduction estimation according to naked eye 3D ghosting contrast reduction data to obtain image layering reduction data;

[0146] Step S245: performing image blur analysis according to the naked eye 3D ghosting contrast reduction data and the image consistency attenuation data to obtain naked eye 3D image blur data;

[0147] Step S246: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D image blur data and the image layering reduction data to obtain naked-eye 3D parallax image distortion data.

[0148] In an embodiment of the present invention, a naked-eye 3D image split detection is performed based on the naked-eye 3D visual spatial dislocation data. The detection is performed by analyzing the image breakage phenomenon caused by inaccurate parallax or inconsistent user viewing angles in the image. Using an image processing algorithm, the parallax mismatch area is detected in the image, that is, the part where the depth level suddenly changes in the image distribution. Then, the split place in the image is identified by an image segmentation technology based on edge detection and local matching. The key to split detection is to mark the fault caused by parallax dislocation in the image to obtain the naked-eye 3D image split data. According to the naked-eye 3D image split data, image consistency attenuation is estimated. In this step, the consistency attenuation of the image is estimated by analyzing the impact of image splitting. Image consistency refers to the continuity and coordination between each perspective of the image. The split data reveals the disordered areas in the image caused by parallax errors or spatial dislocations, and generates image consistency attenuation data by calculating the differences between these areas and the surrounding images. This step uses an image similarity analysis algorithm to match the pixel differences of adjacent area images to predict the degree of visual inconsistency caused by splitting, and adjusts the consistency of the image according to the estimated results. Based on the ghosting data of naked-eye 3D visual images, ghosting contrast reduction detection is performed. The purpose of this step is to analyze the image contrast loss caused by ghosting, locate the ghosting areas in the image through ghosting data, and perform grayscale analysis on the pixel values ​​of these areas. Using the image brightness contrast analysis method, the brightness change of the ghosting area is calculated by comparing the contrast values ​​of the adjacent areas. In this way, the contrast reduction caused by the ghosting effect is detected, and the naked-eye 3D ghosting contrast reduction data is obtained by calculation. According to the naked-eye 3D ghosting contrast reduction data, the image layering reduction is estimated. This step mainly predicts the layering attenuation of the image through ghosting contrast data. Image layering refers to the depth difference between the foreground and background in the image and the perspective effect between the layers. By analyzing the ghosting contrast reduction data, the depth perception deviation between different layers is obtained, and the degree of layering reduction of the overall image is estimated based on this information. The estimation of the image layering reduction is calculated by comparing the brightness and clarity of different depth layers of the image to quantify the attenuation of the layering. Through this method, the image layering reduction data is obtained, and the image blurriness analysis is performed based on the naked eye 3D image blurriness data and the image layering reduction data. This step analyzes the clarity of the image on the overall screen by combining the blurriness data and the layering reduction data, performs sharpness analysis on each pixel of the image, uses image sharpness measurement methods such as the Laplace operator to identify blurry areas, compares these blurry areas with the image layering reduction areas, and analyzes which areas have increased blurriness due to the reduction of layering.This step can accurately measure the blurred area in the image and quantify the blurriness through data analysis, thereby generating naked-eye 3D image blurriness data, which helps adjust display parameters to improve the clarity and depth of the image. Based on the naked-eye 3D image blurriness data and the image layering reduction data, the naked-eye 3D parallax image distortion detection is performed. This step detects the distortion phenomenon in the parallax image by analyzing the blurriness and layering reduction data, and compares the parallax information in the image with the actual display result to detect whether there is obvious distortion in the parallax image. Combined with the blurriness and layering data, the system accurately identifies the distorted areas in the parallax image, especially those parts of the image distortion caused by ghosting, blurring or parallax mismatch. By comparing the different parallax levels of the image and the clarity of each level, the naked-eye 3D parallax image distortion data is generated.

[0149] Preferably, step S3 comprises the following steps:

[0150] Step S31: performing edge color overflow estimation based on naked eye 3D parallax image distortion data to obtain image edge color overflow data;

[0151] Step S32: performing local visual artifact detection according to the image edge color overflow data and the naked eye 3D visual image ghosting data to obtain naked eye 3D local visual artifact data;

[0152] Step S33: estimating user visual fatigue according to the image edge color overflow data and the naked eye 3D local visual artifact data to obtain naked eye 3D user visual fatigue data;

[0153] Step S34: performing naked-eye 3D display abnormality analysis according to naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data.

[0154] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0155] Step S31: performing edge color overflow estimation based on naked eye 3D parallax image distortion data to obtain image edge color overflow data;

[0156] In an embodiment of the present invention, edge color overflow estimation is performed based on the distortion data of the naked-eye 3D parallax image. Edge color overflow is caused by the diffusion of color information of the edge area to the surrounding area when the parallax image is processed at a depth level, resulting in an unnatural image display. The distortion data of the naked-eye 3D parallax image is analyzed, focusing on the edge area of ​​the image, and determining the area of ​​color overflow by comparing the color value change and the depth information. Color space conversion (such as RGB to HSV conversion) is applied to detect the area of ​​sudden change in color saturation so as to accurately mark the color overflow area. This analysis process is combined with an edge detection algorithm to determine the edge area where overflow occurs and estimate the degree of overflow, thereby generating edge color overflow data.

[0157] Step S32: performing local visual artifact detection according to the image edge color overflow data and the naked eye 3D visual image ghosting data to obtain naked eye 3D local visual artifact data;

[0158] In an embodiment of the present invention, local visual artifact detection is performed based on image edge color overflow data and naked-eye 3D visual image ghosting data. Local visual artifacts are usually caused by over-processing or inconsistent image ghosting in image processing, which manifests as unnatural brightness, color or shape deformation in the image. The edge color overflow data is used to determine the artifact area, and the local over-brightening or darkening parts of the image caused by color overflow are identified. Then, these areas are superimposed and analyzed with the ghosting data, and the image contrast and texture analysis algorithm is used to detect whether there is an artifact phenomenon caused by parallax mismatch or ghosting. By comparing the grayscale and color difference of adjacent pixels, the position and type of the artifact are accurately located to obtain the naked-eye 3D local visual artifact data.

[0159] Step S33: estimating user visual fatigue according to the image edge color overflow data and the naked eye 3D local visual artifact data to obtain naked eye 3D user visual fatigue data;

[0160] In an embodiment of the present invention, user visual fatigue is estimated based on image edge color overflow data and naked-eye 3D local visual artifact data. The purpose of this step is to predict the user's visual fatigue level by analyzing unnatural effects in the image (such as color overflow and local artifacts). By establishing a fatigue index model, combined with edge color overflow and artifact data, the impact of abnormal areas in the image on visual comfort is calculated. By analyzing the color difference and brightness changes in the image, combined with existing visual fatigue research, the weights of the impact of color inconsistency and local artifacts on visual fatigue are defined. Then, a weighted average algorithm is used to quantify the fatigue effects of different areas to obtain naked-eye 3D user visual fatigue data.

[0161] Step S34: performing naked-eye 3D display abnormality analysis according to naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data.

[0162] In an embodiment of the present invention, an abnormal analysis of naked-eye 3D display is performed based on naked-eye 3D visual fatigue data. This step detects abnormal conditions in the naked-eye 3D display system by conducting an in-depth analysis of the quantitative data of user visual fatigue, compares the visual fatigue data with the feedback data of the user experience, and evaluates the relationship between visual fatigue and display parameters (such as brightness, contrast, color saturation, etc.). Through this analysis, the root causes of fatigue can be identified, such as unclear images, color distortion, or inaccurate depth perception. Then, combined with the working status of the display system and the ambient lighting conditions, the technical parameters of the naked-eye 3D display are comprehensively analyzed to identify potential abnormalities and generate naked-eye 3D display abnormality data.

[0163] Preferably, step S4 comprises the following steps:

[0164] Step S41: performing image layering rendering on the naked eye 3D visual difference data according to the naked eye 3D display abnormality data to obtain naked eye 3D visual image layering rendering data;

[0165] Step S42: performing environment adaptation compensation processing on the naked eye 3D visual difference data based on the naked eye 3D display abnormality data to obtain naked eye 3D environment adaptation compensation data;

[0166] Step S43: controlling the optical characteristics of the image based on the naked-eye 3D visual image layered rendering data and the naked-eye 3D environment adaptation compensation data to obtain naked-eye 3D image optical characteristics adjustment data;

[0167] Step S44: optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristic adjustment data to obtain naked-eye 3D display optimization data.

[0168] In an embodiment of the present invention, image layering rendering is performed on naked-eye 3D visual difference data based on naked-eye 3D display abnormal data to generate naked-eye 3D visual image layering rendering data. Image layering rendering is to separate and process the naked-eye 3D image according to different depth levels, color levels, contrast and visual features, so as to make independent adjustments in each layer. In actual operation, the naked-eye 3D visual difference data is analyzed to identify the visual differences at different depths, and then layered processing is performed according to the visual features of each layer (such as brightness, contrast, color saturation, etc.). In order to improve the layering effect, depth mapping technology is applied to generate multiple depth layers according to the user's viewing angle and ambient lighting conditions, thereby realizing layered rendering of naked-eye 3D visual images. Each layer is independently rendered according to the corresponding depth information to form naked-eye 3D visual image layering rendering data, and the naked-eye 3D visual difference data is subjected to environmental adaptation compensation processing based on the naked-eye 3D display abnormal data to generate naked-eye 3D environmental adaptation compensation data. Environmental adaptation compensation processing is mainly used to optimize the naked eye 3D display effect and reduce the impact of display abnormalities in different viewing environments. By analyzing the naked eye 3D display abnormality data, the specific situation of the display abnormality, such as color overflow, ghosting, blur, etc., is obtained. Then, in combination with environmental factors (such as ambient light intensity, viewing distance, user visual fatigue, etc.), the naked eye 3D visual difference data is adaptively compensated. The compensation process includes dynamic adjustment of display brightness, contrast, color temperature, etc. to ensure that the image effect is not affected under different environmental conditions and reduce visual discomfort caused by environmental changes. Through this processing, the naked eye 3D environmental adaptation compensation data is generated, and the image optical characteristics are controlled based on the naked eye 3D visual image layered rendering data and the naked eye 3D environmental adaptation compensation data to obtain the naked eye 3D image optical characteristics adjustment data. The image optical characteristics control is to optimize the naked eye 3D display effect, control the image's focal length, light intensity, color distribution and other optical properties, and determine the visual characteristics and display effects of each layer by analyzing the naked eye 3D visual image layered rendering data. Then, the environmental adaptation compensation data is combined with these image layers, and the focal length, brightness, color temperature, contrast, etc. of each layer of the image are dynamically adjusted through optical simulation and control algorithms. Specific operations include adjusting the focus position of images at different depths, optimizing the distribution and intensity of light sources to avoid visual discomfort caused by focal length errors or uneven lighting, generating naked-eye 3D image optical characteristic adjustment data, optimizing naked-eye 3D display based on the naked-eye 3D image optical characteristic adjustment data, and generating naked-eye 3D display optimization data. The goal of naked-eye 3D display optimization is to optimize the display effect of naked-eye 3D images and enhance the user's viewing experience by accurately adjusting the optical characteristics of the image, and to accurately adjust the hardware parameters of the display system (such as the brightness, contrast, color saturation, etc. of the display) using the naked-eye 3D image optical characteristic adjustment data.Then, according to the environmental adaptation compensation data, the display parameters are dynamically adjusted to ensure that the image can maintain the best display effect under different environmental conditions. This step also involves calibrating the optical properties between image layers to reduce artifacts, blur, double images and other problems in the display, ensuring that the presentation effect of each visual layer is optimal. Through these adjustments, the naked eye 3D display optimization data is obtained, which is used to control the display system to achieve accurate and natural naked eye 3D display effects.

[0169] The present invention also provides a naked-eye 3D display control system with real-time data adjustment, which is used to execute the naked-eye 3D display control method with real-time data adjustment as described above. The naked-eye 3D display control system with real-time data adjustment includes:

[0170] Environmental space matrix construction module: used to obtain real-time operation data of naked-eye 3D display; collect surrounding environment based on real-time operation data of naked-eye 3D display to obtain surrounding environment data of naked-eye 3D display; construct environmental space matrix based on surrounding environment data of naked-eye 3D display and real-time operation data of naked-eye 3D display to obtain environmental space matrix data of naked-eye 3D display;

[0171] The parallax image distortion detection module is used to calculate the naked eye 3D visual difference according to the naked eye 3D display environment space matrix data to obtain the naked eye 3D visual difference data; perform the naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain the naked eye 3D parallax image distortion data;

[0172] Display abnormality analysis module: used to estimate user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; perform naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data;

[0173] Naked-eye 3D display optimization module: used to control the image optical characteristics of naked-eye 3D visual difference data according to naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimize the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

[0174] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A naked-eye 3D display control method with real-time data adjustment, characterized in that: The following steps are involved: Step S1: acquiring naked-eye 3D display real-time operation data; collecting surrounding environment data based on the naked-eye 3D display real-time operation data to obtain naked-eye 3D display surrounding environment data; constructing an environment space matrix based on the naked-eye 3D display surrounding environment data and the naked-eye 3D display real-time operation data to obtain naked-eye 3D display environment space matrix data; Step S2: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data; performing naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain naked eye 3D parallax image distortion data; Step S3: estimating user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; Perform naked eye 3D display abnormality analysis based on naked eye 3D user visual fatigue data to obtain naked eye 3D display abnormality data; Step S4: controlling the image optical characteristics of the naked-eye 3D visual difference data according to the naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

2. The naked-eye 3D display control method with real-time data adjustment according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining real-time operation data of naked-eye 3D display; Step S12: collecting surrounding environment data based on the real-time operation data of the naked-eye 3D display to obtain the surrounding environment data of the naked-eye 3D display; Step S13: collecting naked-eye 3D display space environment data according to the naked-eye 3D display surrounding environment data to obtain naked-eye 3D display space environment data; Step S14: measuring the light intensity of the naked-eye 3D display surrounding environment according to the naked-eye 3D display surrounding environment data to obtain the display surrounding environment light intensity data; Step S15: constructing an environment space matrix based on the naked-eye 3D display space environment data, the display surrounding environment light intensity data and the naked-eye 3D display real-time operation data to obtain the naked-eye 3D display environment space matrix data.

3. The naked-eye 3D display control method with real-time data adjustment according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: acquiring the user position of the naked-eye 3D display real-time operation data based on the sensor to obtain the naked-eye 3D display user position data; Step S152: collecting the position of the 3D display screen according to the real-time operation data of the naked-eye 3D display to obtain the position data of the naked-eye 3D display screen; Step S153: measuring the user distance according to the naked-eye 3D display user position data and the naked-eye 3D display screen position data to obtain the naked-eye 3D user viewing distance data; Step S154: calculating the user viewing angle according to the naked-eye 3D display user position data and the naked-eye 3D user viewing distance data to obtain the user viewing angle data; Step S155: constructing a rectangular coordinate system of the naked-eye 3D display space according to the naked-eye 3D display space environment data to obtain the rectangular coordinate system data of the naked-eye 3D environment space; Step S156: constructing an environment space matrix based on the naked eye 3D space rectangular coordinate system data, the display surrounding environment illumination intensity data and the user viewing visual angle data to obtain the naked eye 3D display environment space matrix data.

4. The naked-eye 3D display control method with real-time data adjustment according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing naked eye 3D visual difference calculation according to the naked eye 3D display environment space matrix data to obtain naked eye 3D visual difference data; Step S22: performing parallax image spatial dislocation analysis according to the naked eye 3D visual difference data to obtain naked eye 3D visual spatial dislocation data; Step S23: estimating the ghosting effect of the visual image based on the naked eye 3D visual space dislocation data to obtain the ghosting data of the naked eye 3D visual image; Step S24: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D visual image ghosting data and the naked-eye 3D visual space misalignment data to obtain naked-eye 3D parallax image distortion data.

5. The naked-eye 3D display control method with real-time data adjustment according to claim 4, characterized in that: Step S21 includes the following steps: Step S211: calculating the intensity of reflected light in the naked-eye 3D display environment according to the spatial matrix data of the naked-eye 3D display environment to obtain the intensity data of reflected light in the naked-eye 3D display environment; Step S212: performing halo effect analysis according to the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D halo effect data; Step S213: estimating naked-eye 3D image detail loss according to naked-eye 3D halo effect data to obtain naked-eye 3D image detail loss data; Step S214: estimating display light interference based on the naked-eye 3D display environment reflected light intensity data to obtain naked-eye 3D display light interference data; Step S215: performing naked-eye 3D image abnormality analysis based on the naked-eye 3D display environment space matrix data to obtain naked-eye 3D image abnormality data; Step S216: performing naked-eye 3D visual difference calculation according to the naked-eye 3D display light interference data, the naked-eye 3D image abnormality data and the naked-eye 3D image detail loss data to obtain the naked-eye 3D visual difference data.

6. The naked-eye 3D display control method with real-time data adjustment according to claim 5, characterized in that: Step S215 includes the following steps: Based on the spatial matrix data of the naked eye 3D display environment, the user viewing angle deviation is collected to obtain the user viewing angle deviation data; Performing naked eye 3D parallax mismatch analysis according to the user viewing angle deviation data, thereby obtaining naked eye 3D parallax mismatch data; The left-eye and right-eye image cross probability is estimated according to the naked-eye 3D parallax mismatch data, thereby obtaining the left-eye and right-eye image cross probability data; Perform image depth perception anomaly analysis based on left-eye and right-eye image cross probability data and naked-eye 3D parallax mismatch data to obtain 3D image depth perception anomaly data; Perform 3D image dynamic depth discontinuity detection based on 3D image depth perception abnormal data to obtain 3D image dynamic depth discontinuity data; According to the 3D image dynamic depth discontinuity data and the 3D image depth perception abnormality data, the naked eye 3D stereoscopic effect attenuation is estimated to obtain the naked eye 3D stereoscopic effect attenuation data; According to the naked-eye 3D stereoscopic effect attenuation data and the 3D image dynamic depth discontinuity data, the naked-eye 3D image abnormality analysis is performed to obtain the naked-eye 3D image abnormality data.

7. The naked-eye 3D display control method with real-time data adjustment according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing naked-eye 3D image segmentation detection on the naked-eye 3D visual space dislocation data to obtain naked-eye 3D image segmentation data; Step S242: performing image consistency attenuation estimation based on the naked eye 3D image segmentation data to obtain image consistency attenuation data; Step S243: performing ghost contrast reduction detection based on the naked eye 3D visual image ghost data to obtain naked eye 3D ghost contrast reduction data; Step S244: performing image layering reduction estimation according to naked eye 3D ghosting contrast reduction data to obtain image layering reduction data; Step S245: performing image blur analysis according to the naked eye 3D ghosting contrast reduction data and the image consistency attenuation data to obtain naked eye 3D image blur data; Step S246: performing naked-eye 3D parallax image distortion detection based on the naked-eye 3D image blur data and the image layering reduction data to obtain naked-eye 3D parallax image distortion data.

8. The naked-eye 3D display control method with real-time data adjustment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing edge color overflow estimation based on naked eye 3D parallax image distortion data to obtain image edge color overflow data; Step S32: performing local visual artifact detection according to the image edge color overflow data and the naked eye 3D visual image ghosting data to obtain naked eye 3D local visual artifact data; Step S33: estimating user visual fatigue according to the image edge color overflow data and the naked eye 3D local visual artifact data to obtain naked eye 3D user visual fatigue data; Step S34: performing naked-eye 3D display abnormality analysis according to naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data.

9. The naked-eye 3D display control method with real-time data adjustment according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing image layering rendering on the naked eye 3D visual difference data according to the naked eye 3D display abnormality data to obtain naked eye 3D visual image layering rendering data; Step S42: performing environment adaptation compensation processing on the naked eye 3D visual difference data based on the naked eye 3D display abnormality data to obtain naked eye 3D environment adaptation compensation data; Step S43: controlling the optical characteristics of the image based on the naked-eye 3D visual image layered rendering data and the naked-eye 3D environment adaptation compensation data to obtain naked-eye 3D image optical characteristics adjustment data; Step S44: optimizing the naked-eye 3D display according to the naked-eye 3D image optical characteristic adjustment data to obtain naked-eye 3D display optimization data.

10. A naked-eye 3D display control system with real-time data adjustment, characterized in that: The naked-eye 3D display control method for performing real-time data adjustment according to claim 1, the naked-eye 3D display control system for real-time data adjustment comprises: Environmental space matrix construction module: used to obtain real-time operation data of naked-eye 3D display; collect surrounding environment based on real-time operation data of naked-eye 3D display to obtain surrounding environment data of naked-eye 3D display; construct environmental space matrix based on surrounding environment data of naked-eye 3D display and real-time operation data of naked-eye 3D display to obtain environmental space matrix data of naked-eye 3D display; The parallax image distortion detection module is used to calculate the naked eye 3D visual difference according to the naked eye 3D display environment space matrix data to obtain the naked eye 3D visual difference data; perform the naked eye 3D parallax image distortion detection based on the naked eye 3D visual difference data to obtain the naked eye 3D parallax image distortion data; Display abnormality analysis module: used to estimate user visual fatigue based on naked-eye 3D parallax image distortion data to obtain naked-eye 3D user visual fatigue data; perform naked-eye 3D display abnormality analysis based on naked-eye 3D user visual fatigue data to obtain naked-eye 3D display abnormality data; Naked-eye 3D display optimization module: used to control the image optical characteristics of naked-eye 3D visual difference data according to naked-eye 3D display abnormality data to obtain naked-eye 3D image optical characteristics adjustment data; optimize the naked-eye 3D display according to the naked-eye 3D image optical characteristics adjustment data to obtain naked-eye 3D display optimization data.

Images