Method and System for Optimizing Ultra-High Definition Picture Quality of Smart All-in-One Machine

Abstract

The present invention relates to the technical field of ultra-high-definition picture quality optimization, and discloses a method and system for optimizing the ultra-high-definition picture quality of an intelligent all-in-one machine, including: identifying the display mode of the intelligent all-in-one machine; when the display mode is the first mode, improving the picture quality of the displayed picture to obtain a picture with improved quality; analyzing the improved picture quality of the picture with improved quality, and when the improved picture quality meets the preset display requirements, using the picture with improved quality as the target display picture in the first mode; when the display mode is the second mode, performing edge detection on the second display picture, analyzing the picture quality based on the result of the edge detection, calculating the gray level of the picture when the picture quality does not meet the preset display requirements; adjusting the parameters of the second display picture based on the gray level to obtain an adjusted picture, analyzing the perceived authenticity of the adjusted picture, and when the perceived authenticity meets the preset authenticity, using the adjusted picture as the final display picture in the second mode. The present invention can improve the accuracy of ultra-high-definition picture quality.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-high-definition picture quality optimization, and particularly to a method and system for optimizing the ultra-high-definition picture quality of an intelligent all-in-one machine. Background Art

[0002] Intelligent all-in-one machines play an important role in modern life and work, and ultra-high-definition picture quality optimization is crucial for enhancing the user experience. Developing a method for optimizing the ultra-high-definition picture quality of an intelligent all-in-one machine can significantly improve the image display quality, making the picture clearer, more delicate, and more realistic, bringing an immersive visual enjoyment to users. At the same time, high-quality picture quality can better meet the requirements for image accuracy in professional fields such as design and film production, improving work efficiency and the quality of results. In addition, the optimized ultra-high-definition picture quality also helps to more accurately convey information in scenarios such as education and medical treatment, enhancing the teaching and diagnosis effects.

[0003] Currently, the optimization of the ultra-high-definition picture quality of intelligent all-in-one machines generally adjusts parameters such as color and contrast through software algorithms to achieve picture quality improvement. However, through this method, the degree of picture quality optimization is limited, and problems such as color distortion and detail loss may occur when processing complex image scenes. Since intelligent all-in-one machines need to process different types of image content, including static images and dynamic videos, when facing diverse image data, the existing optimization methods are difficult to achieve comprehensive and accurate picture quality optimization, resulting in an unsatisfactory ultra-high-definition picture quality performance of intelligent all-in-one machines. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the ultra-high-definition picture quality of an intelligent all-in-one machine, and its main purpose is to improve the accuracy of the ultra-high-definition picture quality of the intelligent all-in-one machine.

[0005] To achieve the above object, a method for optimizing the ultra-high-definition picture quality of an intelligent all-in-one machine provided by the present invention includes:

[0006] Obtain an intelligent all-in-one machine with the picture quality to be optimized, analyze the light perception conditions of the display environment corresponding to the intelligent all-in-one machine, and based on the light perception conditions, perform light perception adjustment on the intelligent all-in-one machine to obtain an adjusted intelligent all-in-one machine, and identify the display mode of the adjusted intelligent all-in-one machine;

[0007] When the display mode is the first mode, obtain the first display screen of the adjusted intelligent all-in-one machine, analyze the first picture quality of the first display screen, and when the first picture quality does not meet the display requirements, perform frequency domain transformation on the first display screen to obtain a frequency domain transformation screen, identify the real component information and imaginary component information of the frequency domain transformation screen, and based on the real component information and the imaginary component information, perform picture quality improvement on the display screen to obtain a picture quality improved screen;

[0008] Analyze the enhanced picture quality of the picture quality enhanced picture. When the enhanced picture quality does not meet the preset display requirements, return to execute the step of performing frequency domain transformation on the first display picture. When the enhanced picture quality meets the preset display requirements, use the picture quality enhanced picture as the target display picture in the first mode;

[0009] When the display mode is the second mode, perform edge detection on the second display picture corresponding to the second mode. Based on the result of the edge detection, perform background separation on the second display picture to obtain a background separated picture. Analyze the separated picture quality of the background separated picture. When the separated picture quality does not meet the preset display requirements, calculate the gray level of the background separated picture;

[0010] Based on the gray level, adjust the picture parameters of the second display picture to obtain an adjusted picture, and analyze the perceived authenticity of the adjusted picture. When the perceived authenticity does not meet the preset authenticity, return to execute the step of adjusting the picture parameters of the second display picture based on the gray level. When the perceived authenticity meets the preset authenticity, use the adjusted picture as the final display picture in the second mode.

[0011] Optionally, the performing light sensing adjustment on the smart all-in-one machine based on the light sensing condition to obtain an adjusted smart all-in-one machine includes:

[0012] Query the light sensing data corresponding to the light sensing condition;

[0013] Perform statistical analysis on the light sensing data to determine the light sensing distribution state of the light sensing condition;

[0014] Query the usage scenario of the smart all-in-one machine and the user's usage requirements;

[0015] Based on the usage scenario and the user's usage requirements, combined with the light sensing distribution state, perform light sensing adjustment on the smart all-in-one machine to obtain an adjusted smart all-in-one machine.

[0016] Optionally, the analyzing the first picture quality of the first display picture includes:

[0017] Identify the brightness value, contrast value, and color saturation of the first display picture;

[0018] Based on the brightness value, the contrast value, and the color saturation, use the following formula to calculate the quality evaluation value of the first display picture:

[0019] ;

[0020] Wherein, Represents the quality assessment value, Represents the brightness value, Represents the contrast value, Represents the color saturation, Represents The evaluation weight of, Represents The evaluation weight of, Represents The evaluation weight of;

[0021] Based on the quality assessment value, determine the first picture quality of the first display picture.

[0022] Optionally, the identifying the real component information and the imaginary component information of the frequency-domain transformed picture includes: constructing a frequency spectrum chart of the frequency-domain transformed picture;

[0023] Query the horizontal amplitude information and the vertical amplitude information of the frequency spectrum chart;

[0024] Based on the horizontal amplitude information, determine the phase information of the frequency-domain transformed picture;

[0025] Based on the vertical amplitude information, determine the amplitude information of the frequency-domain transformed picture;

[0026] Based on the phase information and the amplitude information, determine the real component information and the imaginary component information of the frequency-domain transformed picture.

[0027] Optionally, the enhancing the picture quality of the display picture based on the real component information and the imaginary component information to obtain a picture with enhanced quality includes:

[0028] Based on the real component information and the imaginary component information, determine the noise frequency components, high-frequency components and color frequency distribution of the display picture;

[0029] Perform denoising processing on the display picture based on the noise frequency components to obtain a denoised picture;

[0030] Perform detail enhancement on the denoised picture based on the high-frequency components to obtain an enhanced picture;

[0031] Perform color correction on the enhanced picture based on the color frequency distribution to obtain a picture with enhanced quality.

[0032] Optionally, the analyzing the quality of the picture with enhanced quality includes:

[0033] Use the following formula to calculate the mean square error of the image of the picture with enhanced quality:

[0034] ;

[0035] Among them, represents the mean square error of the image, represents the pixel value of the original image corresponding to the image with improved image quality at the coordinate point , represents the pixel value of the image with improved image quality at the coordinate point , represents the length of the image with improved image quality, represents the width of the image with improved image quality, i represents the horizontal axis coordinate point, and j represents the vertical axis coordinate point;

[0036] Based on the mean square error of the image, analyze the image quality improvement of the image with improved image quality.

[0037] Optionally, the edge detection of the second display screen corresponding to the second mode includes:

[0038] Perform frame division on the second display screen to obtain divided frames;

[0039] Perform filtering on the divided frames to obtain a filtered image;

[0040] Calculate the gradient value of the filtered image;

[0041] Based on the gradient value, perform double-threshold processing on the filtered image to obtain a threshold-processed image;

[0042] Perform edge tracking on the threshold-processed image to obtain the image edge.

[0043] Optionally, based on the result of the edge detection, perform background separation on the second display screen to obtain a background-separated image, including:

[0044] Based on the result of the edge detection, determine the edge key points of the second display screen;

[0045] Based on the edge key points, construct an image anchor box for the second display screen;

[0046] Based on the image anchor box, perform adaptive anchoring on the second display screen to obtain an anchored image;

[0047] Based on the anchored image, perform background separation on the second display screen to obtain a background-separated image.

[0048] Optionally, the separation image quality of the background-separated image includes:

[0049] Calculate the mean square error of the separation image of the background-separated image;

[0050] Based on the mean square error of the separated screen, calculate the peak signal-to-noise ratio of the background separated screen using the following formula:

[0051] ;

[0052] where, represents the peak signal-to-noise ratio, represents the mean square error of the separated screen, represents the maximum pixel value of the background separated screen;

[0053] Based on the peak signal-to-noise ratio, determine the separated screen quality of the background separated screen.

[0054] To solve the above problems, the present invention also provides a system for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine. The system includes:

[0055] A display mode recognition module, configured to obtain an intelligent all-in-one machine with a picture quality to be optimized, analyze the light-sensing conditions of the display environment corresponding to the intelligent all-in-one machine, perform light-sensing adjustment on the intelligent all-in-one machine based on the light-sensing conditions to obtain an adjusted intelligent all-in-one machine, and recognize the display mode of the adjusted intelligent all-in-one machine;

[0056] A picture quality improvement module, configured to, when the display mode is the first mode, obtain the first display picture of the adjusted intelligent all-in-one machine, analyze the first picture quality of the first display picture, and when the first picture quality does not meet the display requirements, perform frequency-domain transformation on the first display picture to obtain a frequency-domain transformed picture, recognize the real component information and imaginary component information of the frequency-domain transformed picture, and perform picture quality improvement on the display picture based on the real component information and the imaginary component information to obtain a picture quality improved picture;

[0057] A picture quality analysis module, configured to analyze the improved picture quality of the picture quality improved picture, and when the improved picture quality does not meet the preset display requirements, return to execute the step of performing frequency-domain transformation on the first display picture, and when the improved picture quality meets the preset display requirements, use the picture quality improved picture as the target display picture of the first mode;

[0058] A second display picture analysis module, configured to, when the display mode is the second mode, perform edge detection on the second display picture corresponding to the second mode, perform background separation on the second display picture based on the result of the edge detection to obtain a background separated picture, analyze the separated screen quality of the background separated picture, and when the separated screen quality does not meet the preset display requirements, calculate the gray level of the background separated picture;

[0059] The picture quality evaluation module is used to adjust the picture parameters of the second display screen based on the gray level to obtain an adjusted picture, analyze the perceived authenticity of the adjusted picture, and when the perceived authenticity does not meet the preset authenticity, return to execute the step of adjusting the picture parameters of the second display screen based on the gray level. When the perceived authenticity meets the preset authenticity, the adjusted picture is used as the final display picture of the second mode.

[0060] The present invention can perform targeted light sense adjustment by analyzing the light sense conditions of the corresponding display environment of the smart all-in-one machine, improving the visibility and comfort of the picture; when the display mode is the first mode, the present invention can obtain the current display information of the smart all-in-one machine by acquiring the first display picture of the adjusted smart all-in-one machine as a specific basis for picture quality optimization; the present invention can help users separate the foreground and background in the picture by performing edge detection on the second display picture corresponding to the second mode, providing a basis for subsequent background separation; the present invention can make the image better present details and layering under different display conditions and improve the overall quality of the picture by adjusting the picture parameters of the second display picture based on the gray level to obtain an adjusted picture; finally, the present invention can obtain a high-quality display picture to meet the display requirements of the smart all-in-one machine by using the adjusted picture as the final display picture of the second mode when the perceived authenticity meets the preset authenticity. Therefore, the present invention can improve the accuracy of the ultra-high definition picture quality of the smart all-in-one machine. Description of the Drawings

[0061] Figure 1 It is a schematic flowchart of a method for optimizing the ultra-high definition picture quality of a smart all-in-one machine provided by an embodiment of the present invention;

[0062] Figure 2 It is a functional module diagram of a system for optimizing the ultra-high definition picture quality of a smart all-in-one machine provided by an embodiment of the present invention;

[0063] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] The embodiments of the present application provide a method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine. The execution subject of the method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0066] Referring to Figure 1 As shown, it is a schematic flowchart of a method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine provided by an embodiment of the present invention. In this embodiment, the method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine includes:

[0067] S1. Obtain an intelligent all-in-one machine with picture quality to be optimized, analyze the light perception conditions of the display environment corresponding to the intelligent all-in-one machine, and based on the light perception conditions, perform light perception adjustment on the intelligent all-in-one machine to obtain an adjusted intelligent all-in-one machine, and identify the display mode of the adjusted intelligent all-in-one machine.

[0068] In the embodiments of the present invention, by obtaining the intelligent all-in-one machine with picture quality to be optimized, the specific device that needs to optimize the picture quality can be determined, providing a target for subsequent analysis and processing. The device information to be optimized can be obtained by connecting to the communication interface or management system of the intelligent all-in-one machine. Modes such as network connection and USB connection can be used to interact with the device to obtain the identification information, current settings, etc. of the device.

[0069] Among them, the intelligent all-in-one machine refers to an intelligent device integrating multiple functions and technologies. Generally, it has a high-resolution liquid crystal display screen or other advanced display technologies, can present clear and vivid images and video content, and a powerful processor, such as a high-performance central processing unit (CPU) and a graphics processing unit (GPU), which can quickly process various complex tasks, including image and video playback, multitasking, running various application programs, etc.

[0070] Furthermore, the embodiment of the present invention can adjust the light perception in a targeted manner by analyzing the light perception conditions of the display environment corresponding to the smart all-in-one machine, thereby improving the visibility and comfort of the picture. The light perception conditions refer to a series of environmental factors and characteristics related to light, which affect people's perception of light and the presentation effect of objects in the light environment.

[0071] Optionally, the light sensing condition can be obtained by deploying light sensors around the smart all-in-one machine to measure parameters such as the intensity and color temperature of the ambient light.

[0072] Furthermore, the embodiment of the present invention adjusts the light perception of the smart all-in-one machine based on the light perception condition, so that the display of the smart all-in-one machine can be adapted to different ambient light conditions, thereby improving the user experience.

[0073] As an embodiment of the present invention, the light sensing of the smart all-in-one machine is adjusted based on the light sensing condition to obtain an adjusted smart all-in-one machine, including: querying the light sensing data corresponding to the light sensing condition, performing statistical analysis on the light sensing data to determine the light sensing distribution state of the light sensing condition, querying the usage scenarios and user usage requirements of the smart all-in-one machine, and adjusting the light sensing of the smart all-in-one machine based on the usage scenarios and the user usage requirements combined with the light sensing distribution state to obtain an adjusted smart all-in-one machine.

[0074] Among them, the light sensing data includes light intensity, color temperature and light uniformity. The usage scenarios are such as conference rooms, exhibition halls, etc., and the corresponding requirements are different depending on the usage scenarios.

[0075] Optionally, the light perception data can be collected by a light sensor, and the light perception distribution state can be obtained by statistically analyzing the average value, maximum value, minimum value and standard deviation of the light intensity, color temperature and light uniformity corresponding to different time periods in the light perception data. The adjustment of the smart all-in-one machine can be obtained by adjusting parameters such as the screen brightness, contrast and color temperature of the smart all-in-one machine. If the ambient light is strong, the screen brightness can be appropriately increased. If the color temperature of the ambient light is warm, the screen color temperature can be adjusted to keep the picture natural.

[0076] Furthermore, the embodiment of the present invention can perform corresponding image quality analysis according to different display modes by identifying and adjusting the display mode of the smart all-in-one machine to better optimize the image quality. Among them, the display mode includes a picture display mode and a video display mode, and the display mode can be identified by reading the setting information of the device and analyzing the type of content currently displayed.

[0077] S2. When the display mode is the first mode, obtain the first display screen of the adjustable intelligent all-in-one machine, analyze the first screen quality of the first display screen, and when the first screen quality does not meet the display requirements, perform frequency domain transformation on the first display screen to obtain a frequency domain transformed screen, identify the real component information and imaginary component information of the frequency domain transformed screen, and based on the real component information and the imaginary component information, improve the picture quality of the display screen to obtain a picture quality improved screen.

[0078] In the embodiment of the present invention, by obtaining the first display screen of the adjustable intelligent all-in-one machine when the display mode is the first mode, the current display information of the intelligent all-in-one machine can be obtained as a specific basis for picture quality optimization. Among them, the first mode refers to the picture display mode, and the display content is displayed in the form of an image.

[0079] Further, in the embodiment of the present invention, by analyzing the first screen quality of the first display screen, the current quality level of the first display screen can be determined to judge whether picture quality improvement is required.

[0080] As an embodiment of the present invention, analyzing the first screen quality of the first display screen includes: identifying the brightness value, contrast value and color saturation of the first display screen, and based on the brightness value, the contrast value and the color saturation, using the following formula to calculate the quality evaluation value of the first display screen:

[0081] ;

[0082] Among them, represents the quality evaluation value, represents the brightness value, represents the contrast value, represents the color saturation, represents the evaluation weight of represents the evaluation weight of represents the evaluation weight of;

[0083] Based on the quality evaluation value, determine the first screen quality of the first display screen.

[0084] Optionally, determining the first screen quality of the first display screen based on the quality evaluation value is determined by comparing the quality evaluation value with a preset evaluation value. The preset evaluation value can be set to 0.8. If the quality evaluation value is not less than 0.8, it means that the first screen quality of the first display screen is qualified.

[0085] It should be understood that when the first picture quality does not meet the display requirements, it means that the quality evaluation value is less than 0.8, indicating that the first picture quality of the first display picture is unqualified and needs to be optimized.

[0086] In the embodiment of the present invention, by performing frequency domain transformation on the first display picture, the frequency domain transformation picture can be obtained to analyze the image information in more detail, so as to determine which parts of the image have low quality and the method for improving the image quality. Among them, the frequency domain transformation refers to converting the image from the spatial domain to the frequency domain, which can be realized by Fourier transform operation.

[0087] Furthermore, in the embodiment of the present invention, by identifying the real component information and imaginary component information of the frequency domain transformation picture, the frequency domain information can be used to perform targeted processing on the image, improving the quality and visual effect of the picture.

[0088] Among them, the real component information refers to the partial amplitude information of the signal in the frequency domain, and the imaginary component information refers to the partial phase information of the signal in the frequency domain and another part of the amplitude information.

[0089] As an embodiment of the present invention, identifying the real component information and imaginary component information of the frequency domain transformation picture includes: constructing a frequency spectrum chart of the frequency domain transformation picture, querying the horizontal amplitude information and vertical amplitude information of the frequency spectrum chart, based on the horizontal amplitude information, determining the phase information of the frequency domain transformation picture, based on the vertical amplitude information, determining the amplitude information of the frequency domain transformation picture, and based on the phase information and the amplitude information, determining the real component information and imaginary component information of the frequency domain transformation picture. Among them, the phase information reflects the relative time relationship between different frequency components. For example, in the Fourier transform of an image, the phase information describes the spatial position relationship of each frequency component in the image, and the amplitude information refers to the intensity or magnitude of the signal.

[0090] Optionally, the frequency spectrum chart can be constructed by combining the frequency domain transformation picture through java software. The querying of the horizontal amplitude information and vertical amplitude information of the frequency spectrum chart can be obtained by traversing all frequency points in the frequency spectrum chart and calculating the amplitude of each frequency point. The determination of the phase information of the frequency domain transformation picture based on the horizontal amplitude information can be obtained by calculating the argument of the complex value in the frequency spectrum chart. The determination of the amplitude information of the frequency domain transformation picture based on the vertical amplitude information can be analyzed by calculating statistical quantities such as the maximum value, minimum value, and average value of the amplitude. The determination of the real component information and imaginary component information of the frequency domain transformation picture based on the phase information and the amplitude information can be calculated by the following formula:

[0091] After knowing the amplitude and phase information, the real component and imaginary component can be calculated by the following formula:

[0092] ;

[0093] ;

[0094] Among them, represents the real component, represents the imaginary component, represents the amplitude, represents the phase.

[0095] Furthermore, in the embodiment of the present invention, based on the real component information and the imaginary component information, the quality of the display screen is improved to obtain a quality-improved screen, which can enhance the perceived texture of the screen to meet the screen viewing needs of the display user corresponding to the smart all-in-one machine.

[0096] As an embodiment of the present invention, the quality of the display screen is improved based on the real component information and the imaginary component information to obtain a quality-improved screen, including: based on the real component information and the imaginary component information, determining the noise frequency components, high-frequency components and color frequency distribution of the display screen, performing denoising processing on the display screen based on the noise frequency components to obtain a denoised screen, performing detail enhancement on the denoised screen based on the high-frequency components to obtain an enhanced screen, and performing color correction on the enhanced screen based on the color frequency distribution to obtain a quality-improved screen.

[0097] Among them, the noise frequency components refer to the manifestations in the frequency domain of the unwanted signal components generated by various factors (such as sensor noise, quantization error, environmental interference, etc.) when x is in the image. The high-frequency components refer to the parts corresponding to the rapidly changing parts in the frequency domain representation of the image. The color frequency distribution refers to the energy distribution of different color channels in the frequency domain in a color image.

[0098] The determination of the noise frequency components, high-frequency components and color frequency distribution of the display screen based on the real component information and the imaginary component information can be achieved by analyzing the real component information and the imaginary component information to determine the frequency regions containing noise to determine the noise frequency components, observing the amplitudes and phases of the real component information and the imaginary component information to determine the high-frequency components, and analyzing the real component information and the imaginary component information to determine the manifestations of different colors in different frequency regions. The high-frequency components are related to the details and textures of the colors, while the low-frequency components affect the overall hue and brightness of the colors.

[0099] Optionally, the denoising process of the display screen based on the noise frequency components to obtain a denoised screen can be achieved through a low-pass filter. The detail enhancement of the denoised screen based on the high-frequency components to obtain an enhanced screen can be realized by appropriately increasing the amplitudes of the high-frequency regions in the real and imaginary components, which can be achieved by multiplying by a gain factor greater than 1. The color correction of the enhanced screen based on the color frequency distribution to obtain a picture quality improved screen can be realized by adjusting the real and imaginary components of the color channels to achieve the correction of color balance. For example, if a certain color channel in the image is too bright or too dark, the overall brightness can be changed by adjusting the real component of that channel in the low-frequency region.

[0100] S3. Analyze the quality of the picture quality improved screen. When the quality of the picture quality improved screen does not meet the preset display requirements, return to execute the step of performing frequency domain transformation on the first display screen. When the quality of the picture quality improved screen meets the preset display requirements, use the picture quality improved screen as the target display screen in the first mode.

[0101] In the embodiment of the present invention, by analyzing the quality of the picture quality improved screen, it can be understood whether the picture quality improvement effect of the picture quality improved screen meets the requirements.

[0102] As an embodiment of the present invention, the analysis of the quality of the picture quality improved screen includes:

[0103] Calculate the mean square error of the image of the picture quality improved screen using the following formula:

[0104] ;

[0105] Where, represents the mean square error of the image, represents the pixel value of the original image corresponding to the picture quality improved screen at the coordinate point , represents the pixel value of the picture quality improved screen at the coordinate point , represents the length of the picture quality improved screen, represents the width of the picture quality improved screen, i represents the horizontal axis coordinate point, and j represents the vertical axis coordinate point;

[0106] Based on the mean square error of the image, analyze the quality of the picture quality improved screen.

[0107] It should be noted that when the calculation result of the mean square error of the image is not greater than 0.2, it indicates that the quality of the picture quality improved screen meets the user's requirements, which can be specifically set according to the actual application requirements.

[0108] In the embodiment of the present invention, when the improved picture quality does not meet the preset display requirements, returning to execute the step of performing frequency domain transformation on the first display picture can timely adjust the picture quality to meet the display requirements.

[0109] Furthermore, when the improved picture quality meets the preset display requirements, using the picture quality improved picture as the target display picture in the first mode can obtain a high-quality display picture to meet the display requirements of the smart all-in-one machine.

[0110] S4. When the display mode is the second mode, perform edge detection on the second display picture corresponding to the second mode. Based on the result of the edge detection, perform background separation on the second display picture to obtain a background separated picture. Analyze the separated picture quality of the background separated picture. When the separated picture quality does not meet the preset display requirements, calculate the gray level of the background separated picture.

[0111] It should be understood that when the display mode is the second mode, it means that the picture display mode of the smart all-in-one machine is the video mode.

[0112] In the embodiment of the present invention, performing edge detection on the second display picture corresponding to the second mode can help the user separate the foreground and background in the picture, providing a basis for subsequent background separation.

[0113] As an embodiment of the present invention, performing edge detection on the second display picture corresponding to the second mode includes: performing picture frame division on the second display picture to obtain a frame-divided picture, performing filtering processing on the frame-divided picture to obtain a filtered picture, calculating the gradient value of the filtered picture, performing double-threshold processing on the filtered picture based on the gradient value to obtain a threshold-processed picture, and performing edge tracking on the threshold-processed picture to obtain a picture edge.

[0114] Among them, the picture frame division refers to dividing the video into multiple frame images. In the setting of this solution, the display picture corresponding to the second mode is the video mode. Therefore, the operation of performing picture frame division on the second display picture can be carried out. The double-threshold processing refers to the process of dividing the gradient amplitude into three categories: strong edges, weak edges, and non-edges according to two set thresholds (high threshold and low threshold).

[0115] Optionally, the frame-divided picture can be obtained by operating a frame division tool, such as through an image processing library in the Python language, performing a frame division operation on the second display picture. The filtered picture can be obtained by performing Gaussian filtering on the frame-divided picture. The gradient value can be calculated through a gradient function.

[0116] Performing edge tracking on the threshold processing screen to obtain the screen edge, which can be obtained by connecting the edge pixels of the threshold processing screen.

[0117] Further, in the embodiment of the present invention, based on the result of the edge detection, background separation is performed on the second display screen, and the obtained background separation screen can process the foreground or background of the image separately, improving the quality and effect of the screen.

[0118] As an embodiment of the present invention, performing background separation on the second display screen based on the result of the edge detection to obtain a background separation screen includes: determining the edge key points of the second display screen based on the result of the edge detection, constructing an image anchor box of the second display screen based on the edge key points, performing adaptive anchoring on the second display screen based on the image anchor box to obtain an anchor box image, and performing background separation on the second display screen based on the anchor box image to obtain a background separation screen. Among them, the image anchor box refers to a technology in the field of image target detection for accurately positioning targets in an image.

[0119] Among them, the adaptive anchor box refers to the process of extracting the target object to be analyzed in the image, avoiding interference from excessive information, and improving the efficiency of image analysis.

[0120] Optionally, the edge key points can be obtained by determining the specific edge pixel points in the second display screen through the result of the edge detection. The image anchor box can be generated using the GIMP tool after determining the size based on the edge key points, and performing adaptive anchoring on the second display screen to obtain an anchor box image.

[0121] Furthermore, in the embodiment of the present invention, by analyzing the separation screen quality of the background separation screen, it can be determined whether the effect of the background separation meets the preset display requirements.

[0122] As an embodiment of the present invention, the separation screen quality of the background separation screen includes: calculating the mean square error of the separation screen of the background separation screen, and calculating the peak signal-to-noise ratio of the background separation screen based on the mean square error of the separation screen using the following formula:

[0123] ;

[0124] Among them, represents the peak signal-to-noise ratio, represents the mean square error of the separation screen, represents the maximum pixel value of the background separation screen;

[0125] Based on the peak signal-to-noise ratio, determine the separation screen quality of the background separation screen.

[0126] Optionally, determining the separation screen quality of the background separation screen based on the peak signal-to-noise ratio can be determined by the peak signal-to-noise ratio calculation result. If the peak signal-to-noise ratio is not less than 30 dB, it indicates that the separation screen quality of the background separation screen is excellent. The mean square error of the separation screen refers to the mean square error of the image of the separation screen and can be calculated by the mean square error function.

[0127] When the separation screen quality does not meet the preset display requirements, calculating the gray level of the background separation screen can help the user understand information about the screen brightness distribution and provide a basis for subsequent screen parameter adjustment.

[0128] Among them, the gray level refers to the range of the brightness level or gray value of the pixels in the image.

[0129] Optionally, the gray level of the background separation screen can be obtained by converting the background separation screen into a grayscale image and calculating the gray value of each pixel.

[0130] S5. Based on the gray level, adjust the screen parameters of the second display screen to obtain an adjusted screen, and analyze the perceived authenticity of the adjusted screen. When the perceived authenticity does not meet the preset authenticity, return to execute the step of adjusting the screen parameters of the second display screen based on the gray level. When the perceived authenticity meets the preset authenticity, use the adjusted screen as the final display screen of the second mode.

[0131] In the embodiment of the present invention, by adjusting the screen parameters of the second display screen based on the gray level to obtain an adjusted screen, the image can better present details and layering under different display conditions, improving the overall quality of the screen.

[0132] Optionally, the process of adjusting the screen parameters of the second display screen based on the gray level to obtain an adjusted screen is as follows: Analyze the distribution of the gray level to understand the overall brightness and contrast characteristics of the image. Then determine the screen parameter adjustment strategy corresponding to different gray levels. For example, for darker gray level areas, the brightness and contrast can be appropriately increased; for brighter gray level areas, the brightness can be reduced to prevent overexposure.

[0133] Furthermore, in the embodiment of the present invention, by analyzing the perceived authenticity of the adjusted screen, it can be judged whether the screen adjustment has achieved the expected effect, avoiding image distortion or unnaturalness caused by over-adjustment or under-adjustment. Among them, the perceived authenticity refers to the degree of closeness between the subjective feeling of a person towards an image, scene or experience and the real situation.

[0134] As an embodiment of the present invention, the analysis of the perceived authenticity of the adjusted screen includes:

[0135] Calculating the structural similarity of the adjusted screen using the following formula:

[0136] ;

[0137] Where, represents the structural similarity, l represents the brightness ratio of the adjusted screen, c represents the contrast of the adjusted screen, s represents the structural degree of the adjusted screen, , , are constants, represents the i-th image in the adjusted screen, represents the original image of the adjusted screen, and M represents the number of comparisons between the adjusted screen and the corresponding original image;

[0138] Based on the structural similarity, determine the perceived authenticity of the adjusted screen.

[0139] It should be noted that when the structural similarity is not less than 0.9, it indicates that the perceived authenticity is excellent. Specifically, it can be set in combination with the actual application scenario. In the above formula, the constants are set according to experience, and their purpose is to adjust the relative importance of the brightness ratio, contrast, and structural degree when calculating the structural similarity. The constants are generally subjectively evaluated for different image pairs, and the evaluation indicators can include image similarity, quality, etc. At the same time, different combinations of constants are used to calculate the structural similarity of these image pairs, and then, by analyzing the correlation between the subjective evaluation results and the structural similarity, the optimal constant values are determined. It should be noted that the values of the constants are not unique and may change with the application scenario and image type. In actual applications, adjustments and optimizations need to be made according to specific situations to obtain more accurate structural similarity calculation results.

[0140] Furthermore, in the embodiment of the present invention, when the perceived authenticity does not meet the preset authenticity, by returning to execute the step of adjusting the screen parameters of the second display screen based on the gray level, the screen quality of the second display screen can be optimized in real time to meet the user's needs.

[0141] Furthermore, in the embodiment of the present invention, when the perceived authenticity meets the preset authenticity, by using the adjusted screen as the final display screen of the second mode, a high-quality display screen can be obtained to meet the display requirements of the intelligent all-in-one machine.

[0142] As Figure 2 shown, it is a functional module diagram of a system for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine provided by an embodiment of the present invention.

[0143] The system 200 for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine according to the present invention can be installed in an electronic device. According to the implemented functions, the system 200 for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine can include a display mode recognition module 201, a picture quality improvement module 202, a picture quality analysis module 203, a second display picture analysis module 204, and a picture quality evaluation module 205. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0144] In this embodiment, the functions of each module / unit are as follows:

[0145] The display mode recognition module 201 is used to obtain the intelligent all-in-one machine with the picture quality to be optimized, analyze the light sense condition of the display environment corresponding to the intelligent all-in-one machine, based on the light sense condition, perform light sense adjustment on the intelligent all-in-one machine to obtain an adjusted intelligent all-in-one machine, and identify the display mode of the adjusted intelligent all-in-one machine;

[0146] The picture quality improvement module 202 is used to, when the display mode is the first mode, obtain the first display picture of the adjusted intelligent all-in-one machine, analyze the first picture quality of the first display picture, and when the first picture quality does not meet the display requirement, perform frequency domain transformation on the first display picture to obtain a frequency domain transformation picture, identify the real component information and imaginary component information of the frequency domain transformation picture, and based on the real component information and the imaginary component information, improve the picture quality of the display picture to obtain a picture quality improved picture;

[0147] The picture quality analysis module 203 is used to analyze the improved picture quality of the picture quality improved picture, and when the improved picture quality does not meet the preset display requirement, return to execute the step of performing frequency domain transformation on the first display picture, and when the improved picture quality meets the preset display requirement, use the picture quality improved picture as the target display picture in the first mode;

[0148] The second display picture analysis module 204 is used to, when the display mode is the second mode, perform edge detection on the second display picture corresponding to the second mode, based on the result of the edge detection, perform background separation on the second display picture to obtain a background separated picture, analyze the separated picture quality of the background separated picture, and when the separated picture quality does not meet the preset display requirement, calculate the gray level of the background separated picture;

[0149] The picture quality evaluation module 205 is configured to adjust the picture parameters of the second display screen based on the gray level to obtain an adjusted picture, analyze the perceived authenticity of the adjusted picture, and when the perceived authenticity does not meet the preset authenticity, return to execute the step of adjusting the picture parameters of the second display screen based on the gray level. When the perceived authenticity meets the preset authenticity, the adjusted picture is used as the final display picture of the second mode.

[0150] Specifically, each module in the system 200 for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine described in the embodiments of the present invention uses the same technical means as the method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine described in the accompanying drawings and can produce the same technical effects, which will not be elaborated here.

[0151] In several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0152] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional module in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a hardware plus software functional module.

[0154] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0155] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention. Any associated drawing marks in the claims should not be regarded as limiting the claimed rights.

[0156] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0157] In addition, it is obvious that the term "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. The terms such as first and second are used to represent names and do not represent any specific order.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. Method for optimizing ultra-high definition picture quality of intelligent all-in-one machine, characterized in that, The method includes: Obtaining an all-in-one intelligent machine with the image quality to be optimized, analyzing the light sensing conditions of the corresponding display environment of the all-in-one intelligent machine, based on the light sensing conditions, performing light sensing adjustment on the all-in-one intelligent machine, and identifying the display mode of the all-in-one intelligent machine after the light sensing adjustment; When the display mode is the first mode, obtaining the first display screen of the all-in-one intelligent machine after the light sensing adjustment, analyzing the first screen quality of the first display screen, when the first screen quality does not meet the display requirements, performing frequency domain transformation on the first display screen to obtain a frequency domain transformed screen, identifying the real component information and the imaginary component information of the frequency domain transformed screen, and based on the real component information and the imaginary component information, improving the image quality of the display screen to obtain an image quality improved screen; Analyzing the improved screen quality of the image quality improved screen, when the improved screen quality does not meet the preset display requirements, returning to execute the step of performing frequency domain transformation on the first display screen, when the improved screen quality meets the preset display requirements, using the image quality improved screen as the target display screen of the first mode; When the display mode is the second mode, performing edge detection on the second display screen corresponding to the second mode, based on the result of the edge detection, separating the background of the second display screen to obtain a background separated screen, analyzing the separated screen quality of the background separated screen, when the separated screen quality does not meet the preset display requirements, calculating the gray level of the background separated screen; Based on the gray level, adjusting the screen parameters of the second display screen to obtain an adjusted screen, and analyzing the perceived authenticity of the adjusted screen, when the perceived authenticity does not meet the preset authenticity, returning to execute the step of adjusting the screen parameters of the second display screen based on the gray level, when the perceived authenticity meets the preset authenticity, using the adjusted screen as the final display screen of the second mode.

2. The method for optimizing the ultra-high definition picture quality of the intelligent all-in-one machine according to claim 1, characterized in that, The performing light sensing adjustment on the all-in-one intelligent machine based on the light sensing conditions includes: Querying the light sensing data corresponding to the light sensing conditions; Performing statistical analysis on the light sensing data to determine the light sensing distribution state of the light sensing conditions; Querying the usage scenario of the all-in-one intelligent machine and the user usage requirements; Based on the usage scenario and the user usage requirements and combining the light sensing distribution state, performing light sensing adjustment on the all-in-one intelligent machine.

3. The method for optimizing the ultra-high definition picture quality of the intelligent all-in-one machine according to claim 1, wherein The analyzing the first screen quality of the first display screen includes: Identifying the brightness value, contrast value and color saturation of the first display screen; Based on the brightness value, the contrast value and the color saturation, using the following formula to calculate the quality evaluation value of the first display screen: α = w1×T1 + w2×T2 + w3×T3 where α represents the quality evaluation value, T1 represents the brightness value, T2 represents the contrast value, T3 represents the color saturation, w1 represents the evaluation weight of T1, w2 represents the evaluation weight of T2, and w3 represents the evaluation weight of T3; Based on the quality evaluation value, determining the first screen quality of the first display screen.

4. The method for optimizing the ultra-high definition picture quality of an all-in-one intelligent machine according to claim 1, wherein, Identifying the real component information and imaginary component information of the frequency-domain transformed picture includes: Constructing a frequency spectrum chart of the frequency-domain transformed picture; Querying the horizontal amplitude information and vertical amplitude information of the frequency spectrum chart; Determining the phase information of the frequency-domain transformed picture based on the horizontal amplitude information; Determining the amplitude information of the frequency-domain transformed picture based on the vertical amplitude information; Determining the real component information and imaginary component information of the frequency-domain transformed picture based on the phase information and the amplitude information.

5. The method for optimizing the ultra-high definition picture quality of an intelligent all-in-one machine according to claim 1, wherein, Enhancing the picture quality of the display picture based on the real component information and the imaginary component information to obtain a picture with enhanced quality includes: Determining the noise frequency components, high-frequency components, and color frequency distribution of the display picture based on the real component information and the imaginary component information; Performing denoising processing on the display picture based on the noise frequency components to obtain a denoised picture; Performing detail enhancement on the denoised picture based on the high-frequency components to obtain an enhanced picture; Performing color correction on the enhanced picture based on the color frequency distribution to obtain a picture with enhanced quality.

6. The method for optimizing the ultra-high definition picture quality of an all-in-one intelligent machine according to claim 1, characterized in that, Analyzing the quality of the picture with enhanced quality of the enhanced picture includes: Calculating the mean squared error of the picture with enhanced quality of the enhanced picture using the following formula: where MSE represents the mean squared error of the image, d1 represents the pixel value of the original image corresponding to the picture with enhanced quality at the coordinate point (i, j), d2 represents the pixel value of the picture with enhanced quality at the coordinate point (i, j), m represents the length of the picture with enhanced quality, n represents the width of the picture with enhanced quality, i represents the horizontal axis coordinate point, and j represents the vertical axis coordinate point; Analyzing the quality of the picture with enhanced quality of the enhanced picture based on the mean squared error of the image.

7. The method for optimizing the ultra-high definition picture quality of the intelligent all-in-one machine according to claim 1, wherein, Performing edge detection on the second display picture corresponding to the second mode includes: Performing frame division on the second display picture to obtain a framed picture; Performing filtering processing on the framed picture to obtain a filtered picture; Calculating the gradient value of the filtered picture; Performing double-threshold processing on the filtered picture based on the gradient value to obtain a threshold-processed picture; Performing edge tracking on the threshold-processed picture to obtain the picture edge.

8. The method for optimizing the ultra-high definition picture quality of an all-in-one intelligent machine according to claim 1, wherein, Performing background separation on the second display picture based on the result of the edge detection to obtain a background-separated picture includes: Determining the edge key points of the second display picture based on the result of the edge detection; Constructing an image anchor box of the second display picture based on the edge key points; Performing adaptive anchoring on the second display picture based on the image anchor box to obtain an anchored image; Performing background separation on the second display picture based on the anchored image to obtain a background-separated picture.

9. The method for optimizing the ultra-high definition picture quality of the intelligent all-in-one machine according to claim 1, characterized in that, The quality of the background-separated picture of the separated picture includes: Calculating the mean squared error of the separated picture of the background-separated picture; Calculating the peak signal-to-noise ratio of the background-separated picture using the following formula based on the mean squared error of the separated picture: where pnsr represents the peak signal-to-noise ratio, δ represents the mean squared error of the separated picture, and X represents the maximum pixel value of the background-separated picture; Determining the quality of the background-separated picture of the separated picture based on the peak signal-to-noise ratio.

10. System for optimizing ultra-high definition picture quality of an intelligent all-in-one machine, characterized in that A method for performing ultra-high definition picture quality optimization of an intelligent all-in-one machine as described in any one of claims 1-9, the system comprising: A display mode recognition module, configured to obtain an intelligent all-in-one machine with picture quality to be optimized, analyze the light sensing conditions of the display environment corresponding to the intelligent all-in-one machine, perform light sensing adjustment on the intelligent all-in-one machine based on the light sensing conditions, and recognize the display mode of the intelligent all-in-one machine after the light sensing adjustment; A picture quality improvement module, configured to, when the display mode is the first mode, obtain the first display picture of the intelligent all-in-one machine after the light sensing adjustment, analyze the first picture quality of the first display picture, and when the first picture quality does not meet the display requirements, perform frequency domain transformation on the first display picture to obtain a frequency domain transformation picture, recognize the real component information and the imaginary component information of the frequency domain transformation picture, and perform picture quality improvement on the display picture based on the real component information and the imaginary component information to obtain a picture quality improved picture; A picture quality analysis module, configured to analyze the improved picture quality of the picture quality improved picture, and when the improved picture quality does not meet the preset display requirements, return to execute the step of performing frequency domain transformation on the first display picture, and when the improved picture quality meets the preset display requirements, use the picture quality improved picture as the target display picture in the first mode; A second display picture analysis module, configured to, when the display mode is the second mode, perform edge detection on the second display picture corresponding to the second mode, perform background separation on the second display picture based on the result of the edge detection to obtain a background separated picture, analyze the separated picture quality of the background separated picture, and when the separated picture quality does not meet the preset display requirements, calculate the gray level of the background separated picture; A picture quality evaluation module, configured to perform picture parameter adjustment on the second display picture based on the gray level to obtain an adjusted picture, and analyze the perceived authenticity of the adjusted picture. When the perceived authenticity does not meet the preset authenticity, return to execute the step of performing picture parameter adjustment on the second display picture based on the gray level. When the perceived authenticity meets the preset authenticity, use the adjusted picture as the final display picture in the second mode.

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