Method and system for optimizing ultra-high-definition image quality of intelligent all-in-one machine

Abstract

The invention relates to the technical field of ultra-high-definition image quality optimization, and discloses an ultra-high-definition image quality optimization method and system for an intelligent all-in-one machine, and the method comprises the steps: recognizing a display mode of the intelligent all-in-one machine; when the display mode is the first mode, improving the image quality of the display image to obtain an image quality improved image; analyzing the improved picture quality of the picture quality improved picture, and when the improved picture quality meets a preset display requirement, taking the picture quality improved picture as a target display picture of the first mode; when the display mode is a second mode, carrying out edge detection on the second display picture, analyzing the picture quality based on an edge detection result, and calculating the gray level of the picture when the picture quality does not meet a preset display requirement; and performing parameter adjustment on the second display picture based on the gray level to obtain an adjusted picture, analyzing the sensing trueness of the adjusted picture, and taking the adjusted picture as a final display picture of the second mode when the sensing trueness meets a preset trueness. According to the invention, the precision of ultra-high-definition image quality can be improved.

Description

Technical Field

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

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

[0003] At present, the optimization of ultra-high-definition image quality of smart all-in-one machines generally adjusts parameters such as color and contrast through software algorithms to achieve improved image quality. However, this method has limited image quality optimization, and may cause color distortion, loss of details and other problems when processing complex image scenes. Since smart all-in-one machines need to process different types of image content, including static images, dynamic videos, etc., it is difficult for existing optimization methods to achieve comprehensive and accurate image quality optimization when facing diverse image data, resulting in unsatisfactory ultra-high-definition image quality performance of smart all-in-one machines. Summary of the invention

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

[0005] To achieve the above-mentioned purpose, the present invention provides a method for optimizing ultra-high-definition image quality of a smart all-in-one machine, comprising: Acquire a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions to obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine; When the display mode is the first mode, a first display screen of the adjusted smart all-in-one machine is obtained, a first screen quality of the first display screen is analyzed, and when the first screen quality does not meet the display requirement, a frequency domain transformation is performed on the first display screen to obtain a frequency domain transformed screen, real component information and imaginary component information of the frequency domain transformed screen are identified, and based on the real component information and the imaginary component information, the image quality of the display screen is improved to obtain an improved image quality screen; analyzing the improved picture quality of the improved picture quality, and when the improved picture quality does not meet the preset display requirement, returning to the step of performing frequency domain transformation on the first display picture, and when the improved picture quality meets the preset display requirement, using the improved picture quality as the target display picture of the first mode; When the display mode is the second mode, edge detection is performed on a second display picture corresponding to the second mode, background separation is performed on the second display picture based on a result of the edge detection to obtain a background separation picture, and a separation picture quality of the background separation picture is analyzed. When the separation picture quality does not meet the preset display requirement, a grayscale level of the background separation picture is calculated; Based on the grayscale, the picture parameters of the second display picture are adjusted to obtain an adjusted picture, and the perceived realism of the adjusted picture is analyzed. When the perceived realism does not meet the preset realism, the step of adjusting the picture parameters of the second display picture based on the grayscale is returned to be executed. When the perceived realism meets the preset realism, the adjusted picture is used as the final display picture of the second mode.

[0006] Optionally, the step of adjusting the light sensitivity of the smart all-in-one machine based on the light sensitivity condition to obtain an adjusted smart all-in-one machine includes: Querying light sensing data corresponding to the light sensing condition; Performing statistical analysis on the light perception data to determine the light perception distribution state of the light perception condition; Query the usage scenarios and user requirements of the smart all-in-one machine; Based on the usage scenario and the user's usage requirements combined with the light sensitivity distribution state, the light sensitivity of the smart all-in-one machine is adjusted to obtain an adjusted smart all-in-one machine.

[0007] Optionally, analyzing a first picture quality of the first display picture includes: Identifying the brightness value, contrast value and color saturation of the first display picture; Based on the brightness value, the contrast value and the color saturation, the quality evaluation value of the first display image is calculated using the following formula: ; in, represents the quality assessment value, Indicates the brightness value, Indicates the contrast value, Indicates color saturation, express The evaluation weight of express The evaluation weight of express The assessment weight of Based on the quality evaluation value, a first picture quality of the first presentation picture is determined.

[0008] Optionally, the identifying the real component information and the 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 the vertical amplitude information of the frequency spectrum chart; Based on the lateral amplitude information, determining the phase information of the frequency domain transformed picture; Based on the longitudinal amplitude information, determining the amplitude information of the frequency domain transformed picture; Real component information and imaginary component information of the frequency domain transformed picture are determined based on the phase information and the amplitude information.

[0009] Optionally, the improving the quality of the display picture based on the real component information and the imaginary component information to obtain the improved picture includes: Based on the real component information and the imaginary component information, determining the noise frequency component, high frequency component and color frequency distribution of the display picture; Performing denoising on the display image based on the noise frequency component to obtain a denoised image; Performing detail enhancement on the denoised image based on the high-frequency components to obtain an enhanced image; Based on the color frequency distribution, color correction is performed on the enhanced picture to obtain a picture with improved image quality.

[0010] Optionally, the analyzing the improved picture quality of the improved picture includes: The image mean square error of the image with improved image quality is calculated using the following formula: ; in, represents the mean square error of the image, Indicates that the original image corresponding to the improved image is at the coordinate point The pixel value of Indicates that the image quality is improved at the coordinate point The pixel value of Indicates the length of the picture that improves the image quality. Indicates the width of the image quality improvement screen, i represents the horizontal coordinate point, and j represents the vertical coordinate point; Based on the image mean square error, an improved picture quality of the improved picture is analyzed.

[0011] Optionally, performing edge detection on the second display picture corresponding to the second mode includes: Framing the second display picture to obtain a framed picture; Performing filtering 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; The edge of the image is tracked by performing edge tracking on the threshold processed image to obtain the edge of the image.

[0012] Optionally, performing background separation on the second display picture based on the result of the edge detection to obtain a background separated picture includes: Based on the result of the edge detection, determining edge key points of the second display picture; Based on the edge key points, construct an image anchor frame of the second display picture; Based on the image anchor frame, adaptively anchor the second display picture to obtain an anchor frame image; Based on the anchor frame image, background separation is performed on the second display picture to obtain a background separated picture.

[0013] Optionally, the separated picture quality of the background separated picture includes: Calculating a mean square error of the separated images of the background separated images; Based on the mean square error of the separated picture, the peak signal-to-noise ratio of the background separated picture is calculated using the following formula: ; in, represents the peak signal-to-noise ratio, represents the mean square error of the separated images, Indicates the maximum pixel value of the background separation picture; Based on the peak signal-to-noise ratio, a separation picture quality of the background separation picture is determined.

[0014] In order to solve the above problems, the present invention also provides a system for optimizing ultra-high-definition image quality of a smart all-in-one machine, the system comprising: A display mode recognition module is used to obtain a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions, obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine; a picture quality improvement module, configured to, when the display mode is the first mode, obtain a first display picture of the adjusted intelligent all-in-one machine, analyze a 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, identify real component information and imaginary component information of the frequency domain transformed picture, and improve the picture quality of the display picture based on the real component information and the imaginary component information to obtain a picture with improved picture quality; a picture quality analysis module, configured to analyze the improved picture quality of the improved picture, and when the improved picture quality does not meet the preset display requirement, return to 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 improved picture as the target display picture of the first mode; a second display picture analysis module, configured to, when the display mode is the second mode, perform edge detection on a second display picture corresponding to the second mode, perform background separation on the second display picture based on a result of the edge detection to obtain a background separation picture, analyze a separation picture quality of the background separation picture, and calculate a grayscale level of the background separation picture when the separation picture quality does not meet the preset display requirement; The image quality assessment module is used to adjust the image parameters of the second display image based on the grayscale to obtain an adjusted image, and analyze the perceived realism of the adjusted image. When the perceived realism does not meet the preset realism, the module returns to the step of adjusting the image parameters of the second display image based on the grayscale. When the perceived realism meets the preset realism, the module uses the adjusted image as the final display image of the second mode.

[0015] The present invention can perform targeted light adjustment by analyzing the light conditions of the display environment corresponding to the smart all-in-one machine, thereby improving the visibility and comfort of the picture; the present invention can obtain the current display information of the smart all-in-one machine by obtaining the first display screen of the adjusted smart all-in-one machine when the display mode is the first mode, so as to serve as a specific basis for optimizing the picture quality; the present invention can help users separate the foreground and background in the picture by performing edge detection on the second display screen corresponding to the second mode, thereby providing a basis for subsequent background separation; the present invention can adjust the picture parameters of the second display screen based on the grayscale level, thereby obtaining an adjusted picture that can make the image better present details and layering under different display conditions, thereby improving the overall quality of the picture; finally, the present invention can obtain a high-quality display screen by using the adjusted picture as the final display screen of the second mode when the perceived realism meets the preset realism, thereby meeting the display requirements of the smart all-in-one machine. Therefore, the present invention can improve the accuracy of ultra-high-definition picture quality of the smart all-in-one machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for optimizing ultra-high-definition image quality of a smart all-in-one machine provided by an embodiment of the present invention; Figure 2 A functional module diagram of a system for optimizing ultra-high-definition image quality of a smart all-in-one machine provided by an embodiment of the present invention; 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

[0017] 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.

[0018] The embodiment of the present application provides a method for optimizing the ultra-high-definition image quality of a smart all-in-one machine. The execution subject of the method for optimizing the ultra-high-definition image quality of a smart all-in-one machine includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for optimizing the ultra-high-definition image quality of a smart 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 it can be 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 networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0019] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing ultra-high definition image quality of a smart all-in-one machine provided by an embodiment of the present invention. In this embodiment, the method for optimizing ultra-high definition image quality of a smart all-in-one machine includes: S1. Obtain a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions, obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine.

[0020] The embodiment of the present invention can determine the specific device that needs to be optimized through the intelligent all-in-one machine for obtaining the image quality to be optimized, provide a target for subsequent analysis and processing, and obtain the device information to be optimized by connecting with the communication interface or management system of the intelligent all-in-one machine. You can use network connection, USB connection, etc. to interact with the device to obtain the identification information, current settings, etc. of the device.

[0021] Among them, the smart all-in-one machine refers to an intelligent device that integrates multiple functions and technologies. It generally has a high-resolution LCD screen or other advanced display technology, can present clear and vivid images and video content, and a powerful processor, such as a high-performance central processing unit (CPU) and graphics processing unit (GPU), which can quickly process various complex tasks, including image and video playback, multitasking, running various applications, etc.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] S2. When the display mode is the first mode, obtain the first display screen of the adjusted 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 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 image quality of the display screen to obtain an improved image quality screen.

[0030] In the embodiment of the present invention, when the display mode is the first mode, the first display screen of the smart all-in-one machine is obtained to obtain the current display information of the smart all-in-one machine, so as to serve as a specific basis for image quality optimization. The first mode refers to the screen display mode, and the display content is displayed in the form of images.

[0031] Furthermore, the embodiment of the present invention can determine the current quality level of the first display picture by analyzing the first picture quality of the first display picture, so as to determine whether picture quality improvement is needed.

[0032] As an embodiment of the present invention, the analyzing the first picture quality of the first display picture includes: identifying a brightness value, a contrast value, and a color saturation of the first display picture, and calculating a quality evaluation value of the first display picture based on the brightness value, the contrast value, and the color saturation using the following formula: ; in, represents the quality assessment value, Indicates the brightness value, Indicates the contrast value, Indicates color saturation, express The evaluation weight of express The evaluation weight of express The assessment weight of Based on the quality evaluation value, a first picture quality of the first presentation picture is determined.

[0033] Optionally, based on the quality assessment value, the first picture quality of the first display picture is determined by comparing the quality assessment value with a preset assessment value, and the preset assessment value can be set to 0.8. If the quality assessment value is not less than 0.8, it indicates that the first picture quality of the first display picture is qualified.

[0034] It should be understood that when the first picture quality does not meet the display requirement, it means that the quality evaluation value is less than 0.8, which means that the first picture quality of the first display picture is unqualified and needs to be optimized.

[0035] In the embodiment of the present invention, by performing frequency domain transformation on the first display image, a frequency domain transformation image is obtained, which can analyze the image information in more detail to determine which parts of the image are of low quality and a method for improving the image quality. The frequency domain transformation refers to converting the image from the spatial domain to the frequency domain, which can be achieved through a Fourier transform operation.

[0036] Furthermore, the embodiment of the present invention can use the frequency domain information to perform targeted processing on the image by identifying the real component information and the imaginary component information of the frequency domain transformed image, thereby improving the quality and visual effect of the image.

[0037] The real component information refers to partial amplitude information reflecting the signal in the frequency domain, and the imaginary component information refers to partial phase information and another partial amplitude information reflecting the signal in the frequency domain.

[0038] As an embodiment of the present invention, the identification of the real component information and imaginary component information of the frequency domain transformation picture includes: constructing a spectrum chart of the frequency domain transformation picture, querying the horizontal amplitude information and the vertical amplitude information of the spectrum chart, determining the phase information of the frequency domain transformation picture based on the horizontal amplitude information, determining the amplitude information of the frequency domain transformation picture based on the vertical amplitude information, and determining the real component information and imaginary component information of the frequency domain transformation picture based on the phase information and the amplitude information. 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 between each frequency component in the image, and the amplitude information refers to the strength or size of the signal.

[0039] Optionally, the spectrum chart can be constructed by combining the frequency domain transformation screen with Java software, and the query of the horizontal amplitude information and the vertical amplitude information of the spectrum chart can be obtained by traversing all frequency points in the spectrum chart and calculating the amplitude of each frequency point. The determination of the phase information of the frequency domain transformation screen based on the horizontal amplitude information can be obtained by calculating the phase angle of the complex value in the spectrum chart. The determination of the amplitude information of the frequency domain transformation screen based on the vertical amplitude information can be obtained by analyzing the vertical amplitude information, for example, by calculating the maximum value, minimum value, average value and other statistical quantities of the amplitude. The determination of the real component information and the imaginary component information of the frequency domain transformation screen based on the phase information and the amplitude information can be calculated by the following formula: Once the amplitude and phase information is known, the real and imaginary components can be calculated using the following formula: ; ; in, represents the real component, represents the imaginary component, Indicates the amplitude, Indicates phase.

[0040] Furthermore, the embodiment of the present invention improves the image quality of the display picture based on the real component information and the imaginary component information, and the obtained image with improved image quality can improve the perceived texture of the picture to meet the picture viewing needs of the display users corresponding to the smart all-in-one machine.

[0041] As an embodiment of the present invention, the image quality of the display picture is improved based on the real component information and the imaginary component information to obtain an improved image quality picture, including: determining the noise frequency component, high-frequency component and color frequency distribution of the display picture based on the real component information and the imaginary component information, denoising the display picture based on the noise frequency component to obtain a denoised picture, enhancing the details of the denoised picture based on the high-frequency component to obtain an enhanced picture, and color correcting the enhanced picture based on the color frequency distribution to obtain an improved image quality picture.

[0042] Among them, the noise frequency component refers to the performance of unnecessary signal components of x in the image due to various factors (such as sensor noise, quantization error, environmental interference, etc.) in the frequency domain; the high-frequency component refers to the part of the image that changes rapidly in the frequency domain representation of the image; and the color frequency distribution refers to the energy distribution of different color channels in the frequency domain in a color image.

[0043] The noise frequency component, high-frequency component and color frequency distribution of the display picture based on the real component information and the imaginary component information can be determined by analyzing the real component information and the imaginary component information to determine the frequency region containing noise to determine the noise frequency component, by observing the amplitude and phase of the real component information and the imaginary component information to determine the high-frequency component, and by analyzing the real component information and the imaginary component information to determine the performance of different colors in different frequency regions, the high-frequency component is related to the details and texture of the color, while the low-frequency component affects the overall hue and brightness of the color.

[0044] Optionally, the denoising process of the display image based on the noise frequency component to obtain the denoised image can be denoised by a low-pass filter, the detail enhancement of the denoised image based on the high-frequency component to obtain the enhanced image can be achieved by appropriately increasing the amplitude of the high-frequency area in the real component and the imaginary component, which can be achieved by multiplying a gain coefficient greater than 1, and the color correction of the enhanced image based on the color frequency distribution to obtain the improved image can be achieved by adjusting the real component and the imaginary component of the color channel to achieve color balance correction. For example, if a color channel in the image is too bright or dim, the overall brightness can be changed by adjusting the real component of the channel in the low-frequency area.

[0045] S3. Analyze the improved picture quality of the improved picture, and when the improved picture quality does not meet the preset display requirements, return to 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 improved picture as the target display picture of the first mode.

[0046] The embodiment of the present invention can understand whether the image quality improvement effect of the image quality improvement picture meets the requirement by analyzing the improved image quality of the image quality improvement picture.

[0047] As an embodiment of the present invention, the analyzing the improved picture quality of the improved picture includes: The image mean square error of the image with improved image quality is calculated using the following formula: ; in, represents the mean square error of the image, Indicates that the original image corresponding to the improved image is at the coordinate point The pixel value of Indicates that the image quality is improved at the coordinate point The pixel value of Indicates the length of the picture that improves the image quality. Indicates the width of the image quality improvement screen, i represents the horizontal coordinate point, and j represents the vertical coordinate point; Based on the image mean square error, an improved picture quality of the improved picture is analyzed.

[0048] It should be noted that when the calculated result of the mean square error of the image is not greater than 0.2, it indicates that the improved image quality of the improved image meets user needs, and the specific setting can be made in combination with actual application needs.

[0049] In the embodiment of the present invention, when the improved picture quality does not meet the preset display requirement, returning to execute the step of performing frequency domain transformation on the first display picture can adjust the picture quality in time to meet the display requirement.

[0050] Furthermore, when the improved image quality meets the preset display requirements, using the improved image quality image as the target display image of the first mode can obtain a high-quality display image to meet the display requirements of the smart all-in-one machine.

[0051] S4. When the display mode is the second mode, edge detection is performed on a second display picture corresponding to the second mode, and based on a result of the edge detection, background separation is performed on the second display picture to obtain a background separation picture, and the separation picture quality of the background separation picture is analyzed. When the separation picture quality does not meet the preset display requirement, the grayscale level of the background separation picture is calculated.

[0052] It should be understood that when the display mode is the second mode, it means that the screen display mode of the smart all-in-one machine is the video mode.

[0053] The embodiment of the present invention can help the user separate the foreground and background in the picture by performing edge detection on the second display picture corresponding to the second mode, thereby providing a basis for subsequent background separation.

[0054] As an embodiment of the present invention, the edge detection of the second display picture corresponding to the second mode includes: framing the second display picture to obtain a framed picture, filtering 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, and edge tracking the threshold processed picture to obtain a picture edge.

[0055] Among them, the picture framing refers to dividing the video into multiple frames of images. In the setting of this solution, the display screen corresponding to the second mode is the video mode, so the picture framing operation can be performed on the second display screen. The dual threshold processing refers to the process of dividing the gradient amplitude into three categories: strong edge, weak edge and non-edge according to the set two thresholds (high threshold and low threshold).

[0056] Optionally, the framed picture can be obtained by operating a frame tool, such as an image processing library in the Python language, by performing a frame operation on the second display picture, the filtered picture can be obtained by filtering the framed picture through a Gaussian filter, and the gradient value can be calculated by a gradient function.

[0057] The edge tracking is performed on the threshold processed picture to obtain the picture edge, which can be obtained by connecting the edge pixels of the threshold processed picture.

[0058] Furthermore, the embodiment of the present invention performs background separation on the second display picture based on the result of the edge detection, and the background separation picture obtained can process the foreground or background of the image separately, thereby improving the quality and effect of the picture.

[0059] As an embodiment of the present invention, the background separation of the second display picture based on the result of the edge detection to obtain the background separation picture includes: determining the edge key points of the second display picture based on the result of the edge detection, constructing an image anchor frame of the second display picture based on the edge key points, performing an adaptive anchor frame on the second display picture based on the image anchor frame to obtain an anchor frame image, and performing background separation on the second display picture based on the anchor frame image to obtain a background separation picture. The image anchor frame refers to a technology in the field of image target detection, which is used to accurately locate targets in images.

[0060] The adaptive anchor frame refers to the process of extracting the target object to be analyzed from the image to avoid the interference of too much information and improve the efficiency of image analysis.

[0061] Optionally, the edge key points can be obtained by determining specific edge pixel points in the second display screen through the result of the edge detection, and the image anchor frame can be generated using the GIMP tool after the size is determined by the edge key points. The second display screen is adaptively anchored to obtain an anchor frame image.

[0062] Furthermore, the embodiment of the present invention can determine whether the background separation effect meets the preset display requirement by analyzing the separation picture quality of the background separation picture.

[0063] As an embodiment of the present invention, the separated picture quality of the background separated picture includes: calculating the separated picture mean square error of the background separated picture, and based on the separated picture mean square error, calculating the peak signal-to-noise ratio of the background separated picture using the following formula: ; in, represents the peak signal-to-noise ratio, represents the mean square error of the separated images, Indicates the maximum pixel value of the background separation picture; Based on the peak signal-to-noise ratio, a separation picture quality of the background separation picture is determined.

[0064] Optionally, the separation picture quality of the background separation picture based on the peak signal-to-noise ratio can be determined by a peak signal-to-noise ratio settlement result. If the peak signal-to-noise ratio is not less than 30dB, it means that the separation picture quality of the background separation picture is excellent. The separation picture mean square error refers to the image mean square error of the separation picture, which can be calculated by a mean square error function.

[0065] When the quality of the separated picture does not meet the preset display requirement, calculating the grayscale of the background separated picture can help the user understand the information about the picture brightness distribution and provide a basis for subsequent picture parameter adjustment.

[0066] The grayscale refers to the brightness level or grayscale value range of pixels in the image.

[0067] Optionally, the grayscale of the background separation picture can be obtained by converting the background separation picture into a grayscale image and calculating the grayscale value of each pixel.

[0068] S5. Based on the grayscale, adjust the picture parameters of the second display picture to obtain an adjusted picture, and analyze the perceived realism of the adjusted picture. When the perceived realism does not meet the preset realism, return to the step of adjusting the picture parameters of the second display picture based on the grayscale. When the perceived realism meets the preset realism, use the adjusted picture as the final display picture of the second mode.

[0069] The embodiment of the present invention adjusts the picture parameters of the second display picture based on the grayscale to obtain an adjusted picture, which can make the image better present details and layering under different display conditions, thereby improving the overall quality of the picture.

[0070] Optionally, the process of adjusting the picture parameters of the second display picture based on the grayscale to obtain the adjusted picture is as follows: analyzing the distribution of grayscale to understand the overall brightness and contrast characteristics of the image. Then determining the picture parameter adjustment strategy corresponding to different grayscales. For example, for darker grayscale areas, the brightness and contrast can be appropriately increased; for brighter grayscale areas, the brightness can be reduced to prevent overexposure.

[0071] Furthermore, the embodiment of the present invention can determine whether the picture adjustment has achieved the expected effect by analyzing the perceived realism of the adjusted picture, thereby avoiding picture distortion or unnaturalness caused by over-adjustment or under-adjustment. The perceived realism refers to the degree of closeness between a person's subjective feeling of an image, scene or experience and the actual situation.

[0072] As an embodiment of the present invention, the analyzing the perceived realism of the adjusted picture includes: The structural similarity of the adjustment picture is calculated using the following formula: ; in, represents the structural similarity, l represents the brightness ratio of the adjusted image, c represents the contrast of the adjusted image, and s represents the structural degree of the adjusted image. , , is a constant, Indicates adjusting the i-th image in the picture, represents the original image of the adjusted picture, and M represents the number of comparisons between the adjusted picture and the corresponding original image; Based on the structural similarity, the perceived realism of the adjusted picture is determined.

[0073] It should be noted that when the structural similarity is not less than 0.9, it indicates that the perceived authenticity is excellent. The specific setting can be combined with the actual application scenario. In the above formula, the constant is set based on experience. The purpose is to adjust the relative importance of brightness ratio, contrast and structure in calculating structural similarity. The constant is generally obtained by subjectively evaluating different image pairs. The evaluation index may include image similarity, quality, etc. At the same time, different constant combinations are used to calculate the structural similarity of these image pairs. Then, the correlation between the subjective evaluation results and the structural similarity is analyzed to determine the optimal constant value. It should be noted that the value of the constant is not unique and may change with changes in the application scenario and image type. In practical applications, it is necessary to adjust and optimize according to the specific situation to obtain more accurate structural similarity calculation results.

[0074] Furthermore, the embodiment of the present invention can optimize the picture quality of the second display picture in real time to meet user needs by returning to the step of adjusting the picture parameters of the second display picture based on the grayscale when the perceived realism does not meet the preset realism.

[0075] Furthermore, the embodiment of the present invention can obtain a high-quality display picture by using the adjustment picture as the final display picture of the second mode when the perceived reality meets the preset reality, so as to meet the display requirements of the smart all-in-one machine.

[0076] like Figure 2 , is a functional module diagram of a system for optimizing ultra-high-definition image quality of a smart all-in-one machine provided by an embodiment of the present invention.

[0077] The system 200 for optimizing ultra-high-definition image quality of a smart all-in-one machine described in the present invention can be installed in an electronic device. According to the functions implemented, the system 200 for optimizing ultra-high-definition image quality of a smart 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 module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0078] In this embodiment, the functions of each module / unit are as follows: The display mode recognition module 201 is used to obtain a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions, obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine; The image quality improvement module 202 is used to obtain the first display screen of the adjusted intelligent all-in-one machine when the display mode is the first mode, 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 real component information and imaginary component information of the frequency domain transformed screen, and improve the image quality of the display screen based on the real component information and the imaginary component information to obtain an improved image quality screen; The picture quality analysis module 203 is used to analyze the improved picture quality of the improved picture quality, and when the improved picture quality does not meet the preset display requirement, return to 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 improved picture quality as the target display picture of the first mode; The second display picture analysis module 204 is used to perform edge detection on the second display picture corresponding to the second mode when the display mode is the second mode, perform background separation on the second display picture based on the result of the edge detection to obtain a background separation picture, analyze the separation picture quality of the background separation picture, and calculate the grayscale of the background separation picture when the separation picture quality does not meet the preset display requirement; The image quality assessment module 205 is used to adjust the image parameters of the second display picture based on the grayscale to obtain an adjusted picture, and analyze the perceived realism of the adjusted picture. When the perceived realism does not meet the preset realism, it returns to the step of adjusting the image parameters of the second display picture based on the grayscale. When the perceived realism meets the preset realism, the adjusted picture is used as the final display picture of the second mode.

[0079] In detail, each module described in the system 200 for optimizing ultra-high-definition image quality of a smart all-in-one machine described in an embodiment of the present invention adopts the same technical means as the method for optimizing ultra-high-definition image quality of a smart all-in-one machine described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods 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.

[0081] 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 on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0083] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0084] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0085] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. 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.

[0086] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing ultra-high definition image quality of a smart all-in-one machine, characterized in that: The method comprises: Acquire a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions to obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine; When the display mode is the first mode, a first display screen of the adjusted smart all-in-one machine is obtained, a first screen quality of the first display screen is analyzed, and when the first screen quality does not meet the display requirement, a frequency domain transformation is performed on the first display screen to obtain a frequency domain transformed screen, real component information and imaginary component information of the frequency domain transformed screen are identified, and based on the real component information and the imaginary component information, the image quality of the display screen is improved to obtain an improved image quality screen; analyzing the improved picture quality of the improved picture quality, and when the improved picture quality does not meet the preset display requirement, returning to the step of performing frequency domain transformation on the first display picture, and when the improved picture quality meets the preset display requirement, using the improved picture quality as the target display picture of the first mode; When the display mode is the second mode, edge detection is performed on a second display picture corresponding to the second mode, background separation is performed on the second display picture based on a result of the edge detection to obtain a background separation picture, and a separation picture quality of the background separation picture is analyzed. When the separation picture quality does not meet the preset display requirement, a grayscale level of the background separation picture is calculated; Based on the grayscale, the picture parameters of the second display picture are adjusted to obtain an adjusted picture, and the perceived realism of the adjusted picture is analyzed. When the perceived realism does not meet the preset realism, the step of adjusting the picture parameters of the second display picture based on the grayscale is returned to be executed. When the perceived realism meets the preset realism, the adjusted picture is used as the final display picture of the second mode.

2. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The step of adjusting the light sensitivity of the smart all-in-one machine based on the light sensitivity condition to obtain an adjusted smart all-in-one machine includes: Querying light sensing data corresponding to the light sensing condition; Performing statistical analysis on the light perception data to determine the light perception distribution state of the light perception condition; Query the usage scenarios and user requirements of the smart all-in-one machine; Based on the usage scenario and the user's usage requirements combined with the light sensitivity distribution state, the light sensitivity of the smart all-in-one machine is adjusted to obtain an adjusted smart all-in-one machine.

3. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The analyzing the first picture quality of the first display picture includes: Identifying the brightness value, contrast value and color saturation of the first display picture; Based on the brightness value, the contrast value and the color saturation, the quality evaluation value of the first display image is calculated using the following formula: ； in, represents the quality assessment value, Indicates the brightness value, Indicates the contrast value, Indicates color saturation, express The evaluation weight of express The evaluation weight of express The assessment weight of Based on the quality evaluation value, a first picture quality of the first presentation picture is determined.

4. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The identifying of 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 transformation picture; Querying the horizontal amplitude information and the vertical amplitude information of the frequency spectrum chart; Based on the lateral amplitude information, determining the phase information of the frequency domain transformed picture; Based on the longitudinal amplitude information, determining the amplitude information of the frequency domain transformed picture; Real component information and imaginary component information of the frequency domain transformed picture are determined based on the phase information and the amplitude information.

5. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The step of improving the quality of the display picture based on the real component information and the imaginary component information to obtain a picture with improved quality includes: Based on the real component information and the imaginary component information, determining the noise frequency component, high frequency component and color frequency distribution of the display picture; Performing denoising on the display image based on the noise frequency component to obtain a denoised image; Performing detail enhancement on the denoised image based on the high-frequency components to obtain an enhanced image; Based on the color frequency distribution, color correction is performed on the enhanced picture to obtain a picture with improved image quality.

6. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The analyzing the improved picture quality of the improved picture quality comprises: The image mean square error of the image with improved image quality is calculated using the following formula: ； in, represents the mean square error of the image, Indicates that the original image corresponding to the improved image is at the coordinate point The pixel value of Indicates that the image quality is improved at the coordinate point The pixel value of Indicates the length of the picture that improves the image quality. Indicates the width of the image quality improvement screen, i represents the horizontal coordinate point, and j represents the vertical coordinate point; Based on the image mean square error, an improved picture quality of the improved picture is analyzed.

7. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The performing edge detection on the second display picture corresponding to the second mode includes: Framing the second display picture to obtain a framed picture; Performing filtering 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; The edge of the threshold processed picture is tracked to obtain the edge of the picture.

8. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The step of performing background separation on the second display picture based on the result of the edge detection to obtain a background separated picture includes: Based on the result of the edge detection, determining edge key points of the second display picture; Based on the edge key points, construct an image anchor frame of the second display picture; Based on the image anchor frame, adaptively anchor the second display picture to obtain an anchor frame image; Based on the anchor frame image, background separation is performed on the second display picture to obtain a background separated picture.

9. The method for optimizing ultra-high definition image quality of a smart all-in-one machine as claimed in claim 1, characterized in that: The separation picture quality of the background separation picture includes: Calculating a separated picture mean square error of the background separated picture; Based on the mean square error of the separated picture, the peak signal-to-noise ratio of the background separated picture is calculated using the following formula: ； in, represents the peak signal-to-noise ratio, represents the mean square error of the separated images, Indicates the maximum pixel value of the background separation picture; Based on the peak signal-to-noise ratio, a separation picture quality of the background separation picture is determined.

10. The system for optimizing ultra-high-definition image quality of smart all-in-one machines is characterized by: A method for optimizing ultra-high-definition image quality of a smart all-in-one machine as claimed in any one of claims 1 to 9, the system comprising: A display mode recognition module is used to obtain a smart all-in-one machine whose image quality is to be optimized, analyze the light sensing conditions of the display environment corresponding to the smart all-in-one machine, adjust the light sensing of the smart all-in-one machine based on the light sensing conditions, obtain an adjusted smart all-in-one machine, and identify the display mode of the adjusted smart all-in-one machine; a picture quality improvement module, configured to, when the display mode is the first mode, obtain a first display picture of the adjusted intelligent all-in-one machine, analyze a 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, identify real component information and imaginary component information of the frequency domain transformed picture, and improve the picture quality of the display picture based on the real component information and the imaginary component information to obtain a picture with improved picture quality; a picture quality analysis module, configured to analyze the improved picture quality of the improved picture, and when the improved picture quality does not meet the preset display requirement, return to 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 improved picture as the target display picture of the first mode; a second display picture analysis module, configured to, when the display mode is the second mode, perform edge detection on a second display picture corresponding to the second mode, perform background separation on the second display picture based on a result of the edge detection to obtain a background separation picture, analyze a separation picture quality of the background separation picture, and calculate a grayscale level of the background separation picture when the separation picture quality does not meet the preset display requirement; The image quality assessment module is used to adjust the image parameters of the second display image based on the grayscale to obtain an adjusted image, and analyze the perceived realism of the adjusted image. When the perceived realism does not meet the preset realism, the module returns to the step of adjusting the image parameters of the second display image based on the grayscale. When the perceived realism meets the preset realism, the module uses the adjusted image as the final display image of the second mode.

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