Multi-scene adaptive image quality control method and system

Abstract

The invention discloses a multi-scene adaptive image quality control method and system, and particularly relates to the technical field of image control, and the system comprises an intelligent sensing module, an image quality evaluation module, an adaptive adjustment module, a user feedback module and a data storage and analysis module. The method specifically comprises the steps of S1, environment information and image data acquisition and preprocessing, S2, image quality evaluation, S3, image quality adaptive adjustment, S4, user feedback and personalized service, and S5, data storage, analysis and cloud collaboration. By integrating intelligent perception, image quality evaluation, self-adaptive adjustment strategy, user feedback and big data analysis technologies, the image quality can be remarkably improved, different shooting environments can be adapted, and personalized requirements of users can be met, so that the user experience and the applicability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of image control technology, and more specifically, to a multi-scene adaptive image quality control method and system. Background Art

[0002] In the digital age, images and videos have become important carriers of information transmission and expression. Whether it is social media, e-commerce platforms, online education or other Internet application scenarios, high-quality images play a vital role in improving user experience and enhancing information communication effects.

[0003] However, as image resolution and color depth continue to increase, the size of image files also increases, bringing huge challenges to image storage, transmission and processing. Therefore, the development of image quality optimization technology is particularly important.

[0004] Although there are many image quality optimization technologies, such as image compression, image enhancement, image denoising, etc., these technologies still have many limitations in practical applications:

[0005] 1. Traditional image compression algorithms often lead to a significant decrease in image quality, especially at high compression ratios;

[0006] 2. Some image enhancement algorithms may introduce additional noise or distortion, affecting the visual effect of the image;

[0007] 3. Most of the existing image quality optimization technologies are optimized for specific types of images or application scenarios, lacking in versatility and flexibility.

[0008] In view of the above situation, the present invention proposes a multi-scenario adaptive image quality control method and system, which realizes comprehensive optimization and personalized customization of image quality through the collaborative work of an adaptive adjustment module, a user feedback module and a data storage and analysis module. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art, the present invention provides a multi-scene adaptive image quality control method and system to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a multi-scenario adaptive image quality control system, comprising an intelligent perception module, an image quality assessment module, an adaptive adjustment module, a user feedback module and a data storage and analysis module, wherein the intelligent perception module, the image quality assessment module, the adaptive adjustment module, the user feedback module and the data storage and analysis module are connected via a data bus or a network to achieve data interaction and collaborative work.

[0011] Preferably, the intelligent sensing module is used to collect multi-dimensional environmental information and image data of the shooting scene, and also includes an infrared sensor, a gyroscope and an accelerometer to achieve multi-source sensor fusion.

[0012] Preferably, the image quality assessment module is used to perform multi-dimensional quality assessment on the real-time captured image, including clarity, contrast, noise level, color saturation, dynamic range and image stability, and adopts high-speed image processing chips and parallel computing technology to ensure the efficiency and real-time performance of the assessment process.

[0013] Preferably, the adaptive adjustment module is used to generate and apply a refined adjustment strategy including using different algorithms for different types of objects according to scene recognition results and image quality assessment data, introduce a dynamic feedback module during the adjustment process, monitor the adjustment effect in real time and automatically adjust the strategy.

[0014] Preferably, the user feedback module is used to collect and analyze user satisfaction data on image quality, learn users' personalized preferences, and provide an intuitive and easy-to-use interactive adjustment interface, allowing users to manually adjust image parameters and instantly view the effects.

[0015] Preferably, the data storage and analysis module is used for efficient storage, analysis and cloud-based collaborative processing of image data, evaluation results, adjustment strategies and user feedback data, supports big data processing and cloud-based collaborative work, and realizes flexible configuration and sharing of resources.

[0016] A control method, using the above-mentioned multi-scenario adaptive image quality control system, the method comprises the following steps:

[0017] S1. Use the intelligent perception module to start the multi-source sensor to collect multi-dimensional environmental information and image data of the shooting scene in real time, and pre-process the collected data, including denoising and filtering, to improve the accuracy and efficiency of subsequent processing;

[0018] S2, the image quality assessment module receives the image data transmitted by the intelligent perception module, uses the preset multi-dimensional quality assessment indicators to assess the image quality, adopts high-speed image processing chips and parallel computing technology to ensure the efficiency and real-time performance of the assessment process, and quickly generates an image quality assessment report;

[0019] S3, the adaptive adjustment module receives the scene recognition results and image quality assessment data, generates and applies the refined adjustment strategy according to the preset algorithm and strategy library, and introduces the dynamic feedback module during the adjustment process to monitor the adjustment effect in real time, such as comparing the image quality changes before and after the adjustment, and automatically adjusts the adjustment strategy according to the feedback results to ensure that the image quality is continuously optimized to the best state;

[0020] S4. User feedback module collects user satisfaction data on image quality, obtains user feedback through questionnaires and rating systems, analyzes user feedback data, learns user personalized preferences, including color preferences and brightness preferences, and provides an intuitive and easy-to-use interactive adjustment interface, allowing users to manually adjust image parameters, including exposure, white balance and sharpening, and instantly view the adjustment effects;

[0021] S5. The data storage and analysis module efficiently stores image data, evaluation results, adjustment strategies and user feedback data, uses big data to conduct in-depth analysis of the stored data, explores the rules and trends behind the data, and provides data support for system optimization. The data storage and analysis module supports cloud collaboration, migrates some computing tasks and data storage to the cloud, and realizes flexible configuration and sharing of resources.

[0022] Preferably, in S2, the preset multi-dimensional quality assessment indicators include but are not limited to image clarity, contrast, noise level, color saturation, dynamic range and image stability;

[0023] The high-speed image processing chip includes but is not limited to GPU, FPGA or ASIC to accelerate the image processing process. The high-speed image processing chip has powerful parallel processing capabilities and an optimized image processing algorithm library, which can significantly improve the evaluation efficiency;

[0024] The parallel computing technology decomposes the image processing task into multiple subtasks and executes them simultaneously on multiple processors or computing units, so as to reduce the processing time and improve the real-time performance of the evaluation.

[0025] Preferably, in step S3, specifically:

[0026] S3.1, the adaptive adjustment module receives the image quality assessment data and the scene recognition results from the image quality assessment module and the intelligent perception module, and parses the received data to extract key information, such as image quality indicators and scene features, wherein the image quality indicators include but are not limited to clarity, contrast and noise level, and the scene features include but are not limited to lighting conditions, scene type and motion status;

[0027] S3.2, according to the analyzed image quality index and scene characteristics, query the matching adjustment strategy in the algorithm and strategy library pre-set in the adaptive adjustment module, and select the adjustment strategy that best suits the current scene and image quality problem from the matching strategies;

[0028] S3.3, according to the selected adjustment strategy, calculate the required image processing parameters, including exposure compensation value, white balance adjustment parameter and sharpening intensity, configure the calculated parameters into the image processing algorithm, and prepare to adjust the image;

[0029] S3.4. Apply the configured image processing algorithm and parameters to process the image in real time to improve the image quality. During the adjustment process, the adjustment effect can be previewed in real time to preliminarily evaluate the effectiveness of the adjustment strategy.

[0030] S3.5. During the adjustment process, a dynamic feedback module is introduced to monitor the changes in image quality after adjustment in real time, compare the image quality before and after adjustment, and evaluate whether the adjustment effect reaches the expected goal;

[0031] S3.6. Analyze the effectiveness of the adjustment strategy based on the effect evaluation results provided by the dynamic feedback module. If the adjustment effect is not ideal, automatically adjust the adjustment strategy based on the feedback results, try different algorithms and parameter settings, and optimize the adjustment strategy through continuous iteration and adjustment until a satisfactory image quality effect is achieved. Finally, output the adjusted image to a display device or storage medium.

[0032] Technical effects and advantages of the present invention:

[0033] 1. The present invention can collect multi-dimensional information of the shooting environment in real time and perform refined image quality evaluation and adjustment through intelligent perception module, high-speed image quality assessment and adaptive adjustment module, which significantly improves the image quality and adapts to the needs of different shooting environments, thereby enhancing the end-user experience;

[0034] 2. The present invention can collect and analyze user satisfaction data on image quality through the learning function and interactive adjustment interface of the user feedback module, and further learn the user's personalized preferences, so that the system can not only automatically optimize the image quality, but also manually adjust the image parameters according to the user's personalized needs to meet the customized needs of different users;

[0035] 3. The data storage and analysis module of the present invention supports big data processing and cloud collaboration, and can efficiently store and deeply analyze image data, evaluation results and user feedback data, so as to explore the laws and trends behind the data and provide data support for the continuous optimization of the system. In addition, through cloud collaboration, the system realizes flexible configuration and sharing of computing resources and data storage, and improves the overall efficiency and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is the overall system diagram of the present invention.

[0037] Figure 2 It is the overall flow chart of the present invention.

[0038] Figure 3 This is a flow chart of step S3 of the present invention.

[0039] The accompanying drawings are marked as follows: 1. Intelligent perception module; 2. Image quality assessment module; 3. Adaptive adjustment module; 4. User feedback module; 5. Data storage and analysis module. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1,

[0042] The present invention provides a multi-scenario adaptive image quality control system, which includes an intelligent perception module 1, an image quality assessment module 2, an adaptive adjustment module 3, a user feedback module 4 and a data storage and analysis module 5. The intelligent perception module 1, the image quality assessment module 2, the adaptive adjustment module 3, the user feedback module 4 and the data storage and analysis module 5 are connected through a data bus or a network to realize data interaction and collaborative work;

[0043] Among them, the intelligent sensing module 1 is used to collect multi-dimensional environmental information and image data of the shooting scene, and also includes an infrared sensor, a gyroscope and an accelerometer to achieve multi-source sensor fusion;

[0044] The image quality assessment module 2 is used to conduct multi-dimensional quality assessment of the real-time captured image, including clarity, contrast, noise level, color saturation, dynamic range and image stability. It uses high-speed image processing chips and parallel computing technology to ensure the efficiency and real-time nature of the assessment process.

[0045] The adaptive adjustment module 3 is used to generate and apply a refined adjustment strategy including using different algorithms for different types of objects according to the scene recognition results and image quality assessment data, introduce a dynamic feedback module in the adjustment process, monitor the adjustment effect in real time and automatically adjust the strategy;

[0046] The user feedback module 4 is used to collect and analyze user satisfaction data on image quality, learn the user's personalized preferences, and provide an intuitive and easy-to-use interactive adjustment interface, allowing the user to manually adjust image parameters and instantly view the effects;

[0047] The data storage and analysis module 5 is used for efficient storage, analysis and cloud-based collaborative processing of image data, evaluation results, adjustment strategies and user feedback data, supports big data processing and cloud-based collaborative work, and realizes flexible configuration and sharing of resources.

[0048] Specifically, in the above system, the intelligent sensing module 1 first collects multi-dimensional environmental information of the shooting scene, such as lighting conditions, motion status, etc. and image data in real time through integrated infrared sensors, gyroscopes, accelerometers and other multi-source sensors. This information is comprehensively processed through data fusion technology to provide basic data support for subsequent image quality evaluation and adjustment;

[0049] Image quality assessment module 2 uses high-speed image processing chips and parallel computing technology to perform multi-dimensional quality assessment on real-time captured images, including key indicators such as clarity, contrast, noise level, color saturation, dynamic range and image stability. This efficient and real-time assessment capability ensures that the system can quickly identify the quality of the image;

[0050] Adaptive adjustment module 3 generates and applies refined adjustment strategies based on scene recognition results and image quality assessment data. These strategies use different image processing algorithms for optimization according to different types of objects and scene characteristics. At the same time, by introducing a dynamic feedback module, the system can monitor the adjustment effect in real time and automatically adjust the strategy as needed to ensure continuous optimization of image quality.

[0051] The user feedback module 4 is responsible for collecting user satisfaction data on image quality and learning the user's personalized preferences through analysis. In addition, it also provides an intuitive and easy-to-use interactive adjustment interface, allowing users to manually adjust image parameters and instantly view the adjustment effects. This user participation mechanism enhances the flexibility and personalized service capabilities of the system;

[0052] The data storage and analysis module 5 is responsible for the efficient storage and in-depth analysis of the image data, evaluation results, adjustment strategies and user feedback data generated in the entire system. By supporting big data processing and cloud collaboration, the module realizes flexible configuration and sharing of resources, providing strong support for the continuous optimization and upgrading of the system.

[0053] In summary, the multi-scenario adaptive image quality control system provided by the present invention realizes a closed-loop control process from environmental perception to image quality optimization by integrating multiple functional modules such as intelligent perception, image quality assessment, adaptive adjustment, user feedback and data storage and analysis, thereby significantly improving image quality and user experience.

[0054] Embodiment 2,

[0055] This embodiment provides a control method, using the above system, the method comprising the following steps:

[0056] S1. Use the intelligent sensing module 1 to start the multi-source sensor to collect multi-dimensional environmental information and image data of the shooting scene in real time, and pre-process the collected data, including denoising and filtering, to improve the accuracy and efficiency of subsequent processing.

[0057] Specifically, in this step, the intelligent sensing module 1 integrates a variety of sensors, such as infrared sensors, gyroscopes, accelerometers, and image sensors, which can capture multi-dimensional environmental information and image data of the shooting scene in real time. These data include but are not limited to lighting conditions, temperature, humidity, motion status, and pixel values ​​of the image. The collected raw data often contains noise and redundant information. These factors will affect the accuracy and efficiency of subsequent processing. Therefore, the data needs to be preprocessed to provide a high-quality data foundation for subsequent image quality evaluation and optimization, thereby improving the accuracy and efficiency of the entire system.

[0058] S2, the image quality assessment module 2 receives the image data transmitted by the intelligent perception module 1, uses the preset multi-dimensional quality assessment index to assess the image quality, adopts the high-speed image processing chip and parallel computing technology to ensure the efficiency and real-time performance of the assessment process, and quickly generates an image quality assessment report;

[0059] Among them, the preset multi-dimensional quality evaluation indicators include but are not limited to image clarity, contrast, noise level, color saturation, dynamic range and image stability. High-speed image processing chips include but are not limited to GPU, FPGA or ASIC to accelerate the image processing process. High-speed image processing chips have powerful parallel processing capabilities and optimized image processing algorithm libraries, which can significantly improve the evaluation efficiency. Parallel computing technology decomposes image processing tasks into multiple subtasks and executes them simultaneously on multiple processors or computing units to reduce processing time and improve the real-time performance of evaluation.

[0060] Specifically, the purpose of this step is mainly to conduct a comprehensive and accurate quality assessment of the image data transmitted by the intelligent perception module 1. Through the preset multi-dimensional quality assessment indicators, the system can objectively measure the performance of the image in different aspects, such as clarity, contrast, noise level, color saturation, dynamic range and image stability. This comprehensive assessment will help the system to make corresponding adjustments and optimizations based on the evaluation results to improve the overall quality of the image. At the same time, the use of high-speed image processing chips and parallel computing technology can ensure the efficiency and real-time nature of the evaluation process, which is particularly important for real-time image applications that require rapid response and processing, such as video surveillance, autonomous driving and other fields.

[0061] S3, the adaptive adjustment module 3 receives the scene recognition result and the image quality assessment data, generates and applies the refined adjustment strategy according to the preset algorithm and strategy library, and introduces the dynamic feedback module in the adjustment process to monitor the adjustment effect in real time, such as comparing the image quality changes before and after the adjustment, and automatically adjusts the adjustment strategy according to the feedback results to ensure that the image quality is continuously optimized to the best state. Specifically:

[0062] S3.1, the adaptive adjustment module 3 receives the image quality assessment data and the scene recognition results from the image quality assessment module 2 and the intelligent perception module 1, and parses the received data to extract key information, such as image quality indicators and scene features, wherein the image quality indicators include but are not limited to clarity, contrast and noise level, and the scene features include but are not limited to lighting conditions, scene type and motion status;

[0063] S3.2, according to the analyzed image quality index and scene characteristics, query the matching adjustment strategy in the algorithm and strategy library pre-set in the adaptive adjustment module 3, and select the adjustment strategy that best suits the current scene and image quality problem from the matching strategies;

[0064] S3.3, according to the selected adjustment strategy, calculate the required image processing parameters, including exposure compensation value, white balance adjustment parameter and sharpening intensity, configure the calculated parameters into the image processing algorithm, and prepare to adjust the image;

[0065] S3.4. Apply the configured image processing algorithm and parameters to process the image in real time to improve the image quality. During the adjustment process, the adjustment effect can be previewed in real time to preliminarily evaluate the effectiveness of the adjustment strategy.

[0066] S3.5. During the adjustment process, a dynamic feedback module is introduced to monitor the changes in image quality after adjustment in real time, compare the image quality before and after adjustment, and evaluate whether the adjustment effect reaches the expected goal;

[0067] S3.6. Analyze the effectiveness of the adjustment strategy based on the effect evaluation results provided by the dynamic feedback module. If the adjustment effect is not ideal, automatically adjust the adjustment strategy based on the feedback results, try different algorithms and parameter settings, and optimize the adjustment strategy through continuous iteration and adjustment until a satisfactory image quality effect is achieved. Finally, output the adjusted image to a display device or storage medium.

[0068] Specifically, the adaptive adjustment module 3 of this step receives image quality assessment data and scene recognition results from the image quality assessment module 2 and the intelligent perception module 1. These data include image quality indicators, such as clarity, contrast and noise level, and scene characteristics, such as lighting conditions, scene type and motion status. The module parses these data and extracts key information. According to the parsed image quality indicators and scene characteristics, the adaptive adjustment module 3 queries the matching adjustment strategies in the preset algorithm and strategy library. These strategies are designed according to image quality problems and scene characteristics, and the module selects the most suitable adjustment strategy for the current situation. After selecting the adjustment strategy, the module calculates the required The image processing parameters, such as exposure compensation value, white balance adjustment parameters and sharpening intensity, are configured into the image processing algorithm to prepare for image adjustment; the configured image processing algorithm and parameters are applied to the image for real-time processing to improve image quality. During the adjustment process, the user can preview the adjustment effect in real time to preliminarily evaluate the effectiveness of the adjustment strategy. The dynamic feedback module monitors the changes in image quality after adjustment in real time and compares the image quality before and after adjustment. Based on the feedback results, the module analyzes the effectiveness of the adjustment strategy and automatically adjusts the strategy, such as adjusting the algorithm and parameter settings. Through continuous iteration and optimization, it ensures that the image quality is continuously optimized to the best state.

[0069] The purpose of this step is to fine-tune the image through the adaptive adjustment module 3 according to the image quality assessment data and the scene recognition results, so as to optimize the image quality to the best state. This step aims to solve the quality problems that may exist in the image in different scenes, such as insufficient clarity, poor contrast, excessive noise, etc., and at the same time consider the scene characteristics, such as the influence of lighting conditions, scene type, motion state, etc. on the image quality, so as to generate and apply the most suitable adjustment strategy for the current scene and image quality problems;

[0070] In general, this step fine-tunes the image through the adaptive adjustment module 3 and continuously optimizes it in combination with the dynamic feedback mechanism, which significantly improves the image quality, enhances the user experience, and enables the system to adapt to changes in different scenarios and image quality issues.

[0071] S4, user feedback module 4 collects user satisfaction data on image quality, obtains user feedback through questionnaires and scoring systems, analyzes user feedback data, learns user personalized preferences, including color preferences and brightness preferences, and provides an intuitive and easy-to-use interactive adjustment interface, allowing users to manually adjust image parameters, including exposure, white balance and sharpening, and instantly view the adjustment effects.

[0072] The main purpose of this step is to further enhance the user experience and meet the personalized needs of users by collecting and analyzing user satisfaction data on image quality and providing an interactive adjustment interface.

[0073] S5, data storage and analysis module 5 efficiently stores image data, evaluation results, adjustment strategies and user feedback data, uses big data to conduct in-depth analysis of the stored data, explores the rules and trends behind the data, and provides data support for system optimization. Data storage and analysis module 5 supports cloud collaboration, migrates some computing tasks and data storage to the cloud, and realizes flexible configuration and sharing of resources.

[0074] Specifically, this step is responsible for efficiently and orderly storing image data, evaluation results, adjustment strategies and user feedback data. These data are stored in a safe and reliable storage medium for subsequent analysis and use. Using big data analysis technology, the module deeply explores the laws and trends behind the stored data. Through statistical analysis of data, machine learning and other methods, the module discovers the correlation and potential laws between data, and provides data support for system optimization. Based on the results of big data analysis, the module provides suggestions and guidance for the continuous optimization of the system. These suggestions may involve the adjustment of algorithm parameters, optimization of processing procedures, improvement of user interfaces, etc., aiming to improve system performance and user experience. The module supports cloud collaboration and migrates some computing tasks and data storage to the cloud. Through the flexible configuration and sharing of cloud computing resources and storage resources, the module realizes the maximum utilization of resources and the scalability of the system. Cloud collaboration also helps to achieve real-time synchronization and sharing of data, and improve the collaborative efficiency of the system.

[0075] The purpose of this step is to efficiently and orderly store image data, evaluation results, adjustment strategies and user feedback data, and to deeply explore the laws and trends behind these data through big data analysis technology, so as to provide strong data support for the continuous optimization of the system. In addition, by supporting cloud collaboration, this step also aims to achieve flexible configuration and sharing of computing resources and data storage, and improve the overall efficiency and scalability of the system.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. Multi-scene adaptive image quality control system, characterized by: The system comprises an intelligent perception module (1), an image quality assessment module (2), an adaptive adjustment module (3), a user feedback module (4) and a data storage and analysis module (5), wherein the intelligent perception module (1), the image quality assessment module (2), the adaptive adjustment module (3), the user feedback module (4) and the data storage and analysis module (5) are connected via a data bus or a network to achieve data interaction and collaborative work.

2. The multi-scene adaptive image quality control system according to claim 1, characterized in that: The intelligent sensing module 1 is used to collect multi-dimensional environmental information and image data of the shooting scene, and also includes an infrared sensor, a gyroscope and an accelerometer to achieve multi-source sensor fusion.

3. The multi-scene adaptive image quality control system according to claim 2, characterized in that: The image quality assessment module (2) is used to perform multi-dimensional quality assessment on the real-time captured image, including clarity, contrast, noise level, color saturation, dynamic range and image stability, and adopts a high-speed image processing chip and parallel computing technology to ensure the efficiency and real-time performance of the assessment process.

4. The multi-scene adaptive image quality control system according to claim 3, characterized in that: The adaptive adjustment module (3) is used to generate and apply a refined adjustment strategy including using different algorithms for different types of objects according to the scene recognition results and image quality assessment data, and introduce a dynamic feedback module during the adjustment process to monitor the adjustment effect in real time and automatically adjust the strategy.

5. The multi-scene adaptive image quality control system according to claim 4, characterized in that: The user feedback module (4) is used to collect and analyze user satisfaction data on image quality, learn the user's personalized preferences, and provide an intuitive and easy-to-use interactive adjustment interface, allowing the user to manually adjust image parameters and instantly view the effects.

6. The multi-scene adaptive image quality control system according to claim 5, characterized in that: The data storage and analysis module (5) is used to efficiently store, analyze and collaboratively process image data, evaluation results, adjustment strategies and user feedback data in the cloud, support big data processing and collaborative work in the cloud, and realize flexible configuration and sharing of resources.

7. A control method, using the multi-scene adaptive image quality control system according to claim 6, characterized in that: The method comprises the following steps: S1, environmental information and image data collection and preprocessing, using the intelligent perception module (1) to start the multi-source sensor to collect multi-dimensional environmental information and image data of the shooting scene in real time, and preprocess the collected data, including denoising and filtering, to improve the accuracy and efficiency of subsequent processing; S2, image quality assessment, the image quality assessment module (2) receives the image data transmitted by the intelligent perception module (1), uses preset multi-dimensional quality assessment indicators to assess the quality of the image, adopts high-speed image processing chips and parallel computing technology to ensure the efficiency and real-time performance of the assessment process, and quickly generates an image quality assessment report; S3, adaptive adjustment of image quality. The adaptive adjustment module (3) receives the scene recognition results and image quality assessment data, generates and applies a refined adjustment strategy based on a preset algorithm and strategy library, and introduces a dynamic feedback module during the adjustment process to monitor the adjustment effect in real time, such as comparing the image quality changes before and after the adjustment, and automatically adjusts the adjustment strategy based on the feedback results to ensure that the image quality is continuously optimized to the best state. S4. User feedback and personalized service. The user feedback module (4) collects user satisfaction data on image quality, obtains user feedback through questionnaires and a rating system, analyzes user feedback data, learns user personalized preferences, including color preferences and brightness preferences, and provides an intuitive and easy-to-use interactive adjustment interface, allowing users to manually adjust image parameters, including exposure, white balance, and sharpening, and instantly view the adjustment effects; S5. Data storage, analysis and cloud collaboration. The data storage and analysis module (5) efficiently stores image data, evaluation results, adjustment strategies and user feedback data, uses big data to conduct in-depth analysis of the stored data, explores the laws and trends behind the data, and provides data support for system optimization. The data storage and analysis module (5) supports cloud collaboration, migrates some computing tasks and data storage to the cloud, and realizes flexible configuration and sharing of resources.

8. The control method according to claim 7, characterized in that: In S2, the preset multi-dimensional quality assessment indicators include but are not limited to image clarity, contrast, noise level, color saturation, dynamic range, and image stability; The high-speed image processing chip includes but is not limited to GPU, FPGA or ASIC to accelerate the image processing process. The high-speed image processing chip has powerful parallel processing capabilities and an optimized image processing algorithm library, which can significantly improve the evaluation efficiency; The parallel computing technology decomposes the image processing task into multiple subtasks and executes them simultaneously on multiple processors or computing units, so as to reduce the processing time and improve the real-time performance of the evaluation.

9. The control method according to claim 7, characterized in that: In step S3, specifically; S3.1, data analysis and information extraction, the adaptive adjustment module (3) receives the image quality assessment data and the scene recognition result from the image quality assessment module (2) and the intelligent perception module (1), analyzes the received data, and extracts key information, such as image quality indicators and scene features, wherein the image quality indicators include but are not limited to clarity, contrast and noise level, and the scene features include but are not limited to lighting conditions, scene type and motion state; S3.2, adjustment strategy selection, according to the analyzed image quality index and scene characteristics, query the matching adjustment strategy in the algorithm and strategy library pre-set in the adaptive adjustment module (3), and select the adjustment strategy that best suits the current scene and image quality problem from the matching strategies; S3.3, image processing parameter calculation, according to the selected adjustment strategy, calculate the required image processing parameters, including exposure compensation value, white balance adjustment parameter and sharpening intensity, configure the calculated parameters into the image processing algorithm, and prepare for image adjustment; S3.4, real-time image adjustment and preview, apply the configured image processing algorithm and parameters to process the image in real time to improve the image quality. During the adjustment process, the adjustment effect can be previewed in real time to preliminarily evaluate the effectiveness of the adjustment strategy; S3.5, Dynamic feedback and effect evaluation, during the adjustment process, a dynamic feedback module is introduced to monitor the changes in image quality after adjustment in real time, compare the image quality before and after adjustment, and evaluate whether the adjustment effect reaches the expected goal; S3.6, strategy optimization and iterative adjustment, analyze the effectiveness of the adjustment strategy based on the effect evaluation results provided by the dynamic feedback module. If the adjustment effect is not ideal, automatically adjust the adjustment strategy based on the feedback results, try different algorithms and parameter settings, and optimize the adjustment strategy through continuous iteration and adjustment until a satisfactory image quality effect is achieved. Finally, the adjusted image is output to the display device or storage medium.

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