Video quality detection system based on image processing technology

Abstract

The invention discloses a video quality detection system based on an image processing technology, and the system comprises an image extraction module which is used for extracting an image of each frame in a video and obtaining the time interval information between adjacent frames, a brightness calculation module which is used for calculating the brightness value distribution in each frame and the brightness value distribution between the adjacent frames, forming a brightness change data sequence, and outputting the brightness change data sequence. The brightness change analysis module is used for analyzing the intra-frame and inter-frame brightness change rate based on the brightness change data sequence, and the brightness regulation and control module is used for regulating and controlling the brightness of the video frame according to the brightness change rate analysis result so as to optimize the visual comfort; according to the video quality detection system based on the image processing technology, the stability and watching comfort of videos are remarkably improved, the video quality detection system is suitable for various hardware devices and scene applications, and the intra-frame and inter-frame brightness change rate is analyzed, regulated and controlled to solve the problem that the watching visual comfort is reduced due to environment illumination fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of video quality detection and optimization, and in particular to a video quality detection system based on image processing technology. Background Art

[0002] In the field of video quality detection, detection systems based on image processing technology are widely used to analyze and optimize the quality of video frames to enhance the viewing experience. Among them, the rate of change of brightness is an important factor affecting video stability and viewing comfort. However, in actual application scenarios, due to fluctuations in ambient light, the brightness of video frames may suddenly change or slowly drift, resulting in reduced visual adaptability of viewers, which in turn affects the overall viewing experience. Existing video quality detection methods usually rely on single-frame brightness distribution statistics or local area brightness mean calculation to identify abnormal brightness changes. However, these methods only focus on the brightness characteristics within a single frame and fail to fully consider the brightness change trend across frames. Therefore, they have significant limitations when dealing with ambient light fluctuations.

[0003] Furthermore, traditional video quality detection systems lack effective strategies for controlling the rate of brightness change across frames. In complex lighting environments, such as outdoor scenes or those with frequently changing light sources, existing methods struggle to distinguish between brightness changes within the video content itself and external lighting interference, resulting in reduced detection accuracy. Furthermore, some video quality optimization methods employ computationally complex deep learning models for brightness compensation, but this often increases computational overhead and makes it difficult to meet the demands of real-time processing. Consequently, existing technologies are unable to effectively control the rate of brightness change within and across frames when responding to ambient lighting fluctuations, which can easily lead to a decrease in viewing comfort. Summary of the Invention

[0004] The purpose of the present invention is to provide a video quality detection system based on image processing technology, which analyzes and controls the brightness change rate within each frame and between frames to solve the problem of reduced viewing visual comfort caused by ambient light fluctuations.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a video quality detection system based on image processing technology, the system comprising:

[0006] The image extraction module is used to extract the image of each frame in the video and obtain the time interval information between adjacent frames;

[0007] The brightness calculation module connected to the image extraction module is used to calculate the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence;

[0008] A brightness change analysis module connected to the brightness calculation module is used to analyze the brightness change rate within a frame and between frames based on the brightness change data sequence;

[0009] The brightness control module is connected to the brightness change analysis module and is used to adjust the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis results.

[0010] Preferably, the image extraction module extracts the image of each frame in the video and obtains the time interval information between adjacent frames, including calculating the time interval between adjacent frames to ensure the stability of time estimation. The specific formula is: 2 =kR 3 ;

[0011] Where T represents the time interval between two frames, k represents the proportional constant, and R represents the amplitude of the brightness change between frames;

[0012] After calculation, the time interval information is input into the brightness calculation module.

[0013] Preferably, the brightness calculation module calculates the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence, including calculating the average brightness of the current frame, setting a threshold for illumination influence, and calculating the brightness change rate within the frame and across frames. The specific formula is:

[0014] Where p represents the brightness change rate, A represents the initial rate of brightness change, B represents the threshold of illumination influence, C represents a constant, D represents the illumination intensity of the current frame, and e represents the base of the natural logarithm.

[0015] A brightness change data sequence is formed and input into the brightness change analysis module.

[0016] Preferably, the brightness change analysis module analyzes the brightness change rate within and between frames based on the brightness change data sequence, including determining the brightness of the current frame, setting the reference brightness, and calculating the brightness change actually perceived by the human eye. The specific formula is:

[0017] Where E represents the brightness change perceived by the human eye, q represents the perception adjustment coefficient, S represents the actual brightness of the current frame, and S0 represents the reference brightness value;

[0018] Combined with the analysis results, it is determined whether the brightness change will affect visual comfort, and the data is transmitted to the brightness control module.

[0019] Preferably, the brightness control module controls the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis result, including calculating the brightness adjustment amount expected by the human eye, calculating the actual adjustable brightness, and adjusting the brightness to ensure that the final brightness adjustment meets the adaptation range of the human eye. The specific formula is: Q d =Q s ;

[0020] Among them, Qd Indicates the desired brightness adjustment amount, Q s Indicates the actual brightness adjustment amount;

[0021] Output optimized brightness data and use it for final video frame optimization.

[0022] Preferably, the brightness calculation module includes a partition processing unit for dividing each frame of image into multiple regions and calculating the brightness value distribution of each region respectively.

[0023] Preferably, the brightness control module includes a feedback adjustment unit for dynamically adjusting the brightness of the video frame according to a visual comfort parameter preset by the user.

[0024] Preferably, a display module is further included, for outputting the optimized video image to a display device in real time.

[0025] Preferably, the brightness control module further includes an adaptive adjustment unit for optimizing the brightness of the video frame in real time according to changes in external environmental illumination.

[0026] Preferably, the brightness change analysis module and the brightness control module are connected via a prediction model, so as to predict the brightness change trend in advance and perform pre-control.

[0027] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0028] This video quality detection system based on image processing technology uses an image extraction module to extract the image of each frame in the video and obtain the time interval information between adjacent frames. The brightness calculation module calculates the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence. The brightness change analysis module analyzes the brightness change rate within the frame and across frames based on the brightness change data sequence. The brightness control module controls the brightness of the video frame based on the brightness change rate analysis results to optimize visual comfort. This system can not only accurately calculate the brightness distribution of a single frame, but also deeply analyze the brightness change trend across frames. This overcomes the shortcomings of existing technologies that only focus on the brightness characteristics of a single frame. It distinguishes the brightness changes of the video content from external light interference in real time, effectively improving the detection accuracy and reliability in complex lighting environments. It can dynamically adjust the brightness of the video frame to optimize the viewing experience, reduce visual fatigue, and significantly improve the stability and viewing comfort of the video. While ensuring the brightness control effect, it reduces the computational complexity and can meet the needs of real-time processing. It is applicable to various hardware devices and scenarios. It analyzes and controls the brightness change rate within each frame and across frames to solve the problem of reduced viewing visual comfort caused by ambient light fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a connection diagram of the system modules of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, the present invention provides a technical solution: a video quality detection system based on image processing technology, the system comprising:

[0032] The image extraction module is used to extract the image of each frame in the video and obtain the time interval information between adjacent frames;

[0033] The brightness calculation module connected to the image extraction module is used to calculate the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence;

[0034] A brightness change analysis module connected to the brightness calculation module is used to analyze the brightness change rate within a frame and between frames based on the brightness change data sequence;

[0035] The brightness control module is connected to the brightness change analysis module and is used to adjust the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis results.

[0036] The core of this system lies in the detection and control of brightness changes based on image processing technology. First, the image extraction module extracts each frame from the video stream and accurately calculates the time interval between adjacent frames to ensure temporal consistency in subsequent brightness analysis. Subsequently, the brightness calculation module uses statistical methods to calculate the distribution of pixel brightness values within each frame and generate a brightness change data sequence that reflects the brightness fluctuations in the video image. The brightness change analysis module models and calculates this data sequence, identifying local brightness changes within frames and overall brightness fluctuation trends across frames. It then identifies abnormal brightness change patterns, such as flicker and high-contrast jumps, by setting thresholds or using machine learning methods. Finally, the brightness control module adjusts the brightness distribution of the video frames based on the analysis results, optimizing the video image through methods such as dynamic range compression, contrast equalization, or local adaptive enhancement, ensuring a stable and comfortable viewing experience for viewers in various lighting environments. Furthermore, this system can be combined with real-time video processing technology to achieve low-latency brightness optimization and control, making it suitable for applications such as live broadcasts and video conferencing. This system can improve visual comfort during video viewing and effectively alleviate visual fatigue and discomfort caused by drastic brightness fluctuations. Through a precise brightness control mechanism, the system can reduce common problems such as flickering at high brightness and loss of detail in overly dark scenes, thereby improving video quality and content readability. In addition, compared to traditional fixed brightness adjustment methods, this system can intelligently perceive dynamic changes in video content and adaptively optimize the brightness of the picture to adapt it to different ambient lighting conditions, thereby improving the viewing experience. In terms of commercial applications, the system can be widely used in video playback devices, online video platforms, television broadcasting systems, etc., to ensure that viewers can obtain the best visual experience under different viewing devices and viewing conditions. In addition, the system can also combine artificial intelligence algorithms for personalized brightness optimization, providing customized video enhancement solutions for different users, improving user satisfaction, and providing more competitive technical support for the video industry.

[0037] The image extraction module extracts the image of each frame in the video and obtains the time interval information between adjacent frames, including calculating the time interval between adjacent frames to ensure the stability of time estimation. The specific formula is: T 2 =kR 3 ;

[0038] Where T represents the time interval between two frames, k represents the proportional constant, and R represents the amplitude of the brightness change between frames;

[0039] After calculation, the time interval information is input into the brightness calculation module.

[0040] The system processes the video stream frame by frame through the image extraction module and obtains the time interval information of adjacent frames. 2 =kR 3The system models the relationship between the brightness variation (R) and the time interval (T) between adjacent frames to reflect the impact of brightness changes on time estimation. The proportionality constant k in the formula is used to correct for time interval calculation errors in different scenarios, ensuring accurate time estimates in both rapidly changing and stable scenes. Through this calculation, the system can dynamically adapt to fluctuations in inter-frame brightness changes while ensuring the accuracy and robustness of time interval estimation. This time interval information is then input into the brightness calculation module as a reference for subsequent brightness distribution and variation analysis, providing the necessary time domain support for the brightness variation analysis module. By adopting a calculation formula that relates inter-frame brightness changes to time intervals, this system effectively improves the stability and accuracy of time estimation, significantly reducing time calculation errors in video scenes with drastic brightness fluctuations. Furthermore, this method can flexibly adapt to different brightness variation conditions, ensuring efficient operation in diverse video processing environments. Compared to traditional fixed time interval processing methods, this system's dynamic adjustment mechanism further enhances the reliability of video quality detection, provides more accurate data support for subsequent brightness control, and optimizes visual comfort and user experience.

[0041] The brightness calculation module calculates the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence, including calculating the average brightness of the current frame, setting the threshold of illumination influence, and calculating the brightness change rate within and across frames. The specific formula is:

[0042] Where p represents the brightness change rate, A represents the initial rate of brightness change, B represents the threshold of illumination influence, C represents a constant, D represents the illumination intensity of the current frame, and e represents the base of the natural logarithm.

[0043] A brightness change data sequence is formed and input into the brightness change analysis module.

[0044] In this embodiment, the brightness calculation module counts the brightness value of each frame of the image, and combines the brightness data of adjacent frames to calculate the average brightness of the current frame, further forming a data sequence of brightness changes within and across frames. The system models the brightness change rate by associating it with light intensity and a light impact threshold to reflect the degree to which lighting conditions influence brightness changes. A, the initial rate, determines the brightness change rate when there is no significant lighting influence; B, the light impact threshold, reflects the degree of light interference with brightness changes; and D, the light intensity of the current frame; its ratio to C, determines the magnitude of the actual lighting environment's influence on brightness changes. The resulting brightness change data sequence is input into the brightness change analysis module, providing basic data support for subsequent analysis of brightness change patterns. This mechanism ensures the accuracy of brightness change rate calculation, especially in scenes with complex lighting conditions, allowing it to dynamically adapt to ambient light changes and stabilize analysis results. By incorporating lighting impact modeling, this implementation effectively reduces the interference of complex lighting conditions on brightness change analysis, improving the accuracy and robustness of brightness change rate calculation. Especially in rapidly changing scenes or environments with uneven lighting conditions, the system can adaptively adjust the brightness change model to ensure data stability. Furthermore, the generated brightness change data sequence provides precise support for subsequent brightness control, ensuring that video brightness optimization more accurately meets actual viewing requirements. Compared with traditional methods, this system shows higher adaptability and reliability under dynamic lighting conditions, providing technical advantages for video quality detection and optimization.

[0045] The brightness change analysis module analyzes the brightness change rate within and across frames based on the brightness change data sequence, including determining the brightness of the current frame, setting the reference brightness, and calculating the actual brightness change perceived by the human eye. The specific formula is:

[0046] Where E represents the brightness change perceived by the human eye, q represents the perception adjustment coefficient, S represents the actual brightness of the current frame, and S0 represents the reference brightness value;

[0047] Combined with the analysis results, it is determined whether the brightness change will affect visual comfort, and the data is transmitted to the brightness control module.

[0048] In this embodiment, the brightness change analysis module extracts the actual brightness S of each frame from the brightness change data sequence and sets a reference brightness value S0 as a reference comparison standard. The module uses the formula Calculate the human eye's perception of brightness changes, E. The perceptual adjustment coefficient q in the formula reflects the differences in the human eye's sensitivity to brightness changes in different scenarios. For example, the human eye is more sensitive to brightness changes in dark scenes, so the value of q can be increased. Using the calculated E value, the system quantifies the brightness change perceived by the human eye and, combined with a preset visual comfort threshold, determines whether the brightness change is likely to cause viewing discomfort. If the E value exceeds the threshold, the system transmits the result to the brightness control module, initiating an automatic brightness adjustment mechanism to optimize the viewing experience. By incorporating a calculation formula for how the human eye perceives brightness changes, this implementation more accurately reflects the actual perceptual characteristics of the human visual system and provides a more precise analysis of brightness changes. Compared to traditional analysis methods based solely on physical brightness changes, this implementation demonstrates greater adaptability to dynamic lighting and complex scenes. The system can effectively determine whether brightness changes affect visual comfort, providing a scientific basis for the brightness control module's decision-making and optimizing the overall visual quality of the video. Furthermore, this approach can reduce eye discomfort caused by sudden brightness changes, further improving video viewing comfort and user experience.

[0049] The brightness control module adjusts the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis results. This includes calculating the brightness adjustment amount expected by the human eye, calculating the actual adjustable brightness, and adjusting the brightness to ensure that the final brightness adjustment meets the human eye's adaptation range. The specific formula is: Q d =Q s ;

[0050] Among them, Q d Indicates the desired brightness adjustment amount, Q s Indicates the actual brightness adjustment amount;

[0051] Output optimized brightness data and use it for final video frame optimization.

[0052] In this embodiment, the brightness control module first calculates the brightness adjustment amount Q expected by the human eye based on the brightness change rate analysis result provided by the brightness change analysis module. d , which is based on the estimation of the best adaptation range of the human eye to brightness changes. Then, the system calculates the actual brightness adjustment amount Q that can be achieved based on the actual conditions of video processing (such as brightness difference between frames, device display capabilities, ambient light, etc.). s The system uses the formula Q d =Q sEnsure the consistency between the expected adjustment value and the actual adjustment value, and apply the value to the brightness control of the video frame. Through the adjusted brightness distribution, the overall brightness of the video is optimized to make it consistent with the visual adaptation range of the human eye, thereby improving the viewing experience. The final brightness optimization data will be used for video output to ensure that users can obtain the best viewing effect under various lighting conditions. Through the optimization of the brightness control module, this embodiment can effectively alleviate the visual fatigue caused by excessive or uneven brightness changes in the video, and provide an adaptive brightness adjustment mechanism. Brightness control not only takes into account the physiological characteristics of the human eye to brightness changes, but also combines the constraints in actual video processing to ensure that the adjustment effect takes into account visual comfort and technical feasibility. Compared with the traditional fixed brightness adjustment method, the dynamic control mechanism of this embodiment significantly improves the adaptability and playback quality of the video. Especially under complex environmental lighting, the system can optimize the brightness in a targeted manner, provide clearer and more stable visual effects, and further enhance the user experience.

[0053] The brightness calculation module includes a partition processing unit for dividing each frame image into multiple regions and calculating the brightness value distribution of each region separately. In this embodiment, the brightness calculation module partitions the video frame image through the partition processing unit, dividing each frame into multiple regions (such as gridded regions) to analyze the image brightness distribution in a more fine-grained manner. For each partition, the unit independently calculates its brightness value distribution, including the average brightness, the brightness variation range, and the central trend of the brightness (such as variance or skewness). This method can more accurately reflect the brightness characteristics of different regions in the image, such as the distribution of highlight areas and dark areas. Ultimately, the system integrates the brightness data of all regions to form a brightness distribution model of the frame, providing more detailed data support for the subsequent brightness change analysis module and the brightness control module. This partition processing method can effectively improve the accuracy of brightness analysis, especially in the case of uneven scene brightness, providing a more targeted basis for brightness adjustment. By introducing the partition processing unit, this embodiment can realize regional brightness analysis of the frame image, which can more accurately capture the unevenness of the brightness distribution in the image compared to the overall brightness calculation method. Especially in scenes containing localized areas of high or low brightness, this approach effectively reflects the spatial distribution of brightness, providing more granular data support for subsequent analysis and control of brightness changes. Furthermore, this partitioning approach enhances the flexibility of the brightness calculation module, enabling it to adapt to complex image content and diverse scenarios, improving the accuracy and reliability of the system's brightness analysis and control, thereby further optimizing visual comfort for video viewing.

[0054] The brightness control module includes a feedback adjustment unit, which dynamically adjusts the brightness of the video frame based on user-preset visual comfort parameters. In this embodiment, the feedback adjustment unit of the brightness control module receives user-entered visual comfort parameters (such as brightness threshold, contrast preference, or brightness change sensitivity) and uses these parameters as a reference for the control algorithm. The system first uses the brightness change analysis module to detect the brightness status of the current video frame and compare it with the user-preset parameters. If the brightness is detected to exceed the user-set comfort range, the feedback adjustment unit activates a dynamic adjustment mechanism, calculates the required brightness adjustment based on the difference, and applies it to the brightness control of the current frame in real time. In addition, the feedback adjustment unit has an adaptive optimization function that automatically updates the visual comfort parameters based on the user's historical usage data and ambient lighting conditions to ensure that the adjustment effect is more in line with actual needs. Ultimately, the adjusted brightness data is used to generate an optimized video output, further enhancing the user's viewing experience. By introducing the feedback adjustment unit, this embodiment can dynamically adjust the video brightness according to the user's personalized needs, significantly improving the system's adaptability and user satisfaction. Compared to traditional fixed brightness adjustment mechanisms, this method allows users to set comfort parameters, and the system will adjust the brightness in real time based on these parameters, thereby achieving more precise brightness optimization. In addition, the system can adapt to the user's long-term usage preferences and environmental changes, dynamically optimizing visual effects, further enhancing the flexibility and personalized experience of video playback. This module is particularly suitable for different user groups (such as users who watch videos for a long time or those who are sensitive to brightness changes). While improving viewing comfort, it also reduces visual fatigue caused by uncomfortable brightness changes.

[0055] Also included is a display module for outputting the optimized video image to a display device in real time. In this embodiment, the display module receives the optimized video image data generated by the brightness control module and outputs it to a connected display device, such as a liquid crystal display, an LED screen, or a projection device, in real time. The display module ensures lossless transmission of the video signal through a high-speed interface (such as HDMI, DisplayPort, or USB-C) to maintain the integrity of the image quality. At the same time, the display module dynamically adjusts the output image format in combination with parameters such as the resolution and refresh rate of the display device to ensure that it matches the characteristics of the display device. For video processing scenarios that require real-time performance (such as live broadcasts or video conferencing), the display module uses low-latency image processing technology to ensure that the optimized picture can be output to the display device in the shortest time, avoiding freezes or delays. In addition, the display module supports multi-device synchronous output and can transmit the optimized video image to multiple display terminals at the same time, achieving efficient video content distribution and sharing. By introducing the display module, this embodiment can present the optimized video image to the user in real time, directly reflecting the brightness control effect of the system. The real-time output capability of the display module can meet the needs of various application scenarios, including video editing, real-time live broadcasts, and multimedia presentations. Furthermore, the display module supports lossless signal transmission and dynamic image format adjustment, adapting to the characteristics of different display devices to ensure optimal video quality. Compared to traditional video output methods, this implementation not only maintains the integrity of the optimization effect but also enhances the system's usability and user experience through real-time design, allowing users to intuitively experience the improved visual comfort brought about by brightness optimization.

[0056] The brightness control module further includes an adaptive adjustment unit, which optimizes the brightness of video frames in real time based on changes in ambient lighting. In this embodiment, the adaptive adjustment unit uses an integrated ambient light sensor to monitor changes in external light intensity in real time. It combines the collected light data with preset brightness adjustment rules to dynamically adjust the brightness of the video frames. For example, when ambient light intensity decreases, the adaptive adjustment unit increases the brightness of the video frames to ensure clear details; when ambient light intensity increases, the unit decreases the brightness of the video frames to avoid visual discomfort caused by excessive brightness. The adaptive adjustment unit also has learning and optimization functions, adjusting the brightness optimization strategy based on user usage habits and long-term light change data to better meet the user's visual needs. In addition, to ensure real-time performance, the unit uses a low-latency adjustment algorithm to rapidly apply the calculated results to the brightness control of the video frames, achieving seamless adaptation of lighting changes to video brightness, ensuring the user receives an optimal viewing experience under different lighting conditions. By introducing the adaptive adjustment unit, this embodiment can optimize video brightness based on dynamic changes in ambient lighting, significantly improving the system's adaptability to complex lighting conditions. Compared to traditional fixed brightness adjustment methods, this system effectively reduces the impact of lighting changes on the video viewing experience through real-time monitoring and adjustment. For example, it maintains the comfort and clarity of the video image during daytime and nighttime, or when switching between indoor and outdoor scenes. Furthermore, the adaptive adjustment function optimizes based on user habits, further enhancing the system's personalization and intelligence, making it suitable for a variety of scenarios (such as outdoor presentations and conference room playback).

[0057] The brightness change analysis module is connected to the brightness control module through a prediction model to predict the brightness change trend in advance and perform pre-control. In this embodiment, the brightness change analysis module uses a prediction model to calculate the brightness change trend of future frames based on the brightness change data sequence of the video frame. The prediction model can be trained based on a time series analysis method (such as an ARIMA model), a statistical regression model, or a deep learning method (such as a recurrent neural network such as LSTM, GRU), to establish a trend prediction model for inter-frame brightness changes. This model can identify potential brightness fluctuations in advance, such as severe brightness flickering or gradual changes. The prediction results are then passed to the brightness control module, which performs brightness pre-control in advance according to the predicted value, such as appropriately adjusting the brightness amplitude or mitigating the change speed, thereby reducing the impact of brightness mutations on user visual comfort during video playback. In addition, the system can also dynamically update the parameters of the prediction model through a real-time feedback mechanism, improve prediction accuracy and robustness, and adapt to changes in different video scenes. By introducing a prediction model connection mechanism, this embodiment can effectively improve the system's response speed to brightness changes and reduce visual discomfort or playback quality degradation caused by sudden brightness changes. Compared to passive control methods, this implementation achieves pre-control by predicting brightness trends in advance. This allows for smooth transitions before drastic brightness changes occur, ensuring smooth and comfortable video playback. Furthermore, the predictive model can adapt to different types of video content (such as fast-cut scenes and dynamic camera scenes), enhancing the system's applicability in complex application scenarios. This approach also improves the accuracy and efficiency of brightness control, optimizing the user viewing experience and is particularly suitable for scenarios such as high-quality video playback or live broadcasts.

[0058] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A video quality detection system based on image processing technology, characterized in that: The system comprises: The image extraction module is used to extract the image of each frame in the video and obtain the time interval information between adjacent frames; The brightness calculation module connected to the image extraction module is used to calculate the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence; A brightness change analysis module connected to the brightness calculation module is used to analyze the brightness change rate within a frame and between frames based on the brightness change data sequence; The brightness control module is connected to the brightness change analysis module and is used to adjust the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis results.

2. The video quality detection system based on image processing technology according to claim 1, characterized in that: The image extraction module extracts the image of each frame in the video and obtains the time interval information between adjacent frames, including calculating the time interval between adjacent frames to ensure the stability of time estimation. The specific formula is: 2 =kR 3 ; Where T represents the time interval between two frames, k represents the proportional constant, and R represents the amplitude of the brightness change between frames; After calculation, the time interval information is input into the brightness calculation module.

3. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness calculation module calculates the brightness value distribution within each frame and between adjacent frames to form a brightness change data sequence, including calculating the average brightness of the current frame, setting a threshold for illumination influence, and calculating the brightness change rate within and across frames. The specific formula is: Where p represents the brightness change rate, A represents the initial rate of brightness change, B represents the threshold of illumination influence, C represents a constant, D represents the illumination intensity of the current frame, and e represents the base of the natural logarithm. A brightness change data sequence is formed and input into the brightness change analysis module.

4. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness change analysis module analyzes the brightness change rate within and between frames based on the brightness change data sequence, including determining the brightness of the current frame, setting the reference brightness, and calculating the brightness change actually perceived by the human eye. The specific formula is: Where E represents the brightness change perceived by the human eye, q represents the perception adjustment coefficient, S represents the actual brightness of the current frame, and S0 represents the reference brightness value; Combined with the analysis results, it is determined whether the brightness change will affect visual comfort, and the data is transmitted to the brightness control module.

5. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness control module adjusts the brightness of the video frame to optimize visual comfort based on the brightness change rate analysis results, including calculating the brightness adjustment amount expected by the human eye, calculating the actual adjustable brightness, and adjusting the brightness to ensure that the final brightness adjustment meets the human eye adaptation range. The specific formula is: Q d =Q s ; Among them, Q d Indicates the desired brightness adjustment amount, Q s Indicates the actual brightness adjustment amount; Output optimized brightness data and use it for final video frame optimization.

6. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness calculation module includes a partition processing unit for dividing each frame of image into multiple regions and calculating the brightness value distribution of each region respectively.

7. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness control module includes a feedback adjustment unit for dynamically adjusting the brightness of the video frame according to the visual comfort parameters preset by the user.

8. The video quality detection system based on image processing technology according to claim 1, characterized in that: The system also includes a display module for outputting the optimized video image to a display device in real time.

9. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness control module further includes an adaptive adjustment unit for optimizing the brightness of the video frame in real time according to changes in external ambient light.

10. The video quality detection system based on image processing technology according to claim 1, characterized in that: The brightness change analysis module and the brightness control module are connected via a prediction model, which is used to predict the brightness change trend in advance and perform pre-control.

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