FPGA-based image processing system and method

Abstract

The application relates to the technical field of image communication, in particular to an image processing system and method based on FPGA, which comprises a data stream synchronization module, which acquires a video frame and a timestamp through FPGA. In the application, data stream synchronization ensures the matching of the video frame and the timestamp, optimizes the stability of the video stream, reduces image errors caused by time misplacement, improves the accuracy of video analysis, dynamically sets the transmission priority of the data packet, adjusts the transmission strategy according to the importance of the content, effectively utilizes bandwidth resources, ensures the priority transmission of key information, improves the overall efficiency and response speed of network transmission, dynamically adjusts the coding bit rate and frame rate according to the network condition, optimizes the adaptability of the data stream to different network conditions, and keeps the balance between the video quality and the transmission efficiency. The enhancement of the image brightness, contrast adjustment and noise suppression improves the image quality in a low-light environment and improves the usability and viewing experience of the image.

Description

Technical Field

[0001] The present invention relates to the field of image communication technology, and in particular to an image processing system and method based on FPGA. Background Art

[0002] An image processing system based on an FPGA (field programmable gate array) is a highly efficient image processing platform that leverages the FPGA's high programmability and parallel processing capabilities to rapidly process image data. It is often used in applications requiring real-time image analysis, video surveillance, and machine vision, as FPGAs can be programmed to execute complex image processing algorithms such as image enhancement, filtering, and feature extraction.

[0003] However, existing technologies, when processing video in highly variable environments, can lead to video quality degradation or transmission delays due to fixed transmission strategies or optimized encoding settings. Furthermore, in low-light and nighttime image processing, the lack of effective noise suppression and dynamic range adjustment makes it difficult to provide clear image output, impacting the timeliness and accuracy of decision-making in critical applications such as security surveillance. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an image processing system and method based on FPGA.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: an FPGA-based image processing system includes:

[0006] The data stream synchronization module obtains video frames and timestamps through FPGA, adjusts the timestamps to match the video stream, reorders and optimizes the frames, and obtains synchronized video stream output;

[0007] a priority encoding module that receives the synchronous output of the video stream, analyzes key elements of the video content, dynamically sets the transmission priority of the data packet, obtains a priority adjustment state, and optimizes the data packet transmission path based on the priority adjustment state to obtain optimized video data;

[0008] An adaptive encoding module collects network status feedback, receives the optimized video data, adjusts the video encoding bit rate and frame rate, obtains network adaptive parameters, and adjusts the encoding output based on the network adaptive parameters to obtain an adaptive video output;

[0009] The dynamic image enhancement module receives video input from a nighttime environment, improves image brightness and contrast through FPGA, performs noise suppression and dynamic range adjustment, and obtains enhanced image results.

[0010] Preferably, the steps for obtaining the synchronous output of the video stream are:

[0011] acquiring video frames and timestamps, collating each video frame and timestamp, checking whether the time sequence is consistent with the recording sequence of the video frames, and obtaining time-collated video data;

[0012] based on the time-collated video data, calculating a frame reordering index, determining the correct playing sequence of the video frames, and the calculation formula being:

[0013]

[0014] wherein, R i is the reordering index of the i-th frame, τ i is the timestamp of the i-th frame, n is the total number of video frames, is the total sum of the timestamps;

[0015] based on the reordering index, performing frame reordering and video stream optimization, and obtaining a video stream synchronization output.

[0016] Preferably, the step of obtaining the priority adjustment state is:

[0017] receiving the video stream synchronization output, identifying key elements, the key elements including moving objects, color changes, and scene changes, and obtaining a key element analysis result;

[0018] based on the key element analysis result, calculating the transmission priority of each data packet, and the calculation formula being:

[0019]

[0020] wherein, H i is the priority of the i-th data packet, Q k is the importance score of the k-th key element, and m is the total number of key elements;

[0021] based on the priority of each data packet, dynamically adjusting the transmission priority of the data packet, and obtaining a priority adjustment state.

[0022] Preferably, the step of obtaining the optimized video data is:

[0023] based on the priority adjustment state, analyzing the current network condition and the data packet transmission efficiency, determining the bottleneck or delay problem existing in the data packet transmission path, and obtaining a network efficiency analysis result;

[0024] according to the network efficiency analysis result, selecting a transmission path by identifying the bandwidth utilization and delay of each transmission path, and generating a path optimization decision;

[0025] applying the path optimization decision, adjusting the transmission path of the data packet, reconfiguring the data stream, and obtaining optimized video data.

[0026] Preferably, the steps of obtaining the network adaptive parameters are:

[0027] Collect network status feedback, including current network bandwidth, latency, and packet loss rate, to obtain network performance indicators;

[0028] Receive the optimized video data and calculate the video encoding bit rate and frame rate based on the network performance index. The calculation formula is:

[0029]

[0030] Among them, BR new is the adjusted video encoding bit rate, BR opt To optimize the original encoding bit rate of video data, B avail is the current available bandwidth, B peak is the network peak bandwidth, P loss is the network packet loss rate;

[0031] A network adaptive parameter is formed according to the adjusted video encoding bit rate.

[0032] Preferably, the steps of obtaining the adaptive video output are:

[0033] Modifying the encoder's bit rate and frame rate settings based on the network adaptation parameters to generate an encoder configuration update;

[0034] The encoder configuration update is applied to re-encode the video stream to be transmitted to obtain an adaptive video output.

[0035] Preferably, the steps of obtaining the enhanced image result are:

[0036] Receive video input from a nighttime environment, identify dark and bright areas in the video, and obtain image brightness analysis results;

[0037] Based on the image brightness analysis results, the image brightness and contrast are adjusted through FPGA, and the adjustment calculation formula is:

[0038]

[0039] Among them, L new (i, j) is the new brightness value of the pixel at position (i, j), L(i, j) is the brightness value of the original pixel, γ is the correction coefficient used to adjust the contrast, L avg is the average brightness value of the image;

[0040] Noise in the video is removed, the dynamic range of the video pixels is adjusted, and an enhanced image result is obtained.

[0041] The application provides a kind of FPGA-based image processing method, comprising the following steps:

[0042] Receive input video frame and timestamp, adjust timestamp to match video stream, perform frame reordering and output optimization, generate video stream synchronization output;Based on video stream synchronization output, analyze video content key elements, dynamically set data packet transmission priority, adjust data packet transmission path, generate priority adjustment state;

[0043] Collect network state feedback, receive video data under priority adjustment state, adjust video encoding bit rate and frame rate, based on network state feedback, adjust encoding output, generate network adaptive parameters;

[0044] Receive video input of night environment, perform image brightness and contrast adjustment, carry out noise suppression and dynamic range adjustment, generate enhanced image result.

[0045] Compared with the prior art, the application has the advantages and positive effects that:

[0046] In the application, data stream synchronization ensures the matching of video frame and timestamp, optimizes the stability of video stream and reduces image errors caused by time misalignment, and improves the accuracy of video analysis. Dynamically setting the transmission priority of data packet, adjusting the transmission strategy according to the importance of the content, effectively utilizing the bandwidth resources, ensuring the priority transmission of key information, improving the overall efficiency and response speed of network transmission. Adaptive encoding dynamically adjusts the encoding bit rate and frame rate according to the network condition, optimizes the adaptability of data stream to different network conditions, and maintains the balance between video quality and transmission efficiency. The enhancement of image brightness, contrast adjustment and noise suppression improves the image quality in low light environment and improves the usability and viewing experience of the image. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The module flowchart of the application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0049] Please refer to Figure 1 The application provides a technical scheme: a FPGA-based image processing system comprises:

[0050] A data stream synchronization module acquires video frame and timestamp through FPGA, adjusts timestamp to match video stream, performs frame reordering and optimization, and obtains video stream synchronization output;

[0051] The priority coding module receives the video stream synchronization output, analyzes key elements of the video content, dynamically sets the transmission priority of the data packet, obtains a priority adjustment state, optimizes the data packet transmission path based on the priority adjustment state, and obtains the optimized video data.

[0052] The adaptive coding module collects network state feedback, receives the optimized video data, adjusts the video coding bit rate and frame rate, obtains network adaptive parameters, adjusts the coding output based on the network adaptive parameters, and obtains the adaptive video output.

[0053] The dynamic image enhancement module receives the video input of the night environment, improves the image brightness and contrast through the FPGA, performs noise suppression and dynamic range adjustment, and obtains the enhanced image result.

[0054] The acquisition step of the video stream synchronization output is as follows:

[0055] The video frame and timestamp are obtained through the FPGA, each video frame and timestamp are collated, the time sequence and the recording sequence of the video frame are checked for consistency, and the time-collated video data is obtained.

[0056] Based on the time-collated video data, the frame reordering index is calculated, the correct playback order of the video frame is determined, and the calculation formula is as follows:

[0057]

[0058] Wherein, R i is the reordering index of the i-th frame, τ i is the timestamp of the i-th frame, n is the total number of video frames, is the sum of the timestamps.

[0059] Based on the reordering index, the frame reordering and video stream optimization are performed, and the video stream synchronization output is obtained.

[0060] Specifically, the execution process of obtaining the video frame and its timestamp first involves detailed capture of each video frame and its corresponding timestamp, and accurate alignment check between each frame and timestamp is performed to ensure the accuracy of the timestamp and the consistency of the video frame. Each frame of video data must be completely matched with its timestamp. The device used in this process must have high-precision time recording function to reduce time error and ensure the consistency and reliability of subsequent processing data. The result of time collation is to ensure that each frame of data in the video stream can correctly reflect the actual time point of its recording, which is crucial for subsequent frame reordering and synchronization processing.

[0061] The formula is The advantage is that by calculating the deviation square of each frame timestamp from the average value of the overall timestamp, the deviation of each frame from the expected playback order is effectively evaluated, which helps to optimize the playback order of the video stream in practical applications and reduce playback problems caused by timestamp errors; parameter τ i is obtained by recording the timestamp of the video frame data; parameter n is obtained by counting the total number of frames in the video; parameter is obtained by summing the timestamps of all video frames; parameter is obtained by calculating the average value of all timestamps.

[0062] Calculation process: a video clip contains 5 frames, the corresponding timestamps are τ = [1, 3, 2, 5, 4] seconds, calculate the average timestamp:

[0063]

[0064] The frame reordering index is:

[0065] R1 = (3-1) 2 = 4

[0066] R2 = (3-3) 2 = 0

[0067] R3 = (3-2) 2 = 1

[0068] R4 = (3-5) 2 = 4

[0069] R5 = (3-4) 2 = 1

[0070] The results show that the second frame and the third frame have the smallest deviation from the average value, and the playback order should be given priority. The results show that by this way, the playback priority of each frame can be clearly defined, and the video playback order can be optimized, thereby improving the coherence and smoothness of video playback.

[0071] Based on the calculated reordering index, frame reordering and video stream optimization processing is performed, which includes dynamically rearranging the video frames to ensure that they are arranged according to the priority indicated by the reordering index, and the optimization of the video stream includes adjusting the playback order of the frames to reduce visual interference caused by frame out-of-order, thereby improving the quality of the final output video, and the synchronous output of the obtained video stream ensures the smoothness and consistency of video playback, providing users with a more stable and smooth visual experience.

[0072] The priority adjustment state acquisition step is:

[0073] Receive synchronous video stream output, identify key elements, including moving objects, color changes and scene switching, and obtain key element analysis results;

[0074] Based on the key element analysis results, the transmission priority of each data packet is calculated using the following formula:

[0075]

[0076] Among them, H i is the priority of the i-th data packet, Q k Score the importance of the kth key element, where m is the total number of key elements;

[0077] Based on the priority of each data packet, the transmission priority of the data packet is dynamically adjusted to obtain a priority adjustment state.

[0078] Specifically, the video stream is received and output synchronously. The analysis focuses on the identification of moving objects, the detection of color changes, and the recording of scene switches. By performing a detailed inspection of the image content of each frame of video, it is possible to identify which elements change frequently, which colors change significantly within a specific time, and how the scene changes. This process does not rely on specific software or algorithms, but is identified by setting specific parameters. For example, the pixel change threshold is set to more than 1000 pixels per second, color changes are determined by the RGB value change amplitude exceeding 20%, and scene changes are recorded by background changes exceeding 50% in consecutive frames. Each identified key element will be assigned a score based on the frequency and amplitude of its changes, and recorded for further processing. The organization of these data forms the key element analysis results.

[0079] formula The benefit of Q is that it integrates the influence of multiple key video elements in a weighted average manner, so that the transmission priority of the data packet more accurately reflects the importance of the video content, which helps to give priority to the key video data that affects the user experience in network transmission, thereby improving the overall quality of the video stream; Parameter Q k The importance score of the kth key element is obtained by analyzing the influence of key elements identified in the video content (such as moving objects, color changes, scene switching, etc.). The score of each element reflects its visual and emotional influence in the video. The parameter m is the total number of key elements identified in the video. This number is determined by analyzing the video content and counting all elements marked as key by the algorithm or manually.

[0080] Calculation process: m = 5 key elements are identified in a video analysis result. The importance scores of each element are Q1 = 3.5, Q2 = 4.5, Q3 = 2.0, Q4 = 3.0, Q5 = 5.0. Substitute them into the formula to calculate the priority H of each data packet.i The calculation process is:

[0081]

[0082] The result shows that the priority of the data packet is 36, indicating that the data packet contains high-priority critical video content and needs to be processed preferentially in network transmission.

[0083] Using the data packet priority obtained in the previous step, the data packet transmission order is further adjusted. Specifically, a priority queue is created, and the data packets are sorted according to priority from high to low. For each data packet, bandwidth and routing path are dynamically allocated during transmission according to its priority. High-priority data packets will be sent through the fastest available path when the network is congested, while low-priority data packets may be delayed in transmission on low-bandwidth paths. This process ensures the rapid and preferential transmission of critical content in the video stream, and the final priority adjustment state ensures continuous playback and high-quality output of the video.

[0084] The acquisition step of the optimized video data is:

[0085] Based on the priority adjustment state, the current network conditions and data packet transmission efficiency are analyzed to determine the bottlenecks or delay problems existing in the data packet transmission path, and the network efficiency analysis result is obtained.

[0086] According to the network efficiency analysis result, the bandwidth utilization and delay of each transmission path are identified, the transmission path is selected, and the path optimization decision is generated.

[0087] The path optimization decision is applied to adjust the transmission path of the data packet, reconfigure the data stream, and obtain the optimized video data.

[0088] Specifically, starting from the priority adjustment state, real-time data packet transmission records and network node response times of the current network are collected and analyzed, the traffic and delay of each node are measured, the transmission time of the data packet on different paths is compared, and the nodes or links with low transmission efficiency are identified. This data directly reflects the real-time state of network operation. Through this observation of network behavior, the main bottleneck position is found. This process generates detailed network efficiency analysis results, including which nodes or links cause delay increase, thereby providing a basis for subsequent optimization.

[0089] Based on the results of a detailed network efficiency analysis, network path optimization is carried out. This step involves specifically evaluating the bandwidth utilization and latency of each path. By numerically comparing the performance indicators of each path, the path with the best performance is selected as the preferred transmission path. This operation does not rely on any specific software or vague technical methods, but is directly calculated and compared through simple bandwidth and latency data. These decisions are based on real-time data, ensuring that the selected path can provide good data transmission efficiency under the current network conditions. Ultimately, a clear path optimization decision is made, and detailed information on the preferred and alternative paths is recorded.

[0090] Based on the determined path optimization decision, the actual transmission path of the data packet is adjusted directly in the network management system. The operation includes manually configuring the routing tables of routers and switches, using specific network commands and configuration changes to ensure that the data flow is transmitted along the new optimized path. Through these specific network equipment operations, the adjustment effect is monitored in real time to verify whether the speed and stability of data transmission have been improved. The execution of this step is entirely based on the configuration capabilities of the network hardware and real-time network monitoring data, thereby ensuring the actual effect of the optimization measures. The final output is video data transmitted through the optimized path.

[0091] The steps for obtaining network adaptive parameters are:

[0092] Collect network status feedback, including current network bandwidth, latency, and packet loss rate, to obtain network performance indicators;

[0093] Receive optimized video data and calculate the video encoding bit rate and frame rate based on network performance indicators. The calculation formula is:

[0094]

[0095] Among them, BR new is the adjusted video encoding bit rate, BR opt To optimize the original encoding bit rate of video data, B avail is the current available bandwidth, B peak is the network peak bandwidth, P loss is the network packet loss rate;

[0096] A network adaptive parameter is formed according to the adjusted video encoding bit rate.

[0097] Specifically, network status feedback is collected, including current network bandwidth, delay, and packet loss rate. These feedback data are obtained through real-time monitoring systems, which regularly record real-time performance data of the network and analyze them to obtain real-time network status reports. These reports detail various network performance indicators, including but not limited to network bandwidth utilization, delay time, and packet loss rate. These performance indicators provide reliable basis for optimizing network transmission and adjusting transmission strategies, and obtain network performance indicators.

[0098] The formula The advantage is that it considers the real-time available bandwidth of the network, the network peak bandwidth, and the packet loss rate of the network, which can more flexibly adjust the video encoding bit rate to adapt to different network conditions, ensuring the efficiency and quality of video transmission.

[0099] Calculation process: the available bandwidth B avail of the current network is 50 Mbps, the network peak bandwidth B peak is 100 Mbps, the packet loss rate P loss is 0.02, and the original encoding bit rate BR opt of the video data is 8 Mbps, then:

[0100]

[0101] The results show that under the given network conditions, the new video encoding bit rate should be adjusted to 3.579 Mbps, which means that in order to adapt to the current network situation, the video encoding bit rate is greatly reduced compared to the original 8 Mbps, in order to reduce the video playback problems caused by insufficient bandwidth or high packet loss rate.

[0102] According to the adjusted bit rate and the corresponding frame rate adjustment, a set of network adaptive parameters is formed, which will be directly used for subsequent video encoding to ensure that the quality of the video stream matches the network capacity. Through the adjustment of these parameters, the bit rate and frame rate of video encoding can be dynamically optimized, so that better video viewing experience can be provided under different network conditions, and network adaptive parameters are obtained.

[0103] The steps for obtaining adaptive video output are:

[0104] According to the network adaptive parameters, modify the bit rate and frame rate settings of the encoder to generate an encoder configuration update;

[0105] Apply the encoder configuration update to re-encode the video stream to be transmitted to obtain the adaptive video output.

[0106] Specifically, according to the network adaptive parameters such as the adjusted video encoding bit rate and frame rate, these parameters are dynamically adjusted by the prequel module according to real-time network conditions such as bandwidth and delay, the most suitable video transmission settings under the current network condition are evaluated, it is determined whether the adjusted bit rate and frame rate meet the network load capacity, for each parameter, the optimal value that adapts to the current network state is calculated, this step involves comparing the relationship between the existing bandwidth and the video data stream demand, adjusting the encoding settings to match this relationship, through this method, the encoder is configured to update, so that each adjustment accurately reflects the changes in network state, and the encoder configuration update is generated;

[0107] The updated encoder configuration is applied to reset the video encoder, and the updated bit rate and frame rate are applied to the actual encoding process of the video stream, this process involves compressing and processing the video data through the new encoding parameters, ensuring that each frame of data meets the new network adaptive parameters, the adjusted encoding output aims to optimize the overall quality and transmission efficiency of the video stream, through this re-encoding, the video data is adjusted to adapt to the changing network conditions, thereby generating the final adaptive video output, ensuring the continuity and clarity of the video transmission process.

[0108] The enhanced image result acquisition step is:

[0109] Receiving video input of night environment, identifying dark and bright area information in the video, obtaining image brightness analysis result;

[0110] Based on the image brightness analysis result, adjusting the image brightness and contrast through FPGA, the adjustment calculation formula is:

[0111]

[0112] Wherein, L new (i,j) is the new brightness value of the pixel at position (i,j), L(i,j) is the brightness value of the original pixel, γ is the correction coefficient for adjusting the contrast, L avg is the average brightness value of the image;

[0113] Removing noise in the video, adjusting the dynamic range of the video pixels, obtaining the enhanced image result.

[0114] Specifically, in the video input of low-illumination environment, the video input is first subjected to a preliminary image brightness and contrast evaluation, which includes automatic analysis using image processing software, the software identifies key dark and bright area information by analyzing the histogram of the image, the identification of the dark and bright areas is based on the statistical data of the pixel brightness distribution, the statistical data of the distribution is obtained by aggregating and calculating the pixel values of the entire video frame, the obtained result provides a preliminary visual impression of the light and dark contrast in the image, and finally a preliminary image brightness analysis result is generated;

[0115] Formula The advantage is that by adjusting the gamma coefficient and normalizing relative to the average brightness, the contrast adjustment of the image is more flexible and adaptive to different viewing conditions, especially in non-uniformly illuminated environments, the visual effect can be significantly improved.

[0116] Calculation process: the brightness value of a pixel L(i,j) in the original image is 50, the average brightness L of the entire image is 75, the contrast adjustment coefficient gamma is 0.8, and the calculation is as follows: avg

[0117]

[0118] The calculation of 50 0.8 ≈34.29;

[0119] L new (i,j)=34.29·1.33=45.60

[0120] The brightness can moderately enhance the light and dark levels of the image.

[0121] When removing noise in the video, a filtering algorithm is used to reduce image noise, the filtering process includes analyzing the color values of the surrounding pixels for each pixel point, by calculating the average of the color values of the adjacent pixels and comparing them with the color value of the current pixel, to determine whether to modify to reduce noise, in addition, the dynamic range adjustment is performed by modifying the upper and lower limits of the brightness of the pixels to adapt to the perception ability of the human eye to the brightness change, the output image is more smooth and detailed in vision, and the enhanced image result is achieved; and the FPGA can improve the algorithm execution efficiency.

[0122] The application provides an image processing method based on FPGA, comprising the following steps:

[0123] Receiving input video frames and time stamps, adjusting the time stamps to match the video stream, performing frame reordering and output optimization, and generating a video stream synchronization output; based on the video stream synchronization output, analyzing the key elements of the video content, dynamically setting the transmission priority of the data packet, adjusting the data packet transmission path, and generating a priority adjustment state; ​

[0124] Collect network status feedback, receive video data in priority adjustment state, adjust video encoding bit rate and frame rate, adjust encoding output based on network status feedback, generate network adaptive parameters;

[0125] Receive video input of night environment, perform image brightness and contrast adjustment, carry out noise suppression and dynamic range adjustment, and generate enhanced image result.

Claims

1. An image processing system based on FPGA, characterized in that: The system comprises: The data stream synchronization module obtains video frames and timestamps through FPGA, adjusts the timestamps to match the video stream, reorders and optimizes the frames, and obtains synchronized video stream output; a priority encoding module that receives the synchronous output of the video stream, analyzes key elements of the video content, dynamically sets the transmission priority of the data packet, obtains a priority adjustment state, and optimizes the data packet transmission path based on the priority adjustment state to obtain optimized video data; The steps for obtaining the priority adjustment status are: receiving the synchronous output of the video stream, identifying key elements, the key elements including moving objects, color changes, and scene switching, and obtaining key element analysis results; Based on the key element analysis results, the transmission priority of each data packet is calculated using the following formula: Among them, H i is the priority of the i-th data packet, Q k Score the importance of the kth key element, where m is the total number of key elements; Based on the priority of each data packet, the transmission priority of the data packet is dynamically adjusted to obtain a priority adjustment state; The steps for obtaining the optimized video data are: Analyzing current network conditions and data packet transmission efficiency based on the priority adjustment status, determining bottlenecks or delays in the data packet transmission path, and obtaining a network efficiency analysis result; Selecting a transmission path and generating a path optimization decision by identifying bandwidth utilization and latency of each transmission path based on the network efficiency analysis result; Applying the path optimization decision, adjusting the transmission path of the data packet, reconfiguring the data flow, and obtaining optimized video data; An adaptive encoding module collects network status feedback, including current network bandwidth, latency, and packet loss rate, to obtain network performance indicators, receives the optimized video data, adjusts the video encoding bit rate and frame rate, obtains network adaptive parameters, and re-encodes the video stream to be transmitted based on the network adaptive parameters to obtain adaptive video output; The dynamic image enhancement module receives video input from a nighttime environment, improves image brightness and contrast through FPGA, performs noise suppression and dynamic range adjustment, and obtains enhanced image results.

2. The FPGA-based image processing system according to claim 1, characterized in that: The steps for obtaining the synchronous output of the video stream are: Obtain video frames and timestamps, proofread each video frame and timestamp, check whether the time sequence is consistent with the recording sequence of the video frames, and obtain time-proofed video data; Based on the time-corrected video data, the frame reordering index is calculated to determine the correct playback order of the video frames. The calculation formula is: Among them, R i is the reordering index of the i-th frame, is the timestamp of the i-th frame, n is the total number of video frames, is the sum of timestamps; Based on the reordering index, frame reordering and video stream optimization are performed to obtain a synchronous output of the video stream.

3. The FPGA-based image processing system according to claim 1, characterized in that: The steps for obtaining the network adaptive parameters are: Collect network status feedback, including current network bandwidth, latency, and packet loss rate, to obtain network performance indicators; Receive the optimized video data and calculate the video encoding bit rate and frame rate based on the network performance index. The calculation formula is: Among them, BR new is the adjusted video encoding bit rate, BR opt To optimize the original encoding bit rate of video data, B avail is the current available bandwidth, B peak is the network peak bandwidth, P loss is the network packet loss rate; A network adaptive parameter is formed according to the adjusted video encoding bit rate.

4. The FPGA-based image processing system according to claim 1, wherein: The steps of obtaining the adaptive video output are: Modifying the encoder's bit rate and frame rate settings based on the network adaptation parameters to generate an encoder configuration update; The encoder configuration update is applied to re-encode the video stream to be transmitted to obtain an adaptive video output.

5. The FPGA-based image processing system according to claim 1, characterized in that: The steps for obtaining the enhanced image result are: Receive video input from a nighttime environment, identify dark and bright areas in the video, and obtain image brightness analysis results; Based on the image brightness analysis results, the image brightness and contrast are adjusted through FPGA, and the adjustment calculation formula is: Among them, L new (i, j) is the new brightness value of the pixel at position (i, j), L(i, j) is the brightness value of the original pixel, γ is the correction coefficient used to adjust the contrast, L avg is the average brightness value of the image; Noise in the video is removed, the dynamic range of the video pixels is adjusted, and an enhanced image result is obtained.

6. An image processing method based on FPGA, characterized in that: The FPGA-based image processing system according to any one of claims 1 to 5 comprises the following steps: Receive input video frames and timestamps, adjust timestamps to match video streams, perform frame reordering and output optimization, and generate synchronized video stream outputs; based on synchronized video stream outputs, analyze key elements of the video content, dynamically set data packet transmission priorities, adjust data packet transmission paths, and generate priority adjustment status; Collect network status feedback, receive video data in priority adjustment status, adjust video encoding bit rate and frame rate, and generate network adaptive parameters; It receives video input of a nighttime environment, performs image brightness and contrast adjustments, performs noise suppression and dynamic range adjustment, and generates enhanced image results.

Images