An end-to-end low-latency image transmission method

Abstract

The present invention provides an end-to-end low-latency image transmission method, comprising: acquiring ground target image data collected by an optical device, extracting target area image data and ground target motion state information, simplifying the target area image data according to the ground motion state information; dynamically adjusting the compression ratio of the image data according to the target motion state information and the current environmental conditions to generate compressed image data; formulating and adjusting the transmission strategy based on the compressed image data and the real-time network bandwidth conditions to perform data transmission; decoding the transmitted data to restore the image data during compression and transmission; performing quality assessment on the decoded image data to generate image quality feedback information, and optimizing the image compression and transmission strategy based on the feedback information. The present invention significantly reduces delays, improves image transmission efficiency and quality, and adapts to real-time application requirements in complex environments by dynamically adjusting the compression ratio and transmission strategy.

Description

Technical Field

[0001] The present invention relates to the field of image transmission, and in particular to an end-to-end low-latency image transmission method. Background Art

[0002] In the application scenario of airborne optical equipment, in order to accurately track and guide ground moving targets, it is usually necessary to capture high-quality target images through optical cameras and transmit them in real time. However, the existing image transmission technology faces the following technical difficulties in practical applications:

[0003] The contradiction between real-time performance and low latency: Ground targets move at a high speed, and the images captured by optical devices need to be transmitted to the back-end for processing and analysis in a low-latency manner. However, in a complex network environment, due to bandwidth limitations, signal interference, and excessive data volume, the delay of image transmission often increases, thus affecting the real-time performance and tracking accuracy of the system.

[0004] Balance between image compression and transmission quality: Under limited bandwidth conditions, how to reduce the amount of data through image compression algorithms while ensuring that key details of the target area are not lost is a key issue in achieving high-quality image transmission. In existing technologies, some high-compression ratio algorithms are prone to loss of target area information, while low-compression ratio solutions will increase the burden of network transmission, making it difficult to balance real-time performance and image quality.

[0005] Insufficient adaptability in complex environments: In the application scenarios of airborne devices, the network environment changes frequently and there are many interferences (such as electromagnetic signal interference, dynamic bandwidth fluctuations, etc.). Existing image transmission technologies mostly use fixed compression and transmission strategies, lacking dynamic optimization capabilities for environmental changes, resulting in image quality degradation or system response lag during transmission.

[0006] Therefore, there is an urgent need for a method that can dynamically adjust the image compression and transmission strategy based on the target motion state and complex network environment conditions, so as to achieve real-time, low-latency, high-quality image transmission of ground targets and meet the actual application needs of gas-guided optical equipment in target tracking and guidance. Summary of the invention

[0007] The present invention provides an end-to-end low-latency image transmission method to solve the problem of how to dynamically adjust the compression and transmission strategy of image data based on the motion state information of the target and complex network environment conditions in the application scenario of airborne optical equipment, so as to achieve low-latency and high-quality transmission of real-time images of ground targets to support accurate target tracking and guidance.

[0008] In order to solve the above technical problems, the present invention provides an end-to-end low-latency image transmission method, comprising:

[0009] Acquire ground target image data collected by an optical device, and extract target area image data and motion state information of the ground target; simplify the target area image data according to the motion state information of the ground target to generate simplified target area image data; specifically comprising: acquire ground target image data of continuous frames through an optical device, analyze the target area image data in combination with the motion state information of the ground target, and perform noise removal on the target area image data to obtain simplified target area image data;

[0010] According to the motion state information of the ground target, a compression strategy for the simplified target area image data is formulated; based on the compression strategy and the current environmental conditions, the compression ratio of the simplified target area image data is dynamically adjusted to generate compressed target area image data; specifically comprising: evaluating the dynamic characteristics of the target area, calculating the change amplitude of the simplified target area image data, and determining the compression ratio in combination with the current environmental conditions; compressing the simplified target area image data according to the compression ratio to generate compressed target area image data.

[0011] Based on the compressed target area image data and the real-time network bandwidth conditions, formulate and adjust the image data transmission strategy to transmit the compressed target area image data;

[0012] The receiving end obtains the transmitted compressed target area image data and decodes it, recovers the image data processed during compression and transmission, and generates decoded target area image data;

[0013] Quality assessment is performed based on the decoded target area image data to generate image quality feedback information, and image compression and transmission strategies are optimized according to the feedback information.

[0014] Further, determining the compression ratio in combination with the current environmental conditions includes:

[0015] Calculate the dynamic adjustment factor based on the motion state information of the ground target and the current environmental conditions;

[0016] The compression algorithm parameters in the compression strategy are optimized according to the dynamic adjustment factor to generate a compression ratio of the image data at the current moment.

[0017] Furthermore, the formulating and adjusting the transmission strategy of the image data includes:

[0018] Analyze the real-time network bandwidth status and bandwidth bottleneck, and evaluate the compressed target area image data transmission rate;

[0019] The transmission strategy is adjusted according to the bandwidth bottleneck.

[0020] Furthermore, the step of transmitting the compressed target area image data includes:

[0021] Based on the transmission strategy, the compressed target area image data is transmitted to the receiving end through a low-latency protocol, and the transmission rate is adjusted in real time to adapt to changes in real-time network bandwidth conditions.

[0022] Furthermore, the step of the receiving end acquiring the transmitted compressed target area image data and decoding it comprises:

[0023] According to the decoding strategy corresponding to the compression strategy generated during the transmission process, the key area is restored with high resolution, and the non-key area is restored with low resolution to generate decoded target area image data;

[0024] The decoded target area image is divided into a key area and a non-key area, wherein the key area is the area containing the main target; the non-key area is the area other than the key area, including the background and environment.

[0025] Furthermore, the decoded image data of the target area includes:

[0026] The key area image data is restored with high resolution and the non-key area image data is restored with low resolution.

[0027] Furthermore, the step of performing quality assessment based on the decoded target area image data includes:

[0028] The clarity and noise level of the decoded target area image data are calculated, quality parameters of the target area image data are evaluated, and image quality feedback information is generated.

[0029] Furthermore, the step of optimizing the image compression and transmission strategy includes:

[0030] Analyzing the adaptability of compression ratio and transmission strategy according to the image quality feedback information;

[0031] The compression ratio and transmission parameters are adjusted, and the image data transmission strategy is updated in real time to adapt to the next round of transmission requirements.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) Reduce image transmission delay. In a complex network environment, the present invention dynamically optimizes the transmission strategy according to the real-time network bandwidth status to avoid transmission delay problems caused by insufficient bandwidth or data congestion. In particular, by dynamically adjusting the compression ratio and fragmentation transmission strategy, it ensures that the key areas of the image are transmitted first, thereby significantly reducing transmission delay.

[0034] (2) Improve image quality. Under limited bandwidth conditions, the present invention optimizes the compression ratio through target motion state information, giving priority to ensuring image clarity and detail retention in the target area, reducing the detail loss problem commonly seen in traditional high compression ratio algorithms, and improving the overall quality of the image after decoding at the receiving end.

[0035] (3) Enhance system adaptability and stability. The present invention introduces a real-time feedback mechanism to dynamically update the compression and transmission scheme based on the quality evaluation results of the decoded image, so that the system can adapt to complex environmental conditions such as bandwidth fluctuations and signal interference, and ensure the continuity and robustness of image data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of an end-to-end low-latency image transmission method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0038] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] Example 1: Reference Figure 1 , an embodiment of the present invention provides an end-to-end low-latency image transmission method, which may at least include steps S100-S500:

[0040] S100, acquiring ground target image data collected by an optical device, and extracting target area image data and motion state information of the ground target; simplifying the target area image data according to the motion state information of the ground target to generate simplified target area image data.

[0041] S200, formulating a compression strategy for simplified target area image data according to the motion state information of the ground target; dynamically adjusting the compression ratio of the simplified target area image data based on the compression strategy and current environmental conditions to generate compressed image data.

[0042] S300: Based on the compressed target area image data and the real-time network bandwidth condition, formulate and adjust the image data transmission strategy, and transmit the compressed target area image data.

[0043] S400: The receiving end obtains the transmitted compressed target area image data and decodes it, recovers the image data processed during compression and transmission, and generates decoded target area image data.

[0044] S500: Perform quality assessment based on the decoded target area image data, generate image quality feedback information, and optimize image compression and transmission strategies according to the feedback information.

[0045] Step S100 at least includes steps S110-S130:

[0046] S110: Acquire ground target image data collected by an optical device, and extract image data of the target area.

[0047] The optical device can obtain ground target image data from the optical device. Specifically, the optical device can capture an image sequence through a sensor, input a pixel matrix and a timestamp information of the image, and form image data. ,in Represents at a point in time Captured image data.

[0048] According to the image data , further extract the image of the target area. The tracking algorithm based on the motion state can be combined with the dynamic changes of the target area to obtain the image data. Extract the target-related area and obtain the target area image , that is, at time The target area image at the moment.

[0049] From the target area image In the process, the image data of consecutive frames are extracted to generate an image sequence series of the target area, which will serve as the input for the subsequent image simplification process.

[0050] S120: Analyze the change of the target area according to the motion state information of the ground target, and perform image preprocessing on the target area.

[0051] Based on the motion state information of the ground target , dynamically analyze the changes in the target area. Specifically, Represents the motion state information (e.g., speed, acceleration, direction, etc.) of the ground target at time point t. By comparing the position changes of the ground target at different time points, the dynamic characteristics of the target area are determined.

[0052] Image simplification of the target area: According to the results of the ground target motion state analysis, select the area with large dynamic changes and closely related to the target motion for image preprocessing. That is, select the area with more significant motion changes in the image sequence for preprocessing, and obtain the preprocessed target area , the detail information of this area will be preserved, while other relatively static areas will be blurred or compressed.

[0053] Further preprocessing the target area Simplify the process, remove unnecessary background information and details, and obtain the image data of the target area after preprocessing ,This data contains important information related to the target and removes noise and redundant details.

[0054] S130: performing noise removal processing on the pre-processed image data of the target area to generate simplified image data of the target area.

[0055] The image data of the target area after preprocessing Noise removal is performed. Specifically, a filter-based algorithm, such as median filtering, mean filtering or Gaussian filtering, is used to remove low-frequency noise in the image data caused by sensor noise, environmental interference or image compression.

[0056] After noise removal, a clearer and simplified target area image data is obtained. , the data retains the effective information of the target area, and the noise is greatly reduced. At this time, the simplified target area image data Redundant interference information has been removed in preparation for subsequent data compression and transmission.

[0057] The simplified target area image data The output is used as the input of the subsequent S200 module for further optimization of image compression and transmission strategies.

[0058] The results of each step (e.g. , and ) will continue to be used in subsequent modules (such as S200) to ensure the continuity and consistency of the entire process data.

[0059] Step S200 at least includes steps S210-S230:

[0060] S210: According to the motion state of the ground target, the dynamic characteristics of the target area image are evaluated to determine the adaptability of the compression strategy.

[0061] Based on the simplified target area image data output by the aforementioned S130 module , and the motion status information of ground targets (including dynamic features such as speed and acceleration) to provide input for subsequent image compression strategies.

[0062] Specifically, according to the motion state information of the ground target , by analyzing the dynamic changes of ground targets in the image (such as the rate of change of target position, change of direction, etc.), the dynamic characteristics of the target area image data are evaluated. For example, the following formula can be used to quantify the dynamic characteristics of the target area image data:

[0063] ;

[0064] in, is the motion intensity of the target area, indicating the dynamic change of the ground target at time t, and Respectively represent the horizontal and vertical coordinates of the target at time t, The target is The displacement vector at time.

[0065] based on The dynamic characteristics of the target area image data can further determine whether the image needs a higher compression ratio. If the target area is larger, it means that the target area changes rapidly and the compression ratio may need to be reduced to retain more detail information. If the size of the datagram is smaller, a higher compression ratio can be used to improve transmission efficiency.

[0066] The specific judgment rules can be set as:

[0067] ;

[0068] in, For the compression strategy, and Representing lower and higher compression ratios respectively; A dynamically changing threshold.

[0069] Evaluate results based on dynamic features , determines the compression strategy at the current moment , in preparation for the next step of compression ratio adjustment.

[0070] S220: Dynamically adjust the compression ratio of the simplified target area image data in combination with current environmental conditions.

[0071] Compression strategy based on the output of the aforementioned S210 module and current environmental conditions (such as network bandwidth , signal-to-noise ratio etc.), adjust the compression ratio. and It is a key indicator that describes the current transmission environment.

[0072] According to the current network bandwidth SNR , we can introduce the environmental adjustment factor , used to dynamically adjust the compression ratio. Specifically, the calculation of the environment adjustment factor can be performed by the following formula:

[0073] ;

[0074] in, is the real-time network bandwidth, It is the real-time signal-to-noise ratio, and the scaling factor is a coefficient used to adjust the signal energy calculation result. The environmental adjustment factor reflects the superiority of the current environment and the feasibility of transmission.

[0075] Dynamically adjust compression ratio: Evaluation-based compression strategy and environmental adjustment factors , dynamically adjust the compression ratio of image data. Compression ratio The calculation formula is as follows:

[0076] ;

[0077] in, is the compression ratio at the current moment, is an adjustment factor that controls the sensitivity of the compression ratio. and Different combinations of .

[0078] The compression ratio at the current moment is generated by combining the adjustment coefficient and the current environmental conditions. , used for subsequent compression operations.

[0079] S230: Generate compressed target area image data and optimize the data size for easy transmission.

[0080] Compression ratio based on the output of the aforementioned S220 module And the simplified target area image data , start the image compression operation.

[0081] For the simplified target area image data To compress, use Image compression algorithms such as Compress the image to obtain compressed target area image data .

[0082] The specific operation formula of the compression process is:

[0083] ;

[0084] in, represents the image compression operation, It is the compressed image data of the target area.

[0085] After the image is compressed, the data size is further optimized for subsequent transmission. The size of the image data can be further reduced by removing redundant data and reducing the resolution, making it more adaptable to bandwidth limitations and transmission requirements.

[0086] Generate compressed and optimized target area image data , and prepare for transmission.

[0087] Step S300 at least includes steps S310-S330:

[0088] S310: Based on the compressed target area image data and the real-time network bandwidth status, analyze the bandwidth bottleneck that may be encountered during the transmission process.

[0089] In this step, the input data mainly includes compressed target area image data (such as the image compression data generated in S230) and real-time network bandwidth status.

[0090] The compressed target area image data has been generated through image simplification and compression algorithms (such as a compression ratio optimized based on motion status and current environmental conditions), contains low redundant information, and is convenient for efficient transmission.

[0091] By real-time monitoring of network transmission rate, delay, packet loss rate and other indicators (obtained through the network monitoring system), the current network load status is reflected.

[0092] Based on the compressed target area image data size and network bandwidth conditions, the system evaluates whether a bandwidth bottleneck will occur. Bandwidth bottlenecks usually occur when the network bandwidth is lower than the required transmission bandwidth, resulting in increased delays or data loss during image data transmission.

[0093] The calculation formula for setting the bandwidth bottleneck is:

[0094] ;

[0095] in, is the network bandwidth utilization, is the compressed image data size of the target area, This is the time required for actual data transmission.

[0096] when Exceeding the maximum carrying capacity of the network bandwidth ,Right now , the system identifies the bandwidth bottleneck and needs to adapt and adjust the bandwidth.

[0097] S320: Formulate an image data transmission strategy that adapts to bandwidth conditions.

[0098] First, input image data, bandwidth bottleneck analysis results (such as the bandwidth utilization rate obtained in S310) ), compressed target area image data, and real-time network bandwidth status.

[0099] By analyzing bandwidth bottlenecks, the system can And the compressed target area image data size , determine the transmission strategy.

[0100] Specifically, the system first considers whether the image data needs to be further compressed to accommodate the lower bandwidth. If the bandwidth conditions allow, the image data can be transmitted at the original compression ratio; otherwise, the system will dynamically adjust the compression ratio. Set the compression adjustment ratio , used to indicate the further compression ratio of image data:

[0101] ;

[0102] in, is the compressed data size after adjustment, The compression ratio adjustment factor is usually calculated by bandwidth utilization. In addition, the system will evaluate whether it is necessary to fragment the transmission and cut the image data into multiple packets to adapt to the network environment with large bandwidth fluctuations. This process uses a fragmentation algorithm ,in:

[0103] ;

[0104] in is the number of packets.

[0105] Furthermore, if the network bandwidth If the fluctuation is large, the system will adopt a dynamic adjustment mechanism based on real-time feedback to modify the compression strategy and transmission strategy at any time to ensure real-time data transmission and image quality.

[0106] S330: Adjust the transmission strategy according to the real-time network bandwidth conditions, and transmit the compressed target area image data from the sending end to the receiving end through the transmission protocol.

[0107] Based on the real-time bandwidth status and transmission strategy, the system starts the image data transmission. During the transmission process, the system uses the protocol (such as The image data is transmitted from the sender to the receiver according to the predetermined strategy. If the network bandwidth changes, the transmission protocol will adjust the data flow rate and transmission path in real time. At this time, the system will dynamically adjust the data flow according to the following formula:

[0108] ;

[0109] in:

[0110] is the actual data transfer rate.

[0111] is the real-time network bandwidth,

[0112] The network bandwidth limit.

[0113] if If the data transmission rate is greater than the current bandwidth, the system will temporarily slow down the transmission rate or use data compression to control the transmission rate.

[0114] During the transmission process, a reliable transmission protocol (such as ) or low latency protocols (such as ) to ensure data integrity and real-time performance. The protocol layer adjusts the size and frequency of data packets according to bandwidth conditions and data size to ensure that data packets are not lost and latency is as low as possible.

[0115] For example, at low bandwidths, the system may use The protocol is used because it has a faster transmission speed, but it requires network bandwidth support. The transmission protocol will dynamically adjust the size of the packet. and sending interval to ensure that the transmission does not cause bandwidth overload.

[0116] Step S400 at least includes steps S410-S430:

[0117] S410: The receiving end receives the transmitted compressed image data of the target area and performs decoding processing to restore the image data processed during the compression and transmission process;

[0118] Specifically, after receiving the transmitted compressed target area image data, the receiving end will decode the image data based on a predetermined decoding strategy; the transmitted compressed target area image data includes image data processed by compressed sensing, wherein the key areas of the image are processed with high resolution, while the non-key areas are compressed with low resolution.

[0119] Among them, in the image, the key area refers to the area containing important information related to the target motion. For the image decoding and compression process, these areas are of great significance to the image clarity and subsequent target recognition and tracking. The key area usually includes the target itself and its surrounding dynamic environment, and contains detailed information of the target, such as the target edge, contour, etc. In practical applications, the key area usually refers to the pixel area containing the target.

[0120] In contrast to the key area, the non-key area refers to the area in the image that does not contain important target information, usually including the background, the environment area far away from the target, or static objects. These areas have little effect on the tracking and identification of the target, so low-resolution processing or higher compression ratio can be used to reduce the amount of data and transmission bandwidth requirements.

[0121] The receiving end obtains the compressed data stream transmitted from the sending end, where the data stream includes compressed target area image data and related transmission information (such as timestamp, image data blocks, compression parameters, etc.).

[0122] According to the decoding strategy generated during the transmission process (the strategy defined in the S230 module), the receiving end uses the corresponding decoding algorithm to decode the image data. This process includes high-resolution decoding of key areas and low-resolution recovery of image data for non-key areas.

[0123] Key areas (such as target objects, motion trajectories, etc.) are restored using standard image decoding algorithms. The formula is:

[0124] ;

[0125] in, represents the decoded key area image, is the compressed key area data, is the high-resolution restoration parameter, is the decoding function.

[0126] Low-resolution decoding: Non-critical areas are decoded using compressed sensing technology. The formula is:

[0127] ;

[0128] in, represents the decoded non-critical area image, It is the compressed non-critical area data. is the low-resolution restoration parameter.

[0129] S420: Perform quality assessment on the decoded target area image data, check key factors such as image clarity and noise level, and assess the quality of the image after transmission.

[0130] After the decoded image data is restored, the receiving end evaluates the image quality.

[0131] The sharpness of the decoded image is evaluated by calculating the gradient, contrast and other features of the decoded image. Specifically, an image sharpness measurement method such as Laplace transform or Operator is evaluated. The formula is:

[0132] ;

[0133] in, represents the sharpness measure of the image, It is the image gradient, which calculates the degree of change around the image pixel.

[0134] Noise level assessment: Use noise estimation algorithms such as mean square error Peak Signal Ratio ) to evaluate the noise in the image. The formula is:

[0135] ;

[0136] in, Represents the decoded image data, represents the original image data, is the total number of image pixels. This formula calculates the mean square error between the decoded image and the original image.

[0137] calculate The formula is:

[0138] ;

[0139] in, is the maximum pixel value of the image, usually 255 (for 8-bit images).

[0140] By combining image clarity and noise level, a comprehensive image quality score is calculated. The formula is

[0141] ;

[0142] in, and is the weight coefficient, adjusted according to different quality standards, Represents the overall quality score.

[0143] S430: Generate image quality feedback information and provide evaluation data on image quality.

[0144] Specifically, based on the quality evaluation result, the receiving end generates image quality feedback information, which may include: image clarity, noise level, The specific values ​​of parameters such as image quality serve as an objective description of image quality.

[0145] Comprehensive quality score generated by combining various quality indicators , as the feedback result. This score can be used to determine whether the image meets the system requirements or needs further optimization through the corresponding threshold.

[0146] The quality assessment results are transmitted to the user or system monitoring end through the display interface or communication module to inform the current status of the image quality and provide corresponding suggestions or warnings.

[0147] If the image quality score is lower than the set threshold, the system may trigger actions such as retransmission, adjustment of compression parameters, or change of transmission strategy.

[0148] Step S500 at least includes steps S510-S530:

[0149] S510: Analyze problems or bottlenecks in the compression and transmission process based on the image quality feedback information from the receiving end.

[0150] Specifically, the receiving end obtains feedback data on key factors such as image clarity and noise level through the decoded image quality evaluation feedback information (see the image quality feedback information generated in S420). The receiving end will evaluate the loss of image data during compression and transmission based on the feedback data. Specifically, the feedback information includes the following aspects:

[0151] Image clarity : The receiver evaluates the detail recovery of important areas in the image, especially the clarity of key areas. For example, whether the details of the target area in the image are effectively preserved.

[0152] Noise Level : Evaluate whether there is excessive noise or artifacts in the image, and the relationship between the size of the noise and the image compression ratio.

[0153] Transmission bandwidth : Changes in network bandwidth may cause bottlenecks or delays in the transmission process.

[0154] Based on the above feedback, the receiving end analyzes the problems in the transmission process. The sources of the problem may include the degradation of image quality (such as insufficient clarity or excessive noise), as well as data transmission delays or packet loss caused by bandwidth limitations. For example, assuming that the received image clarity C is low, it means that the compression process may cause the loss of image details, or the packet loss rate is high during the transmission process, and the balance between compression ratio and bandwidth needs to be considered in subsequent adjustments.

[0155] S520: Adjust the image data compression ratio and transmission strategy according to the analysis results.

[0156] Further, the receiving end adjusts the compression ratio and transmission strategy of the image according to the problem analysis result in S510. At this time, the following factors need to be considered:

[0157] Compression ratio adjustment : If the image quality feedback shows that the image clarity is not up to standard ( If the compression ratio is low, the compression ratio should be reduced (i.e., increased ) to retain more image details. At this time, the compression ratio The adjustment should be based on the transmission bandwidth The conditions must be balanced, and even if the compression ratio increases, it cannot exceed the bandwidth limit.

[0158] Transmission strategy adjustment : If the image clarity is good, but the network bandwidth is low, it may cause a transmission bottleneck. The receiving end will The feedback information can be used to adjust the image data transmission strategy. For example, the layered transmission technology is used to give priority to the transmission of high-quality image data in key areas, and the non-key areas are compressed and transmitted with lower image quality. It can be expressed as the following formula:

[0159] ;

[0160] in, represents the transmission strategy of high-quality images, represents the transmission strategy of low-quality images, and are predefined quality and bandwidth thresholds.

[0161] Specifically, if the bandwidth If the bandwidth is low, the receiving end may adopt dynamic bandwidth management to adjust the priority of data transmission by monitoring the bandwidth status in real time to ensure priority transmission of key image areas.

[0162] At this time, the compression ratio of the image data and transmission strategy The adjustment is based on the analysis results of the received feedback information to ensure the maximization of image quality and the optimal use of bandwidth resources.

[0163] S530: Update the transmission and compression scheme in real time.

[0164] Further, based on the compression ratio adjusted in S520 and transmission strategy , the receiving end will update its image data transmission and compression strategy in real time. This process requires the system to dynamically adjust the compression and transmission strategy based on the current network bandwidth and image quality feedback information each time a new image data is transmitted. The updated transmission and compression scheme involves the following key steps:

[0165] According to the current bandwidth conditions and image quality feedback , the receiving end adjusts the parameters of the compression algorithm in real time, such as compression ratio and image quality priority. This allows the transmission efficiency and image quality to be adaptively improved when the network bandwidth changes.

[0166] During the transmission process, the receiving end adaptively adjusts the transmission mode of image data according to bandwidth changes and image quality feedback. For example, when the bandwidth is sufficient, higher quality image data is transmitted; when the bandwidth is limited, the quality of non-critical areas is reduced through a layered transmission strategy to ensure the integrity of important image areas.

[0167] At this point, each round of compression and transmission strategy adjustment made by the receiving end will be fed back to the system as a new input, forming a real-time dynamic adjustment mechanism. The receiving end continuously updates its transmission and compression scheme based on the image quality feedback data to ensure that each image transmission can achieve the expected effect.

[0168] The receiving end evaluates the image quality (S420) and bandwidth feedback Perform analysis and adjust the image compression ratio and transmission strategy in real time to ensure the best balance between image quality and transmission efficiency.

[0169] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing the embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An end-to-end low-latency image transmission method, characterized in that: The method comprises the following steps: Acquire ground target image data collected by an optical device, and extract target area image data and motion state information of the ground target; simplify the target area image data according to the motion state information of the ground target to generate simplified target area image data; specifically comprising: acquire ground target image data of continuous frames through an optical device, analyze the target area image data in combination with the motion state information of the ground target, and perform noise removal on the target area image data to obtain simplified target area image data, and use the following formula to quantify the dynamic characteristics of the simplified target area image data: ； in, is the motion intensity of the target area, indicating the dynamic change of the ground target at time t. and Respectively represent the horizontal and vertical coordinates of the ground target at time t; According to the motion state information of the ground target, a compression strategy for the simplified target area image data is formulated; based on the compression strategy and the current environmental conditions, the compression ratio of the simplified target area image data is dynamically adjusted to generate compressed target area image data; specifically comprising: evaluating the dynamic characteristics of the target area, calculating the change amplitude of the simplified target area image data, and determining the compression ratio in combination with the current environmental conditions; compressing the simplified target area image data according to the compression ratio to generate compressed target area image data, specifically: Setting Dynamic Thresholds , according to the intensity of exercise in the target area and dynamic threshold Compare the results and determine the compression strategy ; Calculate the environmental adjustment factor based on the real-time network bandwidth B(t) and signal-to-noise ratio SNR(t) : ； Dynamically adjust the compression ratio of the target area image data based on the compression strategy C(t) and the environmental adjustment factor E(t) : ； in, is an adjustment factor; Based on the compressed target area image data and the real-time network bandwidth conditions, formulate and adjust the image data transmission strategy to transmit the compressed target area image data; The receiving end obtains the transmitted compressed target area image data and decodes it, recovers the image data processed during compression and transmission, and generates decoded target area image data; Based on the decoded target area image data, quality assessment is performed to generate image quality feedback information, and image compression and transmission strategies are optimized according to the feedback information; specifically including: The receiving end calculates the clarity of the decoded target area image and noise level , generating a comprehensive quality score ; ； in, is the decoded target area image gradient; ∑ is the summation symbol; Absolute value operator; The maximum pixel value of the decoded target area image; is the mean square error between the decoded target area image and the original image; function; Represents the decoded target area image data, represents the original image data, The total number of pixels of the decoded target area image; All are weight coefficients; Based on overall quality rating Adjust compression ratio and transmission strategy to achieve end-to-end closed-loop optimization.

2. The end-to-end low-latency image transmission method according to claim 1, characterized in that: The formulating and adjusting the transmission strategy of the image data includes: Analyze the real-time network bandwidth status and bandwidth bottleneck, and evaluate the compressed target area image data transmission rate; The transmission strategy is adjusted according to the bandwidth bottleneck.

3. The end-to-end low-latency image transmission method according to claim 2, characterized in that: The step of transmitting the compressed target area image data comprises: Based on the transmission strategy, the compressed target area image data is transmitted to the receiving end through a low-latency protocol, and the transmission rate is adjusted in real time to adapt to changes in real-time network bandwidth conditions.

4. The end-to-end low-latency image transmission method according to claim 3 is characterized in that: The step of the receiving end acquiring the transmitted compressed target area image data and decoding it comprises: According to the decoding strategy corresponding to the compression strategy generated during the transmission process, the key area is restored with high resolution, and the non-key area is restored with low resolution to generate decoded target area image data; The decoded target area image is divided into a key area and a non-key area, wherein the key area is the area containing the main target; the non-key area is the area other than the key area, including the background and environment.

5. The end-to-end low-latency image transmission method according to claim 4, characterized in that: The decoded image data of the target area includes: The key area image data is restored with high resolution and the non-key area image data is restored with low resolution.

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