A real-time image video compression method of dynamic frame screening

Abstract

The application discloses a kind of real-time image video compression methods of dynamic frame screening, it is related to data compression and transmission technical field, the method includes: obtaining transmission line monitoring video stream, the importance of multiple continuous frames is analyzed by multidimensional importance evaluation index, and importance index is generated;Real-time monitoring network bandwidth and computing power information, dynamically adjust screening threshold;According to the comparison of importance index and screening threshold, locate key frame and redundant frame.Key frame is preferentially transmitted after being encoded by preset encoder, and the encoder retains complete image information;And redundant frame is compressed or skipped by a differentiated processing module, and is transmitted after the transmission of key frame is completed.Solve the technical problems of the prior art, network bandwidth and device computing power resources are insufficient, and then affect the stability and real-time of video transmission, achieve the technical effect of reducing unnecessary data transmission, improving the real-time and stability of video transmission.

Description

Technical Field

[0001] This application relates to the field of data compression and transmission technology, and in particular to a real-time image and video compression method with dynamic frame filtering. Background Technology

[0002] In power transmission line monitoring scenarios, the demand for real-time video data transmission is gradually increasing, especially in remote monitoring and maintenance, where the contradiction between bandwidth and computing power during transmission is becoming increasingly prominent. How to rationally allocate transmission and processing resources under limited bandwidth and computing resources to ensure the timely transmission of high-priority key frames has become a crucial aspect of power transmission line monitoring. Traditional video compression and transmission methods typically rely on fixed compression algorithms or rules, failing to dynamically adapt to changes in the importance of different scenes in the video stream. This results in low transmission efficiency, failure to prioritize the transmission of important information, and inefficient bandwidth utilization, further exacerbating network congestion. Existing methods do not adequately integrate real-time optimization with the dynamic changes in network environment, computing power, and video content, making it difficult to cope with varying monitoring needs and network conditions.

[0003] At present, the relevant technologies have the technical problem that the large amount of data in the video stream of power transmission line monitoring can easily lead to insufficient network bandwidth and equipment computing resources, which in turn affects the stability and real-time performance of video transmission. Summary of the Invention

[0004] This application provides a real-time image and video compression method with dynamic frame filtering, which uses the acquisition of video streams from power transmission line monitoring. This solves the technical problem in the prior art where insufficient network bandwidth and equipment computing power resources affect the stability and real-time performance of video transmission.

[0005] This application provides a real-time image and video compression method with dynamic frame filtering, including:

[0006] The process involves acquiring a video stream of a transmission line under monitoring, analyzing the importance of multiple consecutive frames in the video stream based on a multi-dimensional importance evaluation index, and generating multiple importance indices. Real-time monitoring of network bandwidth and computing power information of a preset network is used to adjust the filtering threshold, generating a preset filtering threshold. The multiple importance indices are compared with the preset filtering threshold to locate key frames and redundant frames. Key frames are those with an importance index greater than or equal to the preset filtering threshold, and redundant frames are those with an importance index less than the preset filtering threshold. The key frames are encoded using a preset encoder and then transmitted with priority. The preset encoder employs an encoding algorithm that preserves complete image information. The redundant frames are input into a differentiation processing module for differential compression processing. After the key frames are transmitted, the differentially compressed redundant frames are transmitted. The differential compression processing includes lightweight compression and skip processing.

[0007] This application proposes a real-time image and video compression method with dynamic frame filtering. First, it acquires a video stream from a power transmission line monitoring system. Then, it analyzes the importance of multiple consecutive frames using a multi-dimensional importance evaluation index to generate an importance index. It monitors network bandwidth and computing power in real time and dynamically adjusts the filtering threshold. Based on a comparison between the importance index and the filtering threshold, it identifies key frames and redundant frames, where key frames are those with higher importance and redundant frames are those with lower importance. Key frames are encoded by a preset encoder and transmitted first, retaining complete image information. Redundant frames are then lightweight compressed or skipped using a differential processing module and transmitted only after the key frames have been transmitted. This method effectively reduces unnecessary data transmission and improves the real-time performance and stability of video transmission. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0009] Figure 1 A flowchart illustrating a real-time image and video compression method with dynamic frame filtering provided in an embodiment of this application;

[0010] Figure 2 This is a schematic diagram illustrating the process of generating importance indicators for a real-time image and video compression method with dynamic frame filtering, provided in an embodiment of this application. Detailed Implementation

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0014] This application provides a real-time image and video compression method with dynamic frame filtering, such as... Figure 1 As shown, the method includes:

[0015] Step S100: Acquire the video stream of the transmission line monitoring to be transmitted. Perform importance analysis on multiple consecutive frames in the video stream based on multi-dimensional importance evaluation indicators to generate multiple importance indicators. Specifically, after acquiring the video stream of the transmission line monitoring to be transmitted, importance analysis is performed to generate importance indicators. For image information entropy, the probability distribution of pixel values ​​is statistically analyzed and calculated using a formula. A uniform distribution results in high information entropy, while abnormal light and shadow changes cause the information entropy to differ from normal frames. Regarding motion complexity, the motion complexity of the first frame is initialized to 1. For subsequent frames, a reference frame set is selected, and motion vector sets are calculated using algorithms such as block matching. The motion vector with the largest modulus is extracted, and the motion complexity is determined based on a preset model and database. The degree of scene change is quantified by extracting feature points using feature extraction algorithms such as SIFT and calculating the feature point matching between adjacent frames. Finally, a weighted fusion method is used to set weights for information entropy, motion complexity, and the degree of scene change, and the importance indicator for each frame is calculated using a formula, providing a quantitative basis for subsequent frame selection and processing.

[0016] In one possible implementation, a video stream of a transmission line monitoring system to be transmitted is acquired. Multiple consecutive frames in the video stream are analyzed for importance based on a multi-dimensional importance assessment index, generating multiple importance indices. Step S100 further includes step S110, where the multi-dimensional importance assessment index includes at least information entropy, motion complexity, and scene change degree. Specifically, when evaluating the transmission line monitoring video stream, information entropy, motion complexity, and scene change degree are important multi-dimensional importance assessment indices. Information entropy measures the uncertainty of image information by calculating the probability distribution of image pixel values. The appearance of situations such as line faults causing electric arcs will increase information entropy. Motion complexity measures the intensity of object motion between frames. After initialization of the first frame, subsequent frames calculate motion vector sets using block matching algorithms and extract the motion vector with the maximum modulus value. The complexity value is determined based on a preset model and database. Scene change degree utilizes feature extraction algorithms to extract feature points and perform adjacent frame matching. Scene changes are quantified based on the matching results; weather changes, equipment additions or removals, etc., will cause scene changes. Combining these three indicators can comprehensively and accurately assess the importance of each frame, providing a basis for subsequent frame selection, compression, and transmission.

[0017] In one possible implementation, a video stream of a transmission line under monitoring is acquired, and multiple consecutive frames in the video stream are analyzed for importance based on a multi-dimensional importance evaluation index to generate multiple importance indices, such as... Figure 2As shown, step S100 further includes step S120, which calculates the information entropy of the multiple consecutive frames to generate multiple information entropies. Specifically, multiple consecutive frames of the transmission line monitoring video stream to be transmitted are sequentially extracted for analysis. For each frame, the range of pixel values ​​is first determined. For example, for a common 8-bit grayscale image, the pixel value range is 0–255. At the same time, a counter is set for each possible pixel value, with an initial value of 0. Each pixel of the image is scanned row by row and column by column, and the number of times each pixel value appears is recorded. For example, if the pixel value of 10 appears 20 times in a certain frame, the counter for the corresponding pixel value of 10 is incremented by 20. When the entire frame image is scanned... After scanning, the frequency data of each pixel value in the frame image is obtained. Based on the obtained frequency data, the probability of each pixel value is calculated. For example, if the total number of pixels in a frame image is 1000, and the pixel value 20 appears 50 times, then the probability of this pixel value is 50 ÷ 1000 = 0.05. Then, using the principle of the information entropy calculation formula, the probability of each pixel value is multiplied by the negative of its base 2 logarithm and then accumulated to obtain the information entropy of the frame image. This process is repeated to calculate the information entropy for each consecutive frame, thereby generating multiple information entropies.

[0018] Step S130: Perform inter-frame motion difference analysis on the multiple consecutive frames to generate multiple motion complexities. Specifically, the motion complexity of the first frame image is set to 1 and recorded as initial data. Then, subsequent analysis begins from the second frame image. Reference frames are selected according to a preset forward frame interval, for example, a preset forward frame interval of 2. When analyzing the second frame image, the first frame image is selected as the reference frame. When analyzing the third frame image, the second and first frames are selected as the reference frame set, and so on. For each current frame, it is compared with the frames in the reference frame set. The motion is determined by analyzing the positional changes of objects or regions in the image. For example, a block matching algorithm can be used to divide the image into several small blocks. The region with the highest matching degree with the current frame block is searched in the reference frames. The motion vector is calculated based on the positional changes of the block. After calculating the motion vectors of all small blocks, the motion vector with the largest modulus is found. The motion complexity of the current frame is determined according to a preset motion complexity assignment rule. The rule can be based on a pre-established database of correspondence between motion vectors and complexity. The corresponding complexity value is determined according to the interval where the modulus of the largest motion vector is located. The above steps are repeated for each consecutive frame to obtain multiple motion complexities.

[0019] Step S140: Perform inter-frame scene change analysis on the multiple consecutive frames to generate multiple scene change levels. Specifically, initialize the scene change level of the first frame image to 1 and store it in the scene change level dataset. Start subsequent analysis from the second frame image. Extract relevant frames according to a preset forward frame interval for feature analysis. For brightness features of different regions in the image, calculate the average brightness value of each region. For example, divide the image into multiple small regions and calculate the average brightness value of all pixels in each small region. For color features, a color histogram can be used to statistically analyze the pixel distribution of each color channel in different intervals. For edge features, use an edge detection algorithm (such as the Sobel operator) to obtain edge information and record the number, direction, and other features of the edges. Compare the current frame with the previous frames in terms of brightness, color, edge, and other features. The comparison is performed on various aspects. For example, in terms of brightness, if the average brightness value of a certain area in the current frame changes more than a certain threshold compared to the previous frame, the amount of brightness change in that area is recorded. For color features, the degree of color change is determined by comparing the differences in the color histogram. For edge features, if the number, direction, or position of the edges changes significantly, the corresponding quantification is also recorded. The changes in brightness, color, and edge features of each area are combined and weighted according to a preset weight allocation method (e.g., brightness change accounts for 40%, color change accounts for 40%, and edge change accounts for 20%) to obtain the scene change value of the current frame relative to the previous frame. The above steps are repeated for each consecutive frame to generate multiple scene change values.

[0020] Step S150: Weighted fusion of the multiple information entropies, multiple motion complexities, and multiple scene change degrees to generate the multiple importance indicators. Specifically, based on the focus of transmission line monitoring and the importance of each evaluation indicator, a weight value is determined for each of the information entropy, motion complexity, and scene change degree. For example, if more emphasis is placed on the information content and scene stability in the image, relatively high weights may be assigned to information entropy and scene change degree; if the motion of objects in the line has a significant impact on the monitoring results, a higher weight is assigned to motion complexity. For each consecutive frame, its corresponding information entropy, motion complexity, and scene change degree are multiplied by their respective weight values, and then the products are added together. For example, if the information entropy of a frame is A, the motion complexity is B, and the scene change degree is C, and their assigned weights are W1, W2, and W3 respectively, then the importance indicator I of that frame is I = A × W1 + B × W2 + C × W3. This calculation is performed on all consecutive frames to generate multiple importance indicators, completing a comprehensive evaluation of the importance of each frame.

[0021] In one possible implementation, inter-frame motion difference analysis is performed on the multiple consecutive frames to generate multiple motion complexities. Step S130 further includes step S131, which initializes the first motion complexity of the first frame image located at the beginning of the multiple consecutive frames to 1 and adds it to the multiple motion complexities. Specifically, initializing the first motion complexity of the first frame image located at the beginning of the multiple consecutive frames to 1 and adding it to the multiple motion complexity sets is mainly to establish a starting benchmark for subsequent calculations. Since the first frame is the starting point, in the absence of previous frames for motion comparison, it is given an initial value to facilitate the subsequent process. Furthermore, because the first frame has important reference value in the subsequent keyframe localization, it is usually defaulted to being a keyframe. Giving it an initial value of 1 helps to determine a relatively basic standard in the overall motion complexity evaluation system.

[0022] Step S132: Extract the second frame image (excluding the first frame image) and a set of second frame images that satisfy a preset forward frame interval from the second frame image from the multiple consecutive frames. Specifically, extract the second frame image (excluding the first frame image) from the multiple consecutive frames to prepare for motion complexity analysis. The motion complexity calculation is a progressive process. Starting from the second frame, more information can be obtained based on the forward frame interval to accurately assess its motion. Extract the set of second frame images related to the second frame image according to the preset forward frame interval. For example, if the preset forward frame interval is set to 4 frames, and the video stream consists of 100 consecutive frames, the image set for the second frame image may include the first frame and other related frames before the second frame (depending on the algorithm's definition rules for related frames). Including the first frame image provides a more comprehensive reference when calculating motion vectors, which helps to more accurately assess the motion changes in the current frame. The forward frame interval is set to improve the accuracy of key frame localization. If only adjacent frames are analyzed, key frames may be misjudged because the motion changes are not obvious in the short term. For example, when an object is slowly accelerating, simply comparing the motion vectors of adjacent frames may not accurately identify the trend of the object's motion changes. However, by referring to multiple forward frames, the changes in motion can be captured more comprehensively, thus accurately identifying key frames and redundant frames.

[0023] Step S133: Analyze the motion vector set of the second frame image relative to each frame image in the second frame image set, and connect it to a preset motion complexity assignment model to generate a second motion complexity for the second frame image, which is then added to the plurality of motion complexities. Specifically, analyze the motion vector set of the second frame image relative to each frame image in the second frame image set. By comparing the pixel differences between the second frame image and each frame in the image set, use algorithms such as block matching to calculate the motion vectors, divide the image into several small blocks, search for the region in the reference frame that best matches the current block, thereby obtaining the motion vector of the block, and summarizing them to form a motion vector set. This process quantifies information such as the motion displacement and direction of objects or image regions between frames. The resulting set of motion vectors is then connected to a preset motion complexity assignment model. This model determines the motion complexity based on pre-defined rules and a database. For example, the database in the model stores the correspondence between different motion vector features and motion complexity. If certain features extracted from the set of motion vectors (such as the maximum motion vector magnitude, the average change in motion vector direction, etc.) meet a specific condition range in the database, the motion complexity of the second frame image can be determined based on the correspondence. The generated second motion complexity is added to multiple motion complexity sets to complete the evaluation and recording of the motion complexity of the second frame image.

[0024] Step S134, and so on, continues to acquire the third frame image and calculate the third motion complexity, until the multiple consecutive frames are traversed and the multiple motion complexities are generated. Specifically, following the same method, the third frame image is acquired sequentially and the above steps are repeated to calculate the third motion complexity. This process continues to traverse all consecutive frames. For each frame processed, the operations of extracting the relevant frame set, analyzing the motion vector set, determining the motion complexity through the model, and adding it to the set are repeated. By progressively advancing, the motion complexity of each frame in the video stream is evaluated, ultimately generating multiple motion complexities covering all consecutive frames. This provides a quantitative evaluation index for the degree of motion change of the frames in the entire video stream, providing an important basis for distinguishing key frames and redundant frames, and helping to make reasonable resource allocation and operations based on the importance of frames during data transmission and processing.

[0025] In one possible implementation, the motion vector set of the second frame image relative to each frame image in the second frame image set is analyzed, and a preset motion complexity assignment model is connected to generate a second motion complexity of the second frame image, which is then added to the plurality of motion complexities. Step S133 further includes step S1331, in which the motion vector with the largest modulus value is extracted from the motion vector set, connected to the preset motion complexity assignment model, and the complexity assignment database is called to filter the complexity identifier sample corresponding to the motion vector with the largest modulus value to generate the second motion complexity. Specifically, after analyzing the frame images and the reference frame set to obtain the motion vector set, this set contains a large amount of vector information about the motion of objects or regions between images. Each motion vector has direction and magnitude (modulus) attributes, reflecting the displacement changes of the corresponding part in the image between frames. Extracting the motion vector with the largest modulus is a key step, because in many cases, the motion change of the object or region corresponding to the motion vector with the largest modulus is the most significant, and it can represent the main motion features of the entire frame image to a certain extent. For example, in the video monitoring of power transmission lines, if the line swings due to strong winds, the motion vector with the largest swing amplitude is likely to have the largest modulus. The motion information of this part plays an important role in judging the importance of the frame. By traversing all vectors in the motion vector set and comparing their modulus values, the vector with the largest modulus can be selected, providing core data for the subsequent connection of the preset motion complexity assignment model, so that the complexity assessment can be focused on the most representative motion information.

[0026] Step S1332, wherein the complexity assignment database includes multiple sets of assignment samples, each set of assignment samples including a motion vector interval sample and a complexity identifier sample. Specifically, the preset motion complexity assignment model is a mechanism used to determine motion complexity based on motion vector information, and the complexity assignment database, as an important support for the model, stores multiple sets of assignment samples. Each set of samples defines a range of motion vector magnitudes, while the complexity indicator sample corresponds to the motion complexity level or value of the corresponding range. For example, a motion vector range sample is [5-10] (magnitude unit), and the corresponding complexity indicator sample is "medium complexity" or a quantified value such as 3 (assuming the complexity value range is 1-5, the larger the value, the higher the complexity). After extracting the motion vector with the largest magnitude, the model uses this motion vector as input and performs a filtering operation in the complexity assignment database. Specifically, the motion vector with the largest magnitude is compared with each motion vector range sample to determine its range. Once the corresponding range is found, the complexity indicator sample associated with that range is obtained. The complexity information represented by this sample is the basis for generating the motion complexity for the current frame. For example, if the extracted motion vector with the largest magnitude is 8, and it is found in the database to fall within the range of [5-10], then the corresponding "medium complexity" indicator or value 3 is used as the second motion complexity of the current frame. In this way, the transformation from raw motion vector data to motion complexity with clear semantics and quantification is achieved, providing an accurate data foundation for subsequent operations such as distinguishing keyframes and redundant frames.

[0027] Step S200 involves real-time monitoring of network bandwidth and computing power information of the preset network to adjust the filtering threshold and generate a preset filtering threshold. Specifically, network monitoring tools, such as SNMP, are used to read port traffic data from network devices and calculate and process it to obtain accurate network bandwidth information. Simultaneously, the system or third-party software is used to obtain parameters such as the utilization rate and frequency of the device's CPU and GPU, and integrate them into a comprehensive computing power evaluation value. A bandwidth-computing power threshold mapping table is pre-built, and bandwidth and computing power intervals are divided based on experimental data and simulation analysis, with corresponding filtering thresholds set. The real-time monitored bandwidth and computing power information are matched with the mapping table, and the corresponding filtering threshold is found according to its interval. This allows for dynamic adjustment of the filtering threshold based on real-time changes in network bandwidth and computing power, adapting to different environments to achieve reasonable frame filtering and transmission.

[0028] In one possible implementation, the network bandwidth and computing power information of the preset network are monitored in real time to adjust the filtering threshold and generate a preset filtering threshold. Step S200 further includes step S210, obtaining a bandwidth-computing power threshold mapping table, wherein the bandwidth-computing power threshold mapping table includes multiple sets of bandwidth intervals, computing power intervals and corresponding filtering thresholds with mapping relationships. Specifically, technical professionals configure the bandwidth-computing power threshold mapping table based on extensive experimental and practical application experience. First, different network bandwidth conditions are categorized. Network bandwidth information encompasses key information such as currently available bandwidth, upload and download speeds, etc. For example, bandwidth is divided into different ranges based on numerical values. The low bandwidth range might be set to less than 10Mbps, suitable for scenarios with poor network conditions; the medium bandwidth range is 10-50Mbps, which can meet general video transmission needs; and the high bandwidth range is greater than 50Mbps, capable of supporting the transmission of large amounts of data such as high-definition video. Each bandwidth range division has undergone testing and analysis of data transmission capabilities in actual network environments to ensure the rationality and accuracy of the classification. Simultaneously, computing power information, i.e., the computing power of the video processing equipment, is considered. Computing power information includes factors such as CPU utilization, GPU utilization, and memory usage. For CPU utilization, different usage ratios reflect the busyness of the device in processing data; GPU utilization is crucial when dealing with tasks such as image processing; and memory usage affects data caching and processing speed. Based on different combinations and value ranges of these factors, different computing power intervals are defined. For example, a low computing power interval may correspond to a situation where CPU utilization is high, GPU utilization is low, and memory usage is high; while a high computing power interval is a situation where CPU utilization is low, GPU utilization is high, and memory usage is reasonable. After defining the bandwidth intervals and computing power intervals, a corresponding filtering threshold is set for each interval with a specific combination of bandwidth and computing power. The filtering threshold is determined based on experience and the needs of video transmission and processing under different bandwidth and computing power conditions, aiming to ensure that a relatively optimized video frame filtering effect can be achieved under different network and device conditions.

[0029] Step S220: Input the network bandwidth information and the computing power information into the bandwidth-computing power threshold mapping table, obtain the bandwidth range and computing power range into which the network bandwidth information and the computing power information fall, and generate the preset filtering threshold. Specifically, the real-time monitored network bandwidth and computing power information are input into a bandwidth-computing power threshold mapping table. For network bandwidth information, for example, if the currently detected available bandwidth is 30Mbps, the upload speed is 5Mbps, and the download speed is 6Mbps, the bandwidth range is determined according to the bandwidth range division standard. For computing power information, assuming the current CPU utilization is 40%, GPU utilization is 70%, and memory usage is 60%, the computing power range is determined by comparing it with the computing power range division standard. Then, the filtering threshold corresponding to the combination of these two ranges is found in the mapping table. Once the corresponding bandwidth range and computing power range combination is found in the mapping table, the corresponding filtering threshold is determined as the preset filtering threshold. This realizes the dynamic generation of filtering thresholds based on the real-time network and device status. When the network bandwidth is good and the computing power is sufficient, the filtering threshold may be appropriately increased, so that more frames are considered important frames for processing and transmission. When the network bandwidth is limited or the computing power is insufficient, the filtering threshold is lowered, and only key frames are prioritized for processing to ensure the stability and smoothness of video transmission, while making reasonable use of resources and improving the overall video monitoring and transmission efficiency.

[0030] In one possible implementation, the network bandwidth and computing power information of the preset network are monitored in real time to adjust the filtering threshold and generate a preset filtering threshold. Step S200 further includes step S230, obtaining the compression feedback information of the video receiving end of the monitoring video stream of the transmission line to be transmitted, wherein the compression feedback information includes video reconstruction quality indicators and smoothness indicators. Specifically, during the video transmission process for power transmission line monitoring, the video receiver is responsible for collecting compression feedback information. For video reconstruction quality indicators, the receiver analyzes the received and decoded video to obtain these indicators. For example, image quality assessment algorithms are used to calculate indicators such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). PSNR measures image quality by calculating the difference between corresponding pixels in the original and reconstructed images, while SSIM evaluates image similarity from three aspects: brightness, contrast, and structure. These indicators are combined to derive a quantitative value for video reconstruction quality. Regarding smoothness indicators, the receiver determines this by monitoring factors such as frame rate changes, number of stutters, and stutter duration during video playback. High smoothness is indicated by a stable frame rate and few stutters; conversely, low smoothness is indicated by high frame rate and short stutter duration. Compression feedback information is fed back to the sender or video processing center via the network for aggregation and analysis. Whether in a wired or wireless network environment, it is essential to ensure the accuracy and timeliness of the feedback information transmission for effective subsequent processing.

[0031] Step S240: If the video reconstruction quality index or smoothness index does not meet the preset reconstruction quality constraints, a filtering optimization instruction is generated. Specifically, the standards for video reconstruction quality and smoothness are preset, i.e., preset reconstruction quality constraints. The constraints are determined based on the actual needs of power transmission line monitoring and the user's expectations for video quality. For example, for monitoring some key components on the power transmission line, the PSNR value of the video reconstruction quality may be required to be no lower than a certain value, and the frame rate may be required to be maintained at a certain level and the number of stutters may not exceed a certain threshold. When the received video reconstruction quality index or smoothness index does not meet the preset reconstruction quality constraints, the system will automatically generate a filtering optimization instruction. If the video reconstruction quality index is lower than the required PSNR or SSIM value, or if the frame rate is too low or stutters are frequent in the smoothness index, the instruction generation mechanism will be triggered. This means that the current video transmission and compression effect has not met the expected standard and needs to be adjusted and optimized.

[0032] Step S250: Optimize the preset screening threshold according to the filtering optimization instruction and a preset step size. Specifically, when the compression information fed back by the video receiver shows that the video reconstruction quality or smoothness does not meet the preset reconstruction quality constraints, the system generates a filtering optimization instruction. Then, historical compression feedback records are retrieved from storage, covering information such as network bandwidth, computing power, screening threshold, and video quality under different past conditions. Data analysis tools are used to analyze the relationship between the screening threshold and video quality changes, such as plotting relationship curves and calculating relevant parameters. A preset step size is determined with the goal of meeting the preset reconstruction quality constraints, and the step size value is optimized considering system stability and resource consumption factors. The screening threshold is then adjusted according to the step size. After each adjustment, the video transmission and compression situation is evaluated. If the requirements are not met, the adjustment by the step size continues and the process is repeated. Simultaneously, network and computing power changes are monitored in real time to adjust the step size or re-analyze the data according to new situations until the video quality meets the standards, achieving efficient, stable, and high-quality video transmission and compression.

[0033] Step S260, wherein the configuration step of the preset step size includes: acquiring historical compression feedback records. Specifically, the system first collects historical compression feedback records from past video transmission processes. The records contain information such as video reconstruction quality indicators, smoothness indicators, and corresponding filtering thresholds under different time periods, network and device conditions. By querying the database or stored files, a large amount of historical data is obtained as the basis for analysis.

[0034] Step S270: Analyze the relationship between the filtering threshold and video reconstruction quality based on the historical compression feedback records. Specifically, based on the historical compression feedback records, analyze the relationship between the filtering threshold and video reconstruction quality and smoothness. For example, by plotting charts or performing data regression analysis, observe how the video reconstruction quality and smoothness change accordingly when the filtering threshold increases or decreases. It is found that when the filtering threshold is too high, although key frames are prioritized, too many redundant frames are skipped or lightly compressed, affecting the overall coherence and integrity of the video, thereby reducing the reconstruction quality and smoothness.

[0035] Step S280: Based on the aforementioned relationship of change, and with the goal of satisfying the preset reconstruction quality constraints for the optimized video reconstruction quality index and smoothness index, a preset step size is generated. Specifically, with the goal of satisfying the preset reconstruction quality constraints for the optimized video reconstruction quality index and smoothness index, the preset step size is determined based on the analyzed relationship of change. If it is found that each decrease in the filtering threshold by a certain value improves the video reconstruction quality and smoothness to a certain extent, then a suitable step size value is calculated based on the magnitude of the improvement and the preset reconstruction quality constraints to be achieved. For example, analysis shows that a decrease in the filtering threshold by 0.1 increases the PSNR value of the video reconstruction quality by 2dB and reduces the number of stutters in the smoothness index by 1. To achieve the preset quality constraints, the PSNR value needs to increase by 5dB and the number of stutters needs to be reduced by 3. Therefore, a suitable step size value can be calculated to gradually adjust the filtering threshold until the preset reconstruction quality constraints are met. At this point, if the threshold is too high, it needs to be appropriately reduced. Through reasonable step size settings, precise optimization is achieved, improving the video transmission and compression effects.

[0036] Step S300: Compare the multiple importance indicators with the preset screening threshold to locate key frames and redundant frames. Key frames are those with an importance indicator greater than or equal to the preset screening threshold, and redundant frames are those with an importance indicator less than the preset screening threshold. Specifically, when locating key frames and redundant frames, the importance indicators of each frame, obtained through multi-faceted analysis and fusion, are first acquired. Simultaneously, the preset screening threshold is determined using a mapping table by monitoring network bandwidth and computing power information. Starting from the first frame of the video stream, the importance indicators of each frame are compared with the preset screening threshold. If the importance indicator of a frame is greater than or equal to the threshold, it is located as a key frame, typically containing critical information such as line fault moments or key equipment actions. If it is less than the threshold, it is a redundant frame, whose information changes, motion amplitude, or scene changes are relatively insignificant. This method accurately distinguishes between the two types of frames in the video stream, providing a basis for subsequent different processing strategies, so as to ensure the transmission of critical information while rationally utilizing resources to adapt to the network and equipment environment.

[0037] Step S400: The keyframe is input into a preset encoder for encoding and then transmitted with priority. The preset encoder employs an encoding algorithm that preserves complete image information. Specifically, the preset encoder is built upon existing high-quality encoding algorithms such as H.265. During encoding, to preserve complete image information, quantization parameters are finely adjusted, more bit resources are allocated to important areas such as key equipment identifiers, and advanced entropy coding technology is utilized. Before inputting the keyframe into the encoder, data preprocessing is performed, such as format and color space conversion. During the encoding operation, intra-frame and inter-frame predictions reduce the amount of data. Appropriate quantization parameters are used when transforming and quantizing residual data, and then entropy coding is used to generate a bitstream. The encoded keyframe is set as a high-priority transmission frame, a priority marker is added at the network protocol level, network devices process it first, and flow control and congestion avoidance mechanisms ensure reliable transmission. The receiving end, having a decoder with the same algorithm, can accurately decode and reconstruct the image, providing data support for power transmission line monitoring.

[0038] Step S500: The redundant frames are input into the differentiation processing module for differential compression processing. After the keyframe transmission is completed, the redundant frames after differential compression are transmitted. The differential compression processing includes lightweight compression and skip processing. Specifically, after a frame is identified as redundant, it is input into the differentiation processing module in sequence through a data channel or queue and an identifier is added for management. In the differential compression processing, lightweight compression includes reducing resolution, such as downsampling high-resolution redundant frames proportionally to reduce the number of pixels; compressing color depth, reducing from true color to indexed color to reduce storage space; and using simple and efficient encoding algorithms to effectively compress repetitive patterns. Skipping processing is based on importance indicators and system status. If the importance indicator is below a threshold and the network is strained, the skipped frame is marked, and the receiving end fills it with interpolation or reference to adjacent frames. After the keyframe transmission is completed, the redundant frames are transmitted in sequence. During transmission, the correct order is ensured according to the identifier. At the same time, network bandwidth and device status are continuously monitored. When bandwidth fluctuates or device load changes, transmission parameters such as rate and data packet size are dynamically adjusted to adapt to the environment, ensure smooth video stream transmission, and make reasonable use of resources.

[0039] In one possible implementation, the redundant frames are input into a differentiation processing module for differential compression. After the key frame transmission is completed, the differentially compressed redundant frames are transmitted. The differential compression includes lightweight compression and skipping. Step S500 further includes step S510, configuring a differentiation judgment threshold, where the differentiation judgment threshold is a threshold for an importance indicator that determines whether to perform lightweight compression or skipping. Specifically, the differentiation judgment threshold must first be configured. This threshold is a threshold for an importance indicator that determines whether to perform lightweight compression or skipping. Setting this threshold requires comprehensive consideration of multiple factors. On one hand, it is necessary to analyze a large amount of transmission line monitoring video data to understand the importance distribution of redundant frames under different scenarios. For example, by collecting video data under different weather conditions (sunny, rainy, windy, etc.), different time periods (daytime, nighttime), and different line operating states (normal operation, pre-fault stage, fault occurrence, etc.), the information entropy, motion complexity, and scene change degree of the redundant frames can be analyzed to determine a suitable range as the threshold setting. Based on this, and considering the current system's resources and actual needs, if network bandwidth is sufficient and device computing power is strong, the differentiation threshold can be appropriately increased, allowing more redundant frames to undergo lightweight compression instead of being skipped directly, thus improving video integrity. Conversely, if network bandwidth is tight or device computing power is limited, the threshold needs to be lowered to reduce data processing volume and prioritize the transmission and processing of key frames and relatively important redundant frames. For example, in a power transmission line fault monitoring scenario with high real-time requirements, in order to transmit key information to the monitoring center as quickly as possible, the threshold may be lowered to skip more less important redundant frames.

[0040] Step S520: When the importance index of the redundant frame is less than the differentiation judgment threshold, the redundant frame is skipped. Specifically, when the importance index of the redundant frame is less than the differentiation judgment threshold, the system decides to skip the redundant frame because, under the current system resource and demand conditions, the information carried by this frame is considered relatively unimportant, and skipping it has little impact on the understanding of the overall video information. For example, if a frame in the video mainly consists of a static background sky and its importance index is very low, below the set threshold, then the system will choose to skip the transmission and processing of this frame to save network bandwidth and device computing resources.

[0041] Step S530: When the importance index of the redundant frame is greater than or equal to the differentiation judgment threshold, the redundant frame is lightly compressed. Specifically, when the importance index of the redundant frame is greater than or equal to the differentiation judgment threshold, the system will lightly compress the redundant frame. This means that the frame contains a certain amount of useful information and needs to be appropriately processed before transmission. For example, although most areas of the image are stationary, a small part of the line components may have slight movement or changes in lighting. If its importance index meets the threshold requirement, the system will use a light compression method, such as reducing resolution, compressing color depth, or using a simple encoding algorithm, to process the frame. This reduces the amount of data while preserving as much useful information as possible before transmission, balancing video quality and system resource utilization efficiency. Through threshold-based judgment and processing, the system can reasonably handle redundant frames, adapting to different power transmission line monitoring scenarios and system conditions.

[0042] This application embodiment acquires video streams from power transmission line monitoring, analyzes the importance of multiple consecutive frames using multi-dimensional importance evaluation metrics to generate importance indices, monitors network bandwidth and computing power in real time, and dynamically adjusts screening thresholds. Based on a comparison of the importance indices and screening thresholds, key frames and redundant frames are identified, with key frames being those with higher importance and redundant frames those with lower importance. Key frames are encoded by a preset encoder and transmitted first, with the encoder retaining complete image information. Redundant frames are lightweight compressed or skipped using a differentiation processing module and transmitted only after the key frames have been transmitted. This achieves the technical effect of reducing unnecessary data transmission and improving the real-time performance and stability of video transmission.

[0043] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A real-time image video compression method of dynamic frame screening, characterized in that, The method comprises the following steps: acquiring a to-be-transmitted power line monitoring video stream, performing importance analysis on a plurality of continuous frames in the to-be-transmitted power line monitoring video stream according to a multi-dimensional importance evaluation index, and generating a plurality of importance indexes; real-time monitoring of network bandwidth information and computing power information of a preset network for screening threshold regulation, generating a preset screening threshold, the computing power information being a computing capability of a video processing device, and the computing power information including CPU usage, GPU utilization, and memory occupation; comparing the plurality of importance indexes with the preset screening threshold to locate key frames and redundant frames, wherein the key frames are frames with importance indexes greater than or equal to the preset screening threshold, and the redundant frames are frames with importance indexes less than the preset screening threshold; inputting the key frames into a preset encoder for encoding and then performing priority transmission, wherein the preset encoder adopts an encoding algorithm that retains complete image information; inputting the redundant frames into a differential processing module for differential compression processing, and then performing transmission of the redundant frames after the differential compression processing, wherein the differential compression processing includes lightweight compression and skip processing; wherein the real-time monitoring of network bandwidth information and computing power information of a preset network for screening threshold regulation includes: acquiring a bandwidth-computing power threshold mapping table, wherein the bandwidth-computing power threshold mapping table includes a plurality of bandwidth intervals, a plurality of computing power intervals, and a plurality of corresponding screening thresholds having a mapping relationship; inputting the network bandwidth information and the computing power information into the bandwidth-computing power threshold mapping table to obtain the bandwidth interval and the computing power interval to which the network bandwidth information and the computing power information fall, and generating the preset screening threshold; the method further comprises: acquiring compression feedback information of a video receiving end of the to-be-transmitted power line monitoring video stream, wherein the compression feedback information includes a video reconstruction quality index and a smoothness index; generating a screening optimization instruction if the video reconstruction quality index or the smoothness index does not meet a preset reconstruction quality constraint; optimizing the preset screening threshold according to the screening optimization instruction at a preset step length; wherein the preset step length configuration step includes: acquiring historical compression feedback records; analyzing the screening threshold, the video reconstruction quality, and the change relationship between the screening threshold according to the historical compression feedback records; generating the preset step length based on the change relationship, with the goal of optimizing the video reconstruction quality index and the smoothness index to meet the preset reconstruction quality constraint; the multi-dimensional importance evaluation index at least includes information entropy, motion complexity, and scene change degree; performing importance analysis on a plurality of continuous frames in the to-be-transmitted power line monitoring video stream according to a multi-dimensional importance evaluation index, and generating a plurality of importance indexes, including: performing information entropy calculation on the plurality of continuous frames to generate a plurality of information entropies; performing inter-frame motion difference analysis on the plurality of continuous frames to generate a plurality of motion complexities; The multiple continuous frames are respectively analyzed for inter-frame scene change, and multiple scene change degrees are generated, including obtaining the change amount of each region in brightness, color and edge, and performing weighted summation according to a preset weight distribution mode to obtain a scene change degree value of the current frame relative to the previous frame; The multiple information entropies, the multiple motion complexities and the multiple scene change degrees are fused to generate the multiple importance indexes; The multiple continuous frames are respectively analyzed for inter-frame motion difference, and multiple motion complexities are generated, including: A first motion complexity of a first frame image located at the first position in the multiple continuous frames is initialized as 1 and added to the multiple motion complexities; Second frame images except the first frame image in the multiple continuous frames are extracted, and a second frame image set satisfying a preset forward inter-frame interval with the second frame image is obtained; A motion vector set of the second frame image relative to each frame image in the second frame image set is analyzed, and a preset motion complexity assignment model is connected to generate a second motion complexity of the second frame image and added to the multiple motion complexities; By analogy, a third frame image is continuously extracted to calculate a third motion complexity until the multiple continuous frames are traversed to generate the multiple motion complexities.

2. A real-time image video compression method of dynamic frame screening according to claim 1, characterized in that, A motion vector set of the second frame image relative to each frame image in the second frame image set is analyzed, and a preset motion complexity assignment model is connected to generate a second motion complexity of the second frame image, including: The motion vector with the maximum modulus value in the motion vector set is extracted, the preset motion complexity assignment model is connected, a complexity assignment database is called to filter a complexity identifier sample corresponding to the motion vector with the maximum modulus value, and the second motion complexity is generated; The complexity assignment database includes multiple groups of assignment samples, and each group of assignment samples includes a motion vector interval sample and a complexity identifier sample.

3. A real-time image video compression method of dynamic frame screening according to claim 1, characterized in that, The redundant frame is input into a differential processing module for differential compression processing, including: A differential judgment threshold is configured, wherein the differential judgment threshold is an importance index threshold for selecting lightweight compression or skip processing; When the importance index of the redundant frame is less than the differential judgment threshold, the redundant frame is processed by skipping; When the importance index of the redundant frame is greater than or equal to the differential judgment threshold, the redundant frame is processed by lightweight compression.

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