Remote image transmission system and method based on compression algorithm

Abstract

The invention relates to the technical field of image transmission, in particular to a remote image transmission system and method based on a compression algorithm, and the method comprises the steps: 1, image blocking and feature extraction; 2, performing block importance dynamic classification; 3, differential compression and layered packaging are carried out; 4, channel adaptive transmission control: dynamically adjusting a transmission strategy according to a real-time channel bit error rate, and when the bit error rate exceeds a threshold value, pausing non-key data layer transmission and migrating non-key blocks to a key data layer according to high compression strength; calculating a bandwidth allocation factor based on the bit error rate and the non-key block proportion, and dynamically controlling the sending amount of a non-key data layer; and step 5, hierarchical reconstruction of the receiving end. By combining a plurality of technologies such as image partitioning, dynamic classification, differential compression, channel adaptive control and receiving end reconstruction, the bandwidth utilization rate is effectively optimized while the image quality is ensured.

Description

Technical Field

[0001] This invention relates to the field of image transmission technology, and in particular to a remote image transmission system and method based on a compression algorithm. Background Technology

[0002] In image transmission applications operating under high-latency, low-bandwidth, and high-error-rate channel environments such as satellite remote sensing and deep space exploration, existing technologies generally employ a transmission architecture of "fixed compression algorithm + unified error correction protection." This type of solution suffers from the following structural defects: To improve transmission efficiency, a high compression ratio is required (such as increasing the quantization step size in JPEG2000). However, increasing the compression ratio weakens the spatial correlation of image data, resulting in a reduction in the redundant information required for error correction decoding. Experiments show that when the compression ratio exceeds 20:1, the error sensitivity increases by more than three times under the same error correction strength.

[0003] If error correction capabilities are enhanced (e.g., by reducing the RS code rate), the effective data rate drops sharply. In a typical satellite channel (bit error rate 10^6), this results in a significant decrease. -3 ~10 -2 In order to ensure basic availability, 30%-50% of bandwidth needs to be sacrificed for error correction, which severely restricts the timeliness of high-resolution image transmission.

[0004] In addition, the edge information of features such as roads and building outlines in satellite images directly affects the value of manual interpretation, but existing technologies suffer from the problem of preserving homogenization: Applying the same level of protection to uniform regions with simple textures (such as water bodies and farmland) and edge regions with complex structures leads to limited error correction resources being consumed by low-value regions. High compression operations cause irreversible damage to edge information: Traditional discrete cosine transform blurs edge gradients under high compression rates. Even if error correction and decoding are successful, defects such as road breaks and building contour distortion in the reconstructed image still lead to interpretation failure.

[0005] Therefore, there is an urgent need for a remote image transmission system and method based on compression algorithms to solve the above problems. Summary of the Invention

[0006] To achieve the above objectives, the present invention provides a remote image transmission system and method based on a compression algorithm, wherein the remote image transmission method based on a compression algorithm includes the following steps: Step 1: Image segmentation and feature extraction: Divide the original remote sensing image into multiple image segments and extract the edge density features and texture complexity features of each image segment; Step 2: Dynamic classification of block importance: Based on the combination relationship between edge density features and texture complexity features, the image blocks are classified into key feature blocks and non-key blocks; Step 3: Differentiated compression and layered packaging: The critical feature blocks are processed with low compression intensity and high protection intensity error correction coding is added. The non-critical blocks are processed with high compression intensity and low protection intensity error correction coding is added. They are then encapsulated into a transmission frame containing a frame header, a critical data layer and a non-critical data layer. Step 4: Channel Adaptive Transmission Control The transmission strategy is dynamically adjusted based on the real-time channel bit error rate. When the bit error rate exceeds the threshold, the transmission of non-critical data layers is suspended and non-critical blocks are migrated to the critical data layer with high compression intensity. The bandwidth allocation factor is calculated based on the bit error rate and the proportion of non-critical blocks, and the amount of non-critical data layer transmission is dynamically controlled. Step 5: Layered reconstruction at the receiving end: Prioritize error correction decoding and retransmission of critical data layers, discard blocks that fail to decode non-critical data layers, and reconstruct damaged non-critical blocks using boundary information of adjacent critical feature blocks.

[0007] Preferably, the extraction of edge density features in step 1 includes: The gradient magnitude of each pixel within an image block is calculated using an edge detection operator; The number of pixels whose gradient magnitude exceeds the gradient magnitude threshold is counted, and the proportion of these pixels to the total number of pixels in the block is calculated as the edge density value. The gradient magnitude threshold is determined by acquiring typical artificial feature sample images containing roads and buildings, calculating the gradient magnitude distribution of the smallest identifiable edge in all samples, and taking the median of the distribution as the initial threshold. The extraction of the texture complexity features includes: Perform frequency domain transformation on image blocks to separate low-frequency and high-frequency components; The proportion of high-frequency component energy to the total energy of the block is calculated as the baseline value for texture complexity. The baseline value is normalized and calibrated based on the image imaging quality parameters to generate the final texture complexity value.

[0008] Preferably, the specific implementation of the dynamic classification of block importance in step 2 is as follows: Set edge density thresholds and texture complexity thresholds, which are dynamically calibrated during the transmission preamble stage: a: The sending end sends test image blocks containing multiple ground features; b: The receiving end generates a quality score based on the edge sharpness of artificial features in the reconstructed image; c: When the quality score is lower than the set requirement, increase the edge density threshold or decrease the texture complexity threshold by a fixed step size; d: Repeat steps a-c until the quality score meets the standard; If the edge density value of an image block is greater than the edge density threshold and the texture complexity value is less than the texture complexity threshold, it is classified as a key feature block.

[0009] Preferably, the specific operations of differential compression in step 3 include: A quantization step size reduction mode is adopted for key features: based on the gradient magnitude distribution histogram within the block, the quantization step size adjustment range that retains more than 90% of the edge information is dynamically selected. Spatial downsampling compression is applied to non-critical blocks: the downsampling rate is selected from a preset mapping table based on the texture complexity value. The higher the texture complexity value, the lower the downsampling rate. The mapping table is established by determining the correspondence between the downsampling rate of different texture types and the image structure similarity through offline experiments. The error correction capability of the high protection strength error correction code in the layered encapsulation is dynamically calculated based on historical channel bit error rate data. Specifically, the average bit error rate of the most recent N transmissions is calculated, and the amount of redundant check data is increased according to the ratio of this average value to the maximum tolerable bit error rate.

[0010] Preferably, the calculation logic for the bandwidth allocation factor in step 4 is as follows: The maximum tolerance bit error rate is set according to the application scenario of the receiving end. A low tolerance value is set for manual interpretation scenarios, and a high tolerance value is set for machine recognition scenarios. The proportion of non-critical blocks is the ratio of the number of non-critical blocks in the current frame to the total number of blocks; The bandwidth allocation factor is proportional to (1 - current bit error rate / maximum tolerable bit error rate) and proportional to the proportion of non-critical blocks; The operation of dynamically controlling the transmission volume of non-critical data layers is as follows: when the bandwidth allocation factor is lower than the set threshold, the number of bytes transmitted by non-critical data layers is reduced proportionally according to the factor value.

[0011] Preferably, the method for reconstructing damaged non-critical blocks in step 5 includes: Locate all key feature blocks adjacent to the damaged block and extract the boundary pixels that border the damaged block; Construct pixel gradient field constraints for damaged blocks based on boundary pixel values; The pixel matrix that meets the gradient field constraints is solved by an iterative optimization algorithm. The optimization objective is to make the transition between the reconstructed region and the boundary pixels smooth. Edge enhancement processing is performed on the reconstruction results: the edge density feature distribution of the original blocks is detected, and the gradient magnitude is adaptively increased at the corresponding positions; The iterative optimization algorithm is the Poisson reconstruction algorithm, specifically including: Dirichlet boundary conditions are defined using boundary pixel values. The gradient field intensity benchmark for the reconstructed region is set based on the original texture complexity value of the damaged block. The pixel values ​​are solved using the discrete Poisson equation, and the iteration terminates when the rate of change of structural similarity between two adjacent reconstruction results is less than the convergence threshold.

[0012] Preferably, the determination condition for "bit error rate exceeding the threshold" in step 4 is as follows: Within a time window of length equal to the channel coherence time, the percentage of times the bit error rate continuously exceeds the threshold is greater than a set proportion; The channel coherence time is determined by calculating the time delay of the autocorrelation function of the bit error rate sequence decaying to a set coefficient.

[0013] Preferably, the retransmission triggering rule in step 5 is as follows: Retransmission is initiated only when the decoding of critical feature blocks fails and its edge density value is greater than the retransmission protection threshold. The retransmission protection threshold is dynamically adjusted based on the available bandwidth: when bandwidth is sufficient, the threshold is lowered to expand the protection range, and when bandwidth is tight, the threshold is raised to focus on core ground features.

[0014] Preferably, in step 3, the frame header also includes an error correction coding type identifier, and the receiving end selects the corresponding decoding strategy based on this identifier: High-protection-strength error correction coding employs a decoding algorithm based on algebraic error correction; Low protection strength error correction coding employs a probabilistic soft-decision-based decoding algorithm; The priority of the decoding strategy selection dynamically switches to a high-protection-strength decoding algorithm when the channel bit error rate increases.

[0015] Accordingly, embodiments of the present invention also provide a remote image transmission system based on a compression algorithm, including a memory configured to store instructions, a processor configured to retrieve the instructions from the memory, and capable of implementing any of the remote image transmission methods based on a compression algorithm described in the embodiments of the present invention when executing the instructions.

[0016] The beneficial effects of this invention are: 1. This invention employs differentiated compression and dynamic classification of block importance, using different compression strategies for critical and non-critical blocks. For critical block segments, a low compression intensity is applied with enhanced error correction protection, thereby reducing spatial information loss due to compression; while for non-critical blocks, a higher compression rate is used, but the error correction protection intensity is reduced. This approach not only maintains the basic image quality but also avoids wasting redundant error correction resources on low-value areas. This technology significantly improves the transmission efficiency of high-resolution images, resolving the contradiction between transmission efficiency and decoding quality under high compression and high bit error rate.

[0017] 2. This invention employs channel-adaptive transmission control to adjust the transmission strategy based on the channel's real-time bit error rate (BER). When the BER is high, transmission of non-critical data layers is automatically suspended and migrated to critical data layers. Error correction redundancy is dynamically adjusted as needed, and bandwidth allocation factors are optimized, effectively reducing bandwidth waste. Through this dynamic error correction and bandwidth allocation strategy, this invention significantly improves bandwidth utilization and data transmission reliability in image transmission under high BER environments.

[0018] 3. This invention uses dynamic classification based on the importance of blocks to assign higher error correction protection strength to key feature blocks in an image, ensuring that information in critical areas is prioritized for protection. This targeted protection strategy avoids the overprotection of low-value areas in traditional methods, ensuring that limited error correction resources are concentrated in important areas, thereby improving the accuracy of image interpretation.

[0019] 4. This invention employs edge density feature extraction and reconstruction technology to perform special processing on edge information during image compression, and reconstructs damaged areas at the receiving end by utilizing the boundary information of adjacent blocks. Specifically, it uses the Poisson reconstruction algorithm to optimize the edge transition of the image, enabling the effective recovery of important edge information even under high compression conditions. This significantly reduces edge blurring and distortion caused by high compression, improving the usability of the image. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a flowchart illustrating the steps involved in extracting texture complexity features using the method of the present invention. Figure 3 This is a flowchart illustrating the calculation logic of the bandwidth allocation factor in step 4 of the method of the present invention. Detailed Implementation

[0022] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0023] Please see Figures 1-3This invention provides a remote image transmission method based on a compression algorithm. First, the original remote sensing image is divided into blocks. For each image block, basic features are extracted by calculating its edge density and texture complexity features. Edge density features reflect the complexity of details within the image, while texture complexity is extracted through frequency domain transformation, representing the fineness of the image surface. These features allow for effective evaluation of the importance of each block within the overall image.

[0024] Furthermore, based on the extracted edge density and texture complexity features, image patches are classified into key feature patches and non-key feature patches. Key feature patches are typically important parts of the image, such as roads and buildings, while non-key feature patches may be background areas or areas with less detail. This classification step provides a basis for subsequent differential compression and data encapsulation.

[0025] In the differentiated compression and layered encapsulation stage, critical feature blocks employ low compression strength with added high-protection-strength error correction coding to ensure that important image information is not lost during transmission. Non-critical blocks, on the other hand, use high compression strength coding with added low-protection-strength error correction coding to reduce data transmission load. These blocks are organized into transmission frames with a frame header, a critical data layer, and non-critical data layers. The frame header contains the basic information required for transmission, while the critical and non-critical data layers contain the compressed and encoded data.

[0026] During transmission, the channel's adaptive transmission control mechanism dynamically adjusts the transmission strategy based on the real-time bit error rate. If the bit error rate exceeds a predetermined threshold, the system will suspend the transmission of non-critical data layers and transfer non-critical blocks to the critical data layer with higher compression intensity. At this time, the system will also calculate the bandwidth allocation factor based on the current bit error rate and the proportion of non-critical blocks, dynamically controlling the transmission volume of non-critical data layers, thereby improving transmission efficiency and ensuring priority transmission of critical ground object blocks.

[0027] The receiving end then reconstructs the image using a layered reconstruction strategy. First, it prioritizes decoding and retransmitting critical data layers to ensure accurate recovery of important data. For non-critical data layers, if decoding fails, unrecoverable blocks are discarded, and reconstruction is performed using boundary information from adjacent critical feature blocks. During reconstruction, pixel values ​​are adjusted based on the boundary pixels of adjacent blocks to ensure that non-critical parts of the image match the surrounding environment as closely as possible, thus guaranteeing overall image quality.

[0028] By employing differentiated compression, layered transmission, and channel adaptive strategies for critical and non-critical blocks, the efficiency of image transmission is improved, while the security and integrity of critical image information are ensured. Especially in environments with unstable channel conditions, it effectively reduces the impact of bit errors on image quality and enhances the reliability of image transmission.

[0029] In one possible implementation, pixels within each image block are first processed using an edge detection operator to calculate the gradient magnitude of each pixel. The core of this step is to quantify the intensity of edges in the image using gradient magnitude, thereby identifying edge regions within the image. Edge regions typically represent important information in the image, such as object boundaries and details; therefore, their extraction is crucial for subsequent image classification.

[0030] Furthermore, for each block, the number of pixels whose gradient magnitude exceeds a preset threshold is counted, and the proportion of these pixels to the total number of pixels in that block is calculated; this proportion is the edge density value. In this way, the edge information intensity of each image block can be determined, thus distinguishing important and unimportant regions in the image. The setting of the gradient magnitude threshold relies on the analysis of typical man-made feature sample images, especially images containing roads and buildings. Edges in these typical images are often our primary concern; therefore, by calculating the gradient magnitude distribution of the smallest identifiable edges in these images, the median of the distribution is selected as the initial threshold for the gradient magnitude, thereby ensuring accurate edge identification.

[0031] In extracting texture complexity features, a frequency domain transformation is first performed on each image block to separate the low-frequency and high-frequency components. Low-frequency components typically reflect the overall structure of the image, while high-frequency components reveal detailed information. By calculating the proportion of high-frequency component energy to the total image energy, a baseline value for texture complexity can be obtained, reflecting the image's level of detail. To better adapt to different image qualities and imaging conditions, this baseline value needs to be normalized and calibrated according to the image's imaging quality parameters to obtain a final texture complexity value.

[0032] This method of extracting edge density and texture complexity features helps to effectively identify important information regions in images and provides a basis for subsequent image transmission. By accurately distinguishing between critical and non-critical regions, it not only improves the efficiency of image transmission but also ensures the priority protection of critical data during transmission. This method can play an important role in image processing, remote sensing data analysis, and other fields in various industries, especially in situations with poor channel conditions or limited bandwidth, where it can maximize image quality.

[0033] In one possible implementation, the step of dynamic classification of image importance aims to adjust the transmission priority of the image in real time based on the edge density and texture complexity characteristics of the image blocks. This dynamic classification method can more accurately identify parts of the image with high transmission quality requirements (such as critical feature blocks), thereby ensuring better protection of important areas during transmission and reducing bandwidth consumption in non-critical areas.

[0034] First, the edge density threshold and texture complexity threshold are dynamically calibrated during the pre-transmission phase. The transmitter first sends a set of test image patches containing various land features, including typical land feature types and covering various detailed areas in the image. The receiver reconstructs the received image and generates a quality score based on the edge sharpness of artificial land features in the reconstructed image. The evaluation criterion for edge sharpness is based on the recovery effect of details and edge regions in the image; sharper edges indicate higher image reconstruction quality.

[0035] When the quality score calculated by the receiver is lower than the preset standard, the system will dynamically adjust the thresholds to improve the classification results. Specifically, the edge density threshold will increase by a fixed step size, while the texture complexity threshold will decrease by a fixed step size. This adjustment helps to optimize the classification criteria so that more important blocks during transmission (such as key feature blocks like roads and buildings) can be accurately identified and prioritized.

[0036] This process is repeated until the received image quality score meets the set requirements. Finally, if the edge density value of an image patch exceeds the edge density threshold and the texture complexity value is less than the texture complexity threshold, then that image patch will be classified as a critical feature patch. This classification strategy ensures that critical regions are better protected during image transmission, for example, by using error correction coding with a lower compression rate and higher protection strength, thus minimizing information loss during image transmission.

[0037] This dynamic calibration mechanism enables the system to adjust its transmission strategy in real time based on different image content and channel conditions, improving image transmission efficiency and ensuring image quality. Under conditions of significant channel variation, it ensures that important parts of the image are prioritized for transmission, thereby optimizing the transmission process and improving the accuracy of image reconstruction.

[0038] In one possible implementation, firstly, a quantization step size reduction mode is adopted for key feature blocks. Key features in the image, such as buildings and roads, usually contain important details and edge information, so this information needs to be preserved as much as possible during compression. In this process, the system dynamically selects a quantization step size by analyzing the gradient magnitude distribution histogram within the image block, so that more than 90% of the edge information can be preserved during compression. The gradient magnitude distribution reflects the edge intensity of different regions in the image, and edge regions are usually crucial to the detail and recognizability of the image. Therefore, adjusting the quantization step size allows these regions to be preserved with higher fidelity during compression, ensuring that the key content of the image is accurately transmitted.

[0039] For non-critical blocks, spatial downsampling compression is employed. Non-critical blocks are typically regions with less detail in the image, such as the background or simple texture areas. To reduce bandwidth consumption, these regions are compressed using spatial downsampling. Specifically, based on the texture complexity value of each block, the system selects an appropriate downsampling rate from a pre-defined mapping table. Higher texture complexity values ​​indicate richer details in the region, thus requiring higher resolution and a lower compression ratio; conversely, regions with lower texture complexity values ​​can use a higher compression ratio, reducing data volume by lowering resolution. This mapping table was established through offline experiments based on the relationship between image structural similarity for different texture types and the downsampling rate, ensuring compression efficiency while minimizing detail loss.

[0040] Furthermore, to improve the reliability of image transmission, high-strength error correction coding in layered encapsulation is employed. Based on historical channel bit error rate data, the system dynamically calculates the error correction capability of the error correction coding. Specifically, it calculates the average bit error rate of the most recent transmissions and automatically adjusts the amount of redundant check data based on the ratio of this average to the maximum tolerable bit error rate. This means that when the transmission channel quality is poor and the bit error rate is high, the system automatically increases redundant data to ensure data integrity; conversely, when the channel quality is good, it reduces redundant data, thereby optimizing transmission efficiency.

[0041] This differentiated compression method can flexibly adjust the compression strategy according to the importance of image regions and changes in the transmission environment. By carefully preserving key regions and efficiently compressing non-critical regions, it can minimize data volume and improve transmission efficiency while ensuring image quality. Furthermore, the dynamic error correction coding adjustment mechanism can maintain data reliability under different transmission conditions, ensuring the accuracy and stability of image transmission.

[0042] In one possible implementation, the calculation logic of the bandwidth allocation factor is designed to achieve dynamic bandwidth management during image transmission, thereby optimizing the balance between image quality and transmission efficiency. This method flexibly adjusts the allocation of bandwidth resources by considering the bit error rate, scene requirements, and the proportion of non-critical blocks, ensuring transmission quality in critical areas while appropriately compressing non-critical areas to reduce bandwidth requirements.

[0043] First, the maximum tolerable bit error rate (BER) setting depends on the application scenario at the receiver. In manual interpretation scenarios, due to the high requirements for image clarity, a lower BER value is typically set, meaning that even a small error will affect the image's recognition accuracy. In machine recognition scenarios, however, images have higher fault tolerance and can tolerate a certain degree of error, thus a higher BER value is set. This differentiated setting helps adjust the BER tolerance level according to actual application needs.

[0044] The proportion of non-critical blocks is then determined based on the ratio of the number of non-critical blocks within the current image frame to the total number of blocks. Non-critical blocks typically refer to parts of the image with less detail and lower requirements for transmission quality. By calculating this ratio, the proportion of areas in the current image frame that do not require priority protection can be determined.

[0045] The bandwidth allocation factor is closely related to the current bit error rate (BER) and the maximum tolerable BER. When the current BER is close to the maximum tolerable BER, the bandwidth allocation factor will be lower, meaning the system will reduce the transmission volume in non-critical areas. Conversely, if the BER is lower, the bandwidth allocation factor will be higher, allowing more bandwidth to be allocated to non-critical areas. Furthermore, the bandwidth allocation factor is also proportional to the proportion of non-critical blocks; the higher the proportion of non-critical blocks, the larger the bandwidth allocation factor, indicating that non-critical areas can receive more bandwidth resources for transmission.

[0046] Dynamic control of the transmission volume of non-critical data layers is achieved through a bandwidth allocation factor. When the bandwidth allocation factor falls below a set threshold, the system proportionally reduces the number of bytes transmitted in the non-critical data layers based on the value of the bandwidth allocation factor. This dynamic adjustment mechanism ensures that, when bandwidth resources are scarce or the bit error rate is high, priority is given to ensuring the transmission quality of critical areas, while appropriately compressing the data volume of non-critical areas, thereby effectively utilizing bandwidth resources.

[0047] This bandwidth allocation strategy can flexibly adjust bandwidth allocation based on real-time transmission quality and application scenario requirements, ensuring high-quality reconstruction of critical areas while efficiently utilizing bandwidth resources and avoiding bandwidth waste during image transmission. Furthermore, dynamically controlling the transmission volume of non-critical data layers prevents the transmission of excessive irrelevant data when channel quality is poor, thereby improving the robustness and efficiency of the entire image transmission system.

[0048] In one possible implementation, the system first locates key feature patches surrounding the damaged patch. These key feature patches typically contain important structural or edge information in the image, and these areas are crucial to the image's sharpness and recognizability. By extracting the boundary pixels between these key patches and the damaged patch, necessary boundary information can be provided for subsequent reconstruction. The values ​​of these boundary pixels will serve as constraints to recover image details from the damaged area.

[0049] Furthermore, pixel gradient field constraints for the damaged blocks are constructed using boundary pixel values. The pixel gradient field is a measure of pixel intensity changes in an image, capable of capturing edge information. Ensuring a smooth transition between the reconstructed region and its surrounding boundaries is crucial in the reconstruction of damaged areas. To achieve this, the system uses an iterative optimization algorithm to solve for a pixel matrix that conforms to the gradient field constraints. The optimization goal is to make the transition between pixel values ​​in the reconstructed region and boundary pixels more natural through the iterative process, avoiding overly abrupt edges.

[0050] This optimization algorithm employs the Poisson reconstruction algorithm, a classic image reconstruction method particularly suitable for smooth image transitions. The Poisson reconstruction algorithm uses boundary pixel values ​​as Dirichlet boundary conditions to ensure the correct transmission of boundary information. Simultaneously, the algorithm incorporates the original texture complexity values ​​of the damaged blocks to set the gradient field intensity benchmark for the reconstructed region. In this way, the system can reasonably set the details of the reconstructed region based on the texture features of the original image, avoiding the loss of texture details.

[0051] Finally, during the reconstruction process, the system performs edge enhancement processing on the reconstruction results. First, it detects the edge density feature distribution of blocks in the original image and adaptively increases the gradient magnitude in these edge regions. In this way, the edge sharpness in the image is enhanced, making the restored image more realistic and natural in terms of detail and texture.

[0052] This method effectively recovers damaged image regions, especially in cases of poor image quality or compression impairment. By combining gradient field constraints and the Poisson reconstruction algorithm, the reconstructed result smoothly transitions with the surrounding area, avoiding unnatural boundaries. Simultaneously, edge enhancement further improves the visual quality of the image, particularly in the rendering of details and textures, ensuring image sharpness and recognizability. This method offers significant advantages in improving image transmission quality, especially in environments with limited bandwidth or unstable channels.

[0053] In one possible implementation, the "bit error rate exceeds the threshold" criterion in step 4 is determined by a method based on a time window and the continuous occurrence of the bit error rate exceeding a set proportion. This criterion helps to accurately determine the transmission quality of the channel and take corresponding control measures when poor channel quality occurs, ensuring the stability and reliability of image transmission.

[0054] First, the condition that the percentage of times the bit error rate (BER) consistently exceeds a threshold is greater than a set proportion is used to determine whether the channel is in a poor state. If the BER repeatedly exceeds the set threshold within a certain time window, it indicates poor channel quality, which may affect the transmission quality of images. By calculating the number of times the BER exceeds the threshold within a certain time window and comparing it with the set proportion, the trend of declining channel quality can be captured more accurately, allowing for timely measures to optimize the transmission process.

[0055] Secondly, the channel coherence time is determined by calculating the autocorrelation function of the bit error rate sequence. The autocorrelation function is a tool for measuring the similarity of signals under different time delays. By analyzing the time delay in which the autocorrelation function of the bit error rate sequence decays to a set coefficient, the channel coherence time can be determined. The channel coherence time reflects the stability of channel quality over a period of time. The longer the channel coherence time, the more stable the signal quality and the smaller the fluctuation in bit error rate. Conversely, if the channel coherence time is short, it indicates that there are large fluctuations in channel quality, frequent fluctuations in bit error rate, which may lead to transmission instability.

[0056] This judgment criterion allows for flexible handling of channel quality fluctuations in actual transmission. In dynamic transmission environments, channel quality may change due to external interference and other factors, and simple real-time bit error rate (BER) determination may not accurately reflect channel stability. By combining the analysis of BER changes within a time window with channel coherence time, the persistence of channel quality can be more effectively judged, thus providing a strong basis for subsequent bandwidth allocation and data transmission strategy adjustments. Furthermore, this judgment criterion can prevent the error recovery mechanism from being prematurely triggered by occasional BER fluctuations, reducing the risk of misjudgment and improving the robustness and efficiency of the entire transmission system.

[0057] In one possible implementation, the retransmission triggering rule in step 5 primarily determines whether a retransmission is necessary based on the decoding results of the key feature blocks and their edge density values. This rule design helps optimize bandwidth utilization and ensures that the quality of core features is effectively protected during transmission.

[0058] Specifically, retransmission is only initiated when decoding of critical feature chunks fails. Critical feature chunks typically contain the most important parts of an image, such as areas with high identification value or high resolution requirements. When these chunks cannot be correctly decoded due to transmission errors, the system considers whether retransmission is necessary. The criterion for this decision is whether the edge density value of the chunk exceeds the retransmission protection threshold. Edge density reflects the richness of edge information within the chunk; generally, edge regions in an image are crucial to its visual appeal and recognizability. A high edge density in a region indicates the presence of important details, and retransmission can effectively restore the quality of that region if decoding fails.

[0059] Furthermore, the retransmission protection threshold is dynamically adjusted based on current bandwidth conditions. When bandwidth is sufficient, the system lowers the threshold to expand the protection range, allowing more critical feature blocks with high edge density to be retransmitted in the event of decoding failure. This adjustment ensures that important image information is adequately protected even under good network conditions. Conversely, when bandwidth is limited, the system raises the threshold, focusing on the most critical feature areas to avoid excessive consumption of network resources due to bandwidth constraints. In this way, the system prioritizes the protection of the most important areas in the image, ensuring that the transmitted image quality does not significantly degrade under limited bandwidth conditions.

[0060] This dynamic threshold adjustment method can flexibly adapt to different transmission scenarios based on actual bandwidth conditions. Its benefits are twofold: firstly, it maximizes image transmission quality, especially ensuring more critical areas are protected when bandwidth is ample; secondly, when bandwidth is limited, it ensures efficient bandwidth utilization and guarantees the quality of core features by increasing the retransmission protection threshold. This strategy optimizes bandwidth utilization while ensuring that critical image content is not affected by transmission errors.

[0061] In one possible implementation, the frame header in step 3 includes a key technical feature—an error correction coding type identifier. This identifier helps the receiver select an appropriate decoding strategy based on different coding types. Specifically, the receiver selects a high-protection-strength or low-protection-strength decoding algorithm based on this identifier to ensure that image data can be decoded efficiently and accurately under various channel conditions.

[0062] When high-strength error correction coding is used in transmission, the receiving end employs algebraic error correction-based decoding algorithms. Algebraic error correction algorithms, by utilizing algebraic structures such as polynomial and finite field operations, can effectively correct errors occurring during transmission, making them particularly suitable for environments with high bit error rates. These algorithms possess strong error recovery capabilities, providing good protection even under poor channel conditions, ensuring that image data can be recovered and correctly decoded at the receiving end.

[0063] In situations with favorable channel conditions and low bit error rates, low-protection-strength error correction coding employs probabilistic soft-decision decoding algorithms. Soft-decision decoding analyzes the probability information of each bit during transmission, rather than simply relying on hard-decision "0" or "1", thus more accurately recovering the original data. In low-bit-error-rate environments, soft-decision decoding can utilize channel resources more efficiently, reduce computational overhead, and decrease bandwidth requirements, thereby improving overall transmission efficiency.

[0064] During actual transmission, when the channel's bit error rate increases, the receiver dynamically switches to a decoding strategy with higher protection strength. This strategy's priority adjustment is based on real-time channel conditions, ensuring that the system automatically employs a stronger error correction protection mechanism when channel quality deteriorates, thereby guaranteeing accurate data transmission. Through this dynamic switching mechanism, the transmission system can adapt to different network conditions, ensuring the stability and reliability of image transmission.

[0065] This decoding strategy selection and dynamic switching mechanism can intelligently adjust the decoding strength according to real-time changes in the channel. When the channel is good, using a low-protection-strength encoding and decoding strategy helps reduce resource consumption and improve transmission efficiency; while when the channel is poor, it automatically switches to a high-protection-strength error correction algorithm, effectively improving transmission robustness and error recovery capabilities. Through this flexible adaptability, the system can maintain high image transmission quality in different network environments while improving bandwidth utilization.

[0066] Accordingly, embodiments of the present invention also provide a remote image transmission system based on a compression algorithm, including a memory configured to store instructions, a processor configured to retrieve the instructions from the memory, and capable of implementing any of the remote image transmission methods based on a compression algorithm described in the embodiments of the present invention when executing the instructions.

[0067] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0068] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for remote image transmission based on a compression algorithm, characterized in that, Includes the following steps: Step 1: Image segmentation and feature extraction: Divide the original remote sensing image into multiple image segments and extract the edge density features and texture complexity features of each image segment; Step 2: Dynamic classification of block importance: Based on the combination relationship between edge density features and texture complexity features, the image blocks are classified into key feature blocks and non-key blocks; Step 3: Differentiated compression and layered packaging: The critical feature blocks are processed with low compression intensity and high protection intensity error correction coding is added. The non-critical blocks are processed with high compression intensity and low protection intensity error correction coding is added. They are then encapsulated into a transmission frame containing a frame header, a critical data layer and a non-critical data layer. Step 4: Channel Adaptive Transmission Control The transmission strategy is dynamically adjusted based on the real-time channel bit error rate. When the bit error rate exceeds the threshold, the transmission of non-critical data layers is suspended and non-critical blocks are migrated to the critical data layer with high compression intensity. The bandwidth allocation factor is calculated based on the bit error rate and the proportion of non-critical blocks, and the amount of non-critical data layer transmission is dynamically controlled. Step 5: Layered reconstruction at the receiving end: Prioritize error correction decoding and retransmission of critical data layers, discard blocks that fail to decode non-critical data layers, and reconstruct damaged non-critical blocks using boundary information of adjacent critical feature blocks.

2. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, Step 1, edge density feature extraction, includes: The gradient magnitude of each pixel within an image block is calculated using an edge detection operator; The number of pixels whose gradient magnitude exceeds the gradient magnitude threshold is counted, and the proportion of these pixels to the total number of pixels in the block is calculated as the edge density value. The gradient magnitude threshold is determined by acquiring typical artificial feature sample images containing roads and buildings, calculating the gradient magnitude distribution of the smallest identifiable edge in all samples, and taking the median of the distribution as the initial threshold. The extraction of the texture complexity features includes: Perform frequency domain transformation on image blocks to separate low-frequency and high-frequency components; The proportion of high-frequency component energy to the total energy of the block is calculated as the baseline value for texture complexity. The baseline value is normalized and calibrated based on the image imaging quality parameters to generate the final texture complexity value.

3. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The specific implementation of the dynamic classification of block importance in step 2 is as follows: Set edge density thresholds and texture complexity thresholds, which are dynamically calibrated during the transmission preamble stage: a: The sending end sends test image blocks containing multiple ground features; b: The receiving end generates a quality score based on the edge sharpness of artificial features in the reconstructed image; c: When the quality score is lower than the set requirement, increase the edge density threshold or decrease the texture complexity threshold by a fixed step size; d: Repeat steps a-c until the quality score meets the standard; If the edge density value of an image block is greater than the edge density threshold and the texture complexity value is less than the texture complexity threshold, it is classified as a key feature block.

4. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The specific operations of differential compression in step 3 include: A quantization step size reduction mode is adopted for key features: based on the gradient magnitude distribution histogram within the block, the quantization step size adjustment range that retains more than 90% of the edge information is dynamically selected. Spatial downsampling compression is applied to non-critical blocks: the downsampling rate is selected from a preset mapping table based on the texture complexity value. The higher the texture complexity value, the lower the downsampling rate. The mapping table is established by determining the correspondence between the downsampling rate of different texture types and the image structure similarity through offline experiments. The error correction capability of the high protection strength error correction code in the layered encapsulation is dynamically calculated based on historical channel bit error rate data. Specifically, the average bit error rate of the most recent N transmissions is calculated, and the amount of redundant check data is increased according to the ratio of this average value to the maximum tolerable bit error rate.

5. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The calculation logic for the bandwidth allocation factor in step 4 is as follows: The maximum tolerance bit error rate is set according to the application scenario of the receiving end. A low tolerance value is set for manual interpretation scenarios, and a high tolerance value is set for machine recognition scenarios. The proportion of non-critical blocks is the ratio of the number of non-critical blocks in the current frame to the total number of blocks; The bandwidth allocation factor is proportional to (1 - current bit error rate / maximum tolerable bit error rate) and proportional to the proportion of non-critical blocks; The operation of dynamically controlling the transmission volume of non-critical data layers is as follows: when the bandwidth allocation factor is lower than the set threshold, the number of bytes transmitted by non-critical data layers is reduced proportionally according to the factor value.

6. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The methods for reconstructing damaged non-critical blocks in step 5 include: Locate all key feature blocks adjacent to the damaged block and extract the boundary pixels that border the damaged block; Construct pixel gradient field constraints for damaged blocks based on boundary pixel values; The pixel matrix that meets the gradient field constraints is solved by an iterative optimization algorithm. The optimization objective is to make the transition between the reconstructed region and the boundary pixels smooth. Edge enhancement processing is performed on the reconstruction results: the edge density feature distribution of the original blocks is detected, and the gradient magnitude is adaptively increased at the corresponding positions; The iterative optimization algorithm is the Poisson reconstruction algorithm, specifically including: Dirichlet boundary conditions are defined using boundary pixel values. The gradient field intensity benchmark for the reconstructed region is set based on the original texture complexity value of the damaged block. The pixel values ​​are solved using the discrete Poisson equation, and the iteration terminates when the rate of change of structural similarity between two adjacent reconstruction results is less than the convergence threshold.

7. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The condition for determining "bit error rate exceeds threshold" in step 4 is as follows: Within a time window of length equal to the channel coherence time, the percentage of times the bit error rate continuously exceeds the threshold is greater than a set proportion; The channel coherence time is determined by calculating the time delay of the autocorrelation function of the bit error rate sequence decaying to a set coefficient.

8. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, The retransmission triggering rule in step 5 is as follows: Retransmission is initiated only when the decoding of critical feature blocks fails and its edge density value is greater than the retransmission protection threshold. The retransmission protection threshold is dynamically adjusted based on the available bandwidth: when bandwidth is sufficient, the threshold is lowered to expand the protection range, and when bandwidth is tight, the threshold is raised to focus on core ground features.

9. The remote image transmission method based on a compression algorithm as described in claim 1, characterized in that, In step 3, the frame header also contains an error correction coding type identifier, and the receiver selects the corresponding decoding strategy based on this identifier: High-protection-strength error correction coding employs a decoding algorithm based on algebraic error correction; Low protection strength error correction coding employs a probabilistic soft-decision-based decoding algorithm; The priority of the decoding strategy selection dynamically switches to a high-protection-strength decoding algorithm when the channel bit error rate increases.

10. A remote image transmission system based on a compression algorithm, characterized in that, The system includes a memory configured to store instructions, a processor configured to retrieve the instructions from the memory, and, when executing the instructions, to implement a remote image transmission method based on a compression algorithm as described in any one of claims 1-9.

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