Satellite image optimization transmission method based on OPENCV image compression technology

Abstract

The invention provides a satellite image optimization transmission method based on an OPENCV picture compression technology, and belongs to the technical field of satellite communication. The balance problem between satellite signal quality and image data transmission is solved; comprising the following steps: S1, acquiring an image to be transmitted, and starting a satellite signal quality detection module according to a signal acquired by the image; s2, a satellite signal quality detection module detects the current satellite signal quality; s3, the compression ratio of the image to be transmitted is automatically adjusted according to a self-adaptive image compression parameter adjustment strategy, compression of the transmitted image is achieved, and the self-adaptive image compression parameter adjustment strategy adjusts the corresponding image compression ratio under different network conditions based on satellite signal quality; s4, the compressed image is transmitted to a data sending module for data transmission; the method is applied to satellite data transmission optimization.

Description

Technical Field

[0001] The invention provides a satellite image optimization transmission method based on OPENCV picture compression technology, belonging to the technical field of satellite communication. Background Art

[0002] With the development of science and technology, satellite communication technology has become an important part of modern communication, especially in the fields of remote monitoring, disaster warning, scientific research, etc. However, due to the limited bandwidth of satellite transmission, the transmission of large amounts of data (such as photos, videos, etc.) has become a challenge. At the same time, image processing technology is also constantly developing. Among them, OPENCV is an open source computer vision library that provides a large number of image processing algorithms that can be used for applications such as image compression, target detection, and face recognition. In addition, network bandwidth optimization technology is also a current research hotspot, aiming to improve the utilization of network bandwidth, reduce network congestion, and improve user experience.

[0003] In order to solve the problem of limited satellite transmission bandwidth, existing technologies mainly reduce the amount of data transmitted by compressing image data. For example, JPEG is a common image compression format that can significantly reduce the size of the image, thereby reducing the bandwidth required for transmission. In addition, there are some image compression methods based on deep learning, which can automatically learn the compression parameters of the image by training neural networks to achieve higher compression efficiency.

[0004] Although existing image compression technologies can solve the problem of limited satellite transmission bandwidth to a certain extent, there are still some problems and shortcomings. First, existing compression methods often require high computational complexity, which is a challenge for embedded devices with limited computing power. Second, existing compression methods may lose a certain amount of image quality, which is unacceptable for some applications (such as medical imaging, remote sensing imaging, etc.). Finally, existing compression methods often cannot adaptively adjust compression parameters according to network conditions, which may result in long transmission time when network conditions are poor, or low transmission efficiency when network conditions are good. Summary of the invention

[0005] In order to overcome the deficiencies in the prior art, the present invention proposes a satellite image optimization transmission method based on OPENCV image compression technology, which can mainly solve the following problems: 1) How to achieve efficient image compression on embedded devices with limited computing power to reduce the amount of transmitted data; 2) How to improve image compression efficiency while ensuring image quality; 3) How to adaptively adjust image compression parameters according to network conditions to improve transmission efficiency, reduce transmission time and other technical issues.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a satellite image optimization transmission method based on OPENCV image compression technology, comprising the following steps: S1: Acquire the image to be transmitted, and start the satellite signal quality detection module according to the signal acquired from the image; S2: The satellite signal quality detection module detects the current satellite signal quality; S3: Automatically adjusting the compression ratio of the image to be transmitted according to an adaptive image compression parameter adjustment strategy to achieve compression of the transmitted image, wherein the adaptive image compression parameter adjustment strategy adjusts the corresponding image compression ratio under different network conditions based on satellite signal quality; S4: Transmit the compressed image to the data sending module for data transmission.

[0007] The satellite signal quality detection module in step S1 monitors the current network status in real time and selects a corresponding compression strategy according to the network status.

[0008] The compression strategy includes the following aspects: Quality adjustment: reduce image quality when bandwidth is low; Resolution adjustment: reduce the image resolution when bandwidth is very limited; Frame rate adjustment: In video transmission, the frame rate is adjusted according to the bandwidth to balance the smoothness and clarity; Format selection: Choose PNG when lossless compression is required, and choose JPEG or WEBP when high compression is required.

[0009] The adaptive image compression parameter adjustment strategy in step S3 utilizes the relevant functions and algorithms in OPENCV to remove redundant information in the image to reduce the size of the image data.

[0010] When compressing an image, an appropriate quality factor is determined based on the specific properties of the image to determine the compression quality.

[0011] The characteristic attributes of an image include content, resolution, and intended use, and the quality factors of an image include color complexity, texture complexity, and brightness distribution.

[0012] The parallel processing method is used to decompose the image compression processing task into multiple subtasks, and the subtasks are assigned to different processing threads for simultaneous execution.

[0013] In the adaptive image compression parameter adjustment strategy, large-size images are divided into small blocks for transmission, and then merged after receiving at the receiving end.

[0014] During the image transmission process, the transmission order of images is determined by sorting the images according to their priorities.

[0015] The priority of an image is defined by image importance and urgency.

[0016] The beneficial effects of the present invention compared with the prior art are as follows: 1. Optimize compression efficiency: By optimizing OPENCV image compression technology, the present invention can more effectively reduce the redundancy of transmitted information and improve the efficiency of image compression, thereby further reducing the bandwidth required for transmission while ensuring image quality.

[0017] 2. Adaptive adjustment: The image compression technology of the present invention can adaptively adjust the compression parameters according to the network conditions, avoiding the problem of long transmission time when the network conditions are poor, or low transmission efficiency when the network conditions are good, thereby improving the stability and efficiency of transmission.

[0018] 3. Utilize the limited bandwidth of RD to realize the transmission of image data, and provide data link guarantee for emergency communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 This is a schematic diagram of data transmission according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of data transmission according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of data transmission according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0020] like Figure 1-3 As shown, the present invention provides a satellite image optimization transmission method based on OPENCV image compression technology, which mainly includes the following steps: S1: Image acquisition; S2: Satellite signal quality detection; S3: Image compression; S4: Data sending.

[0021] The purpose of the satellite signal quality detection step is to respond to different network conditions more flexibly, and based on the satellite signal quality, an adaptive image compression parameter adjustment strategy is proposed. Specifically, the system will monitor the current network status in real time, including key indicators such as bandwidth, delay, and packet loss rate, and dynamically adjust the image compression parameters based on these indicators. Specifically, according to the network status, a set of compression strategies are first defined, such as quality adjustment: reduce image quality when bandwidth is low; resolution adjustment: reduce image resolution when bandwidth is very limited; frame rate adjustment: in video transmission, adjust the frame rate according to bandwidth to balance fluency and clarity; format selection: select PNG when lossless compression is required, and select JPEG or WEBP when high compression rate is required.

[0022] When the network is in good condition, the system will choose a lower compression ratio for compression, or even no compression, to ensure that the receiving end can receive higher quality image data. On the contrary, when the network is in poor condition, such as limited bandwidth, high latency, or severe packet loss, the system will increase the compression ratio, thereby further reducing the amount of data to be transmitted and increasing the transmission speed. This adaptive image compression parameter adjustment strategy enables the system to find the best balance between image quality and transmission efficiency in different network environments.

[0023] For the compression of images, the present invention uses OPENCV, a powerful computer vision library, to compress the image data to be transmitted. OPENCV (Open Source Computer Vision Library) is an open source project that integrates a large number of image processing and computer vision functions. It has efficient algorithms and powerful image processing capabilities, and is particularly suitable for preprocessing and optimizing image data. When compressing images, the size of image data is effectively reduced by removing redundant information in the image using the relevant functions and algorithms in OPENCV. This not only saves storage space, but more importantly, it can significantly improve the efficiency of data transmission in bandwidth-limited scenarios such as satellite communications. When compressing images, based on the specific attributes of the image (such as content, resolution, intended use, etc.), determine the appropriate quality factor of the image (such as color complexity, texture complexity, brightness distribution, etc.) to determine the compression quality.

[0024] In order to improve the efficiency of image compression processing, the present invention also adopts a parallel processing method. Under the modern multi-core processor architecture, by decomposing the image compression processing task into multiple subtasks and assigning them to different processing threads for simultaneous execution, the overall processing speed can be significantly improved.

[0025] In addition, for large-size images, you can consider splitting them into multiple small pieces for transmission and merging them after receiving them. This allows for more flexible management of bandwidth usage during transmission. At the same time, images can be prioritized based on their content and their importance and urgency. Important images can be transmitted first to ensure timely delivery of critical information.

[0026] Specifically, the acquired original image can be divided into multiple regions, and each region is assigned to an independent processing thread for compression processing. This parallel processing method can not only fully utilize the computing power of multi-core processors, but also greatly reduce the overall compression processing time. Especially when processing large-size, high-resolution images, the advantages of parallel processing will be more obvious.

[0027] During the image transmission process, the efficient transmission protocol of satellite communication is used to ensure the stability and efficiency of image data during transmission. Error detection and correction mechanisms are added during the transmission process to reduce the impact of data transmission errors on image quality.

[0028] The present invention will be further described below based on specific embodiments.

[0029] Example 1 The process of RD terminal system processing is as follows Figure 1 As shown, the system starts the RD (Beidou Satellite Short Message System) satellite signal quality detection module through the image acquisition module, and temporarily stores the acquired images. The RD satellite signal quality detection module detects the quality of RD satellite search and quantifies the signal quality into three levels: high, medium, and low. The better the signal quality, the higher the corresponding level. The result is sent to the image compression algorithm engine, and the image parameter adjustment level is determined according to the signal quality. The better the signal quality, the higher the compression ratio. The image compression algorithm engine compresses the temporarily stored image according to the signal quality, and passes the processed results to the RD sending module. The RD sending module sends the compressed image in packets according to the user's configuration. The specific steps are: 1) The RD terminal system starts the image acquisition module (camera component) according to external conditions (customer request, etc.). After successful startup, the image acquisition module generates an instruction to start the RD signal quality detection module. At the same time, the image information is temporarily stored in the device storage module.

[0030] 2) The RD satellite signal quality detection module quantifies the RD beam information output by the RD module and inputs the result into the image compression algorithm engine. The image compression algorithm engine determines the compression ratio based on the signal quality.

[0031] 3) The image compression algorithm engine reads the stored photos, divides the pictures into several parts (the number of parts is determined by the number of cores of the embedded CPU), and sends them to different threads for compression processing. Each thread independently calls the image compression algorithm interface, adds the image compression result to the mark (for splicing after decoding) and writes it into the file.

[0032] 4) After compression is completed, the data is framed (according to the rules of RD packets) and transmitted to the RD sending module.

[0033] Example 2 The process of GS terminal system processing is as follows Figure 2 As shown, the specific steps are: 1) The system activates the signal quality detection module through an advanced sensor array, which is specifically designed to monitor the signal strength of the GS satellite. Once activated, the module immediately conducts a comprehensive signal quality assessment of the surrounding environment and temporarily stores the captured image data in a high-speed buffer.

[0034] GS satellite is not a satellite type or name with a clear direction, but a broader concept. In different contexts, GS satellite may refer to different types of satellites or satellite systems. The GS satellite signal quality detection module can receive signals from satellite navigation systems such as GPS, BDS (Beidou), GALILEO, and GLONASS.

[0035] Satellite signal quality assessment includes: measuring satellite signal stability, quantifying RD modules, etc., all of which are obtained through the terminal by the number of satellites, satellite signal strength, signal-to-noise ratio, baud rate, etc. to evaluate network quality, that is, transmission speed and efficiency.

[0036] 2) The GS satellite signal quality detection module can accurately measure the stability and strength of the GS signal and quantify these indicators into three signal quality levels: excellent, good, and medium. This evaluation result will be transmitted to the image optimization processing unit, which will dynamically adjust the image processing strategy according to the received signal quality level.

[0037] 3) In the image optimization processing unit, a highly intelligent image compression algorithm engine determines the best image compression strategy based on the signal quality level. When the signal quality is good, the engine will use a lower compression ratio to retain more image details; when the signal quality decreases, the engine will automatically increase the compression ratio to ensure that the image data can still be sent efficiently under limited transmission bandwidth.

[0038] 4) After compression, the image data will be divided into data packets that comply with the GS transmission protocol and transmitted through the GS sending module according to the preset sending strategy. During the whole process, the system can monitor the changes in signal quality in real time and dynamically adjust the image processing and transmission strategies to ensure efficient and stable image transmission in different environments.

[0039] Example 3 The system processes Figure 3 As shown, the specific steps are: 1) The system is started by a high-precision image acquisition module (integrated with camera and positioning functions), which is automatically activated when receiving user instructions or meeting specific conditions. Once activated, it immediately captures the image of the target scene and temporarily saves it in the built-in memory.

[0040] 2) The system will start a dedicated Beidou satellite signal quality assessment module, which will receive the Beidou satellite band, comprehensively analyze the Beidou satellite signal quality under the current environment, and divide it into three levels: excellent, good, and general. This assessment result is crucial for the subsequent image compression processing.

[0041] 3) Based on the signal quality evaluation results, the image compression engine will intelligently select appropriate compression parameters. When the signal quality is excellent, the engine will tend to use a lower compression rate to maximize the quality and details of the original image. When the signal quality is average or poor, the engine will automatically increase the compression rate to reduce the amount of data required for transmission, ensuring reliable image transmission in an unstable signal environment.

[0042] 4) After the compression process is completed, the image data will be accurately divided into multiple data packets, each of which meets the transmission standards of the Beidou satellite.

[0043] 5) These data packets will be sent out through the wireless transmission module according to the preset priority and order. The entire system can respond to changes in signal quality in real time and adjust image processing and transmission strategies accordingly to ensure that image data can be transmitted stably and efficiently in various environments.

[0044] The advantages of the present invention are as follows: First, the technical solution of the present invention can effectively reduce the redundancy of transmitted information and improve the efficiency of satellite transmission by optimizing the OPENCV image compression technology, so as to meet the needs of remote monitoring, disaster warning and other fields for large-scale data transmission. Secondly, the technical solution of the present invention can ensure the effectiveness of transmitted information, avoid the accuracy of data analysis affected by image quality loss, and meet the needs of scientific research, medical image transmission and other fields for data accuracy. Finally, the technical solution of the present invention can adaptively adjust the compression parameters according to the network conditions, improve the utilization rate of network bandwidth, and meet the needs of remote sensing image analysis and other fields for big data processing. Therefore, the present invention can be widely used in application fields such as remote monitoring, disaster warning, scientific research, medical image transmission, remote sensing image analysis, etc.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A satellite image optimization transmission method based on OPENCV image compression technology, characterized in that: The following steps are involved: S1: Acquire the image to be transmitted, and start the satellite signal quality detection module according to the signal acquired from the image; S2: The satellite signal quality detection module detects the current satellite signal quality; S3: Automatically adjusting the compression ratio of the image to be transmitted according to an adaptive image compression parameter adjustment strategy to achieve compression of the transmitted image, wherein the adaptive image compression parameter adjustment strategy adjusts the corresponding image compression ratio under different network conditions based on satellite signal quality; S4: Transmit the compressed image to the data sending module for data transmission.

2. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 1, characterized in that: The satellite signal quality detection module in step S1 monitors the current network status in real time and selects a corresponding compression strategy according to the network status.

3. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 2 is characterized in that: The compression strategy includes the following aspects: Quality adjustment: reduce image quality when bandwidth is low; Resolution adjustment: reduce the image resolution when bandwidth is very limited; Frame rate adjustment: In video transmission, the frame rate is adjusted according to the bandwidth to balance the smoothness and clarity; Format selection: Choose PNG when lossless compression is required, and choose JPEG or WEBP when high compression is required.

4. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 1 is characterized in that: The adaptive image compression parameter adjustment strategy in step S3 utilizes the relevant functions and algorithms in OPENCV to remove redundant information in the image to reduce the size of the image data.

5. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 4 is characterized in that: When compressing an image, an appropriate quality factor is determined based on the specific properties of the image to determine the compression quality.

6. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 5 is characterized in that: The characteristic attributes of an image include content, resolution, and intended use, and the quality factors of an image include color complexity, texture complexity, and brightness distribution.

7. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 1 is characterized in that: The parallel processing method is used to decompose the image compression processing task into multiple subtasks, and the subtasks are assigned to different processing threads for simultaneous execution.

8. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 4 is characterized in that: In the adaptive image compression parameter adjustment strategy, large-size images are divided into small blocks for transmission, and then merged after receiving at the receiving end.

9. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 8 is characterized in that: During the image transmission process, the transmission order of images is determined by sorting the images according to their priorities.

10. The satellite image optimization transmission method based on OPENCV image compression technology according to claim 9, characterized in that: The priority of an image is defined by image importance and urgency.

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