A Task-Oriented Image Compression Method, System and Device Assisted by Semantic Information

Abstract

The present application relates to the field of image compression technology. To address the issue of large amounts of image data to be transmitted and limited airborne communication bandwidth in drone communications, a semantic information-assisted task-oriented image compression method, system, and device are disclosed. The method comprises performing semantic segmentation on the original image based on a task knowledge base to obtain a number of task-related information units and background information units, wherein the task knowledge base includes task background, task name, task requirements, and task relevance scores; calculating the weight of each task-related information unit based on the task relevance score and data size of each task-related information unit; allocating bandwidth to each task-related information unit and background information unit based on the weight of each task-related information unit; and performing image compression on each task-related information unit and background information unit based on the bandwidth allocation result to obtain a multi-scale compressed image. This method enables high-quality image transmission under conditions of limited communication bandwidth.

Description

Technical Field

[0001] The present application relates to the field of image compression technology, and more specifically, to a semantic information-assisted task-oriented image compression method, system, and device. Background Art

[0002] Drones typically use aerial cameras and remote sensing systems to capture images of various scenes. During the aerial photography process, drones must forward image data in real time to the control center for analysis and monitoring. However, with the expansion of aerial coverage and the improvement of image quality and accuracy, the amount of image data has increased dramatically.

[0003] To address the limited bandwidth issues in drone communications, image compression algorithms suitable for drone communications have emerged. Real-time image compression significantly reduces data volume and lowers the bandwidth requirements for wireless communications. Traditional image compression algorithms, such as JPEG and JPEG2000, have reached performance bottlenecks due to their inability to capture deeper semantic information within images. Furthermore, the widespread adoption of high-resolution image sensors requires high compression ratios without compromising image reconstruction quality.

[0004] Traditional image compression methods typically aim to achieve pixel-level consistency without considering the performance of subsequent tasks. For example, in drone aerial photography, the images transmitted back are often used for intelligent tasks such as target detection and image classification. At high compression rates, traditional compression schemes can distort image structure and color, potentially leading to a sharp decline in the performance of subsequent tasks. Therefore, traditional image compression algorithms struggle to meet the processing requirements of drone images, posing a challenge to drone communications.

[0005] In recent years, with the development of artificial intelligence and computer hardware, semantic segmentation algorithms have been proposed. These algorithms can extract semantic information by analyzing various semantic features within an image, such as objects, background, and relationship information. Applying different compression rates to different regions of an image based on this semantic information can effectively preserve task-related semantic information within the image, achieve semantic-level image consistency, and effectively ensure the efficiency of subsequent tasks. Therefore, this approach has the potential to address the problem of high-quality image transmission under limited available communication bandwidth in unmanned aerial vehicle wireless communication systems. Consequently, researchers have proposed a series of image semantic communication methods based on intelligent algorithms such as deep learning and convolutional neural networks, which achieve high compression rates by extracting image semantic information.

[0006] However, this image semantic communication method based on intelligent algorithms still has the following defects: it cannot meet dynamically changing task requirements. For example, the specific requirements of the drone aerial photography task (the target of attention) may change with the aerial photography process, that is, the task requirements are dynamically changing, which may cause the image semantic communication method to be unable to correctly transmit information related to the new task requirements. Summary of the Invention

[0007] In order to solve the problem of limited bandwidth in UAV communications, the present invention provides a semantic information-assisted task-oriented image compression method, system and device, which improves the image compression rate while retaining the key semantics of the original image, and can correctly transmit information related to new task requirements when the task requirements are dynamically changing.

[0008] To achieve the above object, according to a first aspect of the present invention, a semantic information-assisted task-oriented image compression method is provided, the method comprising:

[0009] According to the task knowledge base, the original image is semantically segmented to obtain several task-related information units and background information units. The task knowledge base includes task background, task name, task requirements and task relevance score;

[0010] Calculate the weight of each task-related information unit based on the task relevance score and data size of each task-related information unit;

[0011] Allocate bandwidth to each task-related information unit and background information unit based on the weight of each task-related information unit;

[0012] According to the bandwidth allocation result, image compression is performed on each task-related information unit and background information unit to obtain a multi-scale compressed image.

[0013] Furthermore, bandwidth is allocated to each task-related information unit and background information unit based on the weight of each task-related information unit, including allocating a preset proportion of the total signal transmission bandwidth to the background information unit; and allocating the remaining proportion of the total signal transmission bandwidth other than the preset proportion to each task-related information unit based on the weight of each task-related information unit.

[0014] Furthermore, based on the weight of each task-related information unit, the remaining proportion of the total signal transmission bandwidth other than the preset proportion is allocated to each task-related information unit, including calculating the product of the weight of each task-related information unit and the remaining proportion of the total signal transmission bandwidth to obtain the bandwidth of each task-related information unit; when the bandwidth of each task-related information unit is not less than its data size, the bandwidth of the task-related information unit is kept unchanged; when the bandwidth of each task-related information unit is less than its data size, the difference between the data size of the task-related information unit and its bandwidth is allocated to the background information unit.

[0015] Furthermore, based on the task relevance score and data size of each task related information unit, the weight of each task related information unit is calculated, including calculating the product of the task relevance score and data size of each task related information unit; calculating the ratio of the product of each task related information unit to the sum of several products to obtain the weight of each task related information unit.

[0016] Furthermore, according to the task knowledge base, the original image is semantically segmented to obtain a number of task-related information units and background information units, including using an image semantic segmentation model to perform semantic segmentation on the original image according to the task requirements provided by the task knowledge base, and deconstructing the original image into a number of task-related information units and background information units.

[0017] Furthermore, the image semantic segmentation model is jointly composed of the Yolo-World algorithm and the Segment Anything Model algorithm. Through the image semantic segmentation model, according to the task requirements provided by the task knowledge base, the original image is semantically segmented, and the original image is deconstructed into several task-related information units and background information units, including using the domain knowledge base as a training set for the Yolo-World algorithm to train the Yolo-World algorithm, and the domain knowledge base includes image data belonging to different domains and annotated with category labels; through the trained Yolo-world algorithm, according to the task requirements provided by the task knowledge base, target detection is performed on the original image to obtain detection results, and the detection results include task-related target areas; through the Segment Anything Model algorithm, the target in the target area is segmented into independent information units to obtain several task-related information units; the part of the original image other than several task-related information units is used as the background information unit.

[0018] Furthermore, according to the bandwidth allocation result, image compression is performed on each task-related information unit and background information unit to obtain a multi-scale compressed image, including using an image compression algorithm to compress each task-related information unit and background information unit using different compression rates according to the bandwidth allocation result to obtain a multi-scale compressed image.

[0019] According to a second aspect of the present invention, there is also provided a semantic information-assisted task-oriented image compression system, comprising:

[0020] The semantic segmentation module is used to perform semantic segmentation on the original image according to the task knowledge base to obtain several task-related information units and background information units. The task knowledge base includes task background, task name, task requirements and task relevance score;

[0021] A weight calculation module, used to calculate the weight of each task-related information unit based on the task relevance score and data size of each task-related information unit;

[0022] A bandwidth allocation module is used to allocate bandwidth to each task-related information unit and background information unit according to the weight of each task-related information unit;

[0023] The image compression module is used to compress the image of each task-related information unit and the background information unit according to the bandwidth allocation result to obtain a multi-scale compressed image.

[0024] According to the third aspect of the present invention, a semantic information-assisted task-oriented image compression device is also provided, which includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any of the above methods.

[0025] According to a fourth aspect of the present invention, there is further provided a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0026] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0027] The present invention provides a semantic information-assisted task-oriented image compression method, which embeds task requirements in image compression by establishing a task knowledge base; semantically segmenting the original image according to the task requirements to obtain a plurality of task-related information units and background information units; calculating the weight of each task-related information unit according to the task relevance score and data size of each task-related information unit; allocating bandwidth to each task-related information unit and background information unit according to the weight of each task-related information unit, thereby realizing bandwidth allocation based on task relevance; and compressing each task-related information unit and background information unit according to the bandwidth allocation result to obtain a multi-scale compressed image, thereby improving the image compression rate while retaining the key semantics of the original image, achieving the purpose of realizing high-quality image transmission under conditions of limited communication bandwidth, and being able to correctly transmit information related to new task requirements even when the task requirements are dynamically changing. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A flowchart of a semantic information-assisted task-oriented image compression method provided in one embodiment of the present application;

[0030] Figure 2 A flowchart of a semantic information-assisted task-oriented image compression method provided in another embodiment of the present application;

[0031] Figure 3 A schematic diagram of the execution process of a semantic information-assisted task-oriented image compression method provided in an embodiment of the present application;

[0032] Figure 4 A schematic diagram of a task knowledge base provided in an embodiment of the present application;

[0033] Figure 5 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0035] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0036] like Figure 1 As shown, a semantic information-assisted task-oriented image compression method is provided. This method can be executed by a terminal or by a server communicating with the terminal via a network. The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, etc. The server may be a standalone server or a server cluster consisting of multiple servers. This method is described using the terminal as an example and includes the following steps:

[0037] Step 101 : semantically segment the original image according to the task knowledge base to obtain a number of task-related information units and background information units.

[0038] The task knowledge base includes task context, task name, task requirements, and a task relevance score. Task context refers to the scenario for the task, such as drone aerial photography or satellite remote sensing. Task requirements refer to the requirements corresponding to the task context and task name, such as urban traffic monitoring or behavioral assessment. The task relevance score is the score corresponding to the task requirements, which can be obtained through expert scoring, for example.

[0039] Exemplarily, the terminal performs semantic segmentation on the original image through an image semantic segmentation model according to the task requirements provided by the task knowledge base, and deconstructs the original image into several task-related information units and background information units.

[0040] In an embodiment in which an image semantic segmentation model is jointly composed of a Yolo-World algorithm and a Segment Anything Model algorithm, a domain knowledge base is used as a training set for the Yolo-World algorithm to train the Yolo-World algorithm, and the domain knowledge base includes image data belonging to different domains and annotated with category labels; the trained Yolo-world algorithm is used to perform target detection on the original image according to the task requirements provided by the task knowledge base to obtain a detection result, which includes a task-related target area; the Segment Anything Model algorithm is used to segment the target in the target area into independent information units to obtain a number of task-related information units; and the part of the original image other than the several task-related information units is used as a background information unit.

[0041] Step 102 : Calculate the weight of each task-related information unit according to the task-related information unit's task-related information score and data size.

[0042] Exemplarily, the terminal calculates the product of the task relevance score and the data size of each task related information unit; calculates the ratio of the product of each task related information unit to the sum of several products to obtain the weight of each task related information unit.

[0043] Step 103: Allocate bandwidth to each task-related information unit and background information unit according to the weight of each task-related information unit.

[0044] Exemplarily, the terminal allocates a preset proportion of the total signal transmission bandwidth to the background information unit; and allocates the remaining proportion of the total signal transmission bandwidth except the preset proportion to each task-related information unit according to the weight of each task-related information unit.

[0045] Taking into account the different data sizes of each task-related information unit, when the bandwidth allocated to each task-related information unit is less than the data size, the excess bandwidth needs to be fed back to the background information unit. Specifically, based on the weight of each task-related information unit, the remaining proportion of the total signal transmission bandwidth, excluding a preset proportion, is allocated to each task-related information unit. This includes calculating the product of the weight of each task-related information unit and the remaining proportion of the total signal transmission bandwidth, excluding the preset proportion, to obtain the bandwidth of each task-related information unit; if the bandwidth of each task-related information unit is not less than its data size, the bandwidth of the task-related information unit is maintained unchanged; if the bandwidth of each task-related information unit is less than its data size, the difference between the data size of the task-related information unit and its bandwidth is allocated to the background information unit.

[0046] Step 104 : performing image compression on each task-related information unit and background information unit according to the bandwidth allocation result to obtain a multi-scale compressed image.

[0047] The multi-scale compressed image is a compressed image with different compression rates in different regions based on semantic information.

[0048] Exemplarily, the terminal uses an image compression algorithm to compress each task-related information unit and background information unit using different compression rates according to the bandwidth allocation result to obtain a multi-scale compressed image.

[0049] In the above-mentioned semantic information-assisted task-oriented image compression method, task requirements are embedded in image compression by establishing a task knowledge base; according to the task requirements, the original image is semantically segmented to obtain several task-related information units and background information units; the weight of each task-related information unit is calculated according to the task relevance score and data size of each task-related information unit; according to the weight of each task-related information unit, bandwidth is allocated to each task-related information unit and background information unit, thereby realizing bandwidth allocation based on task relevance; according to the bandwidth allocation result, each task-related information unit and background information unit is compressed to obtain a multi-scale compressed image, thereby retaining the key semantics of the original image while improving the image compression rate, achieving the purpose of realizing high-quality image transmission under conditions of limited communication bandwidth, and being able to correctly transmit information related to new task requirements even when the task requirements are dynamically changing.

[0050] In one embodiment, Figure 2 As shown in the figure, for the UAV image transmission scenario with specific tasks, a semantic information-assisted task-oriented image compression method is provided, and a task knowledge base is established to implement an update mechanism to adapt to the dynamic changes of the task.

[0051] The task knowledge base consists of the UAV aerial photography task context, task name, task requirements, and task relevance score. The task requirements primarily provide guidance for image semantic segmentation, driving the decomposition of images into several task-related information units. The task relevance score provides information support for adaptive bandwidth allocation, primarily aiming to achieve bandwidth allocation guided by task relevance.

[0052] The domain knowledge base is the knowledge embedded in the model parameters during the training process of the image semantic segmentation algorithm. It mainly includes annotated image data and is used to implement the training and optimization of the image semantic segmentation algorithm.

[0053] The image semantic segmentation module is composed of the Yolo-World algorithm and the Segment Anything Model (SAM) algorithm. Yolo-World is used to detect targets under task requirements (textual prompt words) and obtain the task-related target areas contained in the image. SAM is then used to segment the targets in the target areas into independent information units, and finally deconstruct the image into background information units and several task-related information units.

[0054] The adaptive bandwidth allocation module combines the task relevance scores and data sizes of task-related information units to calculate the weights of each task-related information unit, as shown in formula (1).

[0055]

[0056] In formula (1), δ i and Size i are the task relevance score and data size of the i-th task-related information unit, and n is the number of deconstructed task-related information units.

[0057] The multi-scale image compression module is used to compress the deconstructed task-related information units and background information units at different compression rates according to the allocated bandwidth, and finally form a multi-scale compressed image with different compression rates in different areas, so as to retain the task-related areas to the greatest extent.

[0058] like Figure 3 As shown in FIG, the execution process of a semantic information-assisted task-oriented image compression method is as follows:

[0059] Step 1, algorithm initialization, such as Figure 4 As shown in the figure, based on the UAV aerial photography mission scenario (taking urban traffic monitoring as an example), the mission information and domain information are analyzed, and the mission knowledge base and domain knowledge base are established (as training sets for adjusting the Yolo-World model). Figure 4 For example, the task requirements for urban traffic monitoring are set to people and cars, and the task relevance scores are set to 1 and 2 respectively. After completing the deployment of the task knowledge base and fine-tuning the algorithm model, proceed to step 2.

[0060] In step 2, the original image and task requirements are fed into the Yolo-World algorithm for object detection, which determines the areas in the original image containing people and vehicles. The detection results are then fed into SAM for semantic segmentation, which decomposes the original image into several task-related information units. After image semantic segmentation is complete, proceed to step 3.

[0061] Step 3: Reserve 10% of the total signal transmission bandwidth for the background information unit. Input the task relevance score provided by the task knowledge base, the data size of the task-related information unit, and the available bandwidth (i.e., 90% of the total signal transmission bandwidth) into the adaptive bandwidth allocation module, and allocate bandwidth to each information unit according to formula (1). During the allocation process, after allocating bandwidth to each task-related information unit, the excess bandwidth (i.e., the difference between the data size of the task-related information unit and its bandwidth if the bandwidth of the task-related information unit is less than its data size) is fed back to the background information unit. After completing the adaptive bandwidth allocation, proceed to step 4.

[0062] In step 4, the bandwidth allocation results and information units are input into the multi-scale image compression module. Various image compression algorithms, such as JPEG, JPEG2000, and PNG, can be used (the demonstration uses the JPEG image compression scheme). Based on the bandwidth allocation results, the image is compressed at different scales for different regions. This creates a multi-scale compressed image with different compression rates for different regions, preserving the task-relevant areas to the greatest extent possible.

[0063] A semantic information-assisted task-oriented image compression method provided in this embodiment compresses images at different scales based on semantic information and regions, which can retain task-related information to the greatest extent possible. By establishing a task knowledge base and designing a corresponding interaction mechanism, the image compression algorithm is guaranteed to effectively compress images under the dynamically changing task background during drone aerial photography.

[0064] After testing, a semantic information-assisted task-oriented image compression method provided in this embodiment was used on the semantic drone dataset released by Graz University of Technology. The original image and the compressed image were simultaneously input into Yolo-World for target detection (confidence level was set to 60%). The image compression rate could reach nearly 3%, while ensuring a target detection rate of more than 90%, achieving effective image compression with a high compression rate while retaining mission-related area information.

[0065] The present application also provides a semantic information-assisted task-oriented image compression system, which includes a semantic segmentation module, a weight calculation module, a bandwidth allocation module and an image compression module.

[0066] Among them, the semantic segmentation module is used to perform semantic segmentation on the original image according to the task knowledge base to obtain several task-related information units and background information units. The task knowledge base includes task background, task name, task requirements and task relevance score.

[0067] The weight calculation module is used to calculate the weight of each task-related information unit according to the task relevance score and data size of each task-related information unit.

[0068] The bandwidth allocation module is used to allocate bandwidth to each task-related information unit and background information unit according to the weight of each task-related information unit.

[0069] The image compression module is used to compress the image of each task-related information unit and the background information unit according to the bandwidth allocation result to obtain a multi-scale compressed image.

[0070] The present application also provides a semantic information-assisted task-oriented image compression device, which can be a computer device, and its internal structure diagram can be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a task-oriented image compression method assisted by semantic information is implemented.

[0071] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0072] like Figure 5 As shown, the present application also provides a computer device, which includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments.

[0073] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0074] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0075] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0077] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A semantic information-assisted task-oriented image compression method, characterized in that: include: Performing semantic segmentation on the original image based on a task knowledge base to obtain a number of task-related information units and background information units. The task knowledge base includes task background, task name, task requirements, and task relevance scores, where the task relevance scores are scores corresponding to the task requirements. Calculating the weight of each task-related information unit based on the task relevance score and data size of each task-related information unit, including calculating the product of the task relevance score and the data size of each task-related information unit; calculating the ratio of the product of each task-related information unit to the sum of several such products to obtain the weight of each task-related information unit; Allocating bandwidth for each task-related information unit and background information unit based on the weight of each task-related information unit includes allocating a preset proportion of the total signal transmission bandwidth to the background information unit; calculating the product of the weight of each task-related information unit and the remaining proportion of the total signal transmission bandwidth other than the preset proportion to obtain the bandwidth of each task-related information unit; if the bandwidth of each task-related information unit is smaller than its data size, maintaining the bandwidth of the task-related information unit unchanged; if the bandwidth of each task-related information unit is not smaller than its data size, allocating the difference between the data size of the task-related information unit and its bandwidth to the background information unit; According to the bandwidth allocation result, image compression is performed on each task-related information unit and background information unit to obtain a multi-scale compressed image.

2. The method according to claim 1, wherein According to the task knowledge base, the original image is semantically segmented to obtain several task-related information units and background information units, including: Through the image semantic segmentation model, according to the task requirements provided by the task knowledge base, the original image is semantically segmented and deconstructed into several task-related information units and background information units.

3. The method according to claim 2, wherein The image semantic segmentation model is composed of the Yolo-World algorithm and the Segment Anything Model algorithm. The image semantic segmentation model performs semantic segmentation on the original image according to the task requirements provided by the task knowledge base, and deconstructs the original image into several task-related information units and background information units, including: The Yolo-World algorithm is trained by using a domain knowledge base as a training set for the Yolo-World algorithm, wherein the domain knowledge base includes image data belonging to different domains and annotated with category labels; Using the trained Yolo-world algorithm, target detection is performed on the original image according to the task requirements provided by the task knowledge base to obtain detection results, which include the target area related to the task; Segmenting the target in the target area into independent information units by using the Segment Anything Model algorithm to obtain several task-related information units; The portion of the original image other than the plurality of task-related information units is used as a background information unit.

4. The method according to claim 1, wherein The step of compressing the task-related information units and the background information units according to the bandwidth allocation result to obtain a multi-scale compressed image includes: Through the image compression algorithm, according to the bandwidth allocation result, different compression rates are used to compress the task-related information units and background information units to obtain multi-scale compressed images.

5. A semantic information-assisted task-oriented image compression system, characterized in that: include: A semantic segmentation module is used to perform semantic segmentation on the original image based on a task knowledge base to obtain a number of task-related information units and background information units. The task knowledge base includes task background, task name, task requirements, and task relevance scores. The task relevance scores are scores corresponding to task requirements. A weight calculation module is used to calculate the product of the task relevance score and the data size of each task related information unit; calculate the ratio of the product of each task related information unit to the sum of several products to obtain the weight of each task related information unit; a bandwidth allocation module, configured to allocate a preset proportion of the total signal transmission bandwidth to the background information unit; calculating the product of the weight of each task-related information unit and the remaining proportion of the total signal transmission bandwidth excluding the preset proportion to obtain the bandwidth of each task-related information unit; if the bandwidth of each task-related information unit is less than its data size, maintaining the bandwidth of the task-related information unit unchanged; if the bandwidth of each task-related information unit is not less than its data size, allocating the difference between the data size of the task-related information unit and its bandwidth to the background information unit; The image compression module is used to compress the image of each task-related information unit and the background information unit according to the bandwidth allocation result to obtain a multi-scale compressed image.

6. A semantic information-assisted task-oriented image compression device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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