CN117440104A - Data compression reconstruction method based on target significance characteristics - Google Patents

Abstract

The specification discloses a data compression reconstruction method based on target significance characteristics, which relates to the technical field of data compression reconstruction and comprises the steps of dividing an original image into a plurality of batches and preprocessing; performing target detection on the preprocessed image by using a Mask R-CNN model to obtain a model detection result; grouping the model detection results to obtain a data set of the required target and other targets; splitting grids of the preprocessed original image, and storing and compressing the grids in groups to obtain other target compression results, background compression results and required target compression results; reconstructing grid images of other targets and backgrounds by adopting a bilinear interpolation method, and reconstructing grid images of a required target by adopting a VAE model to obtain an interpolation result and a reconstruction sample; and splicing the interpolation result and the reconstruction sample to obtain a reconstruction image, so as to solve the problems of redundant information preservation and low accuracy of data reconstruction in the existing data compression reconstruction technology.

Description

A data compression and reconstruction method based on target salience features

Technical field

The invention belongs to the technical field of data compression and reconstruction, and specifically relates to a data compression and reconstruction method based on target salient features.

Background technique

With the continuous development of big data applications, the amount of data from various sensors continues to increase. This growing amount of data is constantly challenging the limits of storage resources. An intelligent algorithm that can achieve data compression to effectively reduce storage space is established. Imminent. There are currently some existing works, which usually use computer vision technologies such as target detection or saliency detection, image segmentation, etc., by dividing the original data into different areas, giving priority to retaining the information of areas containing salient features, thereby maintaining the main content while reducing the amount of data.

However, these existing techniques have some problems when processing complex scenes or images or videos with multiple salient objects. For example, due to misjudgments of target detection models, they may also save some non-critical information, resulting in information redundancy. In addition, the existing technology may effectively use the category information provided by the target detection model to obtain the correlation between targets, so that the interference data can be integrated into the data generation model during the target-related reconstruction process, reducing the accuracy of data reconstruction. sex.

Therefore, current data compression and reconstruction technology has the problem of storing complicated information and low accuracy of data reconstruction when processing images or videos of complex scenes or multiple salient objects.

Contents of the invention

The purpose of the present invention is to provide a data compression and reconstruction method based on target salience characteristics to solve the problem of complicated storage information and accurate data reconstruction when the current data compression and reconstruction technology processes images or videos of complex scenes or multiple salient objects. The problem of low sex.

In order to achieve the above objects, the present invention adopts the following technical solutions:

On the one hand, this specification provides a data compression and reconstruction method based on target salient features, including:

Divide the original images into several batches, and preprocess the original images of the divided target batches;

Use the Mask R-CNN model to perform target detection on the preprocessed original image to obtain the model detection results;

Group the model detection results according to target category labels to obtain the required target data set and other target data sets;

The preprocessed original image is grid-split, and the split grid is grouped, stored and preliminary data compressed according to the attribution relationship with the required target data set and the other target data sets to obtain other Target data compression storage results, background compression storage results, and required target data compression storage results;

The bilinear interpolation method is used to reconstruct the grid images corresponding to the other target data compression storage results and the background compression storage results, and the trained VAE model is used to reconstruct the grid images corresponding to the required target data compression storage results. Reconstruct the lattice image to obtain the interpolation results and reconstructed samples;

The interpolation result and the reconstructed sample are spliced to obtain a complete reconstructed image.

On the other hand, this specification provides a data compression reconstruction device based on target salient features, including:

The preprocessing module is used to divide the original images into several batches and preprocess the original images of the divided target batches;

The target detection module is used to use the Mask R-CNN model to perform target detection on the preprocessed original image and obtain the model detection results;

A target grouping module, used to group the model detection results according to target category labels to obtain the required target data set and other target data sets;

An image compression module, configured to perform grid splitting on the preprocessed original image, and group and store the split grids according to the attribution relationship with the required target data set and the other target data sets. Preliminary data compression to obtain other target data compression storage results, background compression storage results, and required target data compression storage results;

The image reconstruction module is used to use the bilinear interpolation method to reconstruct the grid image corresponding to the other target data compression storage results and the background compression storage results, and use the trained VAE model to reconstruct the required target The grid image corresponding to the data compression storage result is reconstructed to obtain the interpolation result and reconstructed sample;

An image splicing module is used to splice the interpolation result and the reconstructed sample to obtain a complete reconstructed image.

Based on the above technical solution, this specification can achieve the following technical effects:

This method combines the deep learning algorithm Mask R-CNN and the VAE model to more accurately identify salient features in complex scenes, and can handle complex correlations between multiple images with similar salient features. By using the above The method can compress and reconstruct image or video data more accurately, improve the accuracy and real-time performance of data processing while retaining important information, thereby solving the problem of current data compression and reconstruction technology in processing images of complex scenes or multiple salient objects. Or when saving videos, there are problems such as redundant information and low accuracy of data reconstruction.

Description of the drawings

Figure 1 is a schematic flow chart of a data compression and reconstruction method based on target salient features in an embodiment of the present invention.

Figure 2 is a schematic diagram of grid splitting in an embodiment of the present invention.

Figure 3 is a schematic diagram of a variational autoencoder VAE model in an embodiment of the present invention.

Figure 4 is a schematic structural diagram of a data compression and reconstruction device based on target salient features in an embodiment of the present invention.

FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the embodiments of the present invention.

It should be noted that, in order to clearly illustrate the content of the present invention, the present invention enumerates multiple embodiments to further explain different implementations of the present invention, wherein the multiple embodiments are enumerated rather than exhaustive. In addition, for the sake of simplicity of description, contents mentioned in the previous embodiments are often omitted in the later embodiments. Therefore, contents not mentioned in the later embodiments can be referred to the previous embodiments accordingly.

Example 1

Please refer to Figure 1. Figure 1 shows a data compression and reconstruction method based on target salient features provided in this embodiment. In this embodiment, the method includes:

Step 102: Divide the original images into several batches, and preprocess the original images of the divided target batches;

In this embodiment, an implementation manner of step 102 is:

Step 202: Divide the original images into several batches, adjust the image size of the original images of the target batch, and obtain the size-adjusted images;

Specifically, the input image is expressed as , of which/> Represents a batch of image collections, /> Indicates that the timestamp is/> image,/> A label representing a timestamp,/> Represents the number of images in a batch. The preprocessing process of the original image successively completes image size adjustment, color space conversion and denoising.

Image resizing unifies the sizes of images in the same batch so that the input to subsequent algorithms is consistent. Record each image separately The length and width are/> and/> , organize to obtain the length and width collection of the batch of images/> and/> , calculate the maximum value/> and/> , unify the storage size of all images to/> , the expansion part is completed using zero value filling, and the image that completes the above processing is recorded as/> .

Step 204: Perform grayscale processing on the resized image to obtain an image after image color space conversion;

Specifically, the image color space conversion performs grayscale processing on the above-mentioned images, which solves the problem that when general images are stored in RGB format, each pixel in the image needs to be stored in a triple array, which takes up a large space. question. There are currently a variety of grayscale methods that can be used. In order to calculate portability, the present invention mainly uses the averaging method. Assuming that the image will be in RGB format Horizontal No./> in each, vertically/> The array required to store pixels is expressed as , the average calculation formula is as follows:

in, Represents the gray value recorded after grayscale. Furthermore, the logarithmic grayscale transformation is used to map the narrow range of low grayscale values in the original image to a wider range of grayscale intervals, and at the same time map the wider range of high grayscale value intervals to a narrower grayscale interval. . The formula for grayscale conversion is as follows:

Represents the grayscale value after grayscale conversion. Right/> All images in are subjected to the above processing, and the processed results are recorded as/> .

Step 206: Smooth the noise in the image after color space conversion to obtain a preprocessed original image.

Specifically, image denoising smoothes the noise in the above image. This invention uses a general image Gaussian filter to remove noise in the image, and the denoised result is expressed as .

The original image result after completing all the above image preprocessing processes is expressed as , as input to the subsequent feature detection algorithm.

Step 104: Use the Mask R-CNN model to perform target detection on the preprocessed original image to obtain model detection results;

In this embodiment, the model detection results include: target category label, outer frame line where the target is located, target center of gravity, target number, and total target amount.

Specifically, the original image set after preprocessing in step 102 is As input, relevant information about the target contained in the image is obtained based on the target detection model, and the detection results are recorded. The target detection model can be completed using a variety of models, including any of the well-known models such as YOLO, Mask R-CNN, and SSD. Considering that multiple targets are often involved in a single image, the Mask R-CNN (Mask Region-based Convolutional Neural Network) model is mainly used for target detection in the second step. The recorded information after detection includes the target category label (Label), the outer frame line of the target (Bounding Box), the target center of gravity (Center), etc. in each graphic generated by the Mask R-CNN model.

Since in general, a single picture may contain multiple targets, the information is stored in an array. For the preprocessed image collection Every image in/> ,/> , abbreviate the Mask R-CNN model as/> , the detection results of the model are recorded as:/>

in Represents the number/> contained in the image category labels of targets,/> Represents the number/> contained in the image The outer frame line of the target,/> Represents the number/> contained in the image The focus of a goal,/> Indicates the number of the target,/> Indicates that Mask R-CNN is detecting the first/> The total number of detected targets in each image. The model detection results of all images in the image collection can be recorded as:/>

Step 106: Group the model detection results according to target category labels to obtain the required target data set and other target data sets;

Specifically, when performing target detection, there are usually multiple targets in the detection results of the model, but not all targets are required for analysis. In addition, when the detection is started, the model uses the image as the main unit to generate results. When performing data compression in this article, the detected required targets are the main body. There are certain differences between the two. In order to facilitate subsequent algorithm processing, the data is grouped and sorted. Express the target category label required for analysis as ,for All in /> Compose the required target data set for the data results of the label category, expressed as:

Correspondingly, everything is not based on Compose other target data sets for the data results of the label category, expressed as:/>

The original detection result set is divided into two parts, namely .

Step 108: Grid split the preprocessed original image, and perform group storage and preliminary data compression on the split grid according to the attribution relationship with the required target data set and the other target data sets. , obtain other target data compression storage results, background compression storage results, and required target data compression storage results;

In this embodiment, an implementation manner of step 108 is:

Step 302: Grid split the preprocessed original image to obtain several grid images;

Step 304: Group and store the several grid images according to whether they belong to the required target data set, and obtain the required target grid set, other target grid sets and the background grid set;

In this embodiment, the required target grid set is all the grid images within the outer frame line where the required target is located and its range; the other target grid set is the outer frame line where other targets are located and All grid images within its range; the background grid set is all other grid images remaining.

Specifically, referring to Figure 2, for each preprocessed image Both use the same grid for splitting. On the basis of gridding of the image, all the grid images within the outer frame line of the desired target and its range are collectively called the target grid set, expressed as/> . All grid images within the outer frame line and the range of other targets are collectively called other target grid sets, expressed as/> . All other grid images are collectively called the background grid set, denoted as/> . In particular, when a grid appears to belong to both the target grid set and other target grid sets, the grid is divided into the target grid set. As a result, a complete division of the image is achieved, that is,/> .

Based on this, this embodiment is based on the target detection model (such as Yolo, Mask R-CNN, etc.) to extract the target-related areas in the original image, and based on the extracted target frame information and the grid division needs of the image, respectively extract The target grid set, other target grid sets, and background grid sets provide data preprocessing functions for subsequent compression algorithms.

Step 306: Use the Gaussian filtering method to perform preliminary data compression on the grid images in the other target grid sets and the background grid set, and obtain other target data compression storage results and background compression storage results;

In this embodiment, an implementation manner of step 306 is:

Use once The Gaussian convolution kernel of the size processes the grid data in the other target grid collection and obtains the compression storage result of the other target data;

Used twice The sized Gaussian convolution kernel processes the grid data in the background grid set to obtain a background compression storage result.

Specifically, the Gaussian filtering method is used for other target grid sets and background collection/> The grid image in is downsampled. The Gaussian kernel convolution operation (Gaussian filtering) uses a Gaussian convolution kernel to perform a weighted average of the image. Collection of background grids/> The number of convolution kernels used for images in should be greater than for other target grid sets/> The convolution kernel used for the image in . A simple way to do this is to use once/> Gaussian convolution kernel processing/> , used twice Gaussian convolution kernel processing/> . The expression of the convolution kernel is as follows:/>

After using the convolution kernel, delete all even rows and columns to obtain the reduced image.

target grid collection Image in , retaining original resolution. Other target grid collections/> Image in , used once/> After Gaussian convolution kernel processing, the resolution is reduced to the original/> , expressed as/> ;Background grid collection/> Image in , used twice/> After Gaussian convolution kernel processing, the resolution is reduced to the original/> , expressed as/> .

When necessary, the size and number of uses of the convolution kernel can be adjusted according to actual needs. For example, when the data compression ratio needs to be improved, use or/> Wait for a larger Gaussian convolution kernel.

Based on this, this embodiment uses Gaussian filtering with a relatively low computational load to process data compression in other target grid sets and background grid sets. Considering that the amount of target-related information provided in the background grid set is limited, The Gaussian filtering method is repeatedly used on the data in the background grid collection to further reduce its data usage.

Step 308: Input the required target grid set into the VAE model for preliminary data compression, and obtain the required target data compression and storage results.

In this embodiment, before step 308, it also includes:

Use the grid images of the required target grid set as training samples;

The VAE model is trained based on the training samples and the loss function to obtain a trained VAE model.

In this embodiment, an implementation manner of step 308 is:

Input the grid image of the required target grid set into the trained VAE model, use the encoder in it to perform data compression, and obtain the required target data compression storage result.

Specifically, referring to Figure 3, the data of the target grid set is used as the input data set of the variational autoencoder VAE (Variational Autoencoders) model, denoted as . The VAE model assumes that the input data is composed of/> variables/> Composed, this model combines two modules: encoder and decoder. The encoder compresses the input data into unobserved random features, while the decoder maps the compressed data from the feature space back to the data space before data compression. Record the unobserved salient features of the target as/> .

The data generation process of VAE model mainly includes two processes. First, from the prior distribution sample one , and then distribute it according to conditions/> , use/> Generate/> . The VAE model hopes to find a parameter/> Thus maximizing the probability of generating real data:/>

in Represents the parameters of the distribution, where/> Distinctive features can be used/> points obtained

More specifically, the generation result of the VAE model will make its posterior distribution As close as possible to its true posterior distribution/> be consistent. Based on the given training samples/> , the training loss is:

in Represents the KL divergence of the prior and posterior distributions, and its calculation formula is:

After the VAE model training is completed, when performing data storage, the target grid image data uses the encoder Perform data compression and represent the compressed image data as/> .

Therefore, the original image The compressed storage result can be expressed as:/> .

Step 110: Use the bilinear interpolation method to reconstruct the grid images corresponding to the other target data compression storage results and the background compression storage results, and use the trained VAE model to compress the required target data storage results. The corresponding grid image is reconstructed to obtain the interpolation results and reconstructed samples;

In this embodiment, an implementation manner of step 110 is:

Step 402: Use the bilinear interpolation library in OpenCV to perform interpolation processing on the other target data compression storage results and the corresponding grid images in the background compression storage results to obtain interpolation results; the interpolation results include other target data Reconstruction results and background reconstruction results;

Specifically, meshes in other target mesh sets and background mesh sets are treated similarly. More specifically, let a single grid in the collection of grids be , which is a two-dimensional matrix in the form of grayscale image. Define the coordinates of elements in the image as/> , of which/> Positive from left to right,/> Positive from top to bottom. Use the bilinear interpolation library in OpenCV to process, and record the interpolation results as/> and/> .

Step 404: Use the decoder in the trained VAE model to reconstruct the grid image corresponding to the required target data compression storage result, and obtain a reconstruction similar to the grid image of the required target grid set. sample.

Specifically, the images in the target grid collection are generated using the VAE model. The generative model of the VAE model is , of which/> is the encoder,/> is the standard normal distribution. When generating samples, first start from /> Randomly sample one/> , after passing through the decoder, the training data/> Similar samples/> .

Based on this, this embodiment processes the reconstruction process of grid data hierarchically; for other target grid sets and background grid sets that provide less information, a data reconstruction method based on bilinear interpolation is used; for those who need Retain the data in the target grid with richer information and establish the corresponding variational autoencoder VAE model to achieve data compression and reconstruction.

Step 112: Splice the interpolation result and the reconstructed sample to obtain a complete reconstructed image.

Specifically, the result of image reconstruction is expressed as , after integrating the reconstruction results of all images in a batch, the reconstruction results of a batch can be expressed as:/>

In this embodiment, after step 112, it also includes:

After completing one batch of image reconstruction, input the image data of the next batch, and then repeat the above-mentioned steps 102 to 112 to perform the next stage of image reconstruction.

In summary, this method combines the deep learning algorithm Mask R-CNN and the VAE model to more accurately identify salient features in complex scenes, and can handle complex correlations between multiple images with similar salient features. By using the above method, image or video data can be compressed and reconstructed more accurately, while retaining important information, and improving the accuracy and real-time performance of data processing, thereby solving the problem of current data compression and reconstruction technology in processing complex scenes or multiple saliencies. When storing images or videos of objects, there are problems such as complicated storage information and low accuracy of data reconstruction.

Example 2

Please refer to Figure 4. Figure 4 shows a data compression and reconstruction device based on target salient features provided in this embodiment, including:

The preprocessing module is used to divide the original images into several batches and preprocess the original images of the divided target batches;

The target detection module is used to use the Mask R-CNN model to detect targets on the preprocessed original images and obtain model detection results;

A target grouping module, used to group the model detection results according to target category labels to obtain the required target data set and other target data sets;

An image compression module, configured to perform grid splitting on the preprocessed original image, and group and store the split grids according to the attribution relationship with the required target data set and the other target data sets. Preliminary data compression to obtain other target data compression storage results, background compression storage results, and required target data compression storage results;

The image reconstruction module is used to use the bilinear interpolation method to reconstruct the grid image corresponding to the other target data compression storage results and the background compression storage result, and use the trained VAE model to reconstruct the required target The grid image corresponding to the data compression storage result is reconstructed to obtain the interpolation result and reconstructed sample;

An image splicing module is used to splice the interpolation result and the reconstructed sample to obtain a complete reconstructed image.

Optional, preprocessing modules include:

The size adjustment unit is used to divide the original image into several batches, adjust the image size of the original image of the target batch, and obtain the size-adjusted image;

A color adjustment unit, configured to perform grayscale processing on the size-adjusted image to obtain an image after image color space conversion;

A denoising and smoothing unit is used to smooth the noise in the image after color space conversion to obtain a preprocessed original image.

Optional, image compression modules include:

A grid splitting unit, used for grid splitting the preprocessed original image to obtain several grid images;

A grid image grouping unit, configured to group and store the plurality of grid images according to whether they belong to the required target data set, and obtain the required target grid set, other target grid sets and the background grid set;

Other target and background compression units are used to perform preliminary data compression on the other target grid set and the grid images in the background grid set using the Gaussian filtering method, and obtain other target data compression storage results and background compression storage results. ;

The target image compression unit is used to input the required target grid set into the VAE model for preliminary data compression to obtain the required target data compression and storage results.

Optionally, the other target and background compression units include:

Other target compression subunits for use once The Gaussian convolution kernel of the size processes the grid data in the other target grid collection and obtains the compression storage result of the other target data;

Background image compression subunit for use twice The sized Gaussian convolution kernel processes the grid data in the background grid set to obtain a background compression storage result.

Optional, also includes:

A training sample acquisition module, used to use the grid images of the required target grid set as training samples;

A model training module is used to train the VAE model based on the training samples and the loss function, and obtain a trained VAE model.

Optional, image reconstruction modules include:

An interpolation reconstruction unit, configured to use the bilinear interpolation library in OpenCV to perform interpolation processing on the corresponding grid images in the other target data compression storage results and the background compression storage results, to obtain an interpolation result; the interpolation result Including other target data reconstruction results and background reconstruction results;

The VAE model reconstruction unit is configured to use the decoder in the trained VAE model to reconstruct the grid image corresponding to the required target data compression storage result, and obtain the network set with the required target grid. Reconstructed samples with similar grid images.

Based on this, this device combines the deep learning algorithm Mask R-CNN and the VAE model to more accurately identify salient features in complex scenes, and can handle complex correlations between multiple images with similar salient features. By using the above method, image or video data can be compressed and reconstructed more accurately, while retaining important information, and improving the accuracy and real-time performance of data processing, thereby solving the problem of current data compression and reconstruction technology in processing complex scenes or multiple saliencies. When storing images or videos of objects, there are problems such as complicated storage information and low accuracy of data reconstruction.

Example 3

Referring to FIG. 5 , this embodiment provides an electronic device. The electronic device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course may also include other hardware required for services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a data compression reconstruction method based on the target's salient features at the logical level. Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or the combination of software and hardware, etc. That is, the execution subject of the following processing flow is not limited to each logical unit, and can also be hardware. or logic device.

Network interfaces, processors and memories can be connected to each other via bus systems. The above bus can be divided into address bus, data bus, control bus, etc.

Memory is used to store programs. Specifically, the program may include program code including computer operating instructions. Memory may include read-only memory and random access memory and provides instructions and data to the processor.

The processor is used to execute the program stored in the above memory and specifically execute:

Step 102: Divide the original images into several batches, and preprocess the original images of the divided target batches;

Step 104: Use the Mask R-CNN model to perform target detection on the preprocessed original image to obtain model detection results;

Step 106: Group the model detection results according to target category labels to obtain the required target data set and other target data sets;

Step 108: Grid split the preprocessed original image, and perform group storage and preliminary data compression on the split grid according to the attribution relationship with the required target data set and the other target data sets. , obtain other target data compression storage results, background compression storage results, and required target data compression storage results;

Step 110: Use the bilinear interpolation method to reconstruct the grid images corresponding to the other target data compression storage results and the background compression storage results, and use the trained VAE model to compress the required target data storage results. The corresponding grid image is reconstructed to obtain the interpolation results and reconstructed samples;

Step 112: Splice the interpolation result and the reconstructed sample to obtain a complete reconstructed image.

The processor may be an integrated circuit chip that has signal processing capabilities. During the implementation process, each step of the above method can be completed through the integrated logic circuit of the processor's hardware or instructions in the form of software.

Based on the same invention and creation, embodiments of this specification also provide a computer-readable storage medium. The computer-readable storage medium stores one or more programs. The one or more programs are executed by an electronic device including multiple application programs. When, the above-mentioned electronic device is caused to execute a data compression reconstruction method based on target salient features provided by the embodiment corresponding to FIG. 1 to FIG. 3 .

It should be understood by those skilled in the art that embodiments of the present specification may be provided as methods, systems, or computer program products. Thus, the present description may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present specification may take the form of a computer program product embodied on one or more computer-readable storage media having computer-usable program code embodied therein.

In addition, the specific implementation of the above-mentioned device is basically similar to the implementation of the method, so the description is relatively simple. For relevant details, please refer to the partial description of the implementation of the method. Moreover, it should be noted that in each module of the system of the present application, the components are logically divided according to the functions to be implemented. However, the present application is not limited to this, and each component can be divided as needed. Re-divide or combine.

Each embodiment in this specification is described in a progressive manner. The same and similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

The foregoing describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order shown, or sequential order, to achieve desirable results, and multitasking and parallel processing are possible or may be advantageous in certain embodiments.

The above descriptions are only examples of the present application and are not intended to limit the present application. To those skilled in the art, various modifications and variations may be made to this application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application shall be included in the scope of the claims of this application.

Claims (10)

1. The data compression reconstruction method based on the target significance characteristics is characterized by comprising the following steps of:

dividing an original image into a plurality of batches, and preprocessing the original image of a target batch after batch;

performing target detection on the preprocessed original image by using a Mask R-CNN model to obtain a model detection result;

grouping the model detection results according to the target class labels to obtain a required target data set and other target data sets;

splitting grids of the preprocessed original image, and storing the split grids in groups and performing preliminary data compression according to the attribution relation between the grids and the required target data set and the attribution relation between the grids and the other target data sets to obtain other target data compression storage results, background compression storage results and required target data compression storage results;

reconstructing grid images corresponding to the other target data compression storage results and the background compression storage results by adopting a bilinear interpolation method, and reconstructing the grid images corresponding to the required target data compression storage results by adopting a trained VAE model to obtain interpolation results and reconstructed samples;

and splicing the interpolation result and the reconstructed sample to obtain a reconstructed complete image.

2. The method of claim 1, wherein the steps of dividing the original image into batches and preprocessing the batched original image of the target batch include:

dividing an original image into a plurality of batches, and carrying out image size adjustment on the original image of a target batch to obtain an image after size adjustment;

carrying out graying treatment on the image after the size adjustment to obtain an image after the image color space conversion;

and carrying out smoothing treatment on noise in the image after the image color space conversion to obtain a preprocessed original image.

3. The method of claim 2, wherein the model detection result comprises: the target category label, the outline where the target is located, the center of gravity of the target, the target number and the total amount of the target.

4. A method according to claim 3, wherein the steps of splitting the grid of the preprocessed original image, and performing packet storage and preliminary data compression on the split grid according to the attribution relation with the required target data set and the other target data set, to obtain other target data compression storage results, background compression storage results and required target data compression storage results comprise:

grid splitting is carried out on the preprocessed original image, and a plurality of grid images are obtained;

the grid images are stored in groups according to whether the grid images belong to the required target data set or not, and a required target grid set, other target grid sets and a background grid set are obtained;

performing preliminary data compression on grid images in the other target grid sets and the background grid sets by using a Gaussian filtering method to obtain other target data compression storage results and background compression storage results;

and inputting the required target grid set into a VAE model for preliminary data compression to obtain a required target data compression storage result.

5. The method of claim 4, wherein the desired target grid set is all grid images within and around the outline of the desired target; the other target grid sets are all grid images in which the outer frame lines of other targets are located and the range of the outer frame lines; the background grid set is all other grid images remaining.

6. The method of claim 4, wherein the step of obtaining the other target data compression storage result and the background compression storage result by performing preliminary data compression on the grid images in the other target grid set and the background grid set by using a gaussian filtering method comprises:

can be used onceThe size Gaussian convolution kernel processes grid data in the other target grid sets to obtain other target data compression storage results;

is used twiceAnd processing the grid data in the background grid set by a Gaussian convolution kernel of the size to obtain a background compression storage result.

7. The method of claim 4, further comprising, prior to said inputting the desired set of target grids into a VAE model for preliminary data compression, obtaining a desired target data compression storage result:

taking the grid image of the required target grid set as a training sample;

and training the VAE model based on the training sample and the loss function to obtain a trained VAE model.

8. The method of claim 7 wherein the inputting the desired set of target grids into the VAE model for preliminary data compression obtains a desired target data compression storage result by inputting grid images of the desired set of target grids into the trained VAE model for data compression using an encoder therein.

9. The method of claim 8, wherein the steps of reconstructing the grid image corresponding to the other target data compression storage result and the background compression storage result using the bilinear interpolation method, and reconstructing the grid image corresponding to the required target data compression storage result using the trained VAE model, and obtaining the interpolation result and the reconstructed sample comprise:

performing interpolation processing on the other target data compression storage results and the grid images corresponding to the background compression storage results by adopting a bilinear interpolation library in OpenCV to obtain interpolation results; the interpolation result comprises other target data reconstruction results and background reconstruction results;

and reconstructing a grid image corresponding to the required target data compression storage result by adopting a decoder in the trained VAE model to obtain a reconstructed sample similar to the grid image of the required target grid set.

10. A data compression reconstruction device based on a target significance signature, comprising:

the preprocessing module is used for dividing an original image into a plurality of batches and preprocessing the original image of a target batch after batch;

the target detection module is used for carrying out target detection on the preprocessed original image by using a Mask R-CNN model to obtain a model detection result;

the target grouping module is used for grouping the model detection results according to target class labels to obtain a required target data set and other target data sets;

the image compression module is used for splitting grids of the preprocessed original image, and carrying out grouping storage and preliminary data compression on the split grids according to the attribution relation between the grids and the required target data set and the attribution relation between the grids and the other target data sets to obtain other target data compression storage results, background compression storage results and required target data compression storage results;

the image reconstruction module is used for reconstructing grid images corresponding to the other target data compression storage results and the background compression storage results by adopting a bilinear interpolation method, reconstructing grid images corresponding to the required target data compression storage results by adopting a trained VAE model, and obtaining interpolation results and reconstruction samples;

and the image splicing module is used for splicing the interpolation result and the reconstructed sample to obtain a reconstructed complete image.