Satellite image compression method based on linear frequency modulation

Abstract

The invention discloses a satellite image compression method based on linear frequency modulation, and the method comprises the steps: S1, obtaining a satellite image, and carrying out the partitioning of the satellite image through employing a self-adaptive partitioning method, so as to enable different projective transformations in the satellite image to be separated, and obtaining a plurality of image blocks; s2, inputting all the image blocks into a trained neural network model, and predicting to obtain chirp transformation parameters of each image block; s3, performing discrete chirp transformation on each image block according to the chirp transformation parameter corresponding to each image block so as to realize transformation from an image domain to a chirp frequency spectrum domain; s4, in the chirp frequency spectrum domain, the amplitude of the frequency component is quantized, so that continuous frequency spectrum coefficients are mapped into a discrete set; and S5, compressing quantization results of all the image blocks in the step S4 by adopting a lossless algorithm, and then encoding the compressed spectrum data by adopting an encoder to complete compression of the satellite image.

Description

Technical Field

[0001] The invention relates to image compression technology, and in particular to a satellite image compression method based on linear frequency modulation. Background Art

[0002] Image compression, as a key link in computer image processing, faces the challenge of the growing demand for image information storage and transmission. With the development of technology, neural networks provide new ideas for the research direction of image compression methods. Prior art CN 118741145A discloses a performance balanced compression method for satellite images. This method balances the excellent compression performance and poor compression performance of images from the perspective of a multi-image system by combining and arranging images, overcomes the influence of the wooden barrel effect on the practicality of image compression, overcomes the defect of the JPEG2000 standard compression algorithm that the compression quality of multiple images is too different, improves the performance of the standard compression algorithm without increasing the complexity, has the characteristics of easy software and hardware upgrades, and has practical value in satellite image compression and transmission systems.

[0003] Although the above invention has many advantages, it still has the following advantages when used: the invention is essentially still the traditional compression method of JPEG2000. Traditional image compression technology similar to JPEG is limited by its algorithm architecture and will inevitably produce significant block distortion in the reconstructed image. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the satellite image compression method based on linear frequency modulation provided by the present invention solves the problem of distortion in the prior art during image compression.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A satellite image compression method based on linear frequency modulation is provided, which comprises the steps of:

[0007] S1, obtaining a satellite image, and using an adaptive blocking method to block the satellite image, so as to separate different projective transformations in the satellite image, and obtain a plurality of image blocks;

[0008] S2, input all image blocks into the trained neural network model to predict the chirp transformation parameters of each image block;

[0009] S3, performing discrete chirp transformation on each image block according to the chirp transformation parameters corresponding to each image block, so as to realize the conversion from image domain to chirp spectrum domain;

[0010] S4, in the chirp spectrum domain, quantize the amplitude of the frequency component to map the continuous spectrum coefficients into a discrete set;

[0011] S5. Use a lossless algorithm to compress the quantization results of all image blocks in step S4, and then use an encoder to encode the compressed spectrum data to complete the compression of the satellite image.

[0012] Furthermore, the method of using the adaptive blocking method to block the satellite image includes:

[0013] S11, dividing the satellite image into a number of non-overlapping image blocks, and setting an initial value of a matching degree parameter;

[0014] S12, using a two-dimensional projective entropy model to calculate the two-dimensional projective entropy of each image block, and calculating the mean of the two-dimensional projective entropy of all image blocks corresponding to the divided blocks, the two-dimensional projective entropy model is:

[0015]

[0016] Among them, G is the two-dimensional projective entropy; L is the gray level of the satellite image; p ij is the probability of occurrence of the grayscale mean pair (i, j) in the image block; g ij is the matching parameter between the frequency change trend of pixels in the image block and the projection transformation parameter; f(i, j) is the frequency of occurrence of pixels with gray value i and neighborhood gray mean j in the image block; N pairs is the total number of gray mean pairs in the image block; Δθ q is the projection transformation parameter difference of the qth grayscale mean pair; B = min{N L , N w} is the size of the satellite image, N L is the length of the satellite image, N w is the width of the satellite image;

[0017] S13, judging whether the two-dimensional projective entropy of each image block is greater than the corresponding two-dimensional projective entropy mean value or whether the size of the image block is less than the set size, if any of the conditions are met, proceeding to step S14, otherwise the current image block stops being segmented;

[0018] S14, divide the current image block again to obtain a number of image blocks with a size equal to a preset size, and then return to step S12.

[0019] Furthermore, when the satellite image is divided into blocks for the first time, the expression of the number of image blocks b is b=(N L ×N w) / (B×B); when each image is divided into blocks, the size of the image block is [(B / 2 k―1 )×(B / 2 k ―1 )], k is the number of times the current image is segmented.

[0020] Furthermore, the expression for discrete chirp transform of each image block is:

[0021]

[0022] in, is the discrete chirp transform; is the mapping in frequency domain; is the two-dimensional distribution of image grayscale value after projection transformation; is the two-dimensional distribution of the grayscale value of the image; is the chirp transformation parameter predicted by the neural network model; e is the natural logarithm; is the jth pixel in the image block; ΔA is the area of ​​the image block; N is the total number of pixels in the image block.

[0023] Furthermore, the satellite image compression method based on linear frequency modulation also includes decompressing the compressed satellite image:

[0024] S6. performing entropy decoding on the compressed satellite image to restore the compressed data;

[0025] S7. Perform a discrete chirp inverse transform on the entropy decoded data, convert the processed spectrum data back to the image space, and then reconstruct the satellite image; the expression for the discrete chirp inverse transform is:

[0026]

[0027] Furthermore, the neural network model is an autoregressive network, a recurrent neural network, a long short-term memory network, a gated recurrent unit or a gated attention mechanism Transformer;

[0028] The data set for training the neural network model includes a number of image blocks and chirp transformation parameters corresponding to each image block. The number of image blocks are inputs of the neural network model, and the chirp transformation parameters are outputs of the neural network model.

[0029] Furthermore, when dividing the satellite image into blocks, it also includes:

[0030] S15, grouping all image blocks, dividing image blocks of the same size into the same group, obtaining a plurality of groups, and calculating the proportion of each group to all image blocks;

[0031] S16, determining the sampling frequency and sampling times of the image blocks in each group according to the sampling rate of the satellite image and the proportion of each group;

[0032] S17, constructing a corresponding measurement matrix for compression measurement based on the block compression sensing theory according to the sampling times of each image block, and all image blocks in the same group use the same measurement matrix;

[0033] S18. Generate a measurement value vector for each image block as the final image block according to the measurement matrix corresponding to the image block and the gray value vector corresponding to the image block.

[0034] Furthermore, step S16 further includes:

[0035] S161. Calculate the sampling frequency and sampling times of each image block in the group according to the sampling frequency of the satellite image and the proportion of each group:

[0036] R n =SR×P n , M n =R n ×(B / 2 k―1 ) 2

[0037] Among them, R n is the sampling frequency of the image blocks in each group; SR is the sampling rate of the satellite image; p n is the proportion of each group; M n is the sampling times of the image blocks in each group; B is the size of the satellite image;

[0038] S162, determine whether the sampling number of each image block is greater than the sampling upper limit, if so, proceed to step S163, otherwise obtain the sampling number of each image block;

[0039] S163, evenly distributing the number of times exceeding the sampling upper bound to other groups until the sampling number of each image block in each group is less than or equal to the sampling upper bound;

[0040] The expression of the sampling upper bound upper is: upper = 0.9 × (B / 2 k―1 ) 2 .

[0041] The beneficial effects of the present invention are as follows: the purpose of image segmentation in this scheme is different from that in traditional compression methods such as JPEG. The purpose of segmentation in traditional compression methods is to perform independent and parallel processing on each block, thereby improving the efficiency of the entire transform coding algorithm; the adaptive segmentation in this scheme is to separate different local distortions in the image, that is, different projective transformations, so as to fit the corresponding chirp transformation on each different local distortion through a neural network model, so as to achieve a better compression rate, thereby preventing the problem of distortion in the compressed and reconstructed image, thereby ensuring the quality of the reconstructed image.

[0042] The two-dimensional projective entropy calculated by this method can not only reflect the complexity of the image block, but also reflect whether the image block is suitable for chirp transformation, and can help determine the optimal chirp transformation parameters. It should be able to evaluate the difference in projective changes between regions and maximize the distance and difference of projective transformation parameters between different image blocks to ensure a better compression rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of a satellite image compression method based on linear frequency modulation.

[0044] Figure 2 The present invention is a flow chart of another embodiment of a satellite image compression method based on linear frequency modulation. DETAILED DESCRIPTION

[0045] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0046] refer to Figure 1 , Figure 1 A flowchart of a satellite image compression method based on linear frequency modulation is shown; Figure 1 As shown, the method S includes steps S1 to S5.

[0047] In step S1, a satellite image is acquired and partitioned using an adaptive partitioning method to separate different projective transformations in the satellite image to obtain a plurality of image partitions.

[0048] In one embodiment of the present invention, the method of segmenting satellite images using the adaptive segmentation method includes:

[0049] S11, dividing the satellite image into a number of non-overlapping image blocks, and setting an initial value of a matching degree parameter;

[0050] S12, using a two-dimensional projective entropy model to calculate the two-dimensional projective entropy of each image block, and calculating the mean of the two-dimensional projective entropy of all image blocks corresponding to the divided blocks, the two-dimensional projective entropy model is:

[0051]

[0052] Among them, G is the two-dimensional projective entropy; L is the gray level of the satellite image; p ij is the probability of occurrence of the grayscale mean pair (i, j) in the image block; g ij is the matching parameter between the frequency change trend of pixels in the image block and the projection transformation parameter; f(i, j) is the frequency of occurrence of pixels with gray value i and neighborhood gray mean j in the image block; N pairs is the total number of gray mean pairs in the image block; Δθ q is the projection transformation parameter difference of the qth grayscale mean pair; B = min{N L , N w} is the size of the satellite image, N L is the length of the satellite image, N w is the width of the satellite image;

[0053] The divided blocks here are explained as follows: assuming that the image is divided into 4 blocks during the first block division, and the two-dimensional projective entropy of two blocks is less than or equal to the two-dimensional projective entropy mean, and the two blocks is greater than the two-dimensional projective entropy mean, then these two blocks need to be divided again. Assuming that each block is divided into 4 image blocks again, the divided blocks are the image blocks whose two-dimensional projective entropy is greater than the two-dimensional projective entropy mean during the first block division.

[0054] S13, determine whether the two-dimensional projective entropy of each image block is greater than its corresponding two-dimensional projective entropy mean or whether the size of the image block is smaller than the set size. If any of the conditions is met, proceed to step S14, otherwise the current image block stops being segmented.

[0055] The size is set to 32×32 because experiments have shown that there is no obvious difference in the distribution of texture details at this time, and the smaller the image block, the higher the total number of sampling times, so the compression loses its meaning; furthermore, defining the minimum value can prevent serious blocking effects.

[0056] S14, divide the current image block again to obtain a number of image blocks with a size equal to a preset size, and then return to step S12.

[0057] During implementation, the present solution preferably divides the satellite image into blocks for the first time, and the expression for the number of image blocks b obtained is b=(N L ×N w) / (B×B); when each image is divided into blocks, the size of the image block is [(B / 2 K―1 )×(B / 2 K―1 )], k is the number of times the current image is divided into blocks; the preferred preset size of this scheme is [(β / 2 K―1 )×(B / 2 K―1 )].

[0058] In step S2, all image blocks are input into the trained neural network model to predict the chirp transformation parameters of each image block.

[0059] The neural network model of this scheme is preferably an autoregressive network, a recurrent neural network, a long short-term memory network, a gated recurrent unit or a gated attention mechanism Transformer; the data set for training the neural network model includes several image blocks and chirp transformation parameters corresponding to each image block, the several image blocks are the input of the neural network model, and the chirp transformation parameters are the output of the neural network model.

[0060] In step S3, a discrete chirp transform is performed on each image block according to the chirp transform parameters corresponding to each image block, so as to achieve conversion from the image domain to the chirp spectrum domain.

[0061] During implementation, the present solution preferably performs discrete chirp transform on each image block as follows:

[0062]

[0063] in, is the discrete chirp transform; is the mapping in frequency domain; is the two-dimensional distribution of image grayscale value after projection transformation; is the two-dimensional distribution of the grayscale value of the image; is the chirp transformation parameter predicted by the neural network model; e is the natural logarithm; is the jth pixel in the image block; ΔA is the area of ​​the image block; N is the total number of pixels in the image block.

[0064] In step S4, the amplitude of the frequency component is quantized in the chirp spectrum domain to map the continuous spectrum coefficients into a discrete set;

[0065] In step S5, a lossless algorithm is used to compress the quantization results of all image blocks in step S4, and then an encoder is used to encode the compressed spectrum data to complete the compression of the satellite image.

[0066] like Figure 2As shown, in one embodiment of the present invention, the satellite image compression method based on linear frequency modulation further includes decompressing the compressed satellite image:

[0067] S6. performing entropy decoding on the compressed satellite image to restore the compressed data;

[0068] S7. Perform a discrete chirp inverse transform on the entropy decoded data, convert the processed spectrum data back to the image space, and then reconstruct the satellite image; the expression for the discrete chirp inverse transform is:

[0069]

[0070] In one embodiment of the present invention, the satellite image compression method based on linear frequency modulation, when dividing the satellite image into blocks, further includes:

[0071] S15, grouping all image blocks, dividing image blocks of the same size into the same group, obtaining a plurality of groups, and calculating the proportion of each group to all image blocks;

[0072] S16, determining the sampling frequency and sampling times of the image blocks in each group according to the sampling rate of the satellite image and the proportion of each group;

[0073] S17, constructing a corresponding measurement matrix for compression measurement based on the block compression sensing theory according to the sampling times of each image block, and all image blocks in the same group use the same measurement matrix;

[0074] S18. Generate a measurement value vector for each image block as the final image block according to the measurement matrix corresponding to the image block and the gray value vector corresponding to the image block (ie, multiply the measurement matrix by the gray value vector).

[0075] During implementation, the preferred step S16 of this solution further includes:

[0076] S161. Calculate the sampling frequency and sampling times of each image block in the group according to the sampling frequency of the satellite image and the proportion of each group:

[0077] R n =SR×P n , M n =R n ×(B / 2 k―1 ) 2

[0078] Among them, R n is the sampling frequency of the image blocks in each group; SR is the sampling rate of the satellite image; P n is the proportion of each group; M nis the sampling times of the image blocks in each group; B is the size of the satellite image;

[0079] S12, determine whether the sampling number of each image block is greater than the sampling upper limit, if so, proceed to step S13, otherwise obtain the sampling number of each image block;

[0080] S13, evenly distributing the number of times exceeding the sampling upper bound to other groups until the sampling number of each image block in each group is less than or equal to the sampling upper bound;

[0081] The expression of the sampling upper bound upper is: upper = 0.9 × (B / 2 K―1 ) 2 .

[0082] In traditional image compression methods, the block compression process often first divides the image into blocks of the same size, while this scheme uses an adaptive block method, which can group several image blocks of different sizes. In order to verify the effectiveness of the adaptive block technology on the model, the ablation experiment is set as follows:

[0083] The experimental group is set to use the model of ordinary equal-size image block division technology, and the control group is set to use the model of adaptive image block division technology. Experiments are conducted under high bit rate and low bit rate conditions of relevant satellite images, and the average test results are shown in Table 1.

[0084] Table 1 Ablation experiment results of adaptive block module

[0085]

[0086] In Table 1, PSNR is implemented by calculating the mean square error (MSE) between the original image and the reconstructed image to quantify this difference. The smaller the MSE, the closer the two images are, and thus the higher the PSNR value, the better the image quality.

[0087] The results show that in both high and low bit rate cases, the overall model performance using adaptive blocking technology is improved in terms of PSNR indicators compared to the model performance using ordinary blocking technology. Specifically, the average improvement is 1.302dB in high bit rate conditions and 1.366dB in low bit rate conditions. It can be seen that the compression effect of the compression method of this scheme is significantly better than the compression effect of the existing equal-size blocking technology.

Claims

1. A satellite image compression method based on linear frequency modulation, characterized in that: Includes steps: S1, obtaining a satellite image, and using an adaptive blocking method to block the satellite image, so as to separate different projective transformations in the satellite image, and obtain a plurality of image blocks; S2, input all image blocks into the trained neural network model to predict the chirp transformation parameters of each image block; S3, performing discrete chirp transformation on each image block according to the chirp transformation parameters corresponding to each image block, so as to realize the conversion from image domain to chirp spectrum domain; S4, in the chirp spectrum domain, quantize the amplitude of the frequency component to map the continuous spectrum coefficients into a discrete set; S5. Use a lossless algorithm to compress the quantization results of all image blocks in step S4, and then use an encoder to encode the compressed spectrum data to complete the compression of the satellite image.

2. The satellite image compression method based on linear frequency modulation according to claim 1, characterized in that: Methods for segmenting satellite images using an adaptive segmentation method include: S11, dividing the satellite image into a number of non-overlapping image blocks, and setting an initial value of a matching degree parameter; S12, using a two-dimensional projective entropy model to calculate the two-dimensional projective entropy of each image block, and calculating the mean of the two-dimensional projective entropy of all image blocks corresponding to the divided blocks, the two-dimensional projective entropy model is: Among them, G is the two-dimensional projective entropy; L is the gray level of the satellite image; p ij is the probability of occurrence of the grayscale mean pair (i, j) in the image block; g ij is the matching parameter between the frequency change trend of pixels in the image block and the projection transformation parameter; f(i, j) is the frequency of occurrence of pixels with gray value i and neighborhood gray mean j in the image block; N pairs is the total number of gray mean pairs in the image block; Δθ q is the projection transformation parameter difference of the qth grayscale mean pair; B = min{N L , N w } is the size of the satellite image, N L is the length of the satellite image, N w is the width of the satellite image; S13, judging whether the two-dimensional projective entropy of each image block is greater than the corresponding two-dimensional projective entropy mean value or whether the size of the image block is less than the set size, if any of the conditions are met, proceeding to step S14, otherwise the current image block stops being segmented; S14, divide the current image block again to obtain a number of image blocks with a size equal to a preset size, and then return to step S12.

3. The satellite image compression method based on linear frequency modulation according to claim 2, characterized in that: When the satellite image is divided into blocks for the first time, the expression of the number of image blocks b is b = (N L ×N w ) / (B×B); when each image is divided into blocks, the size of the image block is [(B / 2 k-1 )×(B / 2 k-1 )], k is the number of times the current image is segmented.

4. The satellite image compression method based on linear frequency modulation according to claim 1, characterized in that: The expression for discrete chirp transform of each image block is: in, is the discrete chirp transform; is the mapping in frequency domain; is the two-dimensional distribution of image grayscale value after projection transformation; is the two-dimensional distribution of the grayscale value of the image; is the chirp transformation parameter predicted by the neural network model; e is the natural logarithm; is the jth pixel in the image block; ΔA is the area of ​​the image block; N is the total number of pixels in the image block.

5. The satellite image compression method based on linear frequency modulation according to claim 1, characterized in that: It also includes decompression of compressed satellite images: S6. performing entropy decoding on the compressed satellite image to restore the compressed data; S7. Perform a discrete chirp inverse transform on the entropy decoded data, convert the processed spectrum data back to the image space, and then reconstruct the satellite image; the expression for the discrete chirp inverse transform is:

6. The satellite image compression method based on linear frequency modulation according to any one of claims 1 to 5, characterized in that: The neural network model is an autoregressive network, a recurrent neural network, a long short-term memory network, a gated recurrent unit or a gated attention mechanism Transformer; The data set for training the neural network model includes a number of image blocks and chirp transformation parameters corresponding to each image block. The number of image blocks are inputs of the neural network model, and the chirp transformation parameters are outputs of the neural network model.

7. The satellite image compression method based on linear frequency modulation according to claim 2, characterized in that: When satellite images are segmented, the following are also included: S15, grouping all image blocks, dividing image blocks of the same size into the same group, obtaining a plurality of groups, and calculating the proportion of each group to all image blocks; S16, determining the sampling frequency and sampling times of the image blocks in each group according to the sampling rate of the satellite image and the proportion of each group; S17, constructing a corresponding measurement matrix for compression measurement based on the block compression sensing theory according to the sampling times of each image block, and all image blocks in the same group use the same measurement matrix; S18. Generate a measurement value vector for each image block as the final image block according to the measurement matrix corresponding to the image block and the gray value vector corresponding to the image block.

8. The satellite image compression method based on linear frequency modulation according to claim 7, characterized in that: Step S16 further comprises: S161. Calculate the sampling frequency and sampling times of each image block in the group according to the sampling frequency of the satellite image and the proportion of each group: R n =SR×P n ,M n =R n ×(B / 2 k-1 ) 2 Among them, R n is the sampling frequency of the image blocks in each group; SR is the sampling rate of the satellite image; P n is the proportion of each group; M n is the sampling times of the image blocks in each group; B is the size of the satellite image; S162, determine whether the sampling number of each image block is greater than the sampling upper limit, if so, proceed to step S163, otherwise obtain the sampling number of each image block; S163, evenly distributing the number of times exceeding the sampling upper bound to other groups until the sampling number of each image block in each group is less than or equal to the sampling upper bound; The expression of the sampling upper bound upper is: upper = 0.9 × (B / 2 k-1 ) 2 .

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