Edge-enhanced image compression encryption method, device, medium and product

Abstract

The present invention discloses an edge-enhanced image compression encryption method, device, medium and product, and relates to the field of image processing technology. The method comprises: using an edge detection operator to perform edge detection on an original image to obtain edge information; using an image enhancement algorithm to perform image enhancement on the edge information to obtain enhanced edge information; using a chaotic mapping method to perform diffusion encryption on the original image to obtain a chaotic mapping image; performing wavelet transform on the chaotic mapping image to obtain a wavelet subband image; performing scrambling transform on the low-frequency subband image in the wavelet subband image, and performing tensor decomposition on all wavelet subband images to obtain a core tensor and a pattern matrix; performing entropy encryption encoding on the core tensor and the pattern matrix to obtain an encrypted image; and constructing a final image based on the encrypted image and the enhanced edge information. The present invention can make the image edge clearer while ensuring the compression and encryption effects.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an edge enhancement image compression encryption method, device, medium and product. Background Art

[0002] Traditional data transmission and storage is only compressed, without considering the image security issues caused by the "compression-decompression" process. At the same time, images have always been a major problem in information storage, processing, exchange and transmission due to their large amount of information. Therefore, compressing and encrypting image information so that it can be safely transmitted on the channel has been a goal that people have been pursuing for many years. When reconstructing low-bit rate images, edge texture information will be lost. Therefore, it is very necessary to process the edge information of the image to make the reconstructed image edge clearer. Summary of the invention

[0003] The purpose of the present invention is to provide an edge-enhanced image compression and encryption method, device, medium and product, which can make the image edge clearer while ensuring the compression and encryption effects.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] An edge enhancement image compression encryption method, comprising:

[0006] Using an edge detection operator to perform edge detection on the original image to obtain edge information; the original image is a multispectral image;

[0007] An image enhancement algorithm is used to enhance the edge information to obtain enhanced edge information;

[0008] The chaotic mapping method is used to diffuse and encrypt the original image to obtain a chaotic mapping image;

[0009] Performing wavelet transform on the chaotic map image to obtain wavelet subband images; the wavelet subband images include: low-frequency subband images, high-frequency horizontal detail subband images, high-frequency vertical detail subband images and high-frequency diagonal detail subband images;

[0010] Performing scrambling transformation on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image;

[0011] Performing tensor decomposition on the low-frequency sub-band transformation image, the high-frequency horizontal detail sub-band image, the high-frequency vertical detail sub-band image and the high-frequency diagonal detail sub-band image to obtain a core tensor and a mode matrix;

[0012] Perform entropy encryption encoding on the core tensor and the pattern matrix to obtain an encrypted image;

[0013] A final image is constructed according to the encrypted image and the enhanced edge information; the encrypted image in the final image is decompressed and decrypted and then added to the enhanced edge information in the final image to obtain a restored image of the original image.

[0014] Optionally, edge detection is performed on the original image using an edge detection operator to obtain edge information, specifically including:

[0015] An improved Canny operator is used to perform edge detection on the original image to obtain edge information; the improved Canny operator eliminates salt and pepper noise in the original image through adaptive median filtering, calculates gradients by adding 45° and 135° gradient operator templates on the basis of the Sobel operator, determines the edge through the maximum between-class and within-class variance ratio method, and realizes edge detection.

[0016] Optionally, a chaotic mapping method is used to diffuse and encrypt the original image to obtain a chaotic mapping image, which specifically includes:

[0017] The chaotic sequence generated by Lorenz three-dimensional chaotic mapping is used to diffuse and encrypt the original image to obtain a chaotic mapping image.

[0018] Optionally, performing wavelet transform on the chaotic map image to obtain a wavelet subband image specifically includes:

[0019] The chaotic map image is subjected to two-dimensional lifting 9 / 7 wavelet transform to obtain a wavelet subband image.

[0020] Optionally, performing a scrambling transformation on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image specifically includes:

[0021] A key-sensitive Arnold scrambling transform is performed on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image; the key-sensitive Arnold scrambling transform introduces a key into the Arnold scrambling transform.

[0022] Optionally, performing tensor decomposition on the low-frequency sub-band transform image, the high-frequency horizontal detail sub-band image, the high-frequency vertical detail sub-band image, and the high-frequency diagonal detail sub-band image to obtain a core tensor and a pattern matrix, specifically including:

[0023] The Tucker decomposition method is used to perform tensor decomposition on the low-frequency subband transform image, the high-frequency horizontal detail subband image, the high-frequency vertical detail subband image and the high-frequency diagonal detail subband image to obtain the core tensor and the mode matrix.

[0024] Optionally, entropy encryption encoding is performed on the core tensor and the pattern matrix to obtain an encrypted image, specifically including:

[0025] The SPECK coding method is used to entropy encode the core tensor and pattern matrix, and real-time chaotic encryption is performed during the coding process to obtain the encrypted image.

[0026] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the edge enhancement image compression encryption method.

[0027] A computer-readable storage medium stores a computer program, which implements the edge enhancement image compression encryption method when executed by a processor.

[0028] A computer program product comprises a computer program, which implements the edge enhancement image compression encryption method when executed by a processor.

[0029] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] The edge enhancement image compression encryption method provided by the present invention obtains an encrypted image by performing chaotic mapping diffusion encryption, wavelet transform, scrambling transform, tensor decomposition and entropy encryption coding processing on the original image, so that the encrypted image has a higher peak signal-to-noise ratio and better spectrum fidelity, and the information entropy is closer to the maximum value, which can better hide the original image features; by performing edge detection and image enhancement on the original image, the enhanced edge information is obtained, and under the condition of low bit rate, the edge texture information is richer, and the final image is constructed together with the encrypted image, so that the edge of the restored image can be clearer. Therefore, the present invention can make the image edge clearer while ensuring the compression and encryption effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 The present invention provides a flow chart of the edge enhancement image compression encryption method.

[0033] Figure 2 This is a flowchart of the edge enhancement image compression encryption method provided by the present invention.

[0034] Figure 3 This is a comparison chart of the effects of edge detection on the original test image using different edge detection operators provided by the present invention.

[0035] Figure 4 This is a comparison diagram of the effects of edge detection on a test image with 30% salt and pepper noise added using different edge detection operators provided by the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The purpose of the present invention is to provide an edge-enhanced image compression and encryption method, device, medium and product, which can make the image edge clearer while ensuring the compression and encryption effects.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 and Figure 2 As shown, the edge enhancement image compression encryption method provided by the present invention includes:

[0040] S1: Use an edge detection operator to perform edge detection on the original image to obtain edge information. The original image is a multispectral image.

[0041] Preferably, the present invention uses an improved Canny operator to perform edge detection on the original image to obtain edge information. The improved Canny operator eliminates salt and pepper noise in the original image through adaptive median filtering, calculates gradients by adding 45° and 135° gradient operator templates on the basis of the Sobel operator, and determines the edge through the maximum inter-class and intra-class variance ratio method to achieve edge detection.

[0042] Traditional edge detection operators include Roberts operator, Sobel operator, Prewitt operator, Laplacian operator, LOG operator and Canny operator, among which the Canny operator has better detection effect and stronger anti-noise ability.

[0043] The specific process of Canny operator edge detection includes: 1. Gaussian blurring the image to reduce the impact of noise; 2. Calculating the gradient of the image; 3. Non-maximum suppression of the gradient to eliminate edge responses that may be caused by noise; 4. Using the double threshold method to determine the edge; 5. Tracking the edge to form a continuous edge contour.

[0044] The improved Canny operator provided by the present invention first adopts adaptive median filtering to replace traditional Gaussian filtering to eliminate salt and pepper noise, then adds 45° and 135° gradient templates to calculate the gradient, and finally uses the maximum between-class and within-class variance ratio method to replace the double threshold method to determine the edge, which can better connect images.

[0045] The traditional Canny operator, Sobel operator, LOG operator and the improved Canny operator provided by the present invention are used to perform edge detection on the original test image and the test image with 30% salt and pepper noise added, and the experimental results are compared. Figure 3 and Figure 4 As shown. From the comparison results, it can be seen that for the original test image, the Canny operator and the improved Canny operator provided by the present invention are significantly better than the Sobel operator and the LOG operator, and the improved Canny operator provided by the present invention can better retain edge details than the Canny operator, and the detected image edges are more continuous. For noisy images, the Sobel operator and the LOG operator are obviously ineffective and are very sensitive to noise. Although the Canny operator can filter out noise very well, there is still some noise at the edge of the image, and the edge details are poorly connected. The improved Canny operator provided by the present invention can filter out noise very well, and the noise at the edge of the image can also be filtered out very well, the image edge details are better connected, and more details can be retained.

[0046] S2: Use an image enhancement algorithm to enhance the edge information to obtain enhanced edge information.

[0047] Commonly used image enhancement algorithms include: histogram equalization, gray world algorithm, Retinex algorithm, color enhancement algorithm, image denoising algorithm, etc., which will not be described here.

[0048] S3: Use the chaotic mapping method to diffuse and encrypt the original image to obtain a chaotic mapping image.

[0049] Preferably, the Lorenz three-dimensional chaotic mapping is used to generate a chaotic sequence to diffuse and encrypt the original image to obtain a chaotic mapping image.

[0050] The Lorenz three-dimensional chaotic mapping equation is shown in formula (1).

[0051]

[0052] Among them, a, b, and c are the control parameters of the chaotic system. When a=10, b=8 / 3, and c is greater than 24.74, the system is in a chaotic state. x1, y1, and z1 are the initial values ​​of the chaotic system. are three chaotic sequences generated by the chaotic system after a certain number of iterations. Taking the 4-band multispectral image as an example, the chaotic system is iterated by initial parameters, and the first 1000 points are discarded to obtain three chaotic sequences, namely x n ,y n 、z n , through binarization and XOR operation of any two chaotic sequences, six chaotic sequences are obtained, namely X n , Y n , Z n , H n1 , H n2 , H n3 , then select X n , Y n , Z n , H n1 These four chaotic sequences perform diffusion processing on each spectral segment of the multispectral image to encrypt the original multispectral image.

[0053] S4: Perform wavelet transform on the chaotic map image to obtain wavelet subband images. The wavelet subband images include: low-frequency subband images, high-frequency horizontal detail subband images, high-frequency vertical detail subband images, and high-frequency diagonal detail subband images.

[0054] Preferably, a two-dimensional lifting 9 / 7 wavelet transform is performed on the chaotic map image to obtain a wavelet subband image, which can further encrypt the image while eliminating the spatial redundancy of the multispectral image.

[0055] The process of two-dimensional wavelet transform of digital images is: first, one-dimensional column (row) transform of the image is performed, and then one-dimensional row (column) transform of the image is performed. Regardless of whether the row transform is performed first or the column transform is performed first, the final image will always be divided into four regions, namely low-frequency sub-band LL, high-frequency horizontal detail sub-band LH, high-frequency vertical detail sub-band HL and high-frequency diagonal detail sub-band HH. The size of each wavelet sub-band image is 1 / 4 of the original image, which reflects the resolution characteristics of wavelet transform. The low-frequency sub-band LL preserves the information of the original image, and the high-frequency horizontal detail sub-band LH, high-frequency vertical detail sub-band HL and high-frequency diagonal detail sub-band HH preserve the edge and contour information of the original image.

[0056] Compared with the first generation wavelet transform, the lifting step of lifting wavelet is easier to implement, the amount of calculation is greatly reduced, the calculation speed is higher, and the application scope of wavelet transform is expanded. The one-dimensional 9 / 7 wavelet transform based on the lifting algorithm is mainly implemented through three steps, namely odd-even separation, lifting stage and numerical change. The implementation process is shown in equations (2), (3) and (4) respectively.

[0057]

[0058] Among them, x(2n+1) represents the odd sequence obtained by separating the sequence x(n), and x(2n) represents the even sequence obtained by separating the sequence x(n). All are intermediate parameters, d i represents the high frequency part of the sequence x(n), s i represents the low-frequency part of the sequence x(n), and α, β, γ, δ and K are all wavelet transform parameters.

[0059] The 9 / 7 wavelet transform has 9 low-pass filter coefficients and 7 high-pass filter coefficients, as shown in equations (5) and (6).

[0060]

[0061] The low-pass filter coefficient h[n] and the high-pass filter coefficient g[n] are divided into odd and even terms, and then Z-transformed to obtain equation (7).

[0062]

[0063] Among them, H e (z) represents the even-numbered low-pass filter coefficients, H0(z) represents the odd-numbered low-pass filter coefficients, G e (z) represents the even-numbered terms of the high-pass filter coefficient, and G0(z) represents the odd-numbered terms of the high-pass filter coefficient.

[0064] Substituting the four values ​​obtained from the above equation into the polynomial matrix P(z) yields equation (8).

[0065]

[0066] Among them, α=-1.586134342, β=-0.052980118, γ=-0.882911075, δ=0.443506852, K=0.812893066.

[0067] The 9 / 7 wavelet transform has an ideal lossy compression effect. The frequency band after the 9 / 7 wavelet transform is just within the sensitive frequency band of the human eye, making full use of the sensitivity of the human eye to certain frequency bands. Compared with the first generation wavelet, the lifting wavelet has been greatly improved in terms of computational complexity and application prospects.

[0068] S5: Perform scrambling transformation on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image.

[0069] Preferably, a key-sensitive Arnold scrambling transform is performed on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image. The key-sensitive Arnold scrambling transform introduces a key into the Arnold scrambling transform.

[0070] The form of generalized Arnold transform is shown in formula (9).

[0071]

[0072] Where k is the number of iterations, N is the size of the image, A is the scrambling transformation matrix, a and b are initial parameters (when a=b=1, the transformation is a narrow Arnold transformation), (x, y) is the value before the transformation, and (x', y') is the value after the transformation.

[0073] Expand the low-frequency subband into a one-dimensional vector and normalize the pixel value to the range of [0, 255]. In order to improve the key sensitivity and key space of the algorithm, the present invention converts the key (x0, y0) T Introduced into the transformation, the transformation form is shown in formula (10).

[0074]

[0075] The scrambled image can be restored by inverse transformation, and the inverse transformation form is shown in formula (11).

[0076]

[0077] S6: Perform tensor decomposition on the low-frequency subband transform image, the high-frequency horizontal detail subband image, the high-frequency vertical detail subband image, and the high-frequency diagonal detail subband image to obtain a core tensor and a pattern matrix.

[0078] Preferably, the Tucker decomposition method is used to perform tensor decomposition on the low-frequency sub-band transformation image, the high-frequency horizontal detail sub-band image, the high-frequency vertical detail sub-band image and the high-frequency diagonal detail sub-band image to obtain a core tensor and a pattern matrix, thereby eliminating the spectral redundancy and spatial residual redundancy of the multispectral image.

[0079] The tensor decomposition expression is shown in formula (12).

[0080] Y≈G×1A (1) ×2A (2) ×3A (3) (12)

[0081] Among them, A (n1) is a non-negative pattern matrix, G× n1 A (n1) is the n1-mode product of tensor G and matrix A, where n1 is 1, 2, or 3. G is a low-dimensional non-negative core tensor. Y is the approximate tensor obtained after decomposition.

[0082] G in formula (12) is usually represented by l2 norm. To be more suitable for image compression, the present invention uses l1 norm to represent G. Non-negative tensor Tucker decomposition is an optimization problem for solving formula (13).

[0083]

[0084] Wherein, F represents the norm value.

[0085] dGN is iterated by:

[0086]

[0087] Where β=[vec(A (1) ) T ,vec(A (2) ) T ,vec(A (3 ) T ], I is the unit matrix, H is the Hessian matrix, μ is the damping parameter, g is the gradient, y=vec(Y), J is the Jacobian determinant matrix, and vec() represents matrix vectorization.

[0088] According to different wavelet subbands, Replace with or In order to optimize the above formula, the dGN (Damped Gauss-Newton) algorithm is often used. However, due to its large amount of calculation and difficulty in implementation, it is rarely used in multispectral image processing. On this basis, the present invention improves the algorithm to make it more suitable for multispectral image compression. The huge amount of calculation required for the dGN iterative process mainly comes from the calculation of the Hessian matrix H and the gradient g. Therefore, the present invention improves the process and obtains an approximate Hessian matrix (15) after optimization.

[0089] H=G+ZKZ T (15)

[0090] The optimized g is shown in formula (16).

[0091]

[0092] Based on the optimized H and g and using the l1 norm, the approximate core tensor G can be obtained.

[0093] S7: Perform entropy encryption on the core tensor and the pattern matrix to obtain an encrypted image.

[0094] Preferably, the SPECK coding method is used to perform entropy coding on the core tensor and the pattern matrix, and real-time chaotic encryption is performed during the coding process to obtain an encrypted image.

[0095] Entropy coding is a coding process that does not lose any information according to the entropy principle. Common entropy coding mainly includes Shannon coding, Huffman coding, arithmetic coding, SPECK coding, etc. The present invention uses the SPECK coding method. During SPECK coding, the set is divided according to the importance of the coding coefficients. Therefore, during coding, only the data that is more important for image reconstruction needs to be encrypted separately through the chaotic sequence, and the other data is operated according to the original method, and then encoded to obtain the required compressed encryption code stream.

[0096] S8: constructing a final image according to the encrypted image and the enhanced edge information. The encrypted image in the final image is decompressed and decrypted and then added to the enhanced edge information in the final image to obtain a restored image of the original image.

[0097] In summary, the present invention firstly detects the edge information of the image using the improved Canny operator, and then enhances the edge information using the image enhancement method; then uses the Lorenz three-dimensional chaotic mapping to generate a chaotic sequence for image diffusion encryption; then performs a two-dimensional lifting 9 / 7 wavelet transform on the processed image, and performs a key-sensitive Arnold scrambling transform on the obtained low-frequency image, thereby eliminating the spatial redundancy of the multispectral image and further encrypting the image; secondly, the improved fast Tucker decomposition is used for the generated wavelet subband tensor, thereby eliminating the spectral redundancy and spatial residual redundancy of the multispectral image; then the obtained core tensor and pattern matrix are entropy encoded, and real-time chaotic encryption is performed during the encoding process; then the multispectral image is reconstructed by a decompression and decryption algorithm; finally, the enhanced edge information is added to the reconstructed image to obtain the final decompressed image, that is, the restored image of the original image. Compared with the prior art, the present invention has a higher peak signal-to-noise ratio (PSNR) and better spectrum fidelity, and the information entropy is closer to the maximum value, which is better than similar algorithms and can better hide the original image features; in addition, the number of pixels change rate (NPCR) and the unified average changing intensity (UACI) of the present invention are better than similar algorithms and can better resist differential attacks. At the same time, under low bit rate conditions, edge texture information is richer.

[0098] In one embodiment, a computer program product is also provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0099] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0100] In one embodiment, a computer device is also provided, including a memory and a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0101] Furthermore, the computer device also includes an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. 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 through a network connection. When the computer program is executed by the processor, the steps in the above-mentioned method embodiments are implemented.

[0102] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0104] The technical features of the above embodiments may 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.

[0105] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An edge-enhanced image compression encryption method, characterized in that: include: Use edge detection operator to detect the edge of the original image and obtain edge information; The original image is a multispectral image; An image enhancement algorithm is used to enhance the edge information to obtain enhanced edge information; The chaotic mapping method is used to diffuse and encrypt the original image to obtain a chaotic mapping image; Performing wavelet transform on the chaotic map image to obtain wavelet subband images; the wavelet subband images include: low-frequency subband images, high-frequency horizontal detail subband images, high-frequency vertical detail subband images and high-frequency diagonal detail subband images; Performing scrambling transformation on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image; Performing tensor decomposition on the low-frequency sub-band transformation image, the high-frequency horizontal detail sub-band image, the high-frequency vertical detail sub-band image and the high-frequency diagonal detail sub-band image to obtain a core tensor and a mode matrix; Perform entropy encryption encoding on the core tensor and the pattern matrix to obtain an encrypted image; A final image is constructed according to the encrypted image and the enhanced edge information; the encrypted image in the final image is decompressed and decrypted, and then added to the enhanced edge information in the final image to obtain a restored image of the original image; The edge detection operator is used to perform edge detection on the original image to obtain edge information, including: An improved Canny operator is used to perform edge detection on the original image to obtain edge information; the improved Canny operator eliminates salt and pepper noise in the original image through adaptive median filtering, calculates gradients by adding 45° and 135° gradient operator templates on the basis of the Sobel operator, determines the edge through the maximum inter-class and intra-class variance ratio method, and realizes edge detection; The low-frequency sub-band image is subjected to scrambling transformation to obtain a low-frequency sub-band transformed image, which specifically includes: A key-sensitive Arnold scrambling transform is performed on the low-frequency sub-band image to obtain a low-frequency sub-band transformed image; the key-sensitive Arnold scrambling transform introduces a key into the Arnold scrambling transform.

2. The edge enhancement image compression encryption method according to claim 1, characterized in that: The chaotic mapping method is used to diffuse and encrypt the original image to obtain a chaotic mapping image, which specifically includes: The chaotic sequence generated by Lorenz three-dimensional chaotic mapping is used to diffuse and encrypt the original image to obtain a chaotic mapping image.

3. The edge enhancement image compression encryption method according to claim 1, characterized in that: Perform wavelet transform on the chaotic map image to obtain a wavelet subband image, which specifically includes: The chaotic map image is subjected to two-dimensional lifting 9 / 7 wavelet transform to obtain a wavelet subband image.

4. The edge enhancement image compression encryption method according to claim 1, characterized in that: The low-frequency sub-band transform image, the high-frequency horizontal detail sub-band image, the high-frequency vertical detail sub-band image and the high-frequency diagonal detail sub-band image are subjected to tensor decomposition to obtain the core tensor and the mode matrix, including: The Tucker decomposition method is used to perform tensor decomposition on the low-frequency subband transform image, the high-frequency horizontal detail subband image, the high-frequency vertical detail subband image and the high-frequency diagonal detail subband image to obtain the core tensor and the mode matrix.

5. The edge enhancement image compression encryption method according to claim 1, characterized in that: The core tensor and pattern matrix are entropy encrypted to obtain an encrypted image, including: The SPECK coding method is used to entropy encode the core tensor and pattern matrix, and real-time chaotic encryption is performed during the coding process to obtain the encrypted image.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the edge enhancement image compression encryption method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the edge enhancement image compression encryption method according to any one of claims 1 to 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the edge enhancement image compression encryption method according to any one of claims 1 to 5 is implemented.

Images