CN115348455B - Satellite Internet of things image compression method and device - Google Patents
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Abstract
The invention provides a satellite Internet of things image compression method and device. The satellite Internet of things image compression method comprises the following steps: performing dictionary learning-based compression on the acquired Internet of things image to obtain a primary compressed image; and carrying out lossless compression coding based on arithmetic coding on the first-level compressed image to obtain a second-level compressed image. Aiming at an image acquired by a satellite Internet of things terminal, a two-stage image compression model based on sparse representation is provided to realize two-stage lossless compression of the image. The compression process is divided into two steps, in the first step, the transmission of the image is replaced by the transmission of the sparse coefficient after sparse representation, and the first-stage compression of the image is completed; and the second step of carrying out secondary compression coding on the sparse coefficient by utilizing arithmetic coding to form an image compressed bit stream. The compression process is completed. The compression of the large-data-volume images can be completed by utilizing a two-stage image compression model represented in a sparse mode, and the capacity of the satellite internet of things terminal for transmitting the large-data-volume images at one time is achieved.
Description
Technical Field
The invention belongs to the technical field of satellite Internet of things, and particularly relates to a method and a device for compressing images of satellite Internet of things.
Background
The satellite Internet of things has wide coverage region, global coverage can be realized, and the arrangement of terminal equipment of the satellite Internet of things is hardly limited by space; the device is hardly influenced by weather and geographic conditions, and can work all day long; the system has strong survivability and can still normally work under the emergency situations of natural disasters, emergencies and the like; the method has the characteristics of easy provision of uninterrupted network connection for large-range moving targets (airplanes, ships and the like).
Meanwhile, with the development of high-resolution image acquisition technology, the data volume of acquired images is greatly increased, and serious challenges are brought to the compression, storage and transmission of the images.
The multi-beam satellite-borne antenna technology widely adopted by the Internet of things satellite is used for improving the system capacity and supporting the connection requirement of massive terminals so as to provide network connection support and service for massive large-range moving targets more easily, the uplink bandwidth of the Internet of things constellation established and reconstructed at the present stage does not exceed 20M, the Internet of things constellation service is provided by adopting multiple access modes of Frequency Division Multiple Access (FDMA), time Division Multiple Access (TDMA), code Division Multiple Access (CDMA) and time division duplex/frequency division duplex (TDD/FDD), and the transmission time slot and cell channel of a single-terminal user are scheduled so as to ensure the access number of users. And the problem that the uplink bandwidth of the satellite Internet of things is limited in the prior art, so that the satellite Internet of things terminal cannot transmit images with large data volume at a single time is caused.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a satellite Internet of things image compression method and device, which at least partially solve the problem that an image with large data volume cannot be transmitted at a time in the prior art.
In a first aspect, an embodiment of the present disclosure provides a satellite internet of things image compression method, including:
performing dictionary learning-based compression on the acquired Internet of things image to obtain a primary compressed image;
and carrying out lossless compression coding based on arithmetic coding on the first-level compressed image to obtain a second-level compressed image.
Optionally, the internet of things image includes a 360-degree panoramic image of the current position.
Optionally, the obtained internet of things image is compressed based on dictionary learning to obtain a first-level compressed image, including:
obtaining a low-resolution image corresponding to the high-resolution image by matrix low-rank sparse decomposition;
constructing a sample block set based on the generated blocks of the low-resolution image and the high-resolution image;
and performing dictionary training and sparse coefficient solving on the sample block set to obtain a sparse coefficient solution.
Optionally, the performing lossless compression coding based on arithmetic coding on the first-level compressed image to obtain a second-level compressed image includes:
and (4) performing arithmetic coding by taking the sparse coefficient solution as an information source signal to obtain a compressed bit stream.
Optionally, constructing a matrix model in the sample block set based on the generated blocks of the low-resolution image and the high-resolution image is as follows:
Optionally, performing dictionary training and solution of sparse coefficients on the sample block set to obtain a sparse coefficient solution, including:
selecting an initial redundant dictionary D, and solving a sparse coefficient X by adopting an over-complete cosine dictionary and an OMP algorithm;
and updating the atoms of the initial redundant dictionary D according to the sparse coefficient X.
Optionally, the performing lossless compression coding on the first-level compressed image based on arithmetic coding to obtain a second-level compressed image includes:
counting the sequence probability of the sequence information source;
obtaining a cut-off length based on the sequence probability;
intercepting the sequence information source based on the cut-off length to obtain a corresponding sequence interval;
and selecting a code word in the sequence interval, and intercepting the set position of the code word to obtain the arithmetic coding of the sequence information source.
Optionally, the calculation formula in obtaining the cutoff length based on the probability is as follows:
Optionally, selecting a codeword within the sequence interval, and intercepting the set bit of the codeword, including:
and (3) taking any number in the sequence interval as a code word, and intercepting the front L bits of the code word after the decimal point of the binary system.
In a second aspect, an embodiment of the present disclosure further provides a satellite internet of things image compression device, including:
the first-level compression module is used for compressing the acquired Internet of things image based on dictionary learning to obtain a first-level compression image;
and the second-stage compression module is used for carrying out lossless compression coding based on arithmetic coding on the first-stage compressed image to obtain a second-stage compressed image.
The invention provides a satellite Internet of things image compression method and device. According to the satellite Internet of things image compression method, the satellite Internet of things image is subjected to secondary compression, firstly compression based on dictionary learning is carried out, and then lossless compression coding based on arithmetic coding is carried out, so that compression of a large-data-volume image is completed. The purpose of transmitting large-data-volume images at a time by the satellite Internet of things terminal is achieved.
The further compression process is divided into two steps, in the first step, the transmission of the image is replaced by the transmission of the sparse coefficient after sparse representation, and the first-stage compression of the image is completed; and the second step of carrying out secondary compression coding on the sparse coefficient by utilizing arithmetic coding to form an image compressed bit stream. The compression process is completed. The compression of the large-data-volume images can be completed by utilizing a two-stage image compression model represented in a sparse mode, and the purpose that the large-data-volume images are transmitted by the satellite internet of things terminal at one time is achieved. With a higher lossless compression ratio.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.
Fig. 1 is a flowchart of a satellite internet of things image compression method provided by an embodiment of the disclosure;
FIG. 2 is a flow chart of one-level compression based on dictionary learning provided by an embodiment of the present disclosure;
fig. 3 is a flowchart of arithmetic coding provided in an embodiment of the disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
It is to be understood that the embodiments of the present disclosure are described below by way of specific examples, and that other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure herein. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be carried into practice or applied to various other specific embodiments, and various modifications and changes may be made in the details within the description and the drawings without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be further noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, number and proportion of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
As shown in fig. 1, the embodiment discloses a satellite internet of things image compression method, which includes:
performing dictionary learning-based compression on the acquired Internet of things image to obtain a primary compressed image;
and carrying out lossless compression coding based on arithmetic coding on the first-level compressed image to obtain a second-level compressed image.
Optionally, the internet of things image includes a 360-degree panoramic image of the current position.
Optionally, the obtained internet of things image is compressed based on dictionary learning to obtain a first-level compressed image, including:
obtaining a low-resolution image corresponding to the high-resolution image by matrix low-rank sparse decomposition;
constructing a sample block set based on the generated blocks of the low-resolution image and the high-resolution image;
and performing dictionary training and sparse coefficient solving on the sample block set to obtain a sparse coefficient solution.
Optionally, the performing lossless compression coding based on arithmetic coding on the first-level compressed image to obtain a second-level compressed image includes:
and performing arithmetic coding by taking the sparse coefficient solution as an information source signal to obtain a compressed bit stream.
Optionally, constructing a matrix model in the sample block set based on the generated blocks of the low-resolution image and the high-resolution image is as follows:
Optionally, performing dictionary training and solution of sparse coefficients on the sample block set to obtain a sparse coefficient solution, including:
selecting an initial redundant dictionary D, and solving a sparse coefficient X by adopting an over-complete cosine dictionary and an OMP algorithm;
and updating the atoms of the initial redundant dictionary D according to the sparse coefficient X.
Optionally, the performing lossless compression coding on the first-level compressed image based on arithmetic coding to obtain a second-level compressed image includes:
counting the sequence probability of the sequence information source;
obtaining a cut-off length based on the sequence probability;
intercepting the sequence information source based on the cut-off length to obtain a corresponding sequence interval;
and selecting a code word in the sequence interval, and intercepting the set position of the code word to obtain the arithmetic coding of the sequence information source.
Optionally, the calculation formula in obtaining the cutoff length based on the probability is as follows:
Optionally, selecting a codeword within the sequence interval, and intercepting the set bit of the codeword, including:
and (3) taking any number in the sequence interval as a code word, and intercepting the front L bits of the code word after the decimal point of the binary system.
In a specific example, the satellite internet of things terminal device in this embodiment can perform a high-resolution image acquisition function, and can acquire an image in a range of 360 degrees in the current position in real time. The satellite internet of things terminal equipment can provide an image transmission function, and the uplink bandwidth is 20M at most.
The satellite Internet of things terminal equipment has the functions of acquisition and transmission, and the transmission function is divided into two steps, namely preprocessing in one step and transmission in the other step. The preprocessing is to perform lossless compression on the image in advance to form an image compression bit stream; transmission is the process of data upload.
In a two-stage image compression model, the first step of completing the first-stage compression of the image by transmitting a sparse coefficient after sparse representation to replace the transmission of the image; and the second step of carrying out secondary compression coding on the sparse coefficient by utilizing arithmetic coding to form an image compressed bit stream.
And obtaining a degraded image corresponding to the high-resolution image, namely a low-resolution image, by matrix low-rank sparse decomposition, then constructing a sample block set by partitioning the low-resolution image generated by a degradation algorithm and the original high-resolution image, and performing dictionary training and sparse coefficient solving after synthesis.
And (4) performing arithmetic coding by taking the obtained sparse coefficient solution as an information source signal, further compressing the data volume, and finally obtaining a compressed bit stream for transmission.
The dictionary learning-based one-level compression comprises the following steps:
the first-stage compression is to perform image compression based on sparse representation on the image acquired by the terminal to obtain a sparse representation coefficient. As shown in fig. 2.
The dictionary learning algorithm is one of sparse representation dictionary learning methods. Sparse representation is considered to be an efficient and robust feature representation method. In the field of pattern recognition, the conventional representation method uses a linear combination of orthogonal bases in a signal space to express information of an image, so that a problem is simplified, such as a PCA algorithm. And the sparse representation is described by utilizing the linear combination of a few atoms in the dictionary, and is more flexible compared with the traditional expression mode. The sparse representation can be generally regarded as generalized Vector Quantization (VQ), by which the energy of the signal can be concentrated to a few atoms whose coefficients are not 0, thereby further revealing the main features and the intrinsic structure of the signal. The core problems of the sparse representation theory include sparse representation algorithm design, dictionary construction and the like. The sparse representation model is:
wherein
For sample data to be represented (one-dimensional column vector),
either a dictionary or a codebook (one column is one codeword),
a coefficient matrix (one-dimensional column vector, more 0 elements, other non-0 real numbers) is expressed sparsely, and
the number of non-0 is the sparsity of the sparse matrix.
In the construction of a sample set, newly added samples are expected to be enriched as much as possible and represent different characteristics as much as possible, and a dictionary trained by the sample set can be enriched and can express images more accurately and concisely. In the image super-resolution reconstruction process, the construction of a dictionary is a critical problem, whether the image can be accurately sparsely represented depends on the performance of the dictionary to a great extent. The method for constructing the overcomplete dictionary adopts a learning-based method, adopts the idea of machine learning, and constructs a learning dictionary with certain pertinence by learning a certain type of samples, so that target signals are sparsely represented more accurately.
Sparse representation of an image, i.e. representing a given image with as few atoms as possible in an overcomplete dictionary, the general model of sparse representation is as follows:
wherein:
is an overcomplete dictionary;
is the input sample;
as a sparse matrix
A column vector of (a);
is the upper limit of the number of non-zero elements set (i.e., sparsity). If the matrix form is adopted for representation, the general model of sparse representation can be simplified as follows:
the OMP algorithm is an algorithm that orthogonalizes all selected atoms at each step of the decomposition. The OMP algorithm is as follows: in that
Middle and column vector
In order for the signal to be represented,
is composed of
Column dictionary, each column of which calls an atom, requires a solution vector
. Normalizing each column of the matrix D to obtain initial residue
In the ith step of the iteration, the AND signal residue is selected from the matrix D
Best matching column
Namely:
wherein
Is the previously selected N columns of atoms. The residue is updated to
And make it possible to
I.e. updating the coordinates of the first N and j columns of D
The iteration continues until the signal residue reaches a specified accuracy. The expression finally obtained is
Where s represents the set of columns selected in matrix D.
Representing the jth column of matrix D.
And a second step of updating atoms of the initial dictionary according to the sparse coefficient X obtained in the first step. The two steps are alternately carried out, and finally an optimized solution can be obtained.
Two-level lossless compression coding based on arithmetic coding.
The sparse coefficient can be obtained through the steps, and then entropy coding and further compression are carried out on the sparse coefficient.
The basic principle of arithmetic coding is: according to the probability of different symbol sequences of the information source, dividing a [0,1] interval into non-overlapping subintervals, wherein the width of the subintervals is just the probability of each symbol sequence. Therefore, different symbol sequences sent by the information source correspond to the subintervals one to one, any real number in each subinterval can be used for representing the corresponding symbol sequence, the binary decimal is a code word corresponding to the symbol sequence, the probability matching between the length of the binary decimal and the sequence is intercepted, and the efficient coding is realized.
The encoding process is as follows:
step 1, supposing that a binary a1 and a2 symbol sequence source is coded, firstly, the probability of a1 and a2 occurrence is calculated.
Step 2. Calculate the probability p = p (x 1) … p (x 2) of the sequence, then calculate the cut-off length according to the following formula
Where the boxes represent rounded-up.
And 3, intercepting the corresponding interval from left to right according to the symbols of the information source, if the first symbol is a1, intercepting a1 section of the interval from 0 to 1, and if the second symbol is a2, intercepting a2 section of the interval from 0 to a1 on the basis of the previous step, namely, taking the a2 section from 0 to a1, and so on until the last symbol of the sequence.
And 4, taking any number as a code word in the sequence interval calculated in the step 3, and intercepting front L bits after the binary decimal point of the code word to obtain the arithmetic coding of the information source sequence. The arithmetic coding flow chart is shown in fig. 3.
The codes obtained by arithmetically coding the sparse coefficients are obtained, and then the image compressed bit stream is formed for transmission.
This embodiment still discloses a satellite thing networking image compression device, includes:
the first-level compression module is used for compressing the acquired Internet of things image based on dictionary learning to obtain a first-level compression image;
and the second-stage compression module is used for carrying out lossless compression coding based on arithmetic coding on the first-stage compressed image to obtain a second-stage compressed image.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
In the present disclosure, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the block diagrams of devices, apparatuses, devices, systems, etc. referred to in the present disclosure are used merely as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
In addition, as used herein, "or" as used in a list of items beginning with "at least one" indicates a separate list, such that, for example, a list of "A, B or at least one of C" means a or B or C, or AB or AC or BC, or ABC (i.e., a and B and C). Furthermore, the word "exemplary" does not mean that the described example is preferred or better than other examples.
It is also noted that in the systems and methods of the present disclosure, components or steps may be decomposed and/or re-combined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.
Various changes, substitutions, and alterations to the techniques described herein may be made without departing from the techniques of the teachings as defined by the appended claims. Moreover, the scope of the claims of the present disclosure is not limited to the particular aspects of the process, machine, manufacture, composition of matter, means, methods and acts described above. Processes, machines, manufacture, compositions of matter, means, methods, or acts, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or acts.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (8)
1. A satellite Internet of things image compression method is characterized by comprising the following steps:
performing dictionary learning-based compression on the acquired Internet of things image to obtain a primary compressed image;
lossless compression coding based on arithmetic coding is carried out on the first-stage compression-molded image to obtain a second-stage compression image;
carry out the compression based on dictionary study to the thing networking image that obtains, obtain one-level compression image, include:
obtaining a low-resolution image corresponding to the high-resolution image by matrix low-rank sparse decomposition;
constructing a sample block set based on the generated blocks of the low-resolution image and the high-resolution image;
performing dictionary training and sparse coefficient solving on the sample block set to obtain a sparse coefficient solution;
constructing a matrix model in the sample block set based on the generated blocks of the low-resolution image and the high-resolution image as follows:
2. The satellite internet of things image compression method according to claim 1, wherein the internet of things image comprises a 360-degree panoramic image of a current position.
3. The satellite internet of things image compression method according to claim 1, wherein the lossless compression coding based on arithmetic coding is performed on the primary compression-molded image to obtain a secondary compression image, and the method comprises the following steps:
and (4) performing arithmetic coding by taking the sparse coefficient solution as an information source signal to obtain a compressed bit stream.
4. The satellite internet of things image compression method according to claim 1, wherein dictionary training and sparse coefficient solving are performed on a sample block set to obtain a sparse coefficient solution, and the sparse coefficient solution comprises:
selecting an initial redundant dictionary D, and solving a sparse coefficient X by adopting an over-complete cosine dictionary and an OMP algorithm;
and updating the atoms of the initial redundant dictionary D according to the sparse coefficient X.
5. The satellite internet of things image compression method according to claim 1, wherein the lossless compression coding based on arithmetic coding is performed on the primary compression-molded image to obtain a secondary compression image, and the lossless compression coding comprises:
counting the sequence probability of the sequence information source;
obtaining a cut-off length based on the sequence probability;
intercepting a sequence information source based on the cut-off length to obtain a corresponding sequence interval;
and selecting a code word in the sequence interval, and intercepting the set position of the code word to obtain the arithmetic coding of the sequence information source.
6. The satellite internet of things image compression method according to claim 5, wherein a calculation formula in obtaining the cutoff length based on the probability is as follows:
7. The image compression method for the internet of things through the satellite according to claim 5, wherein the steps of selecting a code word in the sequence interval and intercepting the set bit of the code word comprise:
and (3) taking any number in the sequence interval as a code word, and intercepting the front L bits of the code word after the decimal point of the binary system.
8. The utility model provides a satellite thing networking image compression device which characterized in that includes:
the first-level compression module is used for compressing the acquired Internet of things image based on dictionary learning to obtain a first-level compression image;
the second-stage compression module is used for carrying out lossless compression coding on the first-stage compression-molded image based on arithmetic coding to obtain a second-stage compression image;
carry out the compression based on dictionary study to the thing networking image that obtains, obtain one-level compression image, include:
obtaining a low-resolution image corresponding to the high-resolution image by matrix low-rank sparse decomposition;
constructing a sample block set based on the generated blocks of the low-resolution image and the high-resolution image;
performing dictionary training and sparse coefficient solving on the sample block set to obtain a sparse coefficient solution;
constructing a matrix model in the sample block set based on the generated blocks of the low-resolution image and the high-resolution image as follows:
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