CN118353477B - Method, device, equipment and medium for constructing lossy original pattern LDPC source codes - Google Patents
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Abstract
The invention provides a method, a device, equipment and a medium for constructing a lossy original pattern LDPC (low density parity check) source code, which relate to the technical field of data compression and are characterized in that the code rate of an original pattern LDPC code is obtained, the size of an original pattern basic matrix is set according to the code rate and initialized to obtain a first basic matrix, namely an individual in the process of a differential evolution algorithm; after setting parameters of the evolution process, initializing the population to obtain other individuals of the population, and then carrying out mutation, crossover and selection updating operation on each individual in each population respectively until the number of times is limited; and finally, selecting an individual with the lowest distortion value in the population as the optimal individual of the current iteration. In each evolution process, the invention keeps excellent individuals, eliminates low-level individuals, and guides the search process to the global optimal solution through continuous iterative computation. The method has the advantages of high convergence speed, concise control parameters and reliable performance, and effectively improves the efficiency of the LDPC source code construction of the lossy original pattern.
Description
Technical Field
The invention relates to the technical field of data compression, in particular to a method, a device, equipment and a medium for constructing a lossy original pattern LDPC source code.
Background
In the field of signal processing, the bandwidth resources for storing and transmitting information sources can be effectively saved by lossy compression of data, and the method is a key technology for realizing green communication. The complexity and hardware cost of a communication algorithm can be effectively reduced by utilizing data compression to realize information preprocessing. Lossy compression is a key technology for removing data redundancy in a communication system, and the process only retains the most useful information of the information source, so that the data size can be obviously reduced, the communication bandwidth can be saved, and the burden of device information source acquisition can be reduced. The physical layer of wireless communication realizes the lossy compression of the information source, mainly adopts two modules of information source coding and information source decoding, and respectively realizes the lossy compression and the lossy recovery of data.
The DE (DIFFERENTIAL EVOLUTION ) algorithm is a high-efficiency global optimization algorithm and is also a heuristic search algorithm based on population. Compared with the traditional evolutionary strategy, the mathematical structure and the searching process of the DE algorithm are clear and definite.
The DE algorithm is not used in the construction of the source codes of the low density parity check (Protograph Low DENSITY PARITY CHECK, P-LDPC) of the lossy master pattern at present.
In view of the above, the applicant has studied the prior art and has made the present application.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a medium for constructing a lossy master pattern LDPC (low density parity check) source code, which are used for solving the defects that the source code generated in the existing method is inconsistent with people's preference, does not meet the user's requirements and the like.
In order to solve the technical problems, the invention is realized by the following technical scheme:
A method for constructing a lossy original pattern LDPC source code comprises the following steps:
S1, acquiring a code rate of an original pattern LDPC code, setting the size of an original pattern basic matrix according to the code rate, and initializing to obtain a first basic matrix; regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix of each generation of population;
s2, randomly selecting an integer in a range from 0 to the maximum element to replace any element in the first base matrix according to the maximum number of individuals and the maximum element to obtain other base matrixes in the initial population, namely other individuals;
S3, carrying out mutation operation on each individual in the population by utilizing a differential evolution algorithm according to the mutation probability, namely randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current mutation individual;
Wherein, the maximum individual number is set as K, the maximum population algebra is set as G, and the mutation probability is set as The formula of the mutation operation is:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G;
S4, performing cross operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the cross probability, and performing parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain a current individual cross body;
wherein, the cross probability is set as The formula of the crossover operation is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,An ith row and jth column element for a kth individual in the g generation population;
S5, calculating a distortion value of a current individual crossing body, determining whether to update the current individual, if the distortion value of the current individual crossing body is lower than that of the previous generation, adopting the current individual crossing body to update and replace the current individual, otherwise, keeping the current individual unchanged;
S6, iteratively calculating the distortion value of each individual in each population according to the maximum population algebra and the maximum individual number of each generation of population, and updating and replacing until the population algebra reaches the limited maximum times;
S7, selecting an individual with the lowest distortion value in the population as an optimal individual in the current iteration, and outputting the optimal individual as a result.
The invention also provides a device for constructing the LDPC source codes of the lossy master pattern, which comprises the following components:
The first base matrix acquisition unit is used for acquiring a first base matrix, acquiring the code rate of the original pattern LDPC code, setting the size of the original pattern base matrix according to the code rate and initializing the original pattern base matrix to acquire the first base matrix; and regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix of each generation of population;
The population initializing unit is used for obtaining all individuals in the initial population, randomly selecting integers from 0 to the largest element according to the largest individual number and the largest element to replace any element in the first base matrix, and obtaining other base matrixes, namely other individuals, in the initial population;
The variation unit is used for obtaining variation individuals, performing variation operation on each individual in the population by utilizing a differential evolution algorithm according to variation probability, randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current variation individual; wherein, the maximum individual number is set as K, the maximum population algebra is set as G, and the mutation probability is set as The formula of the mutation operation is:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G;
the crossing unit is used for obtaining a crossing body, carrying out crossing operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the crossing probability, and carrying out parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain a current individual crossing body; wherein, the cross probability is set as The formula of the crossover operation is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,An ith row and jth column element for a kth individual in the g generation population;
The distortion value calculation unit is used for selecting and determining the value of the current individual, calculating the distortion value of the current individual crossing body, determining whether to update the current individual, adopting the current individual crossing body to update and replace the current individual if the distortion value of the current individual crossing body is lower than the distortion value of the previous generation, otherwise, keeping the current individual unchanged;
the iteration unit is used for iteratively calculating the distortion value of each individual, and iteratively calculating the distortion value of each individual in each population according to the maximum population algebra and the maximum individual number of each generation population, and updating and replacing until the population algebra reaches the limited maximum times;
And the output unit is used for obtaining a final result, selecting an individual with the lowest distortion value in the population as the optimal individual of the current iteration, and outputting the optimal individual as the result.
The invention also provides a lossy original pattern LDPC source code construction device which comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to realize the lossy original pattern LDPC source code construction method.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium is stored with computer readable instructions, and the computer readable instructions realize the method for constructing the LDPC source codes of the lossy master pattern when being executed by a processor of equipment where the computer readable storage medium is positioned.
In summary, compared with the prior art, the invention has the following beneficial effects: the invention uses DE algorithm to construct the source code of low density parity check (Protograph Low DENSITY PARITY CHECK, P-LDPC) of the lossy master pattern, generates a series of P-LDPC code basic matrixes (including variants, crossings, etc.) through the operations of variation, crossing, selection updating, etc. of the DE algorithm, and finally generates the P-LDPC code with excellent distortion performance by continuously iterating and selecting the individual with the optimal distortion degree as the result.
The method helps the algorithm to jump out of the current local optimal solution through mutation operation, and searches for a wider source code space; through the cross operation, the new characteristics of the variant individuals can be introduced while the excellent characteristics of part of the original individuals are maintained, and the new individuals with more adaptability are generated, so that the performance of the algorithm is improved; in the selection operation, by comparing the distortion values of the original individual and the crossed individual, an individual having a better distortion value is selected as a part of the next generation population. In each evolution process, the invention keeps excellent individuals, eliminates low-level individuals, guides the searching process to the global optimal solution through continuous iterative calculation, has high convergence speed, simple control parameters and reliable performance, and effectively improves the efficiency of the source code construction of the LDPC of the lossy original pattern.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a method for constructing a source code of a lossy orthographic LDPC according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for constructing a lossy original pattern LDPC source code according to an embodiment of the present invention.
Fig. 3 is a graph showing the rate distortion performance of the coding structure method and the AR3A coding method according to the first embodiment of the present invention.
Fig. 4 is a graph showing the rate distortion performance of the coding construction method and the AR4JA coding method according to the first embodiment of the present invention.
Fig. 5 is a schematic diagram of a lossy orthographic LDPC source code construction apparatus according to a second embodiment of the present invention.
The invention is further described in detail below with reference to the drawings and the specific examples.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, of the embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Example 1
An embodiment of the present invention provides a method for constructing a lossy master LDPC source code, which may be implemented by a lossy master LDPC source code constructing apparatus (hereinafter referred to as a constructing apparatus), and in particular, executed by one or more processors in the constructing apparatus.
In this embodiment, the construction device may be an electronic device equipped with a processor carrying a computer program of the lossy master pattern LDPC source code construction method and the computer program being executable, such as a computer, a smart phone, a smart tablet, etc.
In this embodiment, the DE (DIFFERENTIAL EVOLUTION ) algorithm includes three main operations: mutation, crossover and selection. The algorithm randomly generates an initial population and randomly selects two individuals from the population and randomly alters the third individual with their difference vector. The difference vector is weighted and summed with the third individual according to a certain rule to generate an individual variant, which operation is called mutation.
The operation of combining variant individuals with specific predetermined target individual parameters to generate test individuals is referred to as crossover.
If the fitness value of the test individual is greater than the fitness value of the target individual, the test individual will replace the target individual in the next generation. Otherwise, the target individual will be retained, an operation called selection.
In the evolution process of each generation, the algorithm can keep excellent individuals, eliminate low-level individuals and guide the searching process to a global optimal solution through continuous iterative computation.
The DE algorithm is a high-efficiency global optimization algorithm and is also a heuristic search algorithm based on population. Compared with the traditional evolutionary strategy, the mathematical structure and the searching process of the DE algorithm are clear and definite. The DE algorithm uses differential vectors to perturb the candidate solutions proportionally, producing benign abrupt and more competitive offspring solutions. The most outstanding advantages of the DE algorithm are high convergence speed, concise control parameters and reliable performance. In particular, the DE algorithm has a greater potential in optimizing robustness and search accuracy in the face of the existing nonlinear, multi-modal and inseparable complex problems. Each individual in the population has a solution vector, and intelligent optimization searching is realized by utilizing cooperation and competition among the individuals in the population.
The LC-PEXIT (Lossy Compression Protograph Extrinsic Information Transfer) algorithm for the external information transfer of the lossy compression original pattern generates a series of distortion degrees of a basic matrix (including variants, intersections and the like) of the P-LDPC code through the DE algorithm so as to analyze the performance advantages and disadvantages of the basic matrix when the basic matrix is used for lossy source coding, and the P-LDPC code with excellent distortion performance is finally generated through continuous iteration.
As shown in fig. 1-2, a method for constructing source codes of a lossy orthomode LDPC graph includes steps S1 to S7.
S1, acquiring a code rate of an original pattern LDPC code, setting the size of an original pattern basic matrix according to the code rate, and initializing to obtain a first basic matrix; regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number of each generation of population, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix.
Further, the code rate is setMaximum elementThe first base matrix is:
；
wherein, Representing a first basis matrix, wherein the first basis matrix is m rows and n columns, namely, the 1 st individual in the 1 st generation population; Representation of The m-th row and n-th column elements in the matrix.
S2, randomly selecting 0 to the largest element, namely [0 ],And substituting any element in the first base matrix by an integer in the interval to obtain other base matrices in the initial population, namely other individuals.
Wherein the base matrixRepresented as the kth individual in the g-th generation population.
Then, the LC-PEXIT algorithm of the invention is adopted to calculate the distortion values of all the base matrixes in the population through mutation, crossover, selection and update in turnWherein, the method comprises the steps of, wherein, G is more than or equal to 1 and less than or equal to G, K is more than or equal to 1 and less than or equal to K.
S3, carrying out mutation operation on each individual in the population by utilizing a differential evolution algorithm according to the mutation probability, randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current mutation individual.
Setting the maximum number of individuals as K, the maximum population algebra as G and the mutation probability asFurther, the mutation process is as follows:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G.
And S4, performing cross operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the cross probability, and performing parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain the current individual cross body.
Setting the crossover probability asFurther, the formula of the crossover process is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,Is the ith row and jth column element of the kth individual in the g generation population.
S5, calculating a distortion value of the current individual crossing body, determining whether to update the current individual, if the distortion value of the current individual crossing body is lower than that of the previous generation, adopting the current individual crossing body to update and replace the current individual, otherwise, keeping the current individual unchanged.
Further, if the current individualIs lower than the previous generationThe individual after the cross operation is adopted to replace the current individual for updating; otherwise, the current individual remains unchanged.
Namely, the specific formula is:
；
wherein, For the kth individual in the g-1 generation population,The distortion values calculated for the kth individuals in the g-th generation population,Distortion values calculated for the kth individual in the g-1 generation population.
In this embodiment, when calculating the distortion value, an existing distortion metric may be used to calculate, for example, a Bit Error Rate (BER), that is, a proportion of Error bits in the codeword.
And S6, iteratively calculating the distortion value of each individual in each population according to the maximum population algebra and the maximum individual number of each generation of population, and updating and replacing until the population algebra reaches the limited maximum times.
K=k+1, iteratively calculating the distortion value of each individual in each population and updating and replacing until all the individuals K in the current generation population are calculated, and then calculating all the individuals in the next generation population, namely g=g+1, until the population algebra reaches G.
S7, selecting an individual with the lowest distortion value in the population as an optimal individual in the current iteration, and outputting the optimal individual as a result.
In another embodiment, the coding matrix is designed for source information with source statistics p=0.5, as follows:
Firstly, the size of the basic matrix B is determined, wherein the basic matrix B is initialized by taking 2/5 code rate as an example, namely the size of the basic matrix B is 3*5, the number m of the rows of the basic matrix B is 3, and the number n of the columns of the basic matrix B is 5 (I.e., the 1 st individual in the 1 st generation population) is as follows:
；
Then, after the steps S2-S7, the optimal individual with the lowest distortion value is obtained, namely the final output result ，The coding matrix is as follows:
；
as shown in Table 1, in this embodiment, distortion degree calculation is performed for 2/5 and 4/7 code rates, and the distortion value of the coding base matrix obtained by the method of the present invention is compared with that of the conventional channel coding method, so that it can be seen that the distortion value of the coding base matrix obtained by the coding construction method provided by the present invention is smaller than that of AR3A (Accumulate-repeat-3-accumulate) code and AR4JA (Accumulate-4-jagged-accumulate) code.
TABLE 1
: Representing the basis matrix of the AR3A (Accumulate-repeat-3-accumulate) code. The AR3A code is a classical P-LDPC code and has excellent waterfall area performance.
: Representing the basis matrix of the AR4JA (Accumulate-4-jagged-accumulate) code. The AR4JA code is a classical P-LDPC code and has stable performance close to the capacity limit.
: The basic matrix obtained by the coding construction method provided by the invention is shown.
It can also be found from fig. 3 and fig. 4 that the coding basic matrix obtained by the coding construction method according to the present inventionThe rate distortion performance of the method is closer to the real curve than that of the AR3A code and the AR4JA code, so that the method has more excellent performance.
In summary, compared with the prior art, the invention has the following beneficial effects:
The invention uses DE algorithm to construct the source code of low density parity check (Protograph Low DENSITY PARITY CHECK, P-LDPC) of the lossy master pattern, generates a series of P-LDPC code basic matrixes (including variants, crossings, etc.) through mutation, crossing, selection updating and other operations of the DE algorithm, and finally generates the P-LDPC code with excellent distortion performance by continuously iterating and selecting the individual with the optimal distortion degree as the result.
The method has the advantages of high convergence speed, concise control parameters and reliable performance, and effectively improves the efficiency of the LDPC source code construction of the lossy original pattern.
Example two
As shown in fig. 5, the second embodiment of the present invention further provides a lossy original pattern LDPC source code construction apparatus, including:
The first base matrix acquisition unit is used for acquiring a first base matrix, acquiring the code rate of the original pattern LDPC code, setting the size of the original pattern base matrix according to the code rate and initializing the original pattern base matrix to acquire the first base matrix; and regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix of each generation of population;
The population initializing unit is used for obtaining all individuals in the initial population, randomly selecting integers from 0 to the largest element according to the largest individual number and the largest element to replace any element in the first base matrix, and obtaining other base matrixes, namely other individuals, in the initial population;
The variation unit is used for obtaining variation individuals, performing variation operation on each individual in the population by utilizing a differential evolution algorithm according to variation probability, randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current variation individual; wherein, the maximum individual number is set as K, the maximum population algebra is set as G, and the mutation probability is set as The formula of the mutation operation is:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G;
the crossing unit is used for obtaining a crossing body, carrying out crossing operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the crossing probability, and carrying out parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain a current individual crossing body; wherein, the cross probability is set as The formula of the crossover operation is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,An ith row and jth column element for a kth individual in the g generation population;
The distortion value calculation unit is used for selecting and determining the value of the current individual, calculating the distortion value of the current individual crossing body, determining whether to update the current individual, adopting the current individual crossing body to update and replace the current individual if the distortion value of the current individual crossing body is lower than the distortion value of the previous generation, otherwise, keeping the current individual unchanged;
The iteration unit is used for iteratively calculating the distortion value of each individual, and iteratively calculating the distortion value of each individual in each population according to the maximum individual number of each generation of the maximum population algebra and updating and replacing until the population algebra reaches the limited maximum times;
And the output unit is used for obtaining a final result, selecting an individual with the lowest distortion value in the population as the optimal individual of the current iteration, and outputting the optimal individual as the result.
Example III
The third embodiment of the present invention also provides a lossy original pattern LDPC source code construction apparatus, which includes a memory and a processor, where the memory stores a computer program, and the computer program can be executed by the processor to implement a lossy original pattern LDPC source code construction method as described above.
Example IV
The fourth embodiment of the present invention further provides a computer readable storage medium, where computer readable instructions are stored, where the computer readable instructions implement the method for constructing source codes of lossy orthomode graph LDPC as described above when executed by a processor of an apparatus in which the computer readable storage medium is located.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
References to "first\second" in the embodiments are merely to distinguish similar objects and do not represent a particular ordering for the objects, it being understood that "first\second" may interchange a particular order or precedence where allowed. It is to be understood that the "first\second" distinguishing aspects may be interchanged where appropriate, such that the embodiments described herein may be implemented in sequences other than those illustrated or described herein.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method for constructing a lossy original pattern LDPC source code is characterized by comprising the following steps:
S1, acquiring a code rate of an original pattern LDPC code, setting the size of an original pattern basic matrix according to the code rate, and initializing to obtain a first basic matrix; regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix of each generation of population; wherein, the code rate is set The expression of the first base matrix is:
；
wherein, Representing a first basis matrix, namely, the 1 st individual in the 1 st generation population; Representation of Elements of row m and column n in the matrix;
s2, randomly selecting an integer in a range from 0 to the maximum element to replace any element in the first base matrix according to the maximum number of individuals and the maximum element to obtain other base matrixes in the initial population, namely other individuals;
S3, carrying out mutation operation on each individual in the population by utilizing a differential evolution algorithm according to the mutation probability, randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current mutation individual;
Wherein, the maximum individual number is set as K, the maximum population algebra is set as G, and the mutation probability is set as The formula of the mutation operation is:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G;
S4, performing cross operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the cross probability, and performing parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain a current individual cross body;
wherein, the cross probability is set as The formula of the crossover operation is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,An ith row and jth column element for a kth individual in the g generation population;
S5, calculating a distortion value of a current individual crossing body, determining whether to update the current individual, if the distortion value of the current individual crossing body is lower than that of the previous generation, adopting the current individual crossing body to update and replace the current individual, otherwise, keeping the current individual unchanged;
S6, iteratively calculating the distortion value of each individual in each population according to the maximum population algebra and the maximum individual number of each generation of population, and updating and replacing until the population algebra reaches the limited maximum times;
S7, selecting an individual with the lowest distortion value in the population as an optimal individual in the current iteration, and outputting the optimal individual as a result.
2. The method for constructing source codes of lossy master pattern LDPC according to claim 1, wherein the specific formula of S5 is:
；
wherein, For the kth individual in the g-1 generation population,To represent the distortion values calculated for the kth individuals in the g-th generation population,A distortion value calculated for representing the kth individual in the g-1 generation population.
3. A lossy orthographic LDPC source code construction apparatus, comprising:
The first base matrix acquisition unit is used for acquiring a first base matrix, acquiring the code rate of the original pattern LDPC code, setting the size of the original pattern base matrix according to the code rate and initializing the original pattern base matrix to acquire the first base matrix; and regarding the first base matrix as an individual in the differential evolution algorithm process, and setting related parameters of the differential evolution algorithm process, wherein the parameters comprise: the maximum individual number, the maximum population algebra, the cross probability, the mutation probability and the maximum element in the first base matrix of each generation of population; wherein, the code rate is set The expression of the first base matrix is:
；
wherein, Representing a first basis matrix, namely, the 1 st individual in the 1 st generation population; Representation of Elements of row m and column n in the matrix;
The population initializing unit is used for obtaining all individuals in the initial population, randomly selecting integers from 0 to the largest element according to the largest individual number and the largest element to replace any element in the first base matrix, and obtaining other base matrixes, namely other individuals, in the initial population;
The variation unit is used for obtaining variation individuals, performing variation operation on each individual in the population by utilizing a differential evolution algorithm according to variation probability, randomly selecting a difference vector of two individuals from the population as a random variation source of a third individual, weighting the difference vector, and then summing the difference vector with the third individual according to a rule, thereby generating a current variation individual; wherein, the maximum individual number is set as K, the maximum population algebra is set as G, and the mutation probability is set as The formula of the mutation operation is:
；
wherein, Is the variant of the kth individual in the g generation population,In order to be a function of the rounding-off,，，Three numbers randomly extracted in the intervals of [0, K ],，，Respectively the g generation population，，G is more than or equal to 1 and less than or equal to G;
the crossing unit is used for obtaining a crossing body, carrying out crossing operation on each element of each individual in the population by utilizing a differential evolution algorithm according to the crossing probability, and carrying out parameter mixing on each element of the current variant individual and each element of the original individual so as to obtain a current individual crossing body; wherein, the cross probability is set as The formula of the crossover operation is:
；
wherein, An ith row and jth column element of the intersection of the kth individual in the g-th generation population,An ith row and jth column element that is a variant of the kth individual in the g-th generation population,The ith row and jth column elements of the kth individual in the g generation population
The distortion value calculation unit is used for selecting and determining the value of the current individual, calculating the distortion value of the current individual crossing body, determining whether to update the current individual, adopting the current individual crossing body to update and replace the current individual if the distortion value of the current individual crossing body is lower than the distortion value of the previous generation, otherwise, keeping the current individual unchanged;
the iteration unit is used for iteratively calculating the distortion value of each individual, and iteratively calculating the distortion value of each individual in each population according to the maximum population algebra and the maximum individual number of each generation population, and updating and replacing until the population algebra reaches the limited maximum times;
And the output unit is used for obtaining a final result, selecting an individual with the lowest distortion value in the population as the optimal individual of the current iteration, and outputting the optimal individual as the result.
4. A lossy master LDPC source code construction apparatus comprising a processor and a memory, the memory having stored therein a computer program executable by the processor to implement a lossy master LDPC source code construction method as claimed in any one of claims 1 to 2.
5. A computer readable storage medium, wherein computer readable instructions are stored on the computer readable storage medium, and when executed by a processor of a device in which the computer readable storage medium is located, the computer readable instructions implement a lossy master pattern LDPC source code construction method according to any one of claims 1-2.
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