CN115834885A - Interframe image coding method and system based on sparse representation - Google Patents
Interframe image coding method and system based on sparse representation Download PDFInfo
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Abstract
The invention discloses an interframe image coding method and system based on sparse representation, which relate to the technical field of image processing and comprise the following steps: extracting an intra-frame coding frame in a target image group, and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample; expressing the test samples classified by each coding division mode into linear combinations of training samples under the corresponding coding division mode classification in a linear expression form; acquiring a sparse global optimal solution; residual expression is carried out according to the sparse global optimal solution, and coding division mode judgment is carried out on the inter-frame images in the target image group; and coding the corresponding inter-frame image according to the judgment result of the coding division mode. The invention converts the coding division into the multi-classification problem under sparse representation, reduces the calculation complexity and simultaneously enables the coder applying the coding method to realize the coding of the inter-frame image with higher efficiency.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an interframe image coding method and system based on sparse representation.
Background
With the rapid development of the multimedia field, the demand of multimedia information is continuously increasing. Digital video occupies a large proportion in information transmission and storage, requires a large storage space and transmission bandwidth, and needs to be compressed in order to save resources such as limited bandwidth and storage. Original image and video data have redundancies such as spatial redundancy, temporal redundancy, etc., and elimination of these redundancies can achieve the purpose of data compression.
At present, video coding acceleration becomes a research hotspot, the coding algorithm acceleration is mainly improved aiming at the existing coding algorithm, and the coding efficiency is improved by reducing the calculated amount of the algorithm. The algorithms can be divided into two broad categories: based on statistical analysis algorithms and on machine learning algorithms. However, the former method does not well balance the coding efficiency and the coding computation complexity, so that the overall coding efficiency is still not improved.
Disclosure of Invention
In order to further improve the efficiency of video coding and ensure the quality of the decoded video, the invention provides an interframe image coding method based on sparse representation, which comprises the following steps:
s1: extracting an intra-frame coding frame in a target image group, and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
s2: expressing the test samples classified by each coding division mode into linear combinations of training samples under the corresponding coding division mode classification in a linear expression form;
s3: l under sparse representation according to linear expression 1 Norm and L 2 Sparse global optimal solutions between norms;
s4: according to L 1 Norm and L 2 Residual expression is carried out on the sparse global optimal solution among the norms, and coding division mode judgment is carried out on the inter-frame images in the target image group according to the residual expression;
s5: and coding the corresponding inter-frame image according to the judgment result of the coding division mode.
Further, in the step S2, a linear combination of the training samples is expressed as the following formula:
wherein i is a constant number of a label used for indicating a target code division mode within a value range of the total classification amount of the code division modes,
for the linear combination of training samples corresponding to the target coding partition mode,
is a set of real numbers, m is the eigenvector information dimension of the training sample matrix,
a matrix formed by training samples corresponding to all the coding division modes,
the total amount of training samples corresponding to the target code partition pattern,
is a coefficient vector, and
，
to the eyesFirst in the standard code division mode training sample set
The coefficient vector of each of the training samples,
for the set of real numbers to be used,
and the dimension of the sample size of the training sample matrix corresponding to the target coding mode.
Further, in the step S3, L 1 Norm and L 2 The sparse global optimal solution between norms is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
Further, in the step S4, the residual expression is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
for the target to encode the residual of the partition mode,
for a particular sampling function, for selecting
And setting the coefficient vector not belonging to the target coding division mode to zero.
Further, in the step S1, the feature vector information includes a rate distortion cost and a prediction residual.
The invention also provides an interframe image coding system based on sparse representation, which comprises the following components:
the matrix construction module is used for extracting an intra-frame coding frame in the target image group and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
the linear expression module is used for expressing the test sample of each code division mode classification into a linear combination of the training samples under the corresponding code division mode classification in a linear expression form;
a sparse representation module for performing L under sparse representation according to a linear expression 1 Norm and L 2 Sparse global optimal solutions between norms;
a residual expression module for expressing the residual according to L 1 Norm and L 2 Performing residual expression on sparse global optimal solutions among the norms;
and the coding and dividing module is used for judging the coding and dividing mode of the inter-frame images in the target image group according to the residual expression and coding the corresponding inter-frame images according to the judgment result of the coding and dividing mode.
Further, in the linear expression module, a linear combination of training samples is expressed as follows:
wherein i is a constant number of a label used for indicating a target code division mode within a value range of the total classification amount of the code division modes,
partitioning pattern corresponding training for target codingThe linear combination of the samples is then combined,
is a set of real numbers, m is the eigenvector information dimension of the training sample matrix,
a matrix formed by training samples corresponding to all the coding division modes,
for the total amount of training samples corresponding to the target coding partition mode,
is a coefficient vector, and
，
partitioning pattern training sample set for target coding
The coefficient vector of each of the training samples,
for the set of real numbers to be used,
and the dimension of the sample size of the training sample matrix corresponding to the target coding mode.
Further, in the sparse representation module, L 1 Norm and L 2 The sparse global optimal solution between norms is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
Further, in the residual expression module, the residual expression is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
for the target to encode the residual of the partition mode,
for a particular sampling function, for selecting
And setting the coefficient vector not belonging to the target coding division mode to zero.
Further, in the matrix construction module, the feature vector information includes rate distortion cost and prediction residual.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) According to the interframe image coding method and system based on sparse representation, coding division is converted into a multi-classification problem under sparse representation, so that the coding complexity is reduced, and meanwhile, the coding of interframe images can be realized at higher efficiency by using a coder with the coding method;
(2) The classification training and judgment are carried out in a sparse representation mode, so that the algorithm complexity is greatly reduced;
(3) As the intra-frame coding frame action model is adopted to train the sample data source, and the rate distortion cost and the prediction residual are taken as the characteristic vector information, the coding quality can be ensured while the high-efficiency coding is realized.
Drawings
FIG. 1 is a schematic diagram of the steps of an interframe image coding method based on sparse representation;
fig. 2 is a block diagram of an inter-frame image coding system based on sparse representation.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Examples
As will be understood, sparse representation is a representation in which most or all of the original signal is represented by a linear combination of fewer fundamental signals. Wherein the elementary signals are called atoms and are selected from an overcomplete dictionary; an overcomplete dictionary is aggregated from atoms whose number exceeds the signal dimension. It can be seen that any one signal has a different sparse representation under different atom groups.
Assuming we represent the data set X by an M X N matrix, each row representing a sample and each column representing an attribute of the sample, in general, the matrix is dense, i.e. most elements are not 0. Sparse representation means that a coefficient matrix a (K × N) and a dictionary matrix B (M × K) are found so that B × a restores X as much as possible and a is as sparse as possible, which is a sparse representation of X.
Based on the characteristic of sparse representation, if the coefficient vectors of different coding division modes in the same image group can be found out by reasonably applying the characteristic to interframe coding, the interframe coding division mode judgment which is originally realized by continuously calculating the rate distortion cost of a coding block under each coding depth in a traversing manner can be reduced to residual calculation with smaller calculation amount under sparse representation, thereby realizing efficient interframe coding. Based on this, as shown in fig. 1, the present invention proposes an inter-frame image coding method based on sparse representation, comprising the steps of:
s1: extracting an intra-frame coding frame in a target image group, and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
s2: expressing the test sample of each code division mode classification as a linear combination of training samples under the corresponding code division mode classification in a linear expression form;
s3: l under sparse representation according to linear expression 1 Norm and L 2 Sparse global optimal solutions between norms;
s4: according to L 1 Norm and L 2 Residual expression is carried out on the sparse global optimal solution among the norms, and coding division mode judgment is carried out on the inter-frame images in the target image group according to the residual expression;
s5: and coding the corresponding inter-frame image according to the judgment result of the coding division mode.
Firstly, in the selection of the training samples and the test samples for thinning the classifier, considering that inter-frame images in the same group of pictures (GOP) generally have extremely high motion correlation, and meanwhile, an intra-frame (I-frame) contains all coding information, each coding block in the intra-frame is selected to select each coding division mode training sample, and a part of the training samples is used as the test sample.
Then, to construct a sparse expression classifier, we assume that the category of coding partition has a total of k classification categories (in the VCC coding standard, the value of k is 6, which is no partition NS, quadtree partition QTS, binary tree vertical partition BVS, binary tree horizontal partition BHS, ternary tree vertical partition TVS and ternary tree horizontal partition THS), and each category has a total of k classification categories in the intra-frame coding frame
(Here, the
Scalar by number of samples) training samples
And m is the eigenvector information dimension of the training sample matrix (in this embodiment, the eigenvector information comprises the rate-distortion costPrediction residual, therefore m = 2), i.e. the above-mentioned attribute quantity of the training samples, i is a constant value taking values from 1 to k, expressed as the coding partitioning mode of the ith classification class, j is from 1 to k
Is denoted as the jth training sample. Then any test sample from class i as well, according to the definition of the sparse representation
Can be approximately expressed as a linear combination (1) of the training samples:
since the label of y is initially unknown, y is expressed as a linear combination of all training samples, an
。
Wherein the content of the first and second substances,
the matrix is formed by all training samples of k class coding partition modes.
While
For the coefficient vector of the i-th class encoding partition mode (target encoding partition mode), it can be seen that it is only non-zero with the associated coefficients of the i-th class.
It is easier to solve x, and linear equations can be solved
To be implemented. Here, it can be clearly seen that when m is>n, system of equations
Is overdetermined and the correct x can usually be found as its unique solution. If m is<n, the solution of the above equation is uncertain.The problem of sparse representation is to find a vector x satisfying the above condition, and L of x 0 Norm of
Is minimal and can be expressed as formula (2):
in the formula (I), the compound is shown in the specification,
is L 0 Norm of
A sparse global optimal solution.
However, the problem of finding the rarest solution of the above system of linear equations belongs to the NP-hard problem (all NP problems, i.e. problems solvable in polynomial time with a certain number of operations, can be reduced within the polynomial time complexity), so that if the solution x sought is sparse enough, L is reduced 0 The solution to the minimization problem can be reduced to subsequent L 1 Or even L 2 Minimize the problem, therefore, L 0 The solution to the minimization problem is finally solved in the present invention using the following equation (3),
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
The formula can be solved by a linear programming method.In the ideal situation, the temperature of the air conditioner,
a non-zero element in (a) would be associated with a column in a single object class, then a test sample y may be assigned to that class. But noise and errors in modeling may cause multiple classes to be associated with small non-zero terms, so assigning y to a class of non-zero terms is unreliable. Here, for each class i, settings are made
Is a feature function that selects the coefficients associated with the ith class. For the
，
(wherein the content of the first and second components,
for a particular sampling function, for selecting
And zeroes out the coefficient vectors that do not belong to the target coding partition mode) is a new vector whose only non-zero element is the element in x associated with class i. Using only the coefficients associated with the ith class, a given test sample y can be approximated as
. Then, y can be classified according to these approximate values and assigned to minimize y and
the object class of the residual, specifically the residual, is expressed by the following formula (4),
finally, according to the obtained residual expression, applying the residual expression to other subsequent interframe images in the same image group, and extractingThe coding block under the current coding depth brings the characteristic information into a model, and solves y under the condition of the minimum residual value by calculating the residual value of the coding block, wherein the formula is expressed as
The coding division mode of each coding block under the current coding depth can be obtained, and then the next coding depth is entered until the maximum coding depth or the coding block can not be divided again.
Examples
In order to better understand the technical content of the present invention, the present embodiment explains the present invention in the form of a system structure, as shown in fig. 2, an inter-frame image coding system based on sparse representation includes:
the matrix construction module is used for extracting an intra-frame coding frame in the target image group and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
the linear expression module is used for expressing the test sample of each code division mode classification into a linear combination of training samples under the corresponding code division mode classification in a linear expression form;
the sparse representation module is used for carrying out sparse global optimal solution between the L1 norm and the L2 norm under sparse representation according to a linear expression;
the residual error expression module is used for carrying out residual error expression according to a sparse global optimal solution between the L1 norm and the L2 norm;
and the coding and dividing module is used for judging the coding and dividing mode of the inter-frame images in the target image group according to the residual expression and coding the corresponding inter-frame images according to the judgment result of the coding and dividing mode.
Further, in the linear expression module, the linear combination of the training samples is expressed as the following formula:
wherein i is a value rangeThe index constant for the target coding division mode within the total classification amount of the coding division modes,
for the linear combination of training samples corresponding to the target coding partition mode,
is a set of real numbers, m is the eigenvector information dimension of the training sample matrix,
a matrix formed by training samples corresponding to all the coding division modes,
for the total amount of training samples corresponding to the target coding partition mode,
is a coefficient vector, and
，
partitioning pattern training sample set for target coding
The coefficient vector of each of the training samples,
for the set of real numbers to be used,
and the dimension of the sample size of the training sample matrix corresponding to the target coding mode.
Further, in the sparse representation module, L 1 Norm and L 2 The sparse global optimal solution between norms is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
Further, in the residual expression module, the residual expression is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
the residual of the partitioning mode is coded for the target,
for a particular sampling function, for selecting
And setting the coefficient vector not belonging to the target coding division mode to zero.
Further, in the matrix construction module, the feature vector information includes rate distortion cost and prediction residual.
In summary, the sparse representation-based inter-frame image coding method and system provided by the invention can reduce the computational complexity by converting the coding division into the multi-classification problem under the sparse representation, and enable the encoder using the coding method to realize the coding of the inter-frame image with higher efficiency. Model training and application are performed in a sparse representation mode, so that the algorithm calculation amount in the actual application process is greatly reduced, and the coding efficiency is remarkably improved.
As the intra-frame coding frame action model is adopted to train the sample data source, and the rate distortion cost and the prediction residual are taken as the characteristic vector information, the coding quality can be ensured while the high-efficiency coding is realized.
It should be noted that all the directional indicators (such as upper, lower, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the motion situation, and the like in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
Furthermore, descriptions of the present invention as related to "first," "second," "a," etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or to imply that the number of technical features indicated is indicative. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
Claims (10)
1. An interframe image coding method based on sparse representation is characterized by comprising the following steps:
s1: extracting an intra-frame coding frame in a target image group, and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
s2: expressing the test samples classified by each coding division mode into linear combinations of training samples under the corresponding coding division mode classification in a linear expression form;
s3: l under sparse representation according to linear expression 1 Norm and L 2 Sparse global optimal solutions between norms;
s4: according to L 1 Norm and L 2 Residual expression is carried out on the sparse global optimal solution among the norms, and coding division mode judgment is carried out on the inter-frame images in the target image group according to the residual expression;
s5: and coding the corresponding inter-frame image according to the judgment result of the coding division mode.
2. The sparse representation-based inter-frame image coding method as claimed in claim 1, wherein in the step S2, the linear combination of the training samples is expressed as the following formula:
wherein i is a constant number of a reference number used for indicating a target code division mode within a value range of the total classification amount of the code division modes,
for the linear combination of training samples corresponding to the target code partition mode,
is a set of real numbers, m is the eigenvector information dimension of the training sample matrix,
a matrix formed by training samples corresponding to all the coding division modes,
the total amount of training samples corresponding to the target code partition pattern,
is a coefficient vector, and
，
partitioning pattern training sample set for target coding
The coefficient vector of each of the training samples,
for the set of real numbers to be used,
and the dimension of the sample size of the training sample matrix corresponding to the target coding mode.
3. The sparse representation-based inter-frame image coding method as claimed in claim 2, wherein in said S3 step, L 1 Norm and L 2 The sparse global optimal solution between norms is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
4. The sparse representation-based inter-frame image coding method as claimed in claim 3, wherein in the step S4, the residual expression is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
for the target to encode the residual of the partition mode,
for a particular sampling function, for selecting
And setting the coefficient vector not belonging to the target coding division mode to zero.
5. The sparse representation-based inter-frame image coding method as claimed in claim 1, wherein in the step S1, the feature vector information comprises rate distortion cost and prediction residual.
6. An interframe image coding system based on sparse representation, comprising:
the matrix construction module is used for extracting an intra-frame coding frame in the target image group and respectively constructing a training sample matrix and a test sample matrix by taking the feature vector information of a coding block under each coding depth of the intra-frame coding frame as a training sample;
the linear expression module is used for expressing the test sample of each code division mode classification into a linear combination of training samples under the corresponding code division mode classification in a linear expression form;
a sparse representation module for performing L under sparse representation according to a linear expression 1 Norm and L 2 Sparse global optimal solutions between norms;
a residual expression module for expressing the residual according to L 1 Norm and L 2 Performing residual expression on sparse global optimal solutions among the norms;
and the coding and dividing module is used for judging the coding and dividing mode of the inter-frame images in the target image group according to the residual expression and coding the corresponding inter-frame images according to the judgment result of the coding and dividing mode.
7. The sparse representation-based inter-frame image coding system of claim 6, wherein in the linear expression module, the linear combination of training samples is expressed as the following formula:
wherein i is a constant number of a label used for indicating a target code division mode within a value range of the total classification amount of the code division modes,
for the linear combination of training samples corresponding to the target coding partition mode,
is a set of real numbers, m is the eigenvector information dimension of the training sample matrix,
a matrix formed by training samples corresponding to all the coding division modes,
for the total amount of training samples corresponding to the target coding partition mode,
is a coefficient vector, and
，
partitioning pattern training sample set for target coding
The coefficient vector of each of the training samples,
for the set of real numbers to be used,
and the dimension of the sample size of the training sample matrix corresponding to the target coding mode.
8. The sparse representation-based inter-frame image coding system of claim 7, wherein in the sparse representation module, L 1 Norm and L 2 The sparse global optimal solution between norms is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
is L 1 Norm and L 2 A sparse global optimal solution between the norms,
l being x 1 The norm of the number of the first-order-of-arrival,
l being x 2 And (4) norm.
9. The sparse representation-based inter-frame image coding system of claim 8, wherein in the residual expression module, the residual expression is expressed as the following formula:
in the formula (I), the compound is shown in the specification,
for the target to encode the residual of the partition mode,
for a particular sampling function, for selecting
And setting the coefficient vector not belonging to the target coding division mode to zero.
10. The sparse representation-based inter-frame image coding system of claim 6, wherein in the matrix construction module, the feature vector information comprises rate-distortion cost and prediction residual.
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