CN116489378B - PCA-based 3D LUT data compression and decompression method and system - Google Patents
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Abstract
The invention provides a 3D LUT data compression and decompression method and system based on PCA, which mainly comprise a preprocessing compression part and a chip data reconstruction part. The preprocessing compression part analyzes main component data of a plurality of groups of 3D LUT data, reserves some characteristics which are critical to application and are low in cost, and removes noise or unimportant characteristics, so that the improvement of data processing speed and the reduction of data volume are realized, and a large amount of time and cost are saved; and meanwhile, the chip data reconstruction part carries out data reconstruction based on the main components, and more efficient color processing is realized within the allowable error range.
Description
Technical Field
The invention belongs to the technical field of display. The invention discloses a 3D LUT data compression and decompression method and system based on PCA.
Background
In the field of display technology, 3D LUTs (3D look up tables) are widely used for color calibration of display devices, conversion of image color modes, and presentation of special color effects. In general, the 3D LUT needs to divide the R/G/B channels into N nodes to form N 3 And restoring the information missing in the middle through interpolation.
Since the 3D LUT can describe N in stereoscopic color space 3 The accurate behavior of each color point can be used for processing any nonlinear attribute of display, and also can be used for accurately processing the problems of sudden and large-scale change of color and the like. From a functional mechanism, the LUT has a hardware and software division. The difference of the data volume will cause a large difference of output results, if the setting of N is smaller, the color accuracy will be reduced although the data volume is small; if the N setting is large, the color accuracy is improved although the data amount is large.
To achieve accurate description of color points, a large amount of node information needs to be stored. In particular, the storage of multiple sets of 3D LUT information is required, which occupies a large amount of storage resources. Therefore, a reasonable method needs to be found, and the loss of the information contained in the original data is reduced as much as possible while the data volume is reduced, so as to achieve the purpose of comprehensively analyzing the original data.
PCA (Principal components analysis) as a widely used data dimension reduction algorithm, the core idea is to map data from a high-dimensional linear space to a low-dimensional linear space through linear transformation, and it is desirable that the information amount is maximum in the projection direction, and the price is minimum when the data is reversely reconstructed. The principal feature components (the number is recorded as M) in the data can be found out based on PCA, and the principal features are used for replacing the original data, so that the reduction of the data volume can be realized, and meanwhile, the important components in the data are reserved, namely, the compression process of the data is realized. And reconstructing the stored principal component data in terms of hardware equipment, namely realizing the decompression process of the data.
Disclosure of Invention
The invention provides a 3D LUT data compression and decompression method based on PCA, which comprises a preprocessing compression part and a chip data reconstruction part.
The preprocessing compression part respectively carries out principal component analysis on a plurality of groups of 3D LUT (look-up table) channels R/G/B based on PCA (principal component analysis), obtains principal component components of the R/G/B channels, and can respectively obtain feature vectors of the R/G/B channels after data dimension reduction on the premise that the number of nodes of the 3D LUT channels R/G/B is N and the number of selected principal component feature values/feature vectors is MAnd characteristic valueM is less than N 3 And saving the obtained feature vector and the feature value in a driving chip of the display device.
When the chip data reconstruction part comprises a display device, firstly, the characteristic vector and the characteristic value in the storage space are read; and then, processing the read feature vectors and the feature values based on a chip algorithm realized by PCA, and reconstructing a plurality of groups of 3D LUTs'.
Further, assuming that the 3D LUT group number is K, data compression is achieved without principal component analysisThe number of data to be stored is:the method comprises the steps of carrying out a first treatment on the surface of the The number of data to be stored after data compression based on PCA is:each characteristic value,Individual feature vectorsThe average value of the data storage amounts can be obtained by PCA analysis, and the front-to-back ratio of the data storage amounts is:。
let G channel m=8, n= 5,K =20000
Record the G channel's multiunit 3D LUT sample set asDimension: 5 3 ×20000；
The feature value set after dimension reduction is recorded asDimension: 8X 20000;
the steps of the pretreatment compressing section are as follows:
step one, centralizing a plurality of groups of 3D LUT data sets
wherein ,representing the de-centering of the 3D LUT data set,representing a mean set with dimensions 5 3 ×1，Is of dimension 5 3 The value of x 1, i is 0-19999;
step two, calculating covariance matrix Cov of the data set
；
Step three, a eigenvalue lambda and an eigenvector V of a covariance matrix Cov are obtained;
step four, arranging the feature vectors into a matrix according to the size of the feature values from top to bottom, taking the first 8 rows at the moment as feature vectors P to be saved after dimension reduction, wherein the dimension is 8 multiplied by 5 3 ;
Step five, calculating to obtain a feature value set after dimension reduction
；
Step six, data storage: the feature vector P obtained above is dimensioned as 8×5 3 Feature value setDimension of 8 x 20000 and mean setDimension 5 3 And x 1, storing in a hardware device storage space.
Further, when the display device starts to work, the driving chip loads data from the data storage space to reconstruct the data, and the steps are as follows:
step one, acquiring data: reading the feature vector P from the memory space, and the feature value setSum mean set；
Step two, data reconstruction: feature vector P and feature value set in internal memory based on PCASum mean setProcessing, reconstructing 20000 groups of 3D LUTs', and recording the reconstructed array sample set
Wherein the Q matrix is an all-1 matrix, and the dimension is 1×20000.
The invention also provides a system for realizing the 3D LUT data compression and decompression method based on PCA, which comprises the following steps:
the data compression unit is used for respectively extracting main components of three groups of 3D LUT set R/G/B channels to obtain a characteristic value and a characteristic vector for storage, reducing the dimension of data and realizing the function of data compression;
and the data reconstruction unit is used for realizing data reconstruction based on the stored characteristic values and the characteristic vectors, obtaining a plurality of groups of 3D LUT' sets and realizing a data decompression function.
The invention has the technical effects that: for the situation that the 3D LUT has huge data quantity, high dimensionality and similar characteristics, data analysis is performed based on PCA, and original data possibly with correlation is converted into linear uncorrelation through orthogonal transformation, so that main components of required data are obtained, and data compression is achieved. On one hand, the storage space required by data and the transmission data quantity of the hardware equipment can be greatly reduced, the occupation of storage resources is reduced, and meanwhile, the transmission rate of the hardware equipment is improved; on the other hand, the hardware device can reconstruct the required 3D LUT as much as possible according to the main component data, so that the accuracy of color processing is improved while the reconstruction error is reduced.
Drawings
FIG. 1 is a flow chart of the preprocessing compression of the present invention;
FIG. 2 is a flow chart of the reconstruction of chip data according to the present invention;
fig. 3 shows a system for data compression of a 3D LUT according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the preprocessing data compressing section has the steps of: firstly, based on the obtained color data, respectively calculating the eigenvectors and eigenvalues of a plurality of groups of 3D LUTs on R/G/B three color channels, and carrying out principal component analysis to obtain principal eigenvectors (MXN) of the data 3 ) And characteristic value (M, M<N 3 ) And then, the main characteristic vectors and the characteristic values are stored so as to be conveniently placed in a storage space of a chip, thereby realizing data compression. And meanwhile, the integrity of the stored content of the chip is ensured by a verification mode.
Referring to fig. 2, the chip data reconstruction part has the following steps: when the display equipment works, firstly, the characteristic vector and the characteristic value in the storage space are read; and then, processing the read eigenvectors and eigenvalues based on a chip algorithm realized by PCA to reconstruct a plurality of groups of 3D LUTs' (the reconstructed 3D LUTs are similar to the 3D LUTs).
The preprocessing compression part respectively carries out principal component analysis on three channels of R/G/B of a plurality of groups of 3D LUT (look-up table) based on PCA (principal component analysis), obtains principal component components of the three channels of R/G/B, and can respectively obtain feature vectors of the three channels of R/G/B after data dimension reduction under the assumption that the number of nodes of the three channels of R/G/B of the 3D LUT is N and the number of selected principal component feature values/feature vectors is MAnd characteristic valueM is less than N 3 And saving the obtained feature vector and the feature value in a driving chip of the display device.
When the chip data reconstruction part comprises the display equipment, firstly, the characteristic vector and the characteristic value in the storage space are read; and then, processing the read feature vectors and the feature values based on a chip algorithm realized by PCA, and reconstructing a plurality of groups of 3D LUTs'.
Assuming that the number of 3D LUT groups is K, the number of data to be stored without performing principal component analysis to achieve data compression is:the method comprises the steps of carrying out a first treatment on the surface of the The number of data to be stored after data compression based on PCA is:each characteristic value,Individual feature vectorsThe average value of the data storage amounts can be obtained by PCA analysis, and the front-to-back ratio of the data storage amounts is:。
typically, the value of K is larger, and M and N are smaller, as described below by taking G channel m=8, n= 5,K =20000 as an example (the compression ratio is about 15 calculated by the formula):
record the G channel's multiunit 3D LUT sample set asDimension: 5 3 ×20000；
The feature value set after dimension reduction is recorded asDimension: 8X 20000;
the steps of preprocessing the compression section are as follows:
step one, centralizing a plurality of groups of 3D LUT data sets
；
wherein ,is of dimension 5 3 ×1，Representing a mean set with dimensions 5 3 The value of x 1, i is 0-19999;
step two, calculating covariance matrix Cov of the data set
Step three, a eigenvalue lambda and an eigenvector V of a covariance matrix Cov are obtained;
step four, arranging the feature vectors into a matrix according to the size of the feature values from top to bottom, taking the first 8 rows at the moment as feature vectors P to be saved after dimension reduction, wherein the dimension is 8 multiplied by 5 3 ;
Step five, calculating to obtain a feature value set after dimension reduction
；
Step six, data storage: the feature vector P obtained above is dimensioned as 8×5 3 Feature value setDimension of 8 x 20000 and mean setDimension 5 3 And x 1, storing in a hardware device storage space.
When the display device starts to work, the driving chip loads data from the data storage space to reconstruct the data, and the steps are as follows:
step one, acquiring data: reading the feature vector P from the memory space, and the feature value setSum mean set；
Step two, data reconstruction: feature vector P and feature value set in internal memory based on PCASum mean setProcessing, reconstructing 20000 groups of 3D LUTs', and recording the reconstructed array sample set
The reconstructed 3D LUT' is similar to the 3D LUT
Wherein the Q matrix is an all-1 matrix, and the dimension is 1×20000.
Referring to fig. 3, the present invention further provides a system for implementing a PCA-based 3D LUT data compression and decompression method, including:
the data compression unit is used for respectively extracting main components of three groups of 3D LUT set R/G/B channels to obtain a characteristic value and a characteristic vector for storage, reducing the dimension of data and realizing the function of data compression;
and the data reconstruction unit is used for realizing data reconstruction based on the stored characteristic values and the characteristic vectors, obtaining a plurality of groups of 3D LUT' sets and realizing a data decompression function.
The following is taken as an example of verification of the performance of the compression method and system, and is not limited, and taking a 3D LUT of a display device of a certain model as an example, the test is performed by the data compression and decompression method and system of the present invention. The data compression unit performs principal component analysis on the data of the R/G/B channels to obtain the cumulative contribution degree of each principal component as shown in the following table 1:
TABLE 1 cumulative contribution rate of the principal component of 3D LUT
| accumulated_ratio | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| R | 47.38 | 76.19 | 91.23 | 94.78 | 96.86 | 98.27 | 99.24 | 99.79 | 100.00 |
| G | 42.56 | 68.61 | 80.08 | 87.09 | 93.99 | 99.07 | 99.50 | 99.88 | 100.00 |
| B | 32.31 | 57.02 | 71.59 | 83.07 | 91.02 | 95.94 | 99.06 | 99.74 | 100.00 |
As can be seen from table 1, the cumulative contribution rate of the principal components with the contribution rate of top 5 exceeds 91%, and the cumulative contribution rate threshold (e.g., 83%, 95%, 99%, etc.) may be determined according to the color processing accuracy of the specific practical application, and each principal component constituting the cumulative threshold contribution rate may be determined as the desired principal component, so as to determine the number of characteristic values (e.g., if the top 6 principal components constituting the cumulative contribution rate of 95%, the 6 principal components are determined as the desired principal components, and the number of characteristic values is 6), that is, the compression ratio is determined synchronously.
The data reconstruction means uses the components having high contribution rates as necessary components, that is, the corresponding feature values, feature vectors, and the like. These data will be used as the important basis for the subsequent data reconstruction and color processing. The hardware system will read the data such as the eigenvalue and eigenvector from the storage space, and then carry out the subsequent analysis (reconstructing to obtain multiple sets of 3D LUT' sets for color processing).
The invention provides a method and a system for compressing 3D LUT data, which are used for determining main data characteristics based on PCA, integrating with a hardware system, completing the compression and decompression processes of the 3D LUT data and forming an accurate 3D LUT'. The occupation of storage resources is reduced through the compression process, the transmission rate of hardware equipment is improved, and meanwhile, the main components are utilized for data reconstruction. The compression ratio is flexibly adjusted according to the needs of actual display equipment, compressed data is reasonably stored, and the 3D LUT is efficiently reconstructed, so that the accuracy of subsequent color processing can be ensured.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. The PCA-based 3D LUT data compression and decompression method is characterized by comprising a preprocessing compression part and a chip data reconstruction part;
the preprocessing compression part respectively carries out principal component analysis on a plurality of groups of 3D LUT (look-up table) channels R/G/B based on PCA (principal component analysis), obtains principal component components of the R/G/B channels, and can respectively obtain feature vectors of the R/G/B channels after data dimension reduction on the premise that the number of nodes of the 3D LUT channels R/G/B is N and the number of selected principal component feature values/feature vectors is MAnd eigenvalue->M is less than N 3 Storing the obtained characteristic vector and the characteristic value in a driving chip of the display device;
assuming that the number of 3D LUT groups is K, the number of data to be stored without performing principal component analysis to achieve data compression is:the method comprises the steps of carrying out a first treatment on the surface of the The number of data to be stored after data compression based on PCA is: />Personal characteristic value,/->Personal feature vector sum->The average value of the data storage amounts can be obtained by PCA analysis, and the front-to-back ratio of the data storage amounts is: />；
Let G channel m=8, n= 5,K =20000
Record the G channel's multiunit 3D LUT sample set asDimension: 5 3 ×20000；
The feature value set after dimension reduction is recorded asDimension: 8X 20000;
the steps of the pretreatment compressing section are as follows:
step one, centralizing a plurality of groups of 3D LUT data sets
wherein ,representing the decentralization of the 3D LUT data set,/->Representing a mean set with dimensions 5 3 ×1，/>Is of dimension 5 3 The value of x 1, i is 0-19999;
step two, calculating covariance matrix Cov of the data set
Step three, a eigenvalue lambda and an eigenvector V of a covariance matrix Cov are obtained;
step four, arranging the feature vectors into a matrix according to the size of the feature values from top to bottom, taking the first 8 rows at the moment as feature vectors P to be saved after dimension reduction, wherein the dimension is 8 multiplied by 5 3 ;
Step five, calculating to obtain a feature value set after dimension reduction
Step six, data storage: the feature vector P obtained above is dimensioned as 8×5 3 Feature value setDimension 8 x 20000 and mean set +.>Dimension 5 3 X 1, storing in a hardware device storage space;
when the chip data reconstruction part comprises a display device, firstly, the characteristic vector and the characteristic value in the storage space are read; then, processing the read feature vectors and the feature values based on a chip algorithm realized by PCA, and reconstructing a plurality of groups of 3D LUTs';
when the display device starts to work, the driving chip loads data from the data storage space to reconstruct the data, and the steps are as follows:
step one, acquiring data: reading the feature vector P from the memory space, and the feature value setSum mean set；
Step two, data reconstruction: feature vector P and feature value set in internal memory based on PCASum mean setProcessing, reconstructing 20000 groups of 3D LUTs', and recording the reconstructed array sample set
The reconstructed 3D LUT' is similar to the 3D LUT
Wherein the Q matrix is an all-1 matrix, and the dimension is 1×20000.
2. A system for implementing a PCA-based 3D LUT data compression and decompression method as in claim 1, comprising
The data compression unit is used for respectively extracting main components of three groups of 3D LUT set R/G/B channels to obtain a characteristic value and a characteristic vector for storage, reducing the dimension of data and realizing the function of data compression;
and the data reconstruction unit is used for realizing data reconstruction based on the stored characteristic values and the characteristic vectors, obtaining a plurality of groups of 3D LUT' sets and realizing a data decompression function.
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| CN101000686A (en) * | 2007-01-15 | 2007-07-18 | 浙江大学 | Color control method based on main component analysing |
| CN105788261A (en) * | 2016-04-15 | 2016-07-20 | 浙江工业大学 | Road traffic space data compression method based on PCA and LZW coding |
| CN108141507A (en) * | 2015-10-02 | 2018-06-08 | Vid拓展公司 | Color correction is carried out using look-up table |
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| CN101000686A (en) * | 2007-01-15 | 2007-07-18 | 浙江大学 | Color control method based on main component analysing |
| CN108141507A (en) * | 2015-10-02 | 2018-06-08 | Vid拓展公司 | Color correction is carried out using look-up table |
| CN105788261A (en) * | 2016-04-15 | 2016-07-20 | 浙江工业大学 | Road traffic space data compression method based on PCA and LZW coding |
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