Block-based principal component analysis transformation method and device thereof
The present invention provides a block-based principal component analysis transformation method and a device thereof. The principal component analysis transformation method includes: obtaining an input signal; dividing the input signal and obtaining a plurality of one-dimension vectors corresponding to the divided input signal, wherein a number of the one-dimension vectors is a division number; after arranging the one-dimension vectors to a two-dimension vector, subtracting an average value of the one-dimension vectors of the division number to obtain a zero-mean vector; calculating a covariance matrix of the zero-mean vector; calculating an eigenvector of the covariance matrix; multiplying the zero-mean vector by the eigenvector to obtain a projection coefficient.
1. A blocked-based principal component analysis transformation method, comprising:
obtaining an input signal;
dividing the input signal and obtaining a plurality of one-dimension vectors corresponding to the divided input signal, wherein a number of the plurality of one-dimension vectors is a division number;
after arranging the plurality of one-dimension vectors to a two-dimension vector, subtracting an average value of the plurality of one-dimension vectors of the division number to obtain a zero-mean vector;
calculating a covariance matrix of the zero-mean vector;
calculating an eigenvector of the covariance matrix; and
multiplying the zero-mean vector by the eigenvector to obtain a projection coefficient.
2. The blocked-based principal component analysis transformation method according to claim 1 , wherein the input signal is a one-dimension signal, and the process of dividing the input signal and obtaining the plurality of one-dimension vectors corresponding to the divided input signal comprises:
dividing the one-dimension signal to a plurality of frames and obtaining the plurality of one-dimension vectors according to a plurality of sampling points corresponding the frames.
3. The blocked-based principal component analysis transformation method according to claim 2 , wherein the one-dimension signal is a speech signal.
4. The blocked-based principal component analysis transformation method according to claim 1 , wherein the input signal is a two-dimension signal, and the process of dividing the input signal and obtaining the plurality of one-dimension vectors corresponding to the divided input signal comprises:
dividing the two-dimension signal to a plurality of blocks, obtaining a plurality of two-dimension matrices corresponding to a plurality of pixels of the plurality of blocks and transforming the plurality of two-dimension matrices to the plurality of one-dimension vectors.
5. The blocked-based principal component analysis transformation method according to claim 4 , wherein the two-dimension signal is an image signal.
6. The blocked-based principal component analysis transformation method according to claim 1 , further comprising:
multiplying the projection coefficient by an inverse matrix of the eigenvector to obtain a reconstructed zero-mean vector;
adding the average value to the reconstructed zero-mean vector to obtain a recovered one-dimension vector; and
arranging the recovered one-dimension vector to a recovered input signal.
7. A blocked-based principal component analysis transformation device, comprising:
a processor; and
a memory coupled to the processor, wherein the processor
obtains an input signal;
dividing the input signal and obtaining a plurality of one-dimension vectors corresponding to the divided input signal, wherein a number of the plurality of one-dimension vectors is a division number;
after arranging the plurality of one-dimension vectors to a two-dimension vector, subtracts an average value of the plurality of one-dimension vectors of the division number to obtain a zero-mean vector;
calculates a covariance matrix of the zero-mean vector;
calculates an eigenvector of the covariance matrix; and
multiplying the zero-mean vector by the eigenvector to obtain a projection coefficient.
8. The blocked-based principal component analysis transformation device according to claim 7 , wherein the input signal is a one-dimension signal, and the processor divides the one-dimension signal to a plurality of frames and obtains the plurality of one-dimension vectors according to a plurality of sampling points corresponding to the plurality of frames.
9. The blocked-based principal component analysis transformation device according to claim 7 , wherein the input signal is a two-dimension signal, and the processor divides the two-dimension signal to a plurality of blocks, obtains the plurality of two-dimension matrices corresponding to a plurality of pixels of the plurality of blocks and transforms the plurality of two-dimension matrices to the plurality of one-dimension vectors.
10. The blocked-based principal component analysis transformation device according to claim 7 , wherein the processor multiplies the projection coefficient by an inverse matrix of the eigenvector to obtain a reconstructed zero-mean vector, adds the average value to the reconstructed zero-mean vector to obtain a recovered one-dimension vector, and arranges the recovered one-dimension vector to a recovered input signal.