CN105072446B - The compression sampling and restructing algorithm of a kind of coloured image - Google Patents

The compression sampling and restructing algorithm of a kind of coloured image Download PDF

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CN105072446B
CN105072446B CN201510442038.9A CN201510442038A CN105072446B CN 105072446 B CN105072446 B CN 105072446B CN 201510442038 A CN201510442038 A CN 201510442038A CN 105072446 B CN105072446 B CN 105072446B
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parts
passages
sample rate
channel
coloured image
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CN105072446A (en
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陈建
苏凯雄
杨秀芝
朱宝珠
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Fuzhou University
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Abstract

The present invention relates to the compression sampling and restructing algorithm of a kind of coloured image, coloured image is decomposed into tri- passages of R, G, B and carries out wavelet transformation respectively by coding side, the wavelet conversion coefficient of different layers is measured using different sample rates, and the sparse reconstruct of group is carried out using the correlation of three passages in decoding end.During present invention processing image, the data volume of required sampling is few, and the picture quality of recovery is good.The traditional algorithm independently reconstructed relative to RGB, in low sampling rate, Y-PSNR about improves 1 2dB.

Description

The compression sampling and restructing algorithm of a kind of coloured image
Technical field
The present invention relates to the compression sampling and restructing algorithm of a kind of coloured image.
Background technology
Compressed sensing is a kind of new information processing theory, it breaches nyquist sampling theorem in traditional sampling Limitation so that signal can be to be sampled far below nyquist sampling rate, and reconstructs original with remaining to high probability in decoding end Beginning signal.
Current many scholars have studied application of the compressive sensing theory in gray level image, and its in coloured image should With more rare.Most scholars when compressed sensing is applied to coloured image, using by coloured image transform to RGB or On YUV color spaces, so as to be broken down into three autonomous channels, recycle the compressive sensing theory of gray level image logical to three Road is compressed and reconstructs respectively.But carry out compression reconfiguration coloured image in this way, can not be using between three passages High correlation, cause sampling waste.
Nagesh proposes EJSM algorithms, this is to utilize the phase between three passages during color image compression perceives for the first time Closing property reconstructs, and the method that article proposes the wavelet coefficient joint sparse of three passages extracts the common portion between them, The degree of rarefication of coloured image is reduced, so as to obtain more preferable quality reconstruction.Majumdar propose using the sparse principle of group come Coloured image is reconstructed, is effectively improved the reconstruction quality of coloured image, but calculation matrix is too big, is well in simulations Computer running memory, may be only available for the coloured image of small size.
The content of the invention
In view of this, it is an object of the invention to provide the compression sampling and restructing algorithm of a kind of coloured image, further The sampling efficiency of coloured image is improved, improves reconstruction quality, the natural image compressed sensing for low sampling rate condition.
To achieve the above object, the present invention adopts the following technical scheme that:A kind of compression sampling of coloured image and reconstruct are calculated Method, it is characterised in that:
Programming process includes the following steps:
Step A1:One coloured image is decomposed into R passages, G passages and channel B;
Step A2:The R passages, G passages and channel B are subjected to three layers of wavelet decomposition respectively and obtain corresponding wavelet transformation Coefficient, wherein the R channel decompositions are R1 parts, R2 parts, R3 parts and R4 parts;The G channel decompositions are G1 parts, G2 Partly, G3 parts and G4 parts;The channel B is decomposed into B1 parts, B2 parts, B3 parts and B4 parts;
Step A3:According to the openness distribution sample rate of the wavelet conversion coefficient, the R passages, G passages and B are carried out The block sampling of passage;
Decoding process comprises the following steps:
Step B1:Group1 is merged into R1 parts, G1 parts, B1 parts, by R2 parts, G2 parts, B2 Part merge into group2, by the R3 part, G3 part, B3 part merge into group3, by the R4 part, G4 part, B4 merges into group4 in part;
Step B2:Carry out the sparse reconstruct of group respectively to described group1, group2, group3 and group4;
Step B3:Three are isolated in group1, group2, group3 and group4 after the sparse reconstruct of group is carried out to lead to Four parts in road, and resequence, the wavelet coefficient of the R passages, G passages and the channel B that are reconstructed;
Step B4:Carry out the picture content that wavelet inverse transformation is reconstructed respectively to the R passages, G passages and channel B;
Step B5:The picture content combination of the reconstruct is recovered into the coloured image.
Further, sample rate is allocated as follows in the step A3:Assuming that the coloured image is N × N coloured images, Its sample rate is ratio, and R1 parts account for the 1/64 of total pixel in R passages, make sample rate ratio1=0.9, the R passage of R1 parts Middle R2 parts account for the 3/64 of total pixel, make R3 parts in sample rate ratio2=0.8, the R passage of R2 parts account for the 3/ of total pixel 16, R4 parts account for the 3/4 of total pixel, and the sample rate for making R3 parts is 2 times of R4 fractional-sample rates, i.e., ratio3=2 × Ratio4, to ensure that the whole-sample rate of image is constant, meets following formula:
As available from the above equation:,
The sample rate method of salary distribution as R passages is performed to the G passages and channel B.
The present invention has the advantages that compared with prior art:During present invention processing coloured image, required sampling Data volume is few, and the picture quality of recovery is good, the natural image compressed sensing of low sampling rate condition is particularly suitable for, compared to RGB The traditional algorithm independently reconstructed, in low sampling rate, Y-PSNR about improves 1-2dB.
Brief description of the drawings
Fig. 1 is overview flow chart of the present invention.
Fig. 2 is three layers of wavelet decomposition schematic diagram of one embodiment of the invention R passages.
Fig. 3 is the group1 definition procedure figures of one embodiment of the invention.
Fig. 4 is the group2 definition figures of one embodiment of the invention.
Fig. 5 is the group3 definition figures of one embodiment of the invention.
Fig. 6 is the group4 definition figures of one embodiment of the invention.
Fig. 7 is SL20 restructing algorithms flow chart of the present invention.
Embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is refer to, the present invention provides a kind of compression sampling and restructing algorithm of coloured image, it is characterised in that:
Programming process includes the following steps:
Step A1:One coloured image is decomposed into R passages, G passages and channel B;
Step A2:The R passages, G passages and channel B are subjected to three layers of wavelet decomposition respectively and obtain corresponding wavelet transformation Coefficient, the wherein low frequency of each layer of R passages are distributed as shown in Fig. 2, other passages are also similar with high fdrequency component;The R channel decompositions For R1 parts, R2 parts, R3 parts and R4 parts;The G channel decompositions are G1 parts, G2 parts, G3 parts and G4 parts;Institute State channel B and be decomposed into B1 parts, B2 parts, B3 parts and B4 parts;
Step A3:According to the openness distribution sample rate of the wavelet conversion coefficient, the R passages, G passages and B are carried out The block sampling of passage, the detailed process for distributing sample rate are as follows:Assuming that the coloured image is N × N coloured images, its sample rate For ratio, R1 parts account for the 1/64 of total pixel in R passages, make R2 parts in sample rate ratio1=0.9, the R passage of R1 parts The 3/64 of total pixel is accounted for, makes the part of R3 in sample rate ratio2=0.8, the R passage of R2 parts account for 3/16, the R4 parts of total pixel The 3/4 of total pixel is accounted for, the sample rate for making R3 parts is 2 times, i.e. ratio3=2 × ratio4 of R4 fractional-sample rates, to ensure to scheme The whole-sample rate of picture is constant, meets following formula:
As available from the above equation:,
The sample rate method of salary distribution as R passages is performed to the G passages and channel B.
Decoding process comprises the following steps:
Step B1:Group1 is merged into R1 parts, G1 parts, B1 parts, by R2 parts, G2 parts, B2 Part merge into group2, by the R3 part, G3 part, B3 part merge into group3, by the R4 part, G4 part, B4 merges into group4 in part;
Step B2:Carry out the sparse reconstruct of group respectively to described group1, group2, group3 and group4;
The sparse principle of group is that the same position of three passages is assigned to be identically numbered, and utilizes the correlation between them Property, found in solution and number identical position, make them in this position with certain contact.Using size as 16 × 16 cromograms As exemplified by, if piecemeal size is 2 × 2, then image can be divided into 64 blocks.4 × 1 row for being by each image block scan are sweared Amount, then can show that the definition procedure of group1 is as shown in Figure 3.
Fig. 3 is refer to, first extracts the wavelet coefficient first row in three passages respectively(Preceding 1/64 part is R1, G1, B1), Stacked again in the form of column vector, form 12 × 1 matrixes from R11 to B14, R11, R12, R13, R14 are R1 in wherein Fig. 3 4 elements, G11, G12, G13, G14 are 4 elements of G1, and B11, B12, B13, B14 are 4 elements of B1, group1 be by The identical coordinate definition of relative position into identical numeral, such as R11, G11, B11 in 12 × 1 matrixes that three passages are built up 1 is all defined as, R12, G12, B12 are defined as 2.When organizing sparse reconstruct, to reconstruct first pixel, need to only find Group1 matrixes intermediate value is 1 coordinate(1;5;9), i.e., R, G, B triple channel composition reconstructed image middle position be set to(1;5;9)Picture Element has correlation, their value can be made similar, and here it is the principle for organizing sparse reconstruct.Similarly, 16 × 16 coloured images The definition of group2, group3 and group4 are respectively as shown in Fig. 4, Fig. 5, Fig. 6.
Then, the data of group1, group2, group3 and group4 are reconstructed respectively using SL20 algorithms.Restructuring procedure That is solving-optimizing problem, wherein A is sensing matrix, and y is measured value, and x is wavelet coefficient,Attach most importance to The wavelet coefficient of structure, restructing algorithm flow are as shown in Figure 7.
Step B3:Three are isolated in group1, group2, group3 and group4 after the sparse reconstruct of group is carried out to lead to Four parts in road, and resequence, the wavelet coefficient of the R passages, G passages and the channel B that are reconstructed;Wherein group1 R1 parts, G1 parts and B1 parts are isolated, wherein group2 isolates R2 parts, G2 parts and B2 parts, wherein group3 R3 parts, G3 parts and B3 parts are isolated, wherein group4 isolates R4 parts, G4 parts and B4 parts(Refer to Fig. 1);
Step B4:Carry out the picture content that wavelet inverse transformation is reconstructed respectively to the R passages, G passages and channel B;
Step B5:The picture content combination of the reconstruct is recovered into the coloured image.
In order to verify the effect of the method for the present invention, by taking 512 × 512 coloured image Peppers and Lena as an example, it is assumed that point Block size is 32 × 32, and calculation matrix is bernoulli matrix, to the color image compression sampling of the present invention and restructing algorithm and biography The RGB independence reconstructing methods of system compare, with Y-PSNR PSNR(Unit is dB)Reconstruct evaluation as algorithm refers to Mark.Table 1 gives PSNR of the color image compression reconstructing method under low sampling rate.
Table 1:
As it can be seen from table 1 for coloured image Peppers and Lena, in the case of equal sample rate to image into Row sampling, method PSNR proposed by the present invention are higher about 1-2dB than RGB independence reconstructing methods.It can thus be appreciated that coloured silk proposed by the present invention The compression sampling and restructing algorithm of color image in the case of identical compression ratio, can effectively improve the measurement efficiency of coloured image And reconstruction quality.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with Modification, should all belong to the covering scope of the present invention.

Claims (2)

1. the compression sampling and restructing algorithm of a kind of coloured image, it is characterised in that:
Programming process includes the following steps:
Step A1:One coloured image is decomposed into R passages, G passages and channel B;
Step A2:The R passages, G passages and channel B are subjected to three layers of wavelet decomposition respectively and obtain corresponding wavelet transformation system Number, wherein the R channel decompositions are R1 parts from low to high, R2 parts, R3 parts and R4 parts;The G passages point Solve as G1 parts from low to high, G2 parts, G3 parts and G4 parts;The channel B is decomposed into from low to high B1 parts, B2 parts, B3 parts and B4 parts;
Step A3:According to the openness distribution sample rate of the wavelet conversion coefficient, higher sample rate is set for low frequency part, Relatively low sample rate is set for high frequency section, carries out the block sampling of the R passages, G passages and channel B;
Decoding process comprises the following steps:
Step B1:Group1 is merged into R1 parts, G1 parts, B1 parts, by R2 parts, G2 parts, B2 parts Group2 is merged into, group3 is merged into R3 parts, G3 parts, B3 parts, by R4 parts, G4 parts, B4 portions Divide and merge into group4;
Step B2:Carry out the sparse reconstruct of group respectively to described group1, group2, group3 and group4;
Step B3:Three passages are isolated in group1, group2, group3 and group4 after the sparse reconstruct of group is carried out Four parts, and resequence, the wavelet coefficient of the R passages, G passages and the channel B that are reconstructed;
Step B4:Carry out the picture content that wavelet inverse transformation is reconstructed respectively to the R passages, G passages and channel B;
Step B5:The picture content combination of the reconstruct is recovered into the coloured image.
2. the compression sampling and restructing algorithm of coloured image according to claim 1, it is characterised in that:In the step A3 Sample rate is allocated as follows:Assuming that the coloured image is N × N coloured images that length and width are N number of pixel, its sample rate is R1 parts account for the 1/64 of total pixel in ratio, R passage, make R2 parts in sample rate ratio1=0.9, the R passage of R1 parts account for The 3/64 of total pixel, makes 3/16, the R4 parts that R3 parts in sample rate ratio2=0.8, the R passage of R2 parts account for total pixel account for The 3/4 of total pixel, the sample rate for making R3 parts is 2 times, i.e. ratio3=2 × ratio4 of R4 fractional-sample rates, to ensure image Whole-sample rate it is constant, meet following formula:
As available from the above equation:,
The sample rate method of salary distribution as R passages is performed to the G passages and channel B.
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