CN104680561A - Static image encoding method based on compressed sensing and WBCT (Wavelet Based Contourlet Transform) transform - Google Patents
Static image encoding method based on compressed sensing and WBCT (Wavelet Based Contourlet Transform) transform Download PDFInfo
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- CN104680561A CN104680561A CN201510038054.1A CN201510038054A CN104680561A CN 104680561 A CN104680561 A CN 104680561A CN 201510038054 A CN201510038054 A CN 201510038054A CN 104680561 A CN104680561 A CN 104680561A
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Abstract
The invention relates to the field of digital image processing and the field of static image encoding, aims to realize effective encoding of static images under a compressed sensing framework, lower the hardware and operation loads of a system and increase the running speed of the whole system, and provides a static image encoding method based on compressed sensing and WBCT (Wavelet Based Contourlet Transform) transform. The method comprises the following steps: I, performing WBCT on an image; II, processing the low-frequency component of a source image with an encoding algorithm based on set partitioning in hierarchical trees (SPIHT); III, performing compressed sensing encoding on a high-frequency component. The method is mainly applied to the field of image processing.
Description
Technical field
The present invention relates to digital image processing field, still image coding field.Specifically, the static image coding method based on compressed sensing and WBCT conversion is related to.
Background technology
View data has extremely strong correlativity, and namely image information has a large amount of redundant informations.This is that Image Compression is rely the basis of development, and people maximally utilise this redundant information exactly, reduce required data when representing image as far as possible and carry out compression of images.In general image compression encoding is mainly divided into three large steps: the decorrelation of image represents (conversion), quantizes, entropy code.
The sparse signal of a higher-dimension or compressible signal can be reconstructed by the measured value that the dimension obtained after measuring is lower by compressed sensing, breach the restriction of the traditional nyquist sampling law in signal acquisition aspect, while signal sampling, the compression of data can be carried out.The complexity designed coding side based on the information processing method of CS requires low, and computational requirements is little, and will sample and to combine with coding, directly can obtain a small amount of crucial measurement of pending signal.
Summary of the invention
For overcoming the deficiencies in the prior art, the present invention is intended to realize based on the efficient coding carrying out still image under compressed sensing framework, greatly reduce hardware and the computational burden of system, and then accelerate the travelling speed of whole system, the speed of sampling can be accelerated while reducing sampling matrix scale.For this reason, the technical scheme that the present invention takes is, based on the static image coding method that compressed sensing and WBCT convert, comprises the steps:
The first step: carry out the contourlet transformation (WBCT) based on small echo to image, obtains low frequency respectively and merges component and high frequency fusion component;
Second step: the classical encryption algorithm based on multistage tree set partitioning sequence (Set Partitioning in Hierarchical Trees, SPIHT) is adopted to the low frequency component of source images;
3rd step: compressed sensing Compressed sensing coding is adopted to high fdrequency component.
The specific implementation process of WBCT is: for the wavelet decomposition of any number of plies, using the high-frequency sub-band HL containing detailed information, LH and HH as the object of Directional Decomposition, the identical direction transformation of decomposed class is applied to the high-frequency sub-band on same yardstick, LH: the low high-value that wavelet decomposition obtains; HL: the height place value that wavelet decomposition obtains; HH: the high high-value that wavelet decomposition obtains; In order to meet anisotropy rule, doing the maximum direction transformation of decomposed class at the top of wavelet decomposition, then secondary high level being done to the direction transformation of the decomposed class that reduces by half.
Compress high frequency components perception Compressed sensing encodes:
(1) carry out quantification treatment to each high frequency sub-block, Y=Φ X, X represents original signal, and Φ represents sparse mapping matrix, and in this method, Φ adopts Teoplitz (Toeplitz) matrix;
(2) sparse transformation coefficient Z (Zigzag) line scanning of each data block unit is become to the ordered series of numbers of one dimension, to be conducive to entropy code step below.
Compared with the prior art, technical characterstic of the present invention and effect:
Compressive sensing theory carries out suitable compression to data while Image Coding, decreases sampled data, saves storage space, contains again enough quantity of information simultaneously.
Accompanying drawing explanation
Fig. 1 is the inventive method techniqueflow chart.
Fig. 2 WBCT tri-layers of decomposition chart.
Embodiment
The present invention takes following technical scheme:
The concrete steps of still image coding based on compressed sensing and WBCT conversion are as follows:
The first step: carry out the contourlet transformation (WBCT) based on small echo to image, obtains low frequency respectively and merges component and high frequency fusion component;
Second step: the low frequency component of source images is adopted to the encryption algorithm based on multistage tree set partitioning sequence (Set Partitioning in Hierarchical Trees, SPIHT) optimized;
3rd step: compressed sensing Compressed sensing coding is adopted to high fdrequency component:
Technology path:
1, image sparse
Image sparse is the basis that image carries out compressed sensing.The present invention mainly adopts the contourlet transformation (WBCT) based on small echo.See Fig. 2.
For the limitation of wavelet transformation, represent in order to more effective and process the High dimensional space datas such as image, the present invention adopts the contourlet transformation (WBCT) based on small echo.The implementation procedure of WBCT also uses two-stage and decomposes, first, WBCT adopts wavelet transformation to realize multi-resolution decomposition, efficiently avoid the data redundancy that LP (Laplce) wave filter is introduced, secondly, the high-frequency sub-band travel direction using directional filter banks wavelet decomposition to be obtained decomposes.In the wavelet decomposition stage, WBCT have employed separable filter, and at DFB, (in anisotropic filter (the Directional Filter Banks) stage, have employed the tree-shaped directional filter banks of inseparable iteration be made up of fan-filter.The two-layer conversion of WBCT is all break-even conversion, and therefore WBCT is non-redundancy Transform.
The specific implementation process of WBCT is: for the wavelet decomposition of any number of plies, using the high-frequency sub-band HL containing detailed information, LH and HH as the object of Directional Decomposition, applies the identical direction transformation of decomposed class to the high-frequency sub-band on same yardstick.In order to meet anisotropy rule, the maximum direction transformation of decomposed class can be done at the top of wavelet decomposition, the decomposed class of the direction transformation that then secondary high level reduced by half.Fig. 2 is the decomposing schematic representation of WBCT.
2, low frequency part adopts the design of classical spiht algorithm
There is many amplitude coefficients in low frequency sub-band, most concentration of energy of original image are in lowest frequency subband, therefore, efficient coding can be carried out to these amplitude coefficients in lowest frequency subband, will to a great extent effect diagram as coding quality. the present invention only carries out classical SPIHT coding to low frequency part, make amplitude more concentrated, thus make originally to represent with less bit with the amplitude that more bit represents, namely save the code word of coding, improve compression efficiency.
3, compress high frequency components perception Compressed sensing encodes:
(1) carry out quantification treatment to each high frequency sub-block, Y=Φ X, X represents original signal, and Φ represents sparse mapping matrix, and in this method, Φ adopts Teoplitz (Toeplitz) matrix;
(2) sparse transformation coefficient Z (Zigzag) line scanning of each data block unit is become to the ordered series of numbers of one dimension, to be conducive to entropy code step below.
More efficient in order to obtain, image co-registration process faster, advises the threshold parameter determining to be more conducive to image co-registration by experimental result contrast more repeatedly, make to merge realize fast on basis more rationally with accurately, realize real efficient process.
Claims (3)
1., based on the static image coding method that compressed sensing and WBCT convert, it is characterized in that, comprise the steps:
The first step: carry out the contourlet transformation (WBCT) based on small echo to image, obtains low frequency respectively and merges component and high frequency fusion component;
Second step: the classical encryption algorithm based on multistage tree set partitioning sequence (Set Partitioning inHierarchical Trees, SPIHT) is adopted to the low frequency component of source images;
3rd step: compressed sensing Compressed sensing coding is adopted to high fdrequency component.
2. as claimed in claim 1 based on the static image coding method that compressed sensing and WBCT convert, it is characterized in that, the specific implementation process of WBCT is: for the wavelet decomposition of any number of plies, using the high-frequency sub-band HL containing detailed information, LH and HH as the object of Directional Decomposition, the identical direction transformation of decomposed class is applied to the high-frequency sub-band on same yardstick, LH: the low high-value that wavelet decomposition obtains; HL: the height place value that wavelet decomposition obtains; HH: the high high-value that wavelet decomposition obtains; In order to meet anisotropy rule, doing the maximum direction transformation of decomposed class at the top of wavelet decomposition, then secondary high level being done to the direction transformation of the decomposed class that reduces by half.
3., as claimed in claim 1 based on the static image coding method that compressed sensing and WBCT convert, it is characterized in that, compress high frequency components perception Compressed sensing encodes:
(1) carry out quantification treatment to each high frequency sub-block, Y=Φ X, X represents original signal, and Φ represents sparse mapping matrix, and in this method, Φ adopts Teoplitz (Toeplitz) matrix;
(2) sparse transformation coefficient Z (Zigzag) line scanning of each data block unit is become to the ordered series of numbers of one dimension, to be conducive to entropy code step below.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108419083A (en) * | 2018-03-22 | 2018-08-17 | 南京邮电大学 | A kind of full subband compressed sensing encryption algorithm of image multilevel wavelet |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104063897A (en) * | 2014-06-28 | 2014-09-24 | 南京理工大学 | Satellite hyper-spectral image compressed sensing reconstruction method based on image sparse regularization |
| CN104168483A (en) * | 2014-07-08 | 2014-11-26 | 大连民族学院 | Video compression method and system |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104063897A (en) * | 2014-06-28 | 2014-09-24 | 南京理工大学 | Satellite hyper-spectral image compressed sensing reconstruction method based on image sparse regularization |
| CN104168483A (en) * | 2014-07-08 | 2014-11-26 | 大连民族学院 | Video compression method and system |
Non-Patent Citations (3)
| Title |
|---|
| 席志红 等: ""WBCT变换的SPIHT图像压缩算法的改进"", 《哈尔滨工程大学学报》 * |
| 杨华千 等: ""基于SPIHT的图像加密与压缩关联算法"", 《物理学报》 * |
| 胡欣: ""基于压缩传感的图像重建与编码研究"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108419083A (en) * | 2018-03-22 | 2018-08-17 | 南京邮电大学 | A kind of full subband compressed sensing encryption algorithm of image multilevel wavelet |
| CN108419083B (en) * | 2018-03-22 | 2020-09-15 | 南京邮电大学 | Image multilevel wavelet full subband compressed sensing coding method |
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