CN109194968A - A kind of compression of images cognitive method of fusion message source and channel decoding - Google Patents
A kind of compression of images cognitive method of fusion message source and channel decoding Download PDFInfo
- Publication number
- CN109194968A CN109194968A CN201811069844.6A CN201811069844A CN109194968A CN 109194968 A CN109194968 A CN 109194968A CN 201811069844 A CN201811069844 A CN 201811069844A CN 109194968 A CN109194968 A CN 109194968A
- Authority
- CN
- China
- Prior art keywords
- matrix
- reconstruct
- inverse
- code
- wavelet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
- H04N19/63—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using sub-band based transform, e.g. wavelets
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/132—Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/44—Decoders specially adapted therefor, e.g. video decoders which are asymmetric with respect to the encoder
- H04N19/45—Decoders specially adapted therefor, e.g. video decoders which are asymmetric with respect to the encoder performing compensation of the inverse transform mismatch, e.g. Inverse Discrete Cosine Transform [IDCT] mismatch
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Discrete Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
- Compression Of Band Width Or Redundancy In Fax (AREA)
Abstract
The present invention relates to a kind of compression of images cognitive methods of fusion message source and channel decoding, comprising: sparse transformation；Homogenization: by the sparse matrix W that wavelet sub-band is fixed at a line number value by rows according to the size order of degree of rarefication2, N number of data one vector to be observed of expression of its each column；Sampling observation；Step 4: channel coding: the signal that sampling obtains being encoded using RS code；Transmission；Decoding reconstruct: the codeword information received is demodulated first, then correct codeword information is obtained through the search of syndrome computation, key equation solving and money and good fortune Buddhist nun's algoritic module, good fortune Buddhist nun algoritic module is finally searched for by key equation solving module and money again and obtains reconstruct column vector, and converts coefficient matrices A for the column vector of reconstruct；Inverse homogenization；Inverse sparse transformation.
Description
Technical field
The invention belongs to field of image processings, relate generally to a kind of compression of images perception side based on channel decoding theory
Method.
Background technique
Compressive sensing theory is pointed out, for the signal with sparse characteristic, to be far below the condition of nyquist sampling rate
Signal is observed, still can use limited sample realization to the Exact Reconstruction of signal.Due to natural image signal
Do not have sparse characteristic in pixel domain, therefore realizes signal from picture firstly the need of using wavelet transform or discrete cosine transform
Prime field obtains the characteristic signal with sparse characteristic being made of coefficient in transform domain to the conversion of frequency domain, and then again to this feature
Signal carries out compressed sensing sampling.Number and original image due to the number of samples after sampling far below coefficient in transform domain
Pixel number, it is achieved that the compression to picture signal.
The similitude of channel decoding theory and compressed sensing has attracted numerous scholars to study it, it is also solution
The actual application problem that compressed sensing faces provides possibility.2008, F Parvaresh and B Hassibi were demonstrated
The decoding algorithm of Complex Reed-Solomon (CRS) code can be used as a kind of certainty compressed sensing recovery algorithms.According to this
Theory, (r=c+e, wherein r is received vector to the received vector r for being superimposed with mistake of RS (n, k, 2t) code, and c is code word, and e is mistake
Miss vector) the sampling observation that the 2t syndrome that parity matrix is multiplied is compressed sensing is tieed up with 2t × n.In e
Under the premise of vector degree of rarefication is less than or equal to t, with the RS code decoding algorithm such as BM algorithm, GS algorithm etc. in any one finite field
It, which is reconstructed, can recover error vector e.Traditional compression of images cognitive method reconstruction accuracy is low, data throughput
It is low, in order to comply with the trend of big data era development, meet high-speed data acquisition transmission and the storage of large nuber of images video data
It needs, needs to propose a kind of novel compression of images cognitive method.Reed-solomon code coding/decoding technology is quite mature, existing skill
Art can achieve the throughput of Gbps magnitude, and application reed-solomon code associated translation algorithm realizes the essence of compressed sensing reconstruct
Spend it is quite high, therefore by reed-solomon code be applied to compression of images perception be particularly important.
Summary of the invention
The compression of images for the fusion message source and channel decoding based on reed solomon product code that the purpose of the present invention is to propose to a kind of
Cognitive method can greatly improve data acquisition transmission rate, reduce the pressure of large nuber of images video data storage, improve reconstruct
The precision of images.Technical solution is as follows:
A kind of compression of images cognitive method of fusion message source and channel decoding, including the following steps:
Step 1: sparse transformation
The wavelet transform matrix that a size is n × n is generated according to the method for generating wavelet transform matrix；
Step 2: homogenization
By the sparse matrix W that wavelet sub-band is fixed at a line number value by rows according to the size order of degree of rarefication2, it
N number of data of each column indicate a vector to be observed；
Step 3: sampling observation
Using the parity check matrix H of RS code to coefficient matrix W2It is observed, wherein H is the matrix of a 2k × N, 2k
<<N。
Step 4: channel coding
The signal that sampling obtains is encoded using RS (n, k) code, RS (255,239) or RS generally can be used
The code of (255,223)；
Step 5: transmission
Signal by coded modulation, which enters channel, to be transmitted.
Step 6: decoding reconstruct
The codeword information received is demodulated first, is then searched for through syndrome computation, key equation solving and money
Correct codeword information is obtained with good fortune Buddhist nun's algoritic module, good fortune Buddhist nun algorithm mould is finally searched for by key equation solving module and money again
Block obtains reconstruct column vector, and converts coefficient matrices A for the column vector of reconstruct.
Step 7: inverse homogenization
Wavelet matrix C is rearranged into according to the inverse process of step 2 to the coefficient matrices A that step 6 obtains；
Step 8: inverse sparse transformation
2-d discrete wavelet contravariant is carried out to the coefficient matrix C that step 7 generates with the 2-d discrete wavelet inverse transformation of standard
It changes, obtains reconstruction image D.
Advantages of the present invention:
The present invention proposes a kind of homogenization sparse representation method for the excessive problem of object vector degree of rarefication threshold value, and
And it is based on the homogenization method, propose a kind of New Image compression sensing method of fusion message source and channel decoding.With existing side
Method is compared, and this method can not only realize more accurate compression of images sensing reconstructing, and the hardware superiority high with throughput,
To establish technical foundation for realization high-speed data acquisition transmission and mass data storage.
Detailed description of the invention
Fig. 1 is implementation flow chart of the present invention；
Fig. 2 is n rank wavelet transformation schematic diagram；
Fig. 3 is homogenization sparse representation method schematic diagram proposed by the present invention；
Fig. 4 is for various reconstructing methods to the reconstruct PSNR (dB) of image before and after homogenizing rarefaction representation；
Fig. 5 is reconstruct PSNR (dB) of the various restructing algorithms to different images.
Specific embodiment
The present invention mainly verifies the feasibility of the system model by the way of emulation experiment, and all steps are all by experiment
Verifying, for the compression of images cognitive method for realizing fusion message source and channel decoding, specific implementation step is as follows:
Step 1: sparse transformation
The wavelet transform that a size is n × n is generated according to the method for the generation wavelet transform matrix of standard
Matrix is denoted as W1, as shown in Fig. 2, by wavelet sub-band HH1, LH1, HL1, HH2, LH2, HL2..., LLnThe negated zero of coefficient it is general
Rate is denoted as η respectively1, η2, η3, η4, η5, η6..., η3n+1；
Step 2: homogenization
By wavelet sub-band according to the size order (η of degree of rarefication1<η2=η3<η4<η5=η6<…<η3n+1) by rows at one
The fixed sparse matrix W of a line number value2, N number of data one vector to be observed of expression of its each column, as shown in Figure 3；
Step 3: sampling observation
Using the parity check matrix H of RS code to coefficient matrix W2It is observed；
Step 4: channel coding
The signal that sampling obtains is encoded using RS (n, k) code, RS (255,239) or RS generally can be used
The code of (255,223)；
Step 5: transmission
Signal by coded modulation, which enters channel, to be transmitted.
Step 6: decoding reconstruct
The codeword information received is demodulated first, then obtains syndrome through syndrome computation, through key equation
Error location polynomial Λ (x) and error value multinomial Ω (x) is solved to obtain, is obtained correctly through money search and good fortune Buddhist nun algoritic module
Codeword information finally searches for good fortune Buddhist nun algoritic module by key equation solving module and money again and obtains reconstruct column vector, and will weigh
The column vector of structure is converted into coefficient matrices A.
Step 7: inverse homogenization
Wavelet matrix C is rearranged into according to the inverse process of step 2 to the coefficient matrices A that step 6 obtains；
Step 8: inverse sparse transformation
2-d discrete wavelet contravariant is carried out to the coefficient matrix C that step 7 generates with the 2-d discrete wavelet inverse transformation of standard
It changes, obtains reconstruction image D.
Embodiment is applied to the warp that tetra- width resolution ratio of Lena, Cameraman, Fruits and Peppers is 256 × 256
In allusion quotation image, attached drawing 4 is sampled and again to image using different compressed sensing reconstructing methods under different sample rates
The Y-PSNR obtained after building.Attached drawing 5 is reconstructed to different images using different compressed sensings under different sample rates
The Y-PSNR that method is sampled and obtained after being rebuild.It is obvious that the more existing method of method of the invention has significantly
Performance boost.
Claims (1)
1. a kind of compression of images cognitive method of fusion message source and channel decoding, including the following steps:
Step 1: sparse transformation
The wavelet transform matrix that a size is n × n is generated according to the method for generating wavelet transform matrix；
Step 2: homogenization
By the sparse matrix W that wavelet sub-band is fixed at a line number value by rows according to the size order of degree of rarefication2, it is each
N number of data of column indicate a vector to be observed；
Step 3: sampling observation
Using the parity check matrix H of RS code to coefficient matrix W2It is observed, wherein H is the matrix of a 2k × N, 2k < < N；
Step 4: channel coding
The obtained signal of sampling is encoded using RS (n, k) code, generally can be used RS (255,239) or RS (255,
223) code；
Step 5: transmission
Signal by coded modulation, which enters channel, to be transmitted；
Step 6: decoding reconstruct
The codeword information received is demodulated first, then through the search of syndrome computation, key equation solving and money and good fortune
Buddhist nun's algoritic module obtains correct codeword information, finally searches for good fortune Buddhist nun algoritic module by key equation solving module and money again and obtains
To reconstruct column vector, and coefficient matrices A is converted by the column vector of reconstruct；
Step 7: inverse homogenization
Wavelet matrix C is rearranged into according to the inverse process of step 2 to the coefficient matrices A that step 6 obtains；
Step 8: inverse sparse transformation
2-d discrete wavelet inverse transformation is carried out to the coefficient matrix C that step 7 generates with the 2-d discrete wavelet inverse transformation of standard, is obtained
To reconstruction image D.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811069844.6A CN109194968B (en) | 2018-09-13 | 2018-09-13 | Image compression sensing method fusing information source channel decoding |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811069844.6A CN109194968B (en) | 2018-09-13 | 2018-09-13 | Image compression sensing method fusing information source channel decoding |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109194968A true CN109194968A (en) | 2019-01-11 |
| CN109194968B CN109194968B (en) | 2020-12-25 |
Family
ID=64911064
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811069844.6A Expired - Fee Related CN109194968B (en) | 2018-09-13 | 2018-09-13 | Image compression sensing method fusing information source channel decoding |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109194968B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110708561A (en) * | 2019-09-12 | 2020-01-17 | 北京理工大学 | Underwater information acquisition and transmission method based on compressed sensing and channel coding |
| CN111161128A (en) * | 2019-11-18 | 2020-05-15 | 田树耀 | Image transformation based on frequency domain direction filtering and application thereof in sparse decomposition |
| CN114467087A (en) * | 2019-08-21 | 2022-05-10 | 西门子工业软件公司 | Method, system, method of use, computer program and computer readable medium for storing and retrieving data to and from at least one data storage |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101742313A (en) * | 2009-12-10 | 2010-06-16 | 北京邮电大学 | Method of Distributed Source Coding Based on Compressed Sensing Technology |
| CN102572427A (en) * | 2011-12-24 | 2012-07-11 | 辽宁师范大学 | Multiple description coding and decoding method based on compressed sensing |
| CN103327326A (en) * | 2013-05-30 | 2013-09-25 | 西安交通大学 | SAR image transmission method based on compressed sensing and channel self-adaption |
| CN103596010A (en) * | 2013-11-20 | 2014-02-19 | 天津大学 | Video coding and decoding system based on dictionary learning and compressed sensing |
| CN104504740A (en) * | 2015-01-23 | 2015-04-08 | 天津大学 | Image fusion method of compressed sensing framework |
| US20160249065A1 (en) * | 2015-02-20 | 2016-08-25 | Alcatel-Lucent Usa Inc. | Systems and methods for transmission of compressive sensing data |
| CN107170019A (en) * | 2017-05-25 | 2017-09-15 | 浙江树人学院 | A kind of quick low storage image compression sensing method |
-
2018
- 2018-09-13 CN CN201811069844.6A patent/CN109194968B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101742313A (en) * | 2009-12-10 | 2010-06-16 | 北京邮电大学 | Method of Distributed Source Coding Based on Compressed Sensing Technology |
| CN102572427A (en) * | 2011-12-24 | 2012-07-11 | 辽宁师范大学 | Multiple description coding and decoding method based on compressed sensing |
| CN103327326A (en) * | 2013-05-30 | 2013-09-25 | 西安交通大学 | SAR image transmission method based on compressed sensing and channel self-adaption |
| CN103596010A (en) * | 2013-11-20 | 2014-02-19 | 天津大学 | Video coding and decoding system based on dictionary learning and compressed sensing |
| CN104504740A (en) * | 2015-01-23 | 2015-04-08 | 天津大学 | Image fusion method of compressed sensing framework |
| US20160249065A1 (en) * | 2015-02-20 | 2016-08-25 | Alcatel-Lucent Usa Inc. | Systems and methods for transmission of compressive sensing data |
| CN107170019A (en) * | 2017-05-25 | 2017-09-15 | 浙江树人学院 | A kind of quick low storage image compression sensing method |
Non-Patent Citations (3)
| Title |
|---|
| FARZAD PARVARESH: "Explicit measurements with almost optimal thresholds for compressed sensing", 《2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING》 * |
| TOBIAS SCHNIER: "RSCS: Minimum measurement MMV deterministic compressed sensing based on complex reed solomon coding", 《2015 49TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS》 * |
| 张伟: "基于压缩感知理论的图像融合方法", 《计算机工程与应用》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114467087A (en) * | 2019-08-21 | 2022-05-10 | 西门子工业软件公司 | Method, system, method of use, computer program and computer readable medium for storing and retrieving data to and from at least one data storage |
| CN110708561A (en) * | 2019-09-12 | 2020-01-17 | 北京理工大学 | Underwater information acquisition and transmission method based on compressed sensing and channel coding |
| CN111161128A (en) * | 2019-11-18 | 2020-05-15 | 田树耀 | Image transformation based on frequency domain direction filtering and application thereof in sparse decomposition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109194968B (en) | 2020-12-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102164282B (en) | Coefficient-random-permutation-based compressive sensing method and system for image coding | |
| CN105554501B (en) | Image acquisition and compression method and device | |
| CN107845117B (en) | Hyperspectral Image Compression Method Based on Block Sparse Representation Pattern and Structure Dictionary | |
| CN104159112A (en) | Compressed sensing video transmission method and system based on dual sparse model decoding | |
| CN103595414A (en) | Sparse sampling and signal compressive sensing reconstruction method | |
| CN103368578A (en) | Compressed-sensing-based signal sampling method for distributed wireless sensor network nodes | |
| CN103596010B (en) | Video coding and decoding system based on dictionary learning and compressed sensing | |
| CN109194968A (en) | A kind of compression of images cognitive method of fusion message source and channel decoding | |
| CN105791189B (en) | A Sparse Coefficient Decomposition Method to Improve Reconstruction Accuracy | |
| CN108989817A (en) | A kind of radar data compression method based on reference frame dislocation prediction | |
| CN104811664A (en) | Mine video image compression and transmission method and apparatus | |
| CN101582169A (en) | Distributed hyper spectrum image compression method based on 3D wavelet transformation | |
| CN115114928A (en) | An Interpretable Semantic Communication System Based on Feature Selection | |
| CN104881846A (en) | Structured image compressive sensing restoration method based on double-density dual-tree complex wavelet | |
| CN106899810A (en) | A kind of mine video image fusion method and device | |
| CN110708561A (en) | Underwater information acquisition and transmission method based on compressed sensing and channel coding | |
| CN103026636A (en) | Orthogonal multiple description coding | |
| CN109246437B (en) | An Image Compressed Sensing Method Based on Reed Solomon Code | |
| CN101257629A (en) | A Codec Method for Lossless Compression of Image Sequence Based on Region of Interest | |
| CN103985100A (en) | Partitioned compression sensing method based on self-adaptive observation combinational optimization | |
| CN102281443A (en) | Method for processing compressed sensing image based on optimized hierarchical discrete cosine transform (DCT) | |
| CN113132755A (en) | Extensible man-machine cooperative image coding method and coding system | |
| CN107948644B (en) | Underwater image compression method and transmission method | |
| CN116896651A (en) | Code rate adaptive video semantic communication method and related devices | |
| CN105049870B (en) | A kind of distributed video compressed sensing fast reconstructing method based on degree of rarefication estimation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20201225 Termination date: 20210913 |