CN107155112A - A kind of compressed sensing method for processing video frequency for assuming prediction more - Google Patents
A kind of compressed sensing method for processing video frequency for assuming prediction more Download PDFInfo
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- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
- H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
- H04N19/51—Motion estimation or motion compensation
- H04N19/573—Motion compensation with multiple frame prediction using two or more reference frames in a given prediction direction
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- H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
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- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
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Abstract
The invention discloses a kind of compressed sensing method for processing video frequency for assuming prediction, the structural framing of the processing method includes coding side and decoding end more；Frame in the coding side, video is divided into key frame and non-key frame, according to compressive sensing theory, and key frame and non-key frame obtain measured value by calculation matrix Φ；In the decoding end, the key frame carries out BCS SPL reconstruct, and many prediction hypothesis and residual error reconstruct are then carried out respectively；The non-key frame carries out residual error reconstruct, and the side information produced according to key frame is decoded.A kind of new distributed compression video sensing framework predicted based on MH proposed in the present invention, video can be captured and compressed at low encoding complexity device, and video is effectively rebuild at decoder, better image reconstruction quality can be provided by changing MH BCS SPL frameworks.
Description
Technical field
Perceive and combine the present invention relates to compressed sensing video technique field, more particularly to a kind of many hypothesis piecemeal video compress
Smothing filtering realizes image reconstruction technique.
Background technology
At present, CS (compressed sensing) has reformed signal sampling and processing system by integrated compression and sensing.For
The application of the CS for video is studied from different aspect, video-aware (CVS) is referred to as compressed.At encoder, input video frame
It is grouped into the image sets being made up of key frame and multiple non-key frames.
Key frame is encoded using MPEG/H.264 codings in conventional frame, CS calculation matrix is non-key to sense
Frame, and carry out reconstructed key-frame using the side information generated from the key frame of adjacent reconstruct.The shortcoming of this framework is to still need
Complicated MPEG/H.264 codings.In improvement behind, CS measurements are applied to key frame and non-key frame.Using for sparse
The gradient projection (GPSR) of reconstruction carrys out reconstructed key-frame, and will be non-to reconstruct using the side information produced from the key frame of decoding
Key frame.
With the continuous progress of technology, in order to mitigate huge calculating and memory burden, L.Gan proposes one kind and is based on
The CS with independence assumes that (BCS) is used for 2D images between block and block, and Do etc. carrys out table using the adjacent block in precious decoding frame
Show the block in present frame, to improve the degree of accuracy of side information, and develop residual error method for reconstructing.S.Mun extends Gan BCS,
And rebuild in nearest transform domain, it is characterised by that short transverse is decomposed.These methods are referred to as single hypothesis motion compensation
(SH-MC) scheme, it has some shortcomings.At encoder, due to motion estimation search, the calculating except increasing coder side
Outside complexity, the transport overhead for sending block motion vector is also applied with.In addition, SH-MC it is implicitly assumed that send out in the video frame
Raw motion is uniform block translation model.Because this hypothesis is not always set up, so block artifacts are appeared in the frame of recovery.
In order to solve these problems, E.Taramel et al. proposes a kind of for that will assume that motion compensation (MH-MC) is closed more
And the strategy into BCS, and smothing filtering Landweber is used for video reconstruction (MH-BCS-SPL), it is by finding search
In window all pieces or assume linear combination come find it is more it is accurate assume.MH-MC technologies are with generation more complicated at decoder
Valency improves restorability.Thereafter propose to rebuild assembled scheme using MH and SH based on elastomeric network again, its with Tikhonov
Regularization is rebuild and realizes acceptable performance compared to more complicated cost.Occur updating and dynamic reference as assumed to gather again afterwards
Frame selection algorithm.The method of continuous deployment MH predictions in measurement field and pixel domain, to develop two-stage MH reconstruction models, and R Li
Deng presenting, space-time quantifies and motion is directed at the CVS systems of reconstruction to improve performance etc..But, still there are some problems to need solution
Certainly, because releasing the computation burden of encoder, we can obtain side information (SI), but non-key frame weight by simple algorithm
Structure processing can not be efficiently performed using rough prediction.
The content of the invention
Based on the technical problem of background technology presence, one of the object of the invention is that offer is a kind of new and assumed in advance based on morely
The distributed image compressed sensing Computer Vision framework of survey, wherein calculating three side-information candidates to select to improve tradition
MH prediction algorithms, calculate candidate side information using two-way estimation at solution end, and introduce new algorithm and calculate coefficient correlation, choose
Key side information, recovers non-key frame.
A kind of compressed sensing method for processing video frequency for assuming prediction, the structural framing of the processing method includes coding side more
And decoding end；In the coding side, to improve video reconstruction quality, and according to requirement of real-time, video sequence frame is divided into pass
Key frame and non-key frame, every two frame constitute an image sets (GOP, Group Of Picture), i.e. GOP is equal to 2.Usual odd number
Frame is key frame, and even frame is non-key frame.According to compressive sensing theory, key frame and non-key frame pass through calculation matrix Φ
Measured value is obtained, unlike, the measured rate of key frame is high, and the measured rate of non-key frame is low；In the decoding end, key frame warp
Landweber (BCS SPL) algorithm for reconstructing smoothly projected based on block is crossed to be decoded, then through it is excessive assume prediction algorithm and
After residual error is rebuild, key frame and storage after being rebuild；Non-key frame is carried out after residual error reconstruction, with being produced according to key frame
Side information combined decoding, the non-key frame after being rebuild together.Finally, by decoded key frame and non-key frame according to frame
Sequence integration is into video sequence and exports.
It is preferred that, the side information assumes that prediction MH algorithms are tried to achieve according to decoded adjacent key frame through excessive.
The method and steps for assuming prediction MH algorithms as follows more：
(1) three candidate side information SIi (i=0,1,2) are calculated with bidirectional-movement estimation；
(2) coefficient correlation of non-key frame and three candidate side information is calculated respectively, chooses correlation highest SI information.
(3) in Image Reconstruction, in measurement field, a kind of signal residual error is generated using many hypothesis prediction of SI information, and count
The optimum linear combination assumed is calculated, MS-BCS-SPL technology reengineering images are perceived with improved multiple dimensioned splits' positions.
It is preferred that, assume optimum linear combination according to weighting regularization Tikhonov matrix computations.
A kind of compressed sensing method for processing video frequency for assuming prediction is applied to Computer Vision more.
Compared with prior art, the device have the advantages that being：
A kind of new distributed compression video sensing framework predicted based on MH proposed in the present invention, can be in low complexity
Video is captured and compressed at degree encoder, and effectively rebuilds at decoder video.The framework proposed can be by MH
Prediction and BiME estimate initial edge information.Side information is selected according to coefficient correlation, and for recovering non-key frame.Experimental simulation knot
Fruit shows that framework proposed by the invention can be provided than the original more preferable reconstruction quality of MH-BCS-SPL algorithms.
Brief description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for specification, the reality with the present invention
Applying example is used to explain the present invention together, is not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is a kind of CVS codecs of the compressed sensing method for processing video frequency for assuming prediction proposed by the present invention more
Block diagram；
Fig. 2 is based on many prediction hypothesis for a kind of compressed sensing method for processing video frequency for assuming prediction more and generates side message box
Figure；
Fig. 3 uses the PSNR average values that two kinds of algorithms are drawn under different sampling rates for the non-key frame of Akiyo sequences；
Fig. 4 puts down for the PSNR that the non-key frame of Coastguard sequences is drawn using two kinds of algorithms under different sampling rates
Average；
Fig. 5 is averaged for the PSNR that the non-key frame of Foreman sequences is drawn using two kinds of algorithms under different sampling rates
Value；
Fig. 6 is averaged for the PSNR that the non-key frame of Stefan sequences is drawn using two kinds of algorithms under different sampling rates
Value.
Embodiment
The present invention is made with reference to specific embodiment further to explain.
In traditional MH-BC-SPL structures, compression sensing measures dimension N's by using dimension M × N some substrates Φ
Signal x projection comes composite signal capture and dimension reduction, wherein M<<N.Measurement vector y, which is obtained, is：
Y=Φ x (1)
Wherein x ∈ RN,y∈RM.If x is sparse enough in some conversion bases Ψ, as follows by y optimal reconfigurations x：
Wherein Ψ and Φ are fully irrelevant, and M is sufficiently large.
Compression sampling rate is defined as R=M/N.
Generally, Φ is random matrix so that its Ψ with any selection is irrelevant.In actual applications, most of natures
Signal is not real sparse in any conversion base Ψ.
Then, being become by formula (2) for x reconstruction turn to equation for border：
The problem of being rebuild for the relaxation solved in formula (3), L.Gan proposes a kind of by intensive Φ removals x's
Overall situation sampling, and a kind of block-based CS (BCS) algorithm replaced with the diagonal calculation matrix of block.Used when for each piece
During identical Φ B, Φ can take block diagonal form as follows：
Can the y in the way of block-by-blocki=ΦBxiFormula (1) is rewritten, wherein xi is the block i of image.ΦBIt is that size is MB×
B2Calculation matrix, the measurement speed of BCS algorithms is RB=MB/B2。
In the present invention, we will use improvement BCS-SPL to carry out graphic restoration reconstruction.
1st, the Video Codec structural framing perceived based on many prediction hypothesis distributed compressions
As shown in figure 1, the dotted line left side represents coding side, the right represents decoding end.In coding side partition point video flowing, piecemeal
Key frame and non-key frame.
Allow x1Represent key frame.The method of the Image Reconstruction of key frame is at decoder：
(1) using the piecemeal CS image reconstructing methods (BCS-SPL) based on smooth projection Landweber to key frame x1Enter
Row image initial is reconstructed；
(2) the measured value y to image key frame is passed through1Initial reconstructed image, prediction frame is obtained by how hypothetical prediction
(3) assume prediction generation x on more1WithBetween signal residual error
(4) because measured value y1Can be simply by key frame information x1With its calculation matrix Φ1Row vector Inner product obtain
, by residual signals R1It is mapped in measurement base and obtains measured value D1：
Measured value is rebuild with piecemeal CS image reconstructing methods (BCS-SPL) algorithm based on smooth projection Landweber
D1Obtain initial residual signals
Key frame x1Can be by predicting frameWith initial residual signals residual signals R1Approximate representation
Make x2Non-key frame is represented, it can be by key frame x1The side information of generation is decoded.
As shown in Fig. 2 assume to predict that MH algorithms draw side information SI by more, then non-key frame residual signals R2With it is non-key
Frame x2And side information SI relation is：
D2=Φ2R2=Φ2(x2- SI)=y2-Φ2·SI (6)
Wherein, Φ2Represent non-key frame x respectively with y22Calculation matrix and measured value.
Similarly, measured value D is being rebuild with BCS-SPL algorithms2To the residual error side information of reconstruction
Non-key frame x2Can be by the information reconstruction in information and residual error：
2nd, side information is estimated in measurement field with many prediction hypothesis
As shown in fig. 1, the image reconstruction quality of non-key frame depend heavilys on the quality of the side information of generation.In order to
Make full use of two continuous similitudes between key frame and non-key frame, the side information life based on the MH in measurement field of proposition
It is as shown in Figure 2 into algorithm.
OrderWithThe key frame of adjacent two reconstruct in time-domain, Sn is non-key frame, and algorithm is as follows：
(1) estimate that (BiME) draws initial edge information SI by bidirectional-movement.
(2) many prediction hypothesis are done by initial edge information SI and the non-key frame Sn measured, draws side information SI0
(3) similarly, by initial edge information SI and key frameWithMany prediction hypothesis are done respectively draws side information SI1With
SI2.。
(4) three candidate side information SI by obtainingi(i=0,1,2) calculates its similitude with non-key frame Sn respectively, choosing
Go out similitude highest SIiIt is responsible for reconstruct non-key frame.
(5) using correlation coefficient function r (y1,y2) correlation between two frames is represented, function is as follows：
Wherein y1 and y2 are the measurement vectors of the different masses of piecemeal measurement, and N is the length of measurement vector.
3rd, for doing many hypothesis prediction reconstruct non-key frame steps with side information SI, do and answer in detail as follows.
(1) x is made to represent original image,Represent prognostic chart picture.Then on x withBetween residual signals R can be expressed as
(2) in measurement field, residual signals R can be by formulaCalculate.
(3) algorithm for reconstructing R (D, Φ) is perceived by a kind of compression of images, by formulaDraw approximate weight
Composition pictureTherefore reconstructed image Quality of recovery depends on prognostic chart picture on very big depthPrecision.
The problem of predicting the image similarity most like with original image can be expressed as：
Wherein p (Xref) it is adjacent key frame or the side information generated by estimation.
Due to original image at decoder unknown, Wo MenyongIt is approximate to replace x, and can (8) can be rewritten as
Approximate imageIt may switch to measurement field and be calculated as：
Because measured value y can be measured at decoder, therefore we can improve the degree of accuracy of prediction.Equation (10) can
To be solved by assuming prediction more.
The each block for needing prediction is considered as side information in key frame or the optimum linear group of multiple key frame kind blocks
Close, be designated as
Wherein ω is optimum linearity combination coefficient,The square B being made up of all candidate blocks2× MB gusts, M is assumed that
The sum of prediction block,Middle column vector is that the row of each hypothesis prediction block are represented, (10) are substituted into (9) and obtained:
Wherein it isPenalty term, λ is LaGrange parameter.Γ is that diagonal matrix is expressed as：
H1 ..., hk are yesColumn element.For each piece, then ω can directly be counted by common Tikhonov solutions
Calculate：
By the way that (13) are substituted into (11), prediction block can be obtainedFinally, all prediction blocks and side information SI are placed on one
Rise and rebuild non-key frame.
Confirmatory experiment：In order to assess new frame proposed by the present invention and algorithm, in http://trace.eas.asu.edu/
The standard testing video sequence test experience with QCIF forms is done in yuv/ websites.
The sample rate of key frame is 0.7, and the sample rate of non-key frame is 0.1 to 0.5；Each image block B is big in this experiment
Small is 16 × 16, and reference frame is corresponding to obtain picture search region：± 15 pixels of spatial window size w (image block and its surrounding)
In the range of.
Using algorithm proposed by the invention and original MH-BCS-SPL algorithms, for four sequences (i.e. Akiyo,
Coastguard, Foreman and Stefan) different sampling rates measure the average value of average PSNR performances, the results are shown in Table 1.
Table 1. is using the non-key frame reconstruction quality under the different sample rates of average PSNR (dB) descriptions
(unit：dB).
Conclusion：Data in table 1 describe the non-key frame reconstruction quality of different video under different sample rates.The present invention is carried
The algorithm gone out is compared with MH-BCS-SPL algorithms, and reconstruction quality has 0.3-1dB raising.For moving gentle Akiyo sequences
With motion less violent Coastguard sequences, algorithm proposed by the present invention improves 1dB or so；To motion intense
Foreman and Stefan sequences, algorithm proposed by the present invention improves 0.3dB or so.
It can be seen that our improvement MH-BCS-SPL frameworks provide more preferable figure in whole test scope from Fig. 3-6
As reconstruction quality.For with quick or compound movement sequence, such as Coastguard and Foreman sequences, we are carried
The method gone out shows significant performance gain；For the Akiyo sequences with harmonic motion, performance is also improved.
The foregoing is only a preferred embodiment of the present invention, but protection scope of the present invention be not limited thereto,
Any one skilled in the art the invention discloses technical scope in, technique according to the invention scheme and its
Inventive concept is subject to equivalent substitution or change, should all be included within the scope of the present invention.
Claims (5)
1. a kind of compressed sensing method for processing video frequency for assuming prediction more, the structural framing of the processing method include coding side and
Decoding end, it is characterised in that：In the coding side, video sequence frame is divided into key frame and non-key frame, according to compressed sensing
Theory, key frame and non-key frame obtain measured value by calculation matrix Φ；In the decoding end, key frame, which passes through, is based on block
The Landweber algorithm for reconstructing smoothly projected is decoded, and after then being rebuild through excessive hypothesis prediction algorithm and residual error, obtains weight
Key frame and storage after building；Non-key frame is carried out after residual error reconstruction, combines solution together with the side information produced according to key frame
Code, the non-key frame after being rebuild.Finally, decoded key frame and non-key frame are integrated into video sequence according to frame sequential
Arrange and export.
2. a kind of compressed sensing method for processing video frequency for assuming prediction according to claim 1, it is characterised in that described more
Side information assumes that prediction MH algorithms are tried to achieve according to decoded adjacent key frame through excessive.
3. a kind of compressed sensing method for processing video frequency for assuming prediction according to claim 1, it is characterised in that described more
The method and steps for assuming prediction MH algorithms as follows more：
(1) three candidate side information SIi (i=0,1,2) are calculated with bidirectional-movement estimation；
(2) coefficient correlation of non-key frame and three candidate side information is calculated respectively, chooses correlation highest SI information；
(3) in Image Reconstruction, in measurement field, a kind of signal residual error is generated using many hypothesis prediction of SI information, and calculate vacation
If optimum linear combination, perceive MS-BCS-SPL technology reengineering images with improved multiple dimensioned splits' positions.
4. a kind of compressed sensing method for processing video frequency for assuming prediction according to claim 3 more, it is characterised in that according to
Weight the optimum linear combination that regularization Tikhonov matrix computations are assumed.
5. a kind of compressed sensing method for processing video frequency for assuming prediction according to claim 1-4 are applied to video image more
Processing.
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| CN108259916A (en) * | 2018-01-22 | 2018-07-06 | 南京邮电大学 | Best match interpolation reconstruction method in frame in a kind of distributed video compressed sensing |
| CN109040757A (en) * | 2018-07-20 | 2018-12-18 | 西安交通大学 | A kind of compressed sensing multilayer residual image coding method |
| CN110933429A (en) * | 2019-11-13 | 2020-03-27 | 南京邮电大学 | Video compression sensing and reconstruction method and device based on deep neural network |
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| CN116634209A (en) * | 2023-07-24 | 2023-08-22 | 武汉能钠智能装备技术股份有限公司 | Breakpoint video recovery system and method based on hot plug |
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| CN108111861A (en) * | 2017-12-25 | 2018-06-01 | 辽宁师范大学 | Video elastic movement method of estimation based on 2bit depth pixels |
| CN108111861B (en) * | 2017-12-25 | 2021-06-11 | 辽宁师范大学 | Video elastic motion estimation method based on 2bit depth pixel |
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| CN109040757B (en) * | 2018-07-20 | 2020-11-10 | 西安交通大学 | Compressed sensing multilayer residual image coding method |
| CN110933429A (en) * | 2019-11-13 | 2020-03-27 | 南京邮电大学 | Video compression sensing and reconstruction method and device based on deep neural network |
| CN110933429B (en) * | 2019-11-13 | 2021-11-12 | 南京邮电大学 | Video compression sensing and reconstruction method and device based on deep neural network |
| CN112616052A (en) * | 2020-12-11 | 2021-04-06 | 上海集成电路装备材料产业创新中心有限公司 | Method for reconstructing video compression signal |
| CN116634209A (en) * | 2023-07-24 | 2023-08-22 | 武汉能钠智能装备技术股份有限公司 | Breakpoint video recovery system and method based on hot plug |
| CN116634209B (en) * | 2023-07-24 | 2023-11-17 | 武汉能钠智能装备技术股份有限公司 | Breakpoint video recovery system and method based on hot plug |
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Application publication date: 20170912 |