CN107396131A - A kind of mobile terminal local datastore method based on compressed sensing - Google Patents
A kind of mobile terminal local datastore method based on compressed sensing Download PDFInfo
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- CN107396131A CN107396131A CN201710739486.4A CN201710739486A CN107396131A CN 107396131 A CN107396131 A CN 107396131A CN 201710739486 A CN201710739486 A CN 201710739486A CN 107396131 A CN107396131 A CN 107396131A
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- 239000011159 matrix material Substances 0.000 claims description 50
- 238000012545 processing Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000005236 sound signal Effects 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 4
- 230000008447 perception Effects 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 2
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- 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
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- 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
- H04N19/12—Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
- H04N19/122—Selection of transform size, e.g. 8x8 or 2x4x8 DCT; Selection of sub-band transforms of varying structure or type
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- 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
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/438—Interfacing the downstream path of the transmission network originating from a server, e.g. retrieving encoded video stream packets from an IP network
- H04N21/4385—Multiplex stream processing, e.g. multiplex stream decrypting
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Abstract
A kind of mobile terminal local datastore method based on compressed sensing proposed by the present invention, data are carried out by using compressed sensing technology one-dimensional or two-dimensionally compress and store, when user, which carries out local media data, to be checked, data are recovered by compressed sensing transformation by reciprocal direction and is shown or is played；Preserved present invention is generally directed to the image and video data that mobile terminal is gathered by local camera, in the voice data of local microphone typing, and network application to the view data of local；The present invention can significantly improve the utilization rate of mobile terminal local storage medium, and can ensure the data error very little after recovering.
Description
Technical field
The present invention relates to field of signal processing, and in particular to a kind of mobile terminal local datastore side based on compressed sensing
Method.
Background technology
As user more and more frequently carries out operations using mobile terminal, the media data that mobile terminal preserves is anxious
Increase severely and add.Although high in the clouds storage and a kind of good mode, for convenience of instantaneously browsing and by network and flow restriction,
The daily media data being commonly used of user largely can still be stored in mobile terminal.And scheme because mobile terminal gathers
Picture, audio, the device resolution of video more increase, and the media data collected is increasing.If on shifting
Moved end, then larger pressure can be brought to the storage medium of mobile terminal.Therefore, if can be to being stored in local number
Handled according to largely compression is carried out, this will significantly improve the efficiency that is locally stored of mobile terminal.
The content of the invention
The task of the present invention be propose it is a kind of can carry out largely compression to being stored in local data and handle,
It is easy to improve the date storage method for the efficiency that mobile terminal is locally stored.
To achieve these goals, a kind of mobile terminal local datastore side based on compressed sensing provided by the invention
Method, its concrete technical scheme are as follows：
(1) one-dimensional audio signal, the picture signal of two dimension and vision signal are gathered by sample devices and forms original number
According to, and convert raw data into signal matrix using numerical quantization；
(2) normalization formula is established, and above-mentioned signal matrix is normalized by normalizing formula, formation is returned
One changes signal matrix；
(3) according to compressive sensing theory, the normalized signal matrix obtained from wavelet basis to step 2 is compressed perception
The sparse processing represented, i.e., wavelet basis conversion, i.e. s=Ψ are carried out to normalized signal matrixTΨ is small echo in x, wherein formula
Base, s are sparse signal, finally obtain sparse signal；
(4) projection is compressed to the sparse signal that step 3 obtains using M × N observing matrix Φ to calculate, obtain M dimensions
Observation, i.e. y=Φ s, formed compressed signal；
(5) storage is localized to compressed signal；
(6) when using the data of storage, by solving min | | s | |1S.t.y=Θ x, signal weight is carried out to compressed signal
It is the constraints of preceding paragraph that s.t., which represents consequent, in structure, wherein formula, i.e., solves min on the premise of y=Θ x are met | | s |
|1, wherein Θ=Φ Ψ ^T, and Φ is observing matrix, Ψ is certain wavelet basis, then min | | s | |1To seek each element of s matrixes
The situation of the minimum value of absolute value sum, reconstructed reduction treatment obtain initial data.
Further technical scheme, the audio signal and picture signal gathered in step 1 is all data signal, especially
For picture signal, its expression formula can be indicated by the matrix that rgb value of the scope between [0,255] forms.
Further technical scheme, the normalization formula that step 2 is established are：
Wherein x is one of them specific numerical value in signal matrix, and min is the minimum value in signal matrix, and max is letter
Maximum in number matrix, x*Numerical value after for normalized, and after normalized the value of signal matrix distribution model
It is trapped among between [0,1].
Further technical scheme, the observing matrix that step 4 is established, random Gaussian is studied based on compressive sensing theory
Matrix is almost all uncorrelated to any sparse signal, and observing matrix is arranged to random Gaussian matrix.
Further technical scheme, step 6 solve min by using gradient project algorithms | | s | |1S.t.y=Θ x's
Minimum l1Norm Model, and then renormalization processing is carried out again by reconstructing obtained signal data, finally obtain original media
Data.
Compared with prior art, the beneficial effects of the invention are as follows：The present invention is not changing the storage medium of mobile terminal
Under conditions of capacity, based on compressed sensing principle, largely compression processing, very great Cheng are carried out to being stored in local data
The efficiency that the raising mobile terminal of degree is locally stored.
Brief description of the drawings
Fig. 1 is the compressive sensing theory block flow diagram of the present invention；
Fig. 2 is the flow chart of data processing figure of the present invention.
Embodiment
Any feature disclosed in this specification, unless specifically stated otherwise, can be equivalent by other or with similar purpose
Alternative features are replaced；I.e., unless specifically stated otherwise, each feature is an example in a series of equivalent or similar characteristics
.
Below in conjunction with the accompanying drawings and the embodiment of the present invention is described in detail embodiment.
Pointed out according to Donoho and Candes et al. compressive sensing theories established, consider a time-limited signal of real value
Space RN, random length is n signal x ∈ RN, signal x project to certain group RNOrthogonal basis or tight frame Ψ under conversion coefficient s
=ΨTX is sparse, constructs M × N observing matrix Φ, wherein M is much smaller than N, and the observing matrix is with converting base Ψ not
Correlation, linear transformation is carried out to sparse transformation coefficient vector using observing matrix, measurement collection y=Φ s are obtained, if signal x is in itself
It is sparse, then directly observes obtaining measurement collection y=Φ x from x signals using observing matrix, recover the obtained i.e. former letter of signal
Number, y is the vector of M × 1, then can be by carrying out coding biography to observation collection of the data volume far below original signal because M is much smaller than N
It is defeated；Due to measurement collection y dimension of the dimension well below x, therefore the problem is ill posed, it is difficult to directly reconstruct original letter
Number.However, because x is K sparse, proved based on compressive sensing theory, solution can be passed through：
min||ΨT||pS.t.y=Φ s=Φ ΨTX=Θ x
Norm problem is optimized, just signal x can be reconstructed from accurate or high probability in vectorial y is measured, wherein in formula
S.t. it is the constraints of preceding paragraph to represent consequent, i.e., solves min on the premise of y=Θ x are met | | | ΨTx||1, wherein Θ=
Φ Ψ ^T, and Φ is observing matrix, Ψ is certain wavelet basis, because of s=ΨTX, then min | | ΨTx||1To seek each element of s matrixes
Absolute value sum minimum value situation, specific solution procedure is as shown in Figure 1：
Step1：Sparse transformation, i.e. s=Ψ are carried out to compressible signalTX, obtain sparse signal；
Step2：Sparse signal is projected by observing matrix, the observation of M dimensions is obtained, be i.e. y=Φ s, pressed
Contracting signal；
step3：By solving min | | s | |1S.t.y=Θ x, signal reconstruction is carried out to compressed signal.
As shown in Fig. 2 for the mobile terminal local datastore flow chart of data processing based on compressed sensing, specifically include following
Step：
Step 1：It is one-dimensional signal to sample obtained audio signal, and image and video frame images signal are 2D signal, and
Signal after numerical quantization using obtaining signal matrix；
Step 2：The signal matrix that initial data converts to obtain is normalized, and normalized formula is as follows：
Wherein x is one of them specific numerical value in signal matrix, and min is the minimum value in signal matrix, and max is letter
Maximum in number matrix, x*Numerical value after for normalized, and after normalized the value of signal matrix distribution model
It is trapped among between [0,1]；
Step 3：The signal matrix that step 2 obtains is compressed from wavelet basis and perceives the sparse processing represented.Compression
The theoretical research of perception represents, although many signal values are all non-zeros in practice, under wavelet orthogonal basis, and big portion
The value of point wavelet coefficient is all smaller, and only a small amount of wavelet coefficient value is larger, and these larger conversion coefficients contain
Most information of signal；Wavelet basis conversion, i.e. s=Ψ are carried out to signal matrixTX, obtained s data matrix and original
The same matrix size of signal, it is 0 that simply the numeric distribution in s matrixes, which becomes most numerical value, and the numerical value of a few locations is not
For 0, thus it is described as sparse signal；
Step 4：The sparse signal that step 3 obtains is projected using M × N observing matrix Φ, obtains the sight of M dimensions
Measured value, i.e. y=Φ s；Studied and represented according to compressive sensing theory, observing matrix Φ can meet to reconstruct when being random Gaussian matrix
Condition；Due to M《N, i.e., the matrix size for finally handling obtained low-dimensional compressed signal are much smaller than primary signal matrix size, because
This primary signal is effectively compressed；
Step 5：Storage is localized to the signal data after compression；
Step 6：When needing to check media data, it is necessary to which reduction treatment is reconstructed to data；
, can be with to the compressed signal after compressed sensing processing shown in the step3 of compressed sensing processing step in Fig. 1
By solving min | | s | |1S.t.y=Θ x carry out signal reconstruction.This is a minimum l1The Solve problems of Norm Model.Use ladder
Degree projection algorithm can effectively solve foregoing minimum l1Norm Model.By reconstructing, obtained signal data is counter again to be returned
One change is handled, and finally obtains original media data.
Gradient project algorithms comprise the following steps that：
Input：Information operaor Θ, observation vector y, τ is set as a nonnegative real number, object function threshold value, and greatest iteration
Number；
Output：Echo signal x sparse bayesian learning
Step1. feasible initial point z is chosen0, k=1, β ∈ (0,1), μ ∈ (0,1)；
Step2. when iterations k is less than maximum iteration, negative gradient d is calculatedk=-gk, determine feasible negative gradient side
To otherwise turning step7；
Step3. basisJuice calculates α0, update α0For min (α0, αmin, αmax)；
Step4. linear search：
Determine step-length αkFor α0, β α0, β2α0... in first parameter so that meet following formula：
Step5. according to obtained αk, update next feasible point zk+1=(zk+αkdk)+；
Step6. next feasible point target function value is calculated, if target function value is more than threshold value k=k+1, turns step2；
Step7. iteration, z are stoppedk-1As optimal solutionCalculate echo signal x sparse bayesian learning
The step-by-step procedures of above-mentioned gradient project algorithms refers to document (Mario A.T.Figueiredo, Robert
D.Nowak, Stephen J.Wright.Gradient Projection for Sparse Reconstruction：
Application to Compressed Sensing and Other Inverse Problems.Special Issue on
Convex Optimization for Signal Processing, December 2007.)
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.
Claims (5)
- A kind of 1. mobile terminal local datastore method based on compressed sensing, it is characterised in that comprise the following steps：(1) one-dimensional audio signal, the picture signal and vision signal of two dimension are gathered by sample devices, and utilizes numerical quantization Convert raw data into signal matrix；(2) normalization formula is established, and above-mentioned signal matrix is normalized by normalizing formula, forms normalization Signal matrix；(3) according to compressive sensing theory, the normalized signal matrix obtained from wavelet basis to step 2 is compressed perception and represented Sparse processing, i.e., to normalized signal matrix carry out wavelet basis conversion, i.e. s=ΨTΨ is wavelet basis in x, wherein formula, s For sparse signal, sparse signal is finally obtained；(4) projection is compressed to the sparse signal that step 3 obtains using M × N observing matrix Φ to calculate, obtain the sight of M dimensions Measured value, i.e. y=Φ s, that is, form compressed signal；(5) storage is localized to compressed signal；(6) when using the data of storage, by solving min | | s | |1S.t.y=Θ x carry out signal reconstruction to compressed signal, wherein It is the constraints of preceding paragraph that s.t., which represents consequent, in formula, i.e., solves min on the premise of y=Θ x are met | | s | |1, wherein Θ =Φ Ψ ^T, and Φ is observing matrix, Ψ is certain wavelet basis, then min | | s | |1For ask the absolute value of each element of s matrixes it The situation of the minimum value of sum, reconstructed reduction treatment, obtains initial data.
- 2. the mobile terminal local datastore method based on compressed sensing as claimed in claim 1, it is characterised in that：Collection Audio signal and picture signal are all data signals, and for picture signal, its expression formula can be by scope between [0,255] The matrix of rgb value composition is indicated.
- 3. the mobile terminal local datastore method based on compressed sensing as shown in claim 1, it is characterised in that：Establish Normalizing formula is：<mrow> <msup> <mi>x</mi> <mo>*</mo> </msup> <mo>=</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <mrow> <mi>max</mi> <mo>-</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </mfrac> </mrow>Wherein x is one of them specific numerical value in signal matrix, and min is the minimum value in signal matrix, and max is signal square Maximum in battle array, x*For the numerical value after for normalized, and after normalized the value of signal matrix distribution Between [0,1].
- 4. the mobile terminal local datastore method based on compressed sensing as claimed in claim 1, it is characterised in that：Based on pressure Contracting perception theory research random Gaussian matrix is almost all uncorrelated to any sparse signal, and observing matrix is arranged to random Gaussian square Battle array.
- 5. the mobile terminal local datastore method based on compressed sensing as claimed in claim 1, it is characterised in that：Pass through ladder Spend projection algorithm and solve min | | s | |1S.t.y=Θ x minimum l1Norm Model, and then by reconstructing obtained signal data again Renormalization processing is carried out, finally obtains original media data.
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| US8873622B2 (en) * | 2004-10-15 | 2014-10-28 | Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. | Apparatus and method for generating a coded video sequence by using an intermediate layer motion data prediction |
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