CN107730436A - A kind of wavelet transformation optimization method for accelerating lifting based on GPU - Google Patents

A kind of wavelet transformation optimization method for accelerating lifting based on GPU Download PDF

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Publication number
CN107730436A
CN107730436A CN201711057966.9A CN201711057966A CN107730436A CN 107730436 A CN107730436 A CN 107730436A CN 201711057966 A CN201711057966 A CN 201711057966A CN 107730436 A CN107730436 A CN 107730436A
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data
entered
wavelet transformation
enter
row
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孙自龙
林屹
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Tianjin Cody Information Technology Co Ltd
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Tianjin Cody Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/148—Wavelet transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding

Abstract

The present invention provides a kind of wavelet transformation optimization method for accelerating lifting based on GPU, it is characterised in that：Including：The first step, it would be desirable to carry out path of the view data of wavelet transformation along main frame, equipment end and copy on the main memory 1 of equipment end；Second step, row bound symmetric extension is entered to the data in main memory in the first step 1, data are entered in shared drive, and then data are predicted and renewal is handled；3rd step, row bound symmetric extension is entered again to the data finally obtained in second step, data enter shared drive, and then data are predicted and renewal is handled：4th step, row bound symmetric extension is entered to the data finally obtained in the 3rd step, data enter shared drive, then enter row coefficient scaling：5th step, data are returned in host memory by the path of equipment end, main frame.The present invention is advantageous in that：Its main operational of this product obtains tens times of acceleration lifting, saves the time, reduce energy consumption on the premise of picture signal integrality is ensured.

Description

A kind of wavelet transformation optimization method for accelerating lifting based on GPU
(1) technical field
The present invention relates to image procossing parallel calculating method field, accelerates the small of lifting based on GPU more particularly to a kind of Wave conversion optimization method.
(2) background technology
Image Compression is always popular research direction as the effective means for realizing data compression, wavelet transform DWT (Discrete Wavelet Transform) turns into compression of images by its own good local characteristics and time-frequency characteristic The kernel kernal mapping algorithm of technology, important status is occupied in compression of images field, have important theoretical research value and Practical application meaning.But Wavelet Transformation Algorithm is based on convolution algorithm, computation complexity compared with it is high, committed memory space is big, memory access Often, dependence be present between the adjacency and in conversion process, cause Wavelet Transformation Algorithm to face high definition large scale figure During picture, treatment effeciency reduces, and accelerates compression ratio undesirable.The high direction of research temperature is that hardware platform speeds up to small echo at present Become scaling method, in the hope of obtaining more preferable speed-up ratio.
(3) content of the invention
In order to overcome the weak point of conventional images processing parallel calculating method, added the invention provides one kind based on GPU The wavelet transformation optimization method of speed lifting.
The technical proposal of the invention is realized in this way：
A kind of wavelet transformation optimization method for accelerating lifting based on GPU of this programme, it is characterised in that：Including：
The first step, it would be desirable to carry out path of the view data of wavelet transformation along main frame, equipment end and copy to equipment end On main memory 1；
Second step, row bound symmetric extension is entered to the data in main memory in the first step 1, data enter shared drive It is interior, then data are handled as follows：
1) 1 is predicted：
c1(2n+1)=x (2n+1)+p'[x (2n)+x (2n+2)]
2) 1 is updated：
d1(2n)=x (2n)+u'[c1(2n-1)+c1(2n+1)]
Data after calculating return to main memory 1；
The data finally obtained in second step are entered row bound symmetric extension by the 3rd step again, and data are entered in shared Deposit, then data are handled as follows：
1) 2 are predicted：
c2(2n+1)=c1(2n+1)+p”[d1(2n)+d1(2n+2)]
2) 2 are updated：
d2(2n)=d1(2n)+u”[c1(2n-1)+c1(2n+1)]
Data after calculating enter main memory 2；
4th step, row bound symmetric extension is entered to the data finally obtained in the 3rd step, data enter shared drive, so Laggard row coefficient scaling：
1) coefficient scaling 1：
c3(2n+1)=(- K) c2(2n+1)
2) coefficient scaling 2：
d3(2n)=(1/K) d2(2n)
Data after processing enter main memory 2；
5th step, data matrix transposition, the data procession after processing are carried out to the data finally obtained in the 4th step Convert the judgement that whether terminates, judgement does not terminate data and return to second step to continue to handle, and the data that row-column transform terminates are entered Enter the judgement whether 5 layers of conversion process terminate, judge that unclosed data return to second step and continue to handle, judge successful Data are returned in host memory by the path of equipment end, main frame.
Wherein primary signal is x (n), predictive operator p'=h4/h3, p "=r1/s0；Update operator u'=h3/r3, u "= s0/t0, K=1/t0。
Assuming that the low pass resolution filter coefficient of Wavelet Transformation Algorithm is h0,h1,h2,h3,h4, there is r1=h2-h4-h4h1/h3, s0=h1-h3-h3r0/r1, t0=r0-2r1, r0=h0-2h4h1/h3。
The present invention is advantageous in that：Its main operational of this product takes on the premise of picture signal integrality is ensured Tens times of acceleration lifting is obtained, the time is saved, reduces energy consumption.
(4) illustrate
Fig. 1 is the flow chart of the present invention.
Fig. 2 is Wavelet Transformation Algorithm shared drive schematic diagram of mechanism in the present invention.
Fig. 3 is wavelet transformation algorithm routine block schematic illustration in the present invention.
(5) embodiment
Brief description is made to one embodiment of the present invention below in conjunction with the accompanying drawings.
Such as a kind of Fig. 1 to Fig. 3 wavelet transformation optimization method for accelerating lifting based on GPU, it is characterised in that：Including：
The first step, it would be desirable to carry out path of the view data of wavelet transformation along main frame, equipment end and copy to equipment end On main memory 1；
Second step, row bound symmetric extension is entered to the data in main memory in the first step 1, data enter shared drive It is interior, then data are handled as follows：
1) 1 is predicted：
c1(2n+1)=x (2n+1)+p'[x (2n)+x (2n+2)]
2) 1 is updated：
d1(2n)=x (2n)+u'[c1(2n-1)+c1(2n+1)]
Data after calculating return to main memory 1；
The data finally obtained in second step are entered row bound symmetric extension by the 3rd step again, and data are entered in shared Deposit, then data are handled as follows：
1) 2 are predicted：
c2(2n+1)=c1(2n+1)+p”[d1(2n)+d1(2n+2)]
2) 2 are updated：
d2(2n)=d1(2n)+u”[c1(2n-1)+c1(2n+1)]
Data after calculating enter main memory 2；
4th step, row bound symmetric extension is entered to the data finally obtained in the 3rd step, data enter shared drive, so Laggard row coefficient scaling：
1) coefficient scaling 1：
c3(2n+1)=(- K) c2(2n+1)
2) coefficient scaling 2：
d3(2n)=(1/K) d2(2n)
Data after processing enter main memory 2；
5th step, data matrix transposition, the data procession after processing are carried out to the data finally obtained in the 4th step Convert the judgement that whether terminates, judgement does not terminate data and return to second step to continue to handle, and the data that row-column transform terminates are entered Enter the judgement whether 5 layers of conversion process terminate, judge that unclosed data return to second step and continue to handle, judge successful Data are returned in host memory by the path of equipment end, main frame.
Wherein primary signal is x (n), predictive operator p'=h4/h3, p "=r1/s0；Update operator u'=h3/r3, u "= s0/t0, K=1/t0。
Assuming that the low pass resolution filter coefficient of Wavelet Transformation Algorithm is h0,h1,h2,h3,h4, there is r1=h2-h4-h4h1/h3, s0=h1-h3-h3r0/r1, t0=r0-2r1, r0=h0-2h4h1/h3。
First, the technical program is directed to Wavelet Transformation Algorithm feature, i.e. filter coefficient and view data is floating type Convolution algorithm feature, parser optimization key point and direction of improvement, increase once prediction and once updates step, can be with The expansion of image reconstruction errors is prevented, improves the stability of a system.
Test result indicates that original image can perfectly be recovered using the wavelet transformation of this programme, and it is relatively conventional Wavelet Transformation Algorithm has 3~4 times of acceleration, and lifting scheme achieves good effect.
Secondly, traditional Wavelet Transformation Algorithm lifting scheme is that (In-place) in situ is calculated, and has saved memory cost simultaneously Multiplication number is reduced, but dependence be present between the adjacency in conversion process, is not suitable for Parallel Implementation.Research process It is inside prediction or renewal and be not present phase it was found that the dependence of lifting scheme is existed only between prediction and renewal step Mutually rely on, propose a kind of parallel lifting scheme accordingly, the parallel separation of the parallel of prediction steps and renewal step is realized.Experiment As a result show, lifting scheme achieves tens times of speed-up ratio, while avoids the parallel difficulty for interdepending and bringing.
One embodiment of the present of invention is described in detail above, but the content is only the preferable implementation of the present invention Example, it is impossible to be considered as the practical range for limiting the present invention.All equivalent changes made according to the present patent application scope and improvement Deng, all should still belong to the present invention patent covering scope within.

Claims (2)

  1. A kind of 1. wavelet transformation optimization method for accelerating lifting based on GPU, it is characterised in that：Including：
    The first step, it would be desirable to carry out in the master that path of the view data of wavelet transformation along main frame, equipment end copies to equipment end Deposit on 1；
    Second step, row bound symmetric extension is entered to the data in main memory in the first step 1, data are entered in shared drive, so Data are handled as follows afterwards：
    1) 1 is predicted：
    c1(2n+1)=x (2n+1)+p'[x (2n)+x (2n+2)]
    2) 1 is updated：
    d1(2n)=x (2n)+u'[c1(2n-1)+c1(2n+1)]
    Data after calculating return to main memory 1；
    3rd step, row bound symmetric extension is entered again to the data finally obtained in second step, data enter shared drive, so Data are handled as follows afterwards：
    1) 2 are predicted：
    c2(2n+1)=c1(2n+1)+p”[d1(2n)+d1(2n+2)]
    2) 2 are updated：
    d2(2n)=d1(2n)+u”[c1(2n-1)+c1(2n+1)]
    Data after calculating enter main memory 2；
    4th step, row bound symmetric extension is entered to the data finally obtained in the 3rd step, data enter shared drive, Ran Houjin Row coefficient scaling：
    1) coefficient scaling 1：
    c3(2n+1)=(- K) c2(2n+1)
    2) coefficient scaling 2：
    d3(2n)=(1/K) d2(2n)
    Data after processing enter main memory 2；
    5th step, data matrix transposition is carried out to the data finally obtained in the 4th step, the data procession conversion after processing The judgement whether terminated, judgement do not terminate data and return to second step to continue to handle, and the data that row-column transform terminates enter 5 layers The judgement whether conversion process terminates, judge that unclosed data return to second step and continue to handle, judge successful data Returned to by the path of equipment end, main frame in host memory.
  2. A kind of 2. wavelet transformation optimization method for accelerating lifting based on GPU according to claim 1, it is characterised in that：Its Middle primary signal is x (n), predictive operator p'=h4/h3, p "=r1/s0；Update operator u'=h3/r3, u "=s0/t0, K=1/ t0；
    Assuming that the low pass resolution filter coefficient of Wavelet Transformation Algorithm is h0,h1,h2,h3,h4, there is r1=h2-h4-h4h1/h3, s0= h1-h3-h3r0/r1, t0=r0-2r1, r0=h0-2h4h1/h3。
CN201711057966.9A 2017-11-01 2017-11-01 A kind of wavelet transformation optimization method for accelerating lifting based on GPU Pending CN107730436A (en)

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EP1282075A2 (en) * 2001-07-31 2003-02-05 Ricoh Company, Ltd. Enhancement of compressed images
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Application publication date: 20180223