CN107181943B - A kind of satellite sequence Lossless Image Compression Algorithm method and system based on Mixed Entropy Coding - Google Patents
A kind of satellite sequence Lossless Image Compression Algorithm method and system based on Mixed Entropy Coding Download PDFInfo
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- H04N19/103—Selection of coding mode or of prediction mode
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Abstract
The satellite sequence Lossless Image Compression Algorithm method and system based on Mixed Entropy Coding that the invention discloses a kind of, belong to Image Compression field.The method of the present invention inputs present frame and reference frame image first, and select two kinds of prediction modes: inter-prediction and intra prediction obtain prediction residual.Then prediction residual is mapped to non-negative region, finally devise the mixed self-adapting arithmetic coding based on arithmetic coding and block code, according to the distribution character of residual error data, arithmetic coding and block code are respectively adopted to different data, finally coding result is integrated and is exported.The satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding that The invention also achieves a kind of.Technical solution of the present invention can automatically switch prediction mode according to sequence image spatial redundancy and time redundancy characteristic, so that predicted pixel values are closer to true value, the entropy coder that the present invention designs simultaneously has better coding efficiency, significantly improves Lossless Image Compression Algorithm ratio.
Description
Technical field
The invention belongs to Image Compression fields, more particularly, to a kind of satellite sequence based on Mixed Entropy Coding
Lossless Image Compression Algorithm method and system.
Background technique
Remote sensing technology military surveillance, environmental monitoring, in terms of play very important effect, it is special
It is not that remote sensing images have the characteristics of intuitive, informative.With the development of space technology, especially space military technology
Development, to space remote sensing, more stringent requirements are proposed, mainly includes mentioning for image resolution ratio, spectral resolution and temporal resolution
Height causes image data amount on star to ramp.And by image data from space real-time transmission to ground, it is most of satellite
Main task.In certain application fields, lossless compression is needed, therefore, the sharp increase of space remote sensing image data is faced, needs one kind
Lossless compression method with more high compression performance.
Image data not only spatially has certain correlation, and with the raising of frame frequency, there is also redundancies in time.
In lossless compression method, the redundancy of room and time is not only solved, also to solve coding redundancy, it is both comprehensive to improve
Lossless compression performance.
Current existing lossless compression standard such as JPEG-LS, JPEG2000, JPEG etc. or pertinent literature are solving space
With when time redundancy or computation complexity is higher, the limited defect of computing resource on star is not adapted to, or can not achieve nothing
Damage compression；For coding, the performance of encryption algorithm cannot be given full play to, so there is also certain information is superfluous for the result after coding
It is remaining.Therefore, a kind of complementary lossless compression method is needed in terms of predicting and encoding two.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides a kind of satellites based on Mixed Entropy Coding
Sequence image lossless compression method and system, its object is to be cut automatically according to sequence image spatial redundancy and time redundancy characteristic
Intra prediction and inter-frame forecast mode are changed, and is encoded using ADAPTIVE MIXED entropy coding, image on existing star is thus solved
Compression algorithm complexity height and the high technical problem of information redundance in compress technique.
To achieve the above object, according to one aspect of the present invention, a kind of satellite sequence based on Mixed Entropy Coding is provided
Column Lossless Image Compression Algorithm method and system, this method comprises:
(1) gradient calculates: input picture judges whether there is reference frame, if calculating input pixel periphery without reference to frame
Frame inside gradient between pixel two-by-two, enters step (3)；Otherwise input pixel peripheral image vegetarian refreshments and reference frame respective pixel are calculated
Interframe gradient between point simultaneously calculates the frame inside gradient of input pixel simultaneously；
(2) inter-prediction and amendment: if interframe gradient absolute value sum less than given threshold if utilize interframe gradient and ginseng
It examines frame respective pixel prediction input point pixel and obtains predicted value, and quantify interframe gradient and predicted value is modified；Otherwise enter
Step (3)
(3) intra prediction and amendment: predicted value is obtained using input pixel neighboring pixel prediction input point pixel, and is measured
Change frame inside gradient to be modified predicted value；
(4) residual values are calculated and handled: input pixel value subtracts predicted value and obtains residual values, and residual values are limited in default model
In enclosing, re-maps non-negative region and obtain mapping value mapErrVal；
(5) probability initialization, granny rag Lars point Mixed Entropy Coding: are carried out to section [0, Th] using the distribution of granny rag Lars
Cloth:
Obtain arithmetic coding initialization probability model, wherein P (t) is probability, and σ is the distribution parameter of setting, and Th is setting
Value；If [0, Th] mapErrVal ∈ later, then arithmetic coding is carried out according to probabilistic model, and press whole byte polishing coding result,
Then use mapErrVal update probabilistic model；If mapErrVal ∈ (Th, MAXVAL], then fixed length volume is carried out to mapErrVal
Code；Finally coding result is integrated and is exported.
Further, the step (1) specifically includes:
(11) input picture judges whether there is reference frame, if entering step (12) without reference to frame；Otherwise enter step
Suddenly (13)；
(12) the frame inside gradient of input pixel is calculated,
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 and input pixel
Angle is adjacent, enters step (3)；
(13) it calculates frame inside gradient and calculates the interframe gradient of input pixel, interframe gradient simultaneously are as follows:
Wherein, P5, P4 and P10 be reference frame in and P3, P2 and P8 corresponding pixel points.
Further, the step (2) includes:
(21) judgement (| D4 |+| D5 |+| D6 |) whether≤T1 true, wherein T1 is prediction threshold value；If then entering step
(22)；Otherwise (3) are entered step；
(22) predicted value of the input pixel are as follows:
Wherein,For predicted value, P1 is to input pixel in the pixel value of reference frame corresponding points；
(23) calculate G (inter)=| D4 |+| D5 |+| D6 |, G (inter) is quantified as 10 in section [0, MAXVAL]
10 context models are arranged to predicted value in a gradeIt is modified, wherein MAXVAL is the maximum of input picture gray scale
Value, enters step (4) later.
Further, the step (3) includes:
(31) predicted value are as follows:
(32) D1, D2 and D3 are quantified to obtain context index value, predicted value is carried out by context index value
Amendment.
Further, the step (4) includes:
(41) the prediction residual value of input pixel is calculatedP is input picture point pixel value；
(42) if errVal < 0, errVal=errVal+RANGE is updated；Judge again errVal >=((RANGE+1)/
2) whether true, it is to update errVal=errVal-RANGE, wherein RANGE=MAXVAL+1；
(43) errVal is mapped to non-negative region, mapping value are as follows:
It is another aspect of this invention to provide that providing a kind of satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding
System, the system comprises:
Gradient computing module is used for input picture, reference frame is judged whether there is, if calculating input picture without reference to frame
The plain periphery frame inside gradient between pixel two-by-two, into intra prediction and correction module；Otherwise input pixel neighboring pixel is calculated
Interframe gradient between point and reference frame corresponding pixel points simultaneously calculates the frame inside gradient of input pixel simultaneously；
Inter-prediction and correction module, for judging, if interframe gradient absolute value and be less than given threshold, utilize frame
Between gradient and reference frame respective pixel prediction input point pixel obtain predicted value, and quantify interframe gradient and predicted value repaired
Just；Otherwise enter intra prediction and correction module；
Intra prediction and correction module, for being predicted using input pixel neighboring pixel prediction input point pixel
Value, and quantized frame inside gradient is modified predicted value；
Residual values calculate and processing module, subtract predicted value for input pixel value and obtain residual values, residual values are limited in
In preset range, re-maps non-negative region and obtain mapping value mapErrVal；
Mixed Entropy Coding module, for carrying out probability initialization to section [0, Th] using the distribution of granny rag Lars, granny rag is drawn
This distribution:
Obtain arithmetic coding initialization probability model, wherein P (t) is probability, and σ is the distribution parameter of setting, and Th is setting
Value；If [0, Th] mapErrVal ∈ later, then arithmetic coding is carried out according to probabilistic model, and press whole byte polishing coding result,
Then use mapErrVal update probabilistic model；If mapErrVal ∈ (Th, MAXVAL], then fixed length volume is carried out to mapErrVal
Code；Finally coding result is integrated and is exported.
Further, the gradient computing module specifically includes:
Reference frame judging unit is used for input picture, judges whether there is reference frame, if entering in frame without reference to frame
Gradient computing unit；Otherwise enter inter-frame inside gradient computing unit；
Frame inside gradient computing unit, for calculating the frame inside gradient of input pixel,
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 and input pixel
Angle is adjacent, into intra prediction and correction module；
Inter-frame inside gradient computing unit, for calculate frame inside gradient and simultaneously calculate input pixel interframe gradient, frame
Between gradient are as follows:
Wherein, P5, P4 and P10 be reference frame in and P3, P2 and P8 corresponding pixel points.
Further, which is characterized in that the inter-prediction and correction module include:
Predict judging unit, for judge (| D4 |+| D5 |+| D6 |) whether≤T1 true, wherein T1 is prediction threshold value；
If then entering inter prediction unit；Otherwise enter intra prediction and correction module；
Inter prediction unit, for calculating inter-prediction value, the predicted value of the input pixel are as follows:
Wherein,For predicted value, P1 is to input pixel in the pixel value of reference frame corresponding points；
Inter-prediction amending unit, for calculate G (inter)=| D4 |+| D5 |+| D6 |, in section [0, MAXVAL]
G (inter) is quantified as 10 grades, that is, 10 context models are set to predicted valueIt is modified, wherein MAXVAL is
The maximum value of input picture gray scale enters residual values calculating and processing module later.
Further, the intra prediction and correction module include:
Intraprediction unit, for calculating intra prediction value, predicted value are as follows:
Intra prediction amending unit obtains context index value for being quantified to D1, D2 and D3, passes through context rope
Draw value to be modified predicted value.
Further, the residual values calculate and processing module includes:
Residual computations unit, for calculating the prediction residual value of input pixelP is input picture point
Pixel value；
Residual error limits unit, for limiting residual values within a preset range, if errVal < 0, updates errVal=
errVal+RANGE；Judge whether errVal >=((RANGE+1)/2) be true, is to update errVal=errVal- again
RANGE, wherein RANGE=MAXVAL+1；
Residual error map unit, for errVal to be mapped to non-negative region, mapping value are as follows:
In general, through the invention it is contemplated above technical scheme is compared with the prior art, have following technology special
Sign and the utility model has the advantages that
(1) present invention establishes space-time context and carries out to interframe and intra prediction value according to room and time redundancy properties
Amendment, context environmental situation is enriched in JPEG-LS frame, optimizes prediction model on the basis of context, so that prediction
Value is more nearly pixel true value, a greater degree of to reduce room and time redundancy；
(2) the mixed self-adapting arithmetic coding based on arithmetic coding that the present invention designs can preferably adapt to coded object
Distribution character more plays the performance of arithmetic coding, so that entropy of the coding result further to information source, improves lossless compression
Than；
(3) the satellite sequence Lossless Image Compression Algorithm method according to proposed by the present invention based on Mixed Entropy Coding, can effectively solve
Certainly star transmission bandwidth and big data quantity contradiction.While obtaining high compression ratio, implementation complexity is also able to satisfy on star and counts
The requirement of calculation ability.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 diamond search algorithm schematic diagram of the embodiment of the present invention；
Fig. 3 is the prediction module in interframe of the embodiment of the present invention and frame；
Fig. 4 is granny rag Lars distribution schematic diagram；
Fig. 5 is that the embodiment of the present invention predicts value histogram；
Fig. 6 is the histogram after the mapping of predicted value of the embodiment of the present invention；
Fig. 7 is mixed self-adapting of embodiment of the present invention arithmetic coding block diagram.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
As shown in Figure 1, the present embodiment has following process:
(1) gradient calculates: input picture judges whether there is reference frame, if calculating input pixel periphery without reference to frame
Frame inside gradient between pixel two-by-two, enters step (3), otherwise calculates input pixel peripheral image vegetarian refreshments and reference frame respective pixel
Interframe gradient between point simultaneously calculates the frame inside gradient of input pixel simultaneously；
(11) input picture judges whether there is reference frame, if entering step (12) without reference to frame；Otherwise enter step
Suddenly (13)；The reference frame is any one frame, preferably former frame in former frames of present frame, and first in one group of image sequence
Frame is without reference to frame；
(12) the frame inside gradient of input pixel is calculated,
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 and input pixel
Angle is adjacent, enters step (3)；
(13) it calculates frame inside gradient and calculates the interframe gradient of input pixel, interframe gradient simultaneously are as follows:
D4=P3-P5
D5=P2-P4
D6=P8-P10,
Wherein, P5, P4 and P10 be reference frame in and P3, P2 and P8 corresponding pixel points；The corresponding pixel points use Fig. 2
Shown diamond search method calculates the motion vector between adjacent two frame according to minimum absolute difference and criterion, and motion vector adds institute
Pixel coordinate is selected to be denoted as reference frame corresponding pixel points；By the prediction module in interframe of the embodiment of the present invention as shown in Figure 3 and frame
Carry out gradient calculating.
(2) inter-prediction and amendment: if interframe gradient absolute value sum less than given threshold if utilize interframe gradient and ginseng
It examines frame respective pixel prediction input point pixel and obtains predicted value, and quantify interframe gradient and predicted value is modified；Otherwise enter
Step (3)；
(21) judgement (| D4 |+| D5 |+| D6 |) whether≤T1 true, wherein T1 is prediction threshold value；If then entering step
(22)；Otherwise (3) are entered step；
(22) predicted value of the input pixel are as follows:
Wherein,For predicted value, P1 is to input pixel in the pixel value of reference frame corresponding points；
(23) calculate G (inter)=| D4 |+| D5 |+| D6 |, G (inter) is quantified as 10 in section [0, MAXVAL]
10 context models are arranged to predicted value in a gradeBe modified, wherein MAXVAL be input picture gray scale most
Big value, enters step (4) later.
(3) intra prediction and amendment: predicted value is obtained using input pixel neighboring pixel prediction input point pixel, and is measured
Change frame inside gradient to be modified predicted value；
(31) predicted value are as follows:
(32) D1, D2 and D3 are quantified to obtain context index value, predicted value is carried out by context index value
Amendment；It is illustrated in figure 4 the histogram of the present embodiment prediction value set.
(4) residual values are calculated and handled: input pixel value subtracts predicted value and obtains residual values, and residual values are limited in default model
In enclosing, re-maps non-negative region and obtain mapping value mapErrVal；It is illustrated in figure 5 the mapping value of embodiment；
(41) the prediction residual value of input pixel is calculatedP is input picture point pixel value；
(42) if errVal < 0, errVal=errVal+RANGE is updated；Judge again errVal >=((RANGE+1)/
2) whether true, it is to update errVal=errVal-RANGE, wherein RANGE=MAXVAL+1；
(43) errVal is mapped to non-negative region, mapping value are as follows:
(5) Mixed Entropy Coding: as shown in fig. 6, embodiment mixed self-adapting arithmetic coding specifically: utilize granny rag Lars point
Cloth carries out probability initialization to section [0, Th], obtains arithmetic coding initialization probability model, the distribution of granny rag Lars are as follows:
It is illustrated in figure 7 the distribution curve of granny rag Lars distribution；Wherein, P (t) is probability, and σ is the distribution parameter of setting,
Th is setting value；If [0, Th] mapErrVal ∈ later, then arithmetic coding is carried out according to probabilistic model, and press whole byte polishing
Coding result then uses mapErrVal update probabilistic model；If mapErrVal ∈ (Th, MAXVAL], then to mapErrVal into
Row block code；Finally coding result is integrated and is exported.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (8)
1. a kind of satellite sequence Lossless Image Compression Algorithm method based on Mixed Entropy Coding, which is characterized in that the method includes with
Lower step:
(1) gradient calculates: input picture judges whether there is reference frame, if calculating input pixel periphery two-by-two without reference to frame
Frame inside gradient between pixel, enters step (3)；Otherwise calculate input pixel peripheral image vegetarian refreshments and reference frame corresponding pixel points it
Between interframe gradient and simultaneously calculate input pixel frame inside gradient；
(2) inter-prediction and amendment: if interframe gradient absolute value and be less than given threshold, utilize interframe gradient and reference frame
Respective pixel prediction input point pixel obtains predicted value, and quantifies interframe gradient and be modified to predicted value；Otherwise it enters step
(3)；
(3) predicted value, and quantized frame intra prediction and amendment: are obtained using input pixel neighboring pixel prediction input point pixel
Inside gradient is modified predicted value；
(4) residual values are calculated and handled: input pixel value subtracts predicted value and obtains residual values, and residual values are limited in preset range
It is interior, it re-maps non-negative region and obtains mapping value mapErrVal；The step (4) includes:
(41) the prediction residual value of input pixel is calculatedP is input picture point pixel value；
(42) if errVal < 0, updates errVal=errVal+RANGE；ErrVal >=((RANGE+1)/2) is judged again
It is whether true, it is to update errVal=errVal-RANGE, wherein RANGE=MAXVAL+1；
(43) errVal is mapped to non-negative region, mapping value are as follows:
(5) Mixed Entropy Coding: probability initialization is carried out to section [0, Th] using the distribution of granny rag Lars, the distribution of granny rag Lars:
Obtain arithmetic coding initialization probability model, wherein P (t) is probability, and σ is the distribution parameter of setting, and Th is setting value；
If [0, Th] mapErrVal ∈ later, then arithmetic coding is carried out according to probabilistic model, and press whole byte polishing coding result, with
MapErrVal update probabilistic model is used afterwards；If mapErrVal ∈ (Th, MAXVAL], then block code is carried out to mapErrVal；
Finally coding result is integrated and is exported.
2. a kind of satellite sequence Lossless Image Compression Algorithm method based on Mixed Entropy Coding according to claim 1, feature
It is, the step (1) specifically includes:
(11) input picture judges whether there is reference frame, if entering step (12) without reference to frame；Otherwise it enters step
(13)；
(12) the frame inside gradient of input pixel is calculated,
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 with input pixel angle phase
Neighbour enters step (3)；
(13) it calculates frame inside gradient and calculates the interframe gradient of input pixel, frame inside gradient simultaneously are as follows:
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 with input pixel angle phase
It is adjacent
Interframe gradient are as follows:
Wherein, P5, P4 and P10 be reference frame in and P3, P2 and P8 corresponding pixel points.
3. a kind of satellite sequence Lossless Image Compression Algorithm method based on Mixed Entropy Coding according to claim 2, feature
It is, the step (2) includes:
(21) judgement (| D4 |+| D5 |+| D6 |) whether≤T1 true, wherein T1 is prediction threshold value；If then entering step
(22)；Otherwise (3) are entered step；
(22) predicted value of the input pixel are as follows:
Wherein,For predicted value, P1 is to input pixel in the pixel value of reference frame corresponding points；
(23) calculate G (inter)=| D4 |+| D5 |+| D6 |, G (inter) is quantified as 10 etc. in section [0, MAXVAL]
10 context models are arranged to predicted value in gradeIt is modified, wherein MAXVAL is the maximum value of input picture gray scale,
(4) are entered step later.
4. a kind of satellite sequence Lossless Image Compression Algorithm method based on Mixed Entropy Coding according to claim 3, feature
It is, the step (3) includes:
(31) predicted value are as follows:
(32) D1, D2 and D3 are quantified to obtain context index value, predicted value is modified by context index value.
5. a kind of satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding, which is characterized in that the system comprises:
Gradient computing module is used for input picture, judges whether there is reference frame, if calculating input pixel week without reference to frame
The side frame inside gradient between pixel two-by-two, into intra prediction and correction module；Otherwise calculate input pixel peripheral image vegetarian refreshments and
Interframe gradient between reference frame corresponding pixel points simultaneously calculates the frame inside gradient of input pixel simultaneously；
Inter-prediction and correction module, for judging, if interframe gradient absolute value and be less than given threshold, using interframe ladder
Degree and reference frame respective pixel prediction input point pixel obtain predicted value, and quantify interframe gradient and be modified to predicted value；It is no
Then enter intra prediction and correction module；
Intra prediction and correction module, for obtaining predicted value using input pixel neighboring pixel prediction input point pixel, and
Quantized frame inside gradient is modified predicted value；
Residual values calculate and processing module, subtract predicted value for input pixel value and obtain residual values, residual values are limited in default
In range, re-maps non-negative region and obtain mapping value mapErrVal；The residual values calculate and processing module includes:
Residual computations unit, for calculating the prediction residual value of input pixelP is input picture point pixel
Value；
Residual error limits unit, for limiting residual values within a preset range, if errVal < 0, updates errVal=
errVal+RANGE；Judge whether errVal >=((RANGE+1)/2) be true, is to update errVal=errVal- again
RANGE, wherein RANGE=MAXVAL+1；
Residual error map unit, for errVal to be mapped to non-negative region, mapping value are as follows:
Mixed Entropy Coding module, for carrying out probability initialization, granny rag Lars point to section [0, Th] using the distribution of granny rag Lars
Cloth:
Obtain arithmetic coding initialization probability model, wherein P (t) is probability, and σ is the distribution parameter of setting, and Th is setting value；
If [0, Th] mapErrVal ∈ later, then arithmetic coding is carried out according to probabilistic model, and press whole byte polishing coding result, with
MapErrVal update probabilistic model is used afterwards；If mapErrVal ∈ (Th, MAXVAL], then block code is carried out to mapErrVal；
Finally coding result is integrated and is exported.
6. a kind of satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding according to claim 5, feature
It is, the gradient computing module specifically includes:
Reference frame judging unit is used for input picture, judges whether there is reference frame, if entering frame inside gradient without reference to frame
Computing unit；Otherwise enter inter-frame inside gradient computing unit；
Frame inside gradient computing unit, for calculating the frame inside gradient of input pixel,
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 with input pixel angle phase
Neighbour, into intra prediction and correction module；
Inter-frame inside gradient computing unit, for calculate frame inside gradient and simultaneously calculate input pixel interframe gradient, frame manhole ladder
Degree are as follows:
Wherein, P9, P3, P8 and P2 are adjacent two-by-two, and P3 and P2 are adjacent with input pixel side, P9 and P8 with input pixel angle phase
It is adjacent
Interframe gradient are as follows:
Wherein, P5, P4 and P10 be reference frame in and P3, P2 and P8 corresponding pixel points.
7. a kind of satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding according to claim 6, feature
It is, the inter-prediction and correction module include:
Predict judging unit, for judge (| D4 |+| D5 |+| D6 |) whether≤T1 true, wherein T1 is prediction threshold value；If
Then enter inter prediction unit；Otherwise enter intra prediction and correction module；
Inter prediction unit, for calculating inter-prediction value, the predicted value of the input pixel are as follows:
Wherein,For predicted value, P1 is to input pixel in the pixel value of reference frame corresponding points；
Inter-prediction amending unit, for calculate G (inter)=| D4 |+| D5 |+| D6 |, by G in section [0, MAXVAL]
(inter) 10 grades are quantified as, that is, 10 context models are set to predicted valueIt is modified, wherein MAXVAL is defeated
Enter the maximum value of image grayscale, enters residual values calculating and processing module later.
8. a kind of satellite sequence Lossless Image Compression Algorithm system based on Mixed Entropy Coding according to claim 7, feature
It is, the intra prediction and correction module include:
Intraprediction unit, for calculating intra prediction value, predicted value are as follows:
Intra prediction amending unit obtains context index value for being quantified to D1, D2 and D3, passes through context index value
Predicted value is modified.
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