CN106954065A - Recursive prediction method for compressing image based on gradient orientation histogram - Google Patents
Recursive prediction method for compressing image based on gradient orientation histogram Download PDFInfo
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Abstract
The invention discloses a kind of recursive prediction method for compressing image based on frequency domain direction wave filter, it is low mainly to solve grain direction accuracy in detection in the prior art, and time complexity is high, the problem of predictive coefficient number is more.Its step is：1) prototype anisotropic filter；2) one group of anisotropic filter is generated according to prototype anisotropic filter；3) original-gray image data are read in, and carry out grain direction judgement；4) original-gray image data are extended；5) expanded image data is read out in units of block；6) according to the block number of reading according to setting up Markov prediction；7) the block number evidence to reading is predicted compression；8) judge whether to complete the prediction of all block number evidences, if it is, terminating compression, otherwise data read address saltus step, return to step 5).The present invention can flexibly and accurately detect grain direction, and time complexity is low, and predictive coefficient number is few, improves compression performance, available for the image compression encoding to various digital devices.
Description
Technical field
The present invention relates to technical field of image processing is belonged to, further relate to a kind of method for compressing image, available for pair
The image compression encoding of various digital devices.
Background technology
Information age people increasingly obtain and utilized information by computer, and the multimedia messages after digitizing have
There is mass data, have very big difference between the Computer Storage resource and the network bandwidth that can be provided with Current hardware technology
Away from, therefore the storage and transmission of information cause very big difficulty.Image information is as most important resource on computer, to it
Carry out effective compression processing undoubtedly necessary, can also bring huge benefit.Compression of images is not still various dynamic
State compression of images, the basis of transmission, but also be to influence the key factor of its effect quality.
Predictive coding, as the topmost method of compression of images, is also most basic method, at some to picture compression matter
The field that amount requires higher is often used, such as text image and medical imaging.Predictive coding generally comprises two parts：One
Part is decorrelation, and mostly using fallout predictor, pixel grey scale to be encoded is predicted using encoded neighborhood territory pixel gray value
Value；Another part is to carry out entropy code to predicated error.
Fallout predictor is generally divided into following three types:
(1) global prediction device：To different pictures or to the different pixels point in a width picture, using the pre- of one group of fixation
Coefficient is surveyed to be predicted it；
(2) local predictor：Difference part to image is predicted using different predictive coefficients to it；
(3) fully adaptive fallout predictor：Predictive coefficient changes with the change of image slices vegetarian refreshments.
Main method based on prediction has the intermediate value rim detection MED side used in Joint Photographic Experts Group JPEG-LS
Method, this method from top to bottom, is from left to right encoded pixel-by-pixel to the pixel of piece image, and its essence is that current region is entered
The simple gradient calculation of row one, is predicted then along the direction parallel with border.For current pixel X prediction, it is
Different variable-length encoding patterns are first selected according to the gradient between neighborhood pixels, then current pixel is predicted, will be current
Pixel is subtracted each other with prediction pixel obtains prediction difference, finally carries out entropy code to prediction difference.
Compared to JPEG-LS, the self-adaptive non-loss method for encoding images CALIC based on context is more multiple using one
The miscellaneous adaptive forecasting method GAP based on gradient.This method based on context the horizontal gradient of formwork calculation current pixel and
Vertical gradient, obtains the difference of horizontal gradient and vertical gradient, is then judged whether horizontally or vertically according to difference
Edge, finally according to the intensity of variation at horizontally or vertically edge, suitably chooses the weight calculation current pixel of adjacent pixel
Predicted value.
Although above-mentioned JPEG-LS, CALIC employ the adaptive prediction algorithm based on context and pixel are carried out in advance
Survey, but taken due to both is relatively simple gradient calculation, it is impossible to texture trend, therefore compression accuracy are tracked well
Have to be hoisted.In recent years, many scholars were studied fast intra-mode prediction model selection, and introduced Canny edge sides
To histogram method, the texture features of image are more fully utilized.
One kind is proposed in the article " the image adaptive predictive coding based on LMS " that the super and all inspection of Xiaoization in 2011 is delivered
The Forecasting Methodology of adaptive updates weights, electronic design engineering, 2011.19 (4):p.109-112.Its essence is to being adopted in prediction
Neighborhood pixels constantly update weights, to obtain less prediction difference, although this method prediction is relatively more accurate, due to not having
There are the texture features for making full use of image, time complexity is too high.
, the paper " the Fast Intra-prediction Algorithm research judged based on texture similarity " that burnt long day wise man delivers at it in 2012
In disclose it is a kind of based on texture similarity judge quick selection intra prediction mode method, Xian Electronics Science and Technology University,
2012.Although this method is avoided that full search pattern, and the time complexity of infra-frame prediction is greatly lowered, but is due to various
The prediction of each pixel uses linear prediction under predictive mode, and predictive coefficient number is more, and data to be compressed are added again
Amount.
The content of the invention
It is an object of the invention to overcome the shortcomings of above-mentioned prior art, there is provided a kind of passing based on gradient orientation histogram
Return prognostic chart picture compression method, to reduce time complexity, reduce the number and prediction difference of predictive coefficient, realize compression performance
Lifting.
Realizing the technical scheme of the object of the invention is：By the method based on Sobel gradient orientation histograms and recurrence Ma Er
Can husband's forecast model be combined, first judge the grain direction of image block, then utilize neighbouring some reference pixels in this direction
Recurrence Markov prediction is set up, the weight coefficient of reference pixel in forecast model is obtained using least-squares estimation, is had
Body step includes as follows：
(1) from natural image concentrate read in a width size be M × M, the original-gray image that suffix is .raw forms, its
In, M represents the width and height of original-gray image, × represent multiplication operations；
(2) grain direction judgement is carried out to original image：
(2a) carries out each a line up and down to the original-gray image in step (1), and the border extension of each row in left and right is obtained
Size is (M+2) × (M+2) expanded images；
(2b), according to from left to right, order from top to bottom does not include side using Sobel gradient operators to expanded images
The gradient direction angle of each pixel of boundary's extension is calculated, and then obtains the gradient direction angle matrix G of original-gray image；
Gradient direction angle matrix G is uniformly divided into the block matrix that size is N × N by (2c), reads these block matrix, and will read
Number of times k is taken to be initialized as 1, wherein, N represents block matrix line number and columns, and value is 4 or 8；
(2d) counts the gradient orientation histogram of k-th of block matrix, obtains the corresponding angle of gradient orientation histogram peak value
, and according to the orthogonality relation in image texture direction and image gradient direction, the texture principal direction angle for determining video data block is
(2e) judges whether that the grain direction for completing all original-gray image data blocks judges, if it is, performing step
Suddenly (3), otherwise, k increases 1, return to step (2d) certainly.
(3) the original-gray image data in step (1) are carried out with each three row up and down, the border extension of each three row in left and right,
Obtain size and be (M+6) × (M+6) expanded image data, and data read address is initialized as expanded image data address
Original position；
(4) since data read address, to expanded image data using size as (N+3) × (N+3) block be unit carry out
Read；
(5) according to the block number of reading according to setting up Markov prediction：
The texture principal direction that (5a) is obtained according to step (2), to block number according in include the row of top three, the left side three arrange it is each
Individual pixel, two-dimentional recurrence Markov prediction is set up using with some reference image vegetarian refreshments neighbouring in the grain direction；
(5b) estimates the weights system of reference pixel in recurrence Markov prediction using the method for least-squares estimation
Number；
(6) prediction of block number evidence
(6a) does not include the row of top three to block number in, each pixel that the left side three is arranged, according to what is set up in step (5)
Recurrence Markov prediction is predicted, and obtains predicting value matrix；
Block number is not included the row of top three by (6b) in, each grey scale pixel value that the left side three is arranged, with it in predicted value square
Corresponding predicted value is subtracted each other in battle array, obtains matrix of differences R, and matrix of differences R progress discrete cosine transforms are become
Coefficient matrix T is changed, then transform coefficient matrix T is quantified, entropy code, compressed code is obtained；
(6c) is decoded to compressed code, re-quantization, inverse dct transform, the matrix of differences R ' after being restored, and by R ' with
Matrix of differences R, which is added, to be obtained rebuilding value matrix, according to reconstruction value matrix to pixel at the appropriate address in expanded image data block
Value is updated；
(7) judge whether to complete the pre- measured compressed of all block number evidences, if it is, terminate compression, otherwise, digital independent
Location saltus step is the initial address of next block number evidence, return to step (4).
The present invention compared with prior art, has the following advantages that：
1. reduce time complexity.
The present invention improves selection due to according to Sobel gradient orientation histograms, prejudging the grain direction of image block
The speed of predictive mode, reduces time complexity.
2. improve estimated performance and compression performance.
The present invention is obtained due to original-gray image data are carried out with each three row up and down, the border extension of each three row in left and right
Size is (M+6) × (M+6) expanded image data, and to expanded image data using size as (N+3) × (N+3) block is unit
It is read out, it is contemplated that the reading to the reference pixel reconstructed value of the row row of top three of the left side three of block number evidence, overcomes existing skill
The problem of art is simply inaccurate to result caused by image block, reduces prediction residual, improves estimated performance；While by
The method based on Sobel histogram of gradients is combined with recurrence Markov prediction in the present invention, same block number
The prediction of all pixels in reduces the number of predictive coefficient with reference to same group of weight coefficient, can preferably adapt to figure
As local grain characteristic, more accurately pixel predictors are obtained, prediction difference is reduced, further improves compression performance.
Brief description of the drawings
Fig. 1 be the present invention realize flow chart；
Embodiment
With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail, embodiment
For illustrating the present invention, but it is not limited to the scope of the present invention.
The present invention's of reference picture 1 realizes that step is as follows：
Step one, original-gray image data are read in.
From natural image concentrate read in a width size be M × M, the original-gray image that suffix is .raw forms, wherein, M
The width and height of original-gray image are represented, × represent multiplication operations；
Original-gray image data are carried out grain direction judgement by step 2.
2.1) each a line up and down is carried out to the original-gray image in step one, the border extension of each row in left and right is obtained
Size is (M+2) × (M+2) expanded images, wherein each a line up and down, the border extension of each row in left and right, refer to original ash
Centered on the edge pixel point for spending image, to a line each up and down in expanded image data, each row pixel in left and right carries out mirror
As assignment；
2.2) according to from left to right, order from top to bottom does not include side using Sobel gradient operators to expanded images
The gradient direction angle of each pixel of boundary's extension is calculated：
2.2a) choose Sobel gradient operator pattern matrixs as follows：
Wherein SxFor horizontal direction gradient operator, SyFor vertical direction gradient operator；
2.2b) by SxConvolution is carried out with 3 × 3 neighborhoods of current pixel point, horizontal direction gradient G is obtainedx, and by SyWith working as
3 × 3 neighborhoods of preceding pixel point carry out convolution, obtain vertical direction gradient Gy；
2.2c) according to step (2.2b) result, the gradient direction angle θ (x, y) of current pixel point is calculated：
2.3) gradient direction angle of each pixel is counted, obtains the gradient direction angle matrix of original-gray image
G；
2.4) gradient direction angle matrix G is uniformly divided into the block matrix that size is N × N, reads these block matrix, and will read
Number of times k is taken to be initialized as 1, wherein, N represents block matrix line number and columns, and value is 4 or 8；
2.5) gradient orientation histogram of k-th of block matrix is counted, the corresponding angle of gradient orientation histogram peak value is obtainedAnd according to the orthogonality relation in image texture direction and image gradient direction, determine the texture principal direction angle of video data block
For
2.6) judge whether that the grain direction for completing all original-gray image data blocks judges, if it is, performing step
Rapid three, otherwise, k increases 1, return to step (2.5) certainly.
Pass through above-mentioned steps one and step 2, you can complete to carry out grain direction judgement to original-gray image data, with
Lower step will start to be predicted gray level image compression：
Step 3, extends original-gray image data.
Original-gray image data in step one are carried out with each three row up and down, the border extension of each three row in left and right is obtained
Size is (M+6) × (M+6) expanded image data, and the address that data read address is initialized as into expanded image data is risen
Beginning position, wherein each three row up and down, the border extension of each three row in left and right refers to the edge pixel point using original-gray image in
The heart, to three rows each up and down in expanded image data, each three row pixel in left and right carries out mirror image assignment；
Step 4, since data read address, to expanded image data using size as (N+3) × (N+3) block is unit
It is read out.
4.1) initialize (N+3) × (N+3) two-dimensional matrix and carry out memory block data, and reading times i is initialized as
0, because to not including the row of top three, the prediction of the pixel of block number that the left side three is arranged in is, it is necessary to reference to neighbouring and completed
Some pixel reconstructed values of the block of prediction, therefore in this step, overabsorption go out 3 × 3 matrix unit store it is neighbouring and
Some pixel reconstructed values of the block of prediction are completed；
4.2) since data read address, N+3 data are read, and these data are assigned to the first of two-dimensional matrix
OK；
4.3) data read address is increased into M+6, and i is increased 1 certainly；
4.4) judge whether i is equal to N+3, if it is, the reading of complete block data, otherwise, return to step 4.2).
Step 5, according to the block number of reading according to setting up Markov prediction.
5.1) the texture principal direction obtained according to step 4, to block number according in include the row of top three, the left side three arrange it is each
Individual pixel, two-dimentional recurrence Markov prediction is set up such as using with some reference image vegetarian refreshments neighbouring in the grain direction
Under：
U (i, j)=ρ1u1(i,j)+ρ2u2(i,j)+ρ3u3(i,j)+ρ4u4(i, j)+e (i, j), i, j=0,1,2 ... N
Wherein u (i, j) denotation coordination is the predicted value of the pixel of (i, j), u1(i,j),u2(i,j),u3(i,j),u4
(i, j) represents the gray value of four reference pixels, ρ respectively1,ρ2,ρ3,ρ4The weight coefficient of respectively four reference pixels, e is
White noise；
It is different according to the direction number L of the one of design group of anisotropic filter value, four ginsengs of diverse location can be chosen
Examine pixel, when L=8 exemplified by, the gray value for four reference pixels that eight kinds of grain directions are chosen is as follows：
In 0 degree of direction of level：
u1(i, j)=u (i-1, j)
u2(i, j)=u (i-2, j)
u3(i, j)=u (i-3, j)
u4(i, j)=u (i-2, j-1)
Counterclockwise with x-axis positive axis into the direction of 22.5 degree of angles：
u1(i, j)=u (i, j-1)
u2(i, j)=u (i+1, j-1)
u3(i, j)=u (i+2, j-1)
u4(i, j)=u (i+2, j-2)
In direction counterclockwise with x-axis positive axis in angle of 45 degrees：
u1(i, j)=u (i, j-1)
u2(i, j)=u (i+1, j-1)
u3(i, j)=u (i+1, j-2)
u4(i, j)=u (i+2, j-2)
Counterclockwise with x-axis positive axis into the direction of 67.5 degree of angles：
u1(i, j)=u (i, j-1)
u2(i, j)=u (i+1, j-1)
u3(i, j)=u (i, j-2)
u4(i, j)=u (i+1, j-2)
Counterclockwise with x-axis positive axis into the direction of 90 degree of angles：
u1(i, j)=u (i, j-1)
u2(i, j)=u (i+1, j-1)
u3(i, j)=u (i+1, j-2)
u4(i, j)=u (i+2, j-2)
Counterclockwise with x-axis positive axis into the direction of 112.5 degree of angles：
u1(i, j)=u (i, j-1)
u2(i, j)=u (i-1, j-1)
u3(i, j)=u (i-1, j-2)
u4(i, j)=u (i, j-2)
Counterclockwise with x-axis positive axis into the direction of 135 degree of angles：
u1(i, j)=u (i-1, j)
u2(i, j)=u (i-1, j-1)
u3(i, j)=u (i, j-1)
u4(i, j)=u (i-2, j-2)
Counterclockwise with x-axis positive axis into the direction of 157.5 degree of angles：
u1(i, j)=u (i-1, j)
u2(i, j)=u (i-2, j-1)
u3(i, j)=u (i-1, j-1)
u4(i, j)=u (i, j-1)
In L ＞ 8, quadratic interpolattion is respectively adopted for L kind grain directions, is calculated in grain direction from the close-by examples to those far off
Four grey scale pixel values in the same direction, are used as the gray value of four reference pixels；
5.2) using the method for least-squares estimation, to the weight coefficient of reference pixel in recurrence Markov prediction
Estimated as follows：
5.2a) block number is not included in each pixel that the row of top three, the left side three are arranged Prediction sum squares E (i,
J) it is expressed as follows：
E (i, j)=(u (i, j)-ρ1u1(i,j)-ρ2u2(i,j)-ρ3u3(i,j)-ρ4u4(i,j))2
Wherein u (i, j) denotation coordination is the predicted value of the pixel of (i, j), u1(i,j),u2(i,j),u3(i,j),u4
(i, j) represents the gray value of four reference pixels, ρ respectively1,ρ2,ρ3,ρ4The weight coefficient of respectively four reference pixels, i, j
=1,2 ... N, wherein N represent that block number does not include the row of top three, the line number in the region of the row pixel of the left side three and columns, value in
For 4 or 8；
5.2b) block number is not included in each pixel that the row of top three, the left side three are arranged Prediction sum squares E (i,
J) it is added, obtains the Prediction sum squares E of block number evidencetotal：
WhereinRepresent in i=1,2 ... N, j=1,2 ... it is cumulative to E (i, j) in the range of N；
5.2c) according to the Prediction sum squares E of block number evidencetotalSet up equation group：
WhereinRepresent EtotalTo the weight coefficient ρ of first reference pixel1Seek partial differential,Represent EtotalTo the weight coefficient ρ of second reference pixel2Seek partial differentialRepresent Etotal
To the weight coefficient ρ of the 3rd reference pixel3Seek partial differential,Represent EtotalTo the power of the 4th reference pixel
Value coefficient ρ4Seek partial differential；
5.2d) equation group in step (5.2c) is solved, reference pixel weight coefficient vector is obtainedAnd then
Obtain the weight coefficient ρ of four reference pixels1,ρ2,ρ3,ρ4。
Original picture block data are predicted compression by step 6.
6.1) each pixel of the row of top three, the row of the left side three is not included in block number, according to what is set up in step 5
Recurrence Markov prediction is predicted, and obtains predicting value matrix；
6.2) each grey scale pixel value for not arranging block number including the row of top three, the left side three in, with it in predicted value square
Corresponding predicted value is subtracted each other in battle array, obtains matrix of differences R, and matrix of differences R progress discrete cosine transforms are become
Coefficient matrix T is changed, then transform coefficient matrix T is quantified, entropy code, compressed code is obtained, if wherein by quantization step
Omit, the compressed code of Lossless Compression can be obtained；
6.3) compressed code is decoded, re-quantization, inverse dct transform, the matrix of differences R ' after being restored, and by R ' with
Matrix of differences R, which is added, to be obtained rebuilding value matrix, according to reconstruction value matrix to pixel at the appropriate address in expanded image data block
Value is updated.
Step 7, judges whether to complete the prediction of all block number evidences, if it is, terminate compression, otherwise, digital independent
Location saltus step is the initial address of next block number evidence, return to step four.
Above description is only example of the present invention, does not constitute any limitation of the invention, it is clear that for this
, all may be without departing substantially from the principle of the invention, structure after understand present disclosure and principle for the professional in field
In the case of, the various modifications and variations in form and details are carried out, but these modifications and variations based on inventive concept are still
Within the claims of the present invention.
Claims (5)
1. the recursive prediction method for compressing image based on gradient orientation histogram, specific steps include as follows：
(1) from natural image concentrate read in a width size be M × M, the original-gray image that suffix is .raw forms, wherein, M tables
Show the width and height of original-gray image, × represent multiplication operations；
(2) grain direction judgement is carried out to original image：
(2a) carries out each a line up and down to the original-gray image in step (1), and the border extension of each row in left and right obtains size
For (M+2) × (M+2) expanded images；
(2b), according to from left to right, order from top to bottom is expanded the border that do not include of expanded images using Sobel gradient operators
The gradient direction angle of each pixel of exhibition is calculated, and then obtains the gradient direction angle matrix G of original-gray image；
Gradient direction angle matrix G is uniformly divided into the block matrix that size is N × N by (2c), reads these block matrix, and will read secondary
Number k is initialized as 1, wherein, N represents block matrix line number and columns, and value is 4 or 8；
(2d) counts the gradient orientation histogram of k-th of block matrix, obtains the corresponding angle of gradient orientation histogram peak valueAnd
According to the orthogonality relation in image texture direction and image gradient direction, the texture principal direction angle for determining video data block is
(2e) judges whether that the grain direction for completing all original-gray image data blocks judges, if it is, performing step
(3), otherwise, k increases 1, return to step (2d) certainly.
(3) the original-gray image data in step (1) are carried out with each three row up and down, the border extension of each three row in left and right is obtained
Size is (M+6) × (M+6) expanded image data, and data read address is initialized as into the starting of expanded image data address
Position；
(4) since data read address, to expanded image data using size as (N+3) × (N+3) block is read out for unit；
(5) according to the block number of reading according to setting up Markov prediction：
The texture principal direction that (5a) is obtained according to step (2), each picture of the row of top three, the row of the left side three is not included to block number in
Vegetarian refreshments, two-dimentional recurrence Markov prediction is set up using with some reference image vegetarian refreshments neighbouring in the grain direction；
(5b) estimates the weight coefficient of reference pixel in recurrence Markov prediction using the method for least-squares estimation；
(6) prediction of block number evidence
(6a) does not include the row of top three to block number in, each pixel that the left side three is arranged, according to the recurrence set up in step (5)
Markov prediction is predicted, and obtains predicting value matrix；
Block number is not included the row of top three by (6b) in, each grey scale pixel value that the left side three is arranged, with it in prediction value matrix
Corresponding predicted value is subtracted each other, and obtains matrix of differences R, and obtain transformation series to matrix of differences R progress discrete cosine transforms
Matrix number T, is then quantified, entropy code to transform coefficient matrix T, obtains compressed code；
(6c) is decoded to compressed code, re-quantization, inverse dct transform, the matrix of differences R ' after being restored, and by R ' and difference
Matrix R, which is added, to be obtained rebuilding value matrix, and pixel value at the appropriate address in expanded image data block is entered according to value matrix is rebuild
Row updates；
(7) judge whether to complete the pre- measured compressed of all block number evidences, if it is, terminating compression, otherwise, data read address is jumped
It is changed into the initial address of next block number evidence, return to step (4).
2. the ladder of each pixel of original-gray image according to the method described in claim 1, is calculated wherein in step (2b)
Deflection is spent, is carried out as follows：
It is as follows that (2b1) chooses Sobel gradient operator pattern matrixs：
Wherein SxFor horizontal direction gradient operator, SyFor vertical direction gradient operator；
(2b2) is by SxConvolution is carried out with 3 × 3 neighborhoods of current pixel point, horizontal direction gradient G is obtainedx, and by SyWith current picture
3 × 3 neighborhoods of vegetarian refreshments carry out convolution, obtain vertical direction gradient Gy；
(2b3) calculates the gradient direction angle θ (x, y) of current pixel point according to step (2b2) result：
3. according to the method described in claim 1, wherein in step (4) to expanded image data using size as (N+3) × (N+3)
Block is read out for unit, carries out as follows：
(4a) initializes (N+3) × (N+3) two-dimensional matrix and carrys out memory block data, and reading times i is initialized as into 1；
(4b) reads N+3 data, and be assigned to the first row of two-dimensional matrix since data read address；
Data read address is increased M+6 by (4c), and i is increased into 1 certainly；
(4d) judges whether i is equal to N+3, if it is, the reading of complete block data, otherwise, return to step (4b).
4. according to the method described in claim 1, the two-dimensional Markov forecast model wherein in step (5a), is expressed as follows：
U (i, j)=ρ1u1(i,j)+ρ2u2(i,j)+ρ3u3(i,j)+ρ4u4(i, j)+e (i, j), i, j=0,1,2 ... N
Wherein u (i, j) denotation coordination is the predicted value of the pixel of (i, j), u1(i,j),u2(i,j),u3(i,j),u4(i, j) point
Not Biao Shi four reference pixels gray value, ρ1,ρ2,ρ3,ρ4The weight coefficient of respectively four reference pixels, e (i, j) is white
Noise.
5. according to the method described in claim 1, referred in the estimation recurrence Markov prediction wherein in step (5b)
The weight coefficient of pixel, is carried out as follows：
(5b1) does not include block number in the row of top three, Prediction sum squares E (i, the j) table for each pixel that the left side three is arranged in
Show as follows：
E (i, j)=(u (i, j)-ρ1u1(i,j)-ρ2u2(i,j)-ρ3u3(i,j)-ρ4u4(i,j))2
Wherein u (i, j) denotation coordination is the predicted value of the pixel of (i, j), u1(i,j),u2(i,j),u3(i,j),u4(i, j) point
Not Biao Shi four reference pixels gray value, ρ1,ρ2,ρ3,ρ4The weight coefficient of respectively four reference pixels, i, j=1,2 ...
N, wherein N represent that block number does not include the row of top three, the line number in the region of the row pixel of the left side three and columns in, and value is 4 or 8；
Block number is not included the row of top three by (5b2) in, the Prediction sum squares E (i, j) for each pixel that the left side three is arranged enters
Row is added, and obtains the Prediction sum squares E of block number evidencetotal：
WhereinRepresent in i=1,2 ... N, j=1,2 ... it is cumulative to E (i, j) in the range of N；
(5b3) is according to the Prediction sum squares E of block number evidencetotalSet up equation group：
WhereinRepresent EtotalTo the weight coefficient ρ of first reference pixel1Seek partial differential,Table
Show EtotalTo the weight coefficient ρ of second reference pixel2Seek partial differentialRepresent Etotal3rd is referred to
The weight coefficient ρ of pixel3Seek partial differential,Represent EtotalTo the weight coefficient ρ of the 4th reference pixel4Ask inclined
Differential；
(5b4) is solved to the equation group in step (5b3), obtains reference pixel weight coefficient vectorAnd then obtain four
The weight coefficient ρ of individual reference pixel1,ρ2,ρ3,ρ4。
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