CN108271025B - The coding circuit of depth modelling mode and its coding method in 3D coding and decoding video based on boundary gradient - Google Patents
The coding circuit of depth modelling mode and its coding method in 3D coding and decoding video based on boundary gradient Download PDFInfo
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
- H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
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Abstract
The coding circuit of depth modelling mode and its coding method in the invention discloses a kind of 3D coding and decoding video based on boundary gradient, including input data processing module, coarse search module and smart search module；Input data processing module is the channel that external reference data are inputted into module, and is handled gradient data；Coarse search module is used to carry out initial forecast to depth map；The prediction result that smart search module is used to obtain coarse search carries out essence again and predicts.The present invention can reduce the scramble time, reduce the calculation amount of circuit, and reduce the power consumption of circuit, to promote the performance of entire video coding circuit.
Description
Technical field
The invention belongs to the intraframe predictive coding technical fields of video coding and decoding technology, specifically a kind of to be based on boundary
The pattern-coding circuit of depth modelling and its coding method in the 3D coding and decoding video of gradient.
Background technique
With the continuous development of science and technology, information technology and computer internet are changing people respectively in various degree
Daily life.Nowadays, people obtain information and are mainly derived from multimedia messages, and multimedia messages are using video as core
The heart.3D video compared to common 2D video due to being capable of providing to the effect that user's scene really and naturally reproduces and by industry
With the attention of academia, become one of the hot spot in field of video research.Compared to common 2D video, 3D includes more huge
Big data volume, this all brings certain difficulty to the transmission and preservation of video data.Therefore, 3D video is carried out effective
Compressed encoding just shows particularly significant.
In the intra prediction of the depth image of 3D video, three are predicted compared to Planar, DC of common 2D video, angle
Big Predicting Technique joined DMM depth modelling mode, and DMM can preferably retain the marginal information of depth image, but same with this
When, result in encoder complexity sharp increase.While guaranteeing to synthesize viewpoint quality, the high complexity for how reducing DMM is calculated in advance
Method is at an important research direction.It is divided into DMM1, DMM4 both of which in DMM depth modelling mode again.
It in the prior art, is not also very much, Gustavo for the hardware circuit design of DMM depth modelling mode
Sanchez et al. is 201730thSymposium on Integrated Circuits and Systems Design meeting
On " the Low-Area Scalable Hardware Architecture for DMM-1 Encoder of 3D-HEVC that delivers
Circuit described in Video Coding Standard ", although reducing the circuit of essence search part, also, in each gradient value
Parallel work-flow is carried out in the inter-process of directional information, but it remains as string on for different gradient value directional informations
Row coding, causes the scramble time long, the circuit computing period is longer, influences coding circuit whole work efficiency in this way.
Summary of the invention
The present invention is to solve above-mentioned the shortcomings of the prior art, proposes a kind of 3D video based on boundary gradient
The coding circuit of depth modelling mode and its coding method in encoding and decoding shorten the operation of circuit to can be reduced the scramble time
Period, to promote the performance of entire video coding circuit.
To achieve the above object of the invention, the present invention adopts the following technical scheme:
The coding circuit of depth modelling mode, remembers any depth in a kind of 3D coding and decoding video based on boundary gradient of the present invention
The pixel value for spending the region 4N × 4N in image is original block RU, and wherein N is positive integer, 1≤N≤8；Its main feature is that: the volume
Code circuit includes: input data processing module, coarse search module, smart search module and wedge block model data store module；
The input data processing module receives externally input original block RU, and calculate the original block RU it is upper and lower,
Left and right four borderline 4N-1 gradient values, so that one 4 × (4N-1) a gradient values are obtained on four boundaries, by institute
There is gradient value to be all added, obtains gradient value and Sum, then the binary expression mode of the gradient value and Sum are moved to right into (3+N)
Position, obtains gradient pre-filtering threshold value Thr；Judge on four boundaries with the presence or absence of the ladder less than the gradient pre-filtering threshold value Thr
Angle value, and if it exists, the gradient value whole reset of the gradient pre-filtering threshold value Thr will be less than on four boundaries；Again to i-th
Borderline 4N-1 gradient value successively carries out label；Described i-th borderline 4N-1 gradient value is subjected to descending sort,
To obtain the corresponding sequence that gradient value corresponds to label, it is denoted as i-th of gradient value location information Posi, wherein gradient value is " 0 "
Label be also denoted as " 0 ", 1≤i≤4；With i-th of gradient value location information PosiWith j-th of gradient value location information PosjAs
One group of gradient value directional information, so that 6 groups of gradient value directional informations are obtained, wherein any one group of gradient value directional information is denoted as
Orit, 1≤t≤6；
The coarse search module is according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label point
It is other that the original block RU is split, to obtain KtA coarse search divides block, wherein k-th of coarse search segmentation block be by
The two wedge blocks composition obtained after the original block RU segmentation；Divide block according to k-th of coarse search, in the original block RU
Pixel value carry out mean value computation respectively according to two wedge blocks, obtain the mean value of two wedge blocks and be filled into corresponding wedge shape
In block, to constitute k-th of coarse search prediction block；The coarse search rate distortion costs value of k-th of coarse search prediction block is calculated, thus
Obtain KtThe coarse search rate distortion costs value of a coarse search prediction block；It is thick corresponding to 6 groups of gradient value directional informations of synchronous calculating
Rate distortion costs value is searched for, and therefrom selects coarse search prediction block corresponding to the smallest coarse search rate distortion costs value as most
Excellent coarse search prediction block, i ≠ j, 1≤j≤4；
Two gradient values of the essence search module according to optimal coarse search prediction block in division position, find phase respectively
Two adjacent gradient values, thus according to a gradient value and its two adjacent gradient values and another gradient value and its adjacent
Two gradient values are respectively split the optimal coarse search prediction block, obtain 8 essence search segmentation blocks；Wherein, s-th of essence
Search segmentation block is made of two wedge blocks obtained after original block RU segmentation；It is right according to s-th of essence search segmentation block
Pixel value in the original block RU carries out mean value computation according to two wedge blocks respectively, obtains the mean value of two wedge blocks and fills out
It is charged in corresponding wedge block, to constitute s-th of essence search prediction block；Calculate the smart searching rate of s-th of essence search prediction block
Distortion cost value is distorted generation from this 8 smart searching rates to obtain the essence search rate distortion costs value of 8 essence search prediction blocks
Select prediction block corresponding to the smallest rate distortion costs value as optimal essence in value and minimum coarse search rate distortion costs value
Prediction block is searched for, residual block is finally calculated according to the optimal essence search prediction block and the original block RU, thus with institute
It states optimal essence search prediction block and residual block realizes that the data compression to the original block RU is transmitted；1≤s≤8.
The characteristics of coding method of depth modelling mode, is in a kind of 3D coding and decoding video based on boundary gradient of the present invention
It carries out as follows:
Step 1, the region 4N × 4N in any depth image of note pixel value be original block RU, wherein N is positive integer, 1
≤N≤8；The borderline 4N-1 gradient value in four, upper and lower, left and right for calculating the original block RU, thus on four boundaries
One is obtained 4 × (4N-1) a gradient values；
All gradient values are all added by step 2, obtain gradient value and Sum, then by the two of the gradient value and Sum into
Expression way processed moves to right the position (3+N), obtains gradient pre-filtering threshold value Thr；
Judge on four boundaries with the presence or absence of the gradient value less than gradient pre-filtering threshold value Thr, and if it exists, then by four sides
It is less than the gradient value whole reset of gradient pre-filtering threshold value Thr in boundary；
Step 3 successively carries out label to described i-th borderline 4N-1 gradient value, and borderline to i-th
4N-1 gradient value carries out descending sort, obtains the corresponding sequence that gradient value corresponds to label, is denoted as i-th of gradient value location information
Posi, wherein gradient value is that the label of " 0 " is also denoted as " 0 ", 1≤i≤4；
Step 4, with i-th of gradient value location information PosiWith j-th of gradient value location information PosjAs one group of gradient value
Directional information, so that 6 groups of gradient value directional informations are obtained, wherein any one group of gradient value directional information is denoted as Orit, 1≤t≤
6；I ≠ j, 1≤j≤4；
Step 5, initialization t=1；
Step 6, according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label respectively to described
Original block RU is split, to obtain KtA coarse search divides block, wherein k-th of coarse search segmentation block is by described original
The two wedge blocks composition obtained after block RU segmentation；
Step 7 divides block according to k-th of coarse search, to the pixel value in the original block RU according to two wedge blocks point
Not carry out mean value computation, obtain the mean value of two wedge blocks and be filled into corresponding wedge block, thus constitute k-th of coarse search
Prediction block；
Step 8, the coarse search rate distortion costs value for calculating k-th of coarse search prediction block, to obtain KtA coarse search is pre-
Survey the coarse search rate distortion costs value of block；
T+1 is assigned to t by step 9, judges whether t > 6 is true, if so, then follow the steps 10；Otherwise return step
6；
Step 10 selects corresponding to the smallest coarse search rate distortion costs value from all coarse search rate distortion costs values
Coarse search prediction block is as optimal coarse search prediction block；
Step 11, two gradient values according to optimal coarse search prediction block in division position, find adjacent two respectively
A gradient value；
Step 12, according to a gradient value and its two adjacent gradient values and another gradient value and its adjacent two
Gradient value is respectively split the optimal coarse search prediction block, obtains 8 essence search segmentation blocks；Wherein, s-th of essence search
Segmentation block is made of two wedge blocks obtained after original block RU segmentation；
Step 13 divides block according to s-th of coarse search, to the pixel value in the original block RU according to two wedge blocks point
Not carry out mean value computation, obtain the mean value of two wedge blocks and be filled into corresponding wedge block, thus constitute s-th essence search
Prediction block；
Step 14, the essence search rate distortion costs value for calculating s-th of essence search prediction block, so that it is pre- to obtain 8 essence search
The essence search rate distortion costs value for surveying block, from this 8 essence search rate distortion costs values and minimum coarse search rate distortion costs value
Select prediction block corresponding to the smallest rate distortion costs value as optimal essence search prediction block；
Residual block is calculated according to the optimal essence search prediction block and the original block RU in step 15, thus with institute
It states optimal essence search prediction block and residual block realizes that the data compression to the original block RU is transmitted；1≤s≤8.
Compared with prior art, advantageous effects of the invention are embodied in:
1, the existing depth image intraframe predictive coding circuit of optimization proposed by the present invention, overcomes coarse search in original design
The problem of process code overlong time, proposes a kind of full parellel circuit framework of coarse search module, to all coarse search wedge shapes
Block splitting scheme is calculated simultaneously, the time required to reducing coding.
2, the existing depth image intraframe predictive coding circuit of optimization proposed by the present invention, it is first in input data processing module
Pre-filtering first is carried out according to the threshold value of setting to each borderline gradient value being calculated, the gradient value lower than threshold value is all set
" 0 " will no longer be split for the location of the gradient value of " 0 " in coarse search module later, and reduce the meter of circuit
Calculation amount, while reducing the power consumption of circuit.
3, the existing depth image intra-frame predictive encoding method of optimization proposed by the present invention, changes in original depth image frame
The mode for predicting serial code in DMM1 mode, proposes a kind of intraframe predictive coding algorithm of full parellel, reduces coding and calculates
In the period needed for method operation, save the scramble time.
Detailed description of the invention
Fig. 1 is the pixel value schematic diagram of original block RU in the prior art；
Fig. 2 is coding circuit general frame figure of the present invention；
Fig. 3 is gradient value pre-filtering schematic diagram of the invention；
Fig. 4 is gradient value location information schematic diagram of the present invention；
Fig. 5 is that coarse search of the present invention divides block schematic diagram；
Fig. 6 is coarse search prediction block schematic diagram of the present invention；
Fig. 7 is that coarse search rate distortion costs value of the present invention calculates schematic diagram；
Fig. 8 is present invention essence search segmentation block schematic diagram；
Fig. 9 is that present invention essence searches for prediction block schematic diagram；
Figure 10 is that residual block of the present invention calculates schematic diagram；
Specific embodiment
In the present embodiment, remember that the pixel value in the region 4N × 4N in any depth image is original block RU, wherein N is positive whole
Number, 1≤N≤8；In the present embodiment, the region that N=1, the i.e. original block are one 4 × 4, specific pixel value such as Fig. 1 institute
Show；
As shown in Fig. 2, it includes: defeated for being somebody's turn to do the coding circuit based on depth modelling mode in the 3D coding and decoding video of boundary gradient
Enter data processing module, coarse search module, smart search module and wedge block model data store module；
Input data processing module receives externally input original block RU, and calculates the upper and lower, left and right four of original block RU
A borderline 4N-1 gradient value, so that one 4 × (4N-1) a gradient values are obtained on four boundaries, by all gradient values
It is all added, obtains gradient value and Sum, then the binary expression mode of gradient value and Sum is moved to right into the position (3+N), obtain gradient
Pre-filtering threshold value Thr；Four borderline gradient values are judged whether all less than gradient pre-filtering threshold value Thr, if so, not right
Four borderline gradient values make any change, otherwise, the gradient value that gradient pre-filtering threshold value Thr is less than on four boundaries is complete
Portion's reset, without modification, in the present embodiment, one shares 12 to the gradient value greater than gradient pre-filtering threshold value Thr on four boundaries
It is all added, obtains gradient value and Sum=2+1+45+1+0+1+44+0+0+2+1+1=98 by a gradient value, then by gradient
Value and the binary expression mode 1100010 of Sum move to right (3+1)=4, obtain the binary expression side of gradient pre-filtering threshold value
Formula Thr=110, i.e. Thr=6, it is clear that four borderline gradient values are not all of less than gradient pre-filtering threshold value Thr, therefore will
Gradient value whole reset on four boundaries less than 6, the gradient value greater than 6 without modification, as shown in figure 3, four are borderline
Non- " 0 " gradient value is only left the 44 of 45 and lower boundary of coboundary；I-th of borderline 4N-1 gradient value is successively carried out again
Label；I-th of borderline 4N-1 gradient value is subjected to descending sort, to obtain the corresponding row that gradient value corresponds to label
Sequence is denoted as i-th of gradient value location information Posi, wherein gradient value is that the label of " 0 " is also denoted as " 0 ", 1≤i≤4；With i-th
A gradient value location information PosiWith j-th of gradient value location information PosjAs one group of gradient value directional information, to obtain 6
Group gradient value directional information, wherein any one group of gradient value directional information is denoted as Orit, 1≤t≤6；
Coarse search module is according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label it is right respectively
Original block RU is split, to obtain KtA coarse search divides block, wherein k-th of coarse search segmentation block is by original block RU
The two wedge blocks composition obtained after segmentation is believed according to 6 groups of gradient value directions after gradient pre-filtering in the present embodiment
Breath is only left 1 coarse search and divides block, as shown in figure 5, being that the coarse search divides block；Divide block according to k-th of coarse search,
Mean value computation is carried out according to two wedge blocks to the pixel value in original block RU respectively, obtains the mean value of two wedge blocks and filling
Into corresponding wedge block, so that k-th of coarse search prediction block is constituted, in the present embodiment, as shown in fig. 6, as shown in Fig. 5
Coarse search segmentation block corresponding to coarse search prediction block；The coarse search rate distortion costs value of k-th of coarse search prediction block is calculated,
To obtain KtThe coarse search rate distortion costs value of a coarse search prediction block, in the present embodiment, as shown in fig. 7, in as Fig. 6
The coarse search rate distortion costs value that coarse search prediction block is calculated, value 19；It is synchronous to calculate 6 groups of gradient value directional information institutes
Corresponding coarse search rate distortion costs value, and coarse search corresponding to the smallest coarse search rate distortion costs value is therefrom selected to predict
Block is as optimal coarse search prediction block, in the present embodiment, it is seen that minimum coarse search rate distortion costs value is 19, therefore its is corresponding
Coarse search prediction block is most to have coarse search prediction block, i ≠ j, 1≤j≤4；
Two gradient values of the smart search module according to optimal coarse search prediction block in division position, find adjacent respectively
Two gradient values, thus according to a gradient value and its two adjacent gradient values and another gradient value and its adjacent two
Gradient value is respectively split optimal coarse search prediction block, 8 essence search segmentation blocks is obtained, in the present embodiment, such as Fig. 8 institute
Show, as the optimal coarse search prediction block obtained in coarse search module and its 8 adjacent essence search segmentation blocks；Wherein,
S-th of essence search segmentation block is made of two wedge blocks obtained after original block RU segmentation；According to s-th of essence search segmentation
Block carries out mean value computation according to two wedge blocks to the pixel value in original block RU respectively, obtains the mean value of two wedge blocks simultaneously
It is filled into corresponding wedge block, so that s-th of essence search prediction block is constituted, in the present embodiment, as shown in figure 9, as root
8 essence search prediction blocks being calculated according to 8 essence search segmentation blocks；The smart searching rate for calculating s-th of essence search prediction block is lost
True cost value, to obtain the essence search rate distortion costs value of 8 essence search prediction blocks, in the present embodiment, this 8 essence search
Rate distortion costs value is respectively 82,141,368,421,235,461,159,236, from this 8 essence search rate distortion costs values and
Select prediction block corresponding to the smallest rate distortion costs value pre- as optimal essence search in minimum coarse search rate distortion costs value
Survey block, in the present embodiment, in this 9 rate distortion costs values it is the smallest still be minimum coarse search rate distortion costs value be 19,
I.e. optimal essence search prediction block is identical with optimal coarse search prediction block, is finally counted according to optimal essence search prediction block and original block RU
Calculation obtains residual block, such as Fig. 9, to realize that the data compression to original block RU passes with optimal essence search prediction block and residual block
It is defeated；1≤s≤8.
In the present embodiment, in a kind of 3D coding and decoding video based on boundary gradient the coding method of depth modelling mode be by
Following steps carry out:
Step 1, the region 4N × 4N in any depth image of note pixel value be original block RU, wherein N is positive integer, 1
≤ N≤8, in the present embodiment, as shown in Figure 1, the pixel value of the original block RU in as one 4 × 4 regions；Calculate original block RU
The borderline 4N-1 gradient value in four, upper and lower, left and right, so that one 4 × (4N-1) a gradients are obtained on four boundaries
Value；
All gradient values are all added by step 2, obtain gradient value and Sum, then by the binary form of gradient value and Sum
The position (3+N) is moved to right up to mode, obtains gradient pre-filtering threshold value Thr；
Four borderline gradient values are judged whether all less than gradient pre-filtering threshold value Thr, if so, not to four sides
Gradient value in boundary makes any change, and otherwise, the gradient value that gradient pre-filtering threshold value Thr is less than on four boundaries is all set
" 0 ", without modification, in the present embodiment, one shares 12 ladders to the gradient value greater than gradient pre-filtering threshold value Thr on four boundaries
It is all added, obtains gradient value and Sum=2+1+45+1+0+1+44+0+0+2+1+1=98 by angle value, then by gradient value and
The binary expression mode 1100010 of Sum moves to right (3+1)=4, obtains the binary expression mode of gradient pre-filtering threshold value
Thr=110, i.e. Thr=6, it is clear that four borderline gradient values are not all of less than gradient pre-filtering threshold value Thr, therefore by four
Gradient value whole reset on a boundary less than 6, the gradient value greater than 6 without modification, as shown in figure 3, four are borderline non-
" 0 " gradient value is only left the 44 of 45 and lower boundary of coboundary；
Step 3 successively carries out label to i-th of borderline 4N-1 gradient value, and a to i-th of borderline 4N-1
Gradient value carries out descending sort, obtains the corresponding sequence that gradient value corresponds to label, is denoted as i-th of gradient value location information Posi,
In the present embodiment, as shown in figure 4,4 borderline gradient value location informations of as original block, wherein gradient value is " 0 "
Label be also denoted as " 0 ", 1≤i≤4；
Step 4, with i-th of gradient value location information PosiWith j-th of gradient value location information PosjAs one group of gradient value
Directional information, so that 6 groups of gradient value directional informations are obtained, wherein any one group of gradient value directional information is denoted as Orit, 1≤t≤
6；I ≠ j, 1≤j≤4；
Step 5, initialization t=1；
Step 6, according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label respectively to original
Block RU is split, to obtain KtA coarse search divides block, in the present embodiment, as shown in figure 5, being coarse search segmentation
Block, wherein k-th of coarse search segmentation block is made of two wedge blocks obtained after original block RU segmentation；
Step 7 divides block according to k-th coarse search, to the pixel value in original block RU according to two wedge blocks respectively into
Row mean value computation obtains the mean value of two wedge blocks and is filled into corresponding wedge block, to constitute k-th of coarse search prediction
Block, as shown in fig. 6, being the coarse search prediction block obtained in the present embodiment；
Step 8, the coarse search rate distortion costs value for calculating k-th of coarse search prediction block, to obtain KtA coarse search is pre-
The coarse search rate distortion costs value for surveying block, in the present embodiment, as shown in fig. 7, the coarse search rate of the coarse search prediction block is distorted
Cost value is 19；
T+1 is assigned to t by step 9, judges whether t > 6 is true, if so, then follow the steps 10；Otherwise return step
6；
Step 10 selects corresponding to the smallest coarse search rate distortion costs value from all coarse search rate distortion costs values
Coarse search prediction block is as optimal coarse search prediction block, in the present embodiment, it is clear that minimum coarse search rate distortion costs value is
Coarse search prediction block corresponding to 19, therefore 19 is optimal coarse search prediction block；
Step 11, two gradient values according to optimal coarse search prediction block in division position, find adjacent two respectively
A gradient value；
Step 12, according to a gradient value and its two adjacent gradient values and another gradient value and its adjacent two
Gradient value is respectively split optimal coarse search prediction block, 8 essence search segmentation blocks is obtained, in the present embodiment, such as Fig. 8 institute
Show, as this 8 essence search segmentation blocks；Wherein, s-th of essence search segmentation block is two wedges by obtaining after original block RU segmentation
Shape block composition；
Step 13 divides block according to s-th coarse search, to the pixel value in original block RU according to two wedge blocks respectively into
Row mean value computation obtains the mean value of two wedge blocks and is filled into corresponding wedge block, to constitute s-th of essence search prediction
Block, in the present embodiment, as shown in figure 9,8 essence search prediction blocks i.e. to be obtained according to 8 essence search segmentation blocks；
Step 14, the essence search rate distortion costs value for calculating s-th of essence search prediction block, so that it is pre- to obtain 8 essence search
The essence search rate distortion costs value for surveying block, from this 8 essence search rate distortion costs values and minimum coarse search rate distortion costs value
Select prediction block corresponding to the smallest rate distortion costs value as optimal essence search prediction block, in the present embodiment, this 8 essences
Searching for rate distortion costs value is respectively 82,141,368,421,235,461,159,236, and minimum coarse search rate distortion costs value is
19, therefore optimal essence search prediction block is optimal coarse search prediction block corresponding to minimum coarse search rate distortion costs value；
Residual block is calculated according to optimal essence search prediction block and original block RU in step 15, in the present embodiment, such as schemes
Shown in 10, the as calculating process of residual block, to realize the number to original block RU with optimal essence search prediction block and residual block
According to compression transmission；1≤s≤8.
The present invention carries out the circuit design based on FPGA of intra-frame predictive encoding method for the original block of 4 × 4 sizes, adopts
Behavioral scaling description is carried out with Verilog HDL, is imitated based on Xilinx XC6VLX760 FPGA development board using ISE software
True and comprehensive, the present invention is tested using depth image block as shown in Figure 1, is existed compared to Gustavo Sanchez et al.
" the Low- delivered in 201730th Symposium on Integrated Circuits and Systems Design meeting
Area Scalable Hardware Architecture for DMM-1Encoder of3D-HEVC Video Coding
Circuit in Standard ", for the original block of 4 × 4 sizes, predetermined period number is 132 periods, and the present invention only needs
In 16 periods, it is equivalent to its 12.1%.
Claims (2)
1. the coding circuit of depth modelling mode, remembers in any depth image in a kind of 3D coding and decoding video based on boundary gradient
The region 4N × 4N pixel value be original block RU, wherein N be positive integer, 1≤N≤8；It is characterized in that: the coding circuit packet
It includes: input data processing module, coarse search module, smart search module and wedge block model data store module；
The input data processing module receives externally input original block RU, and calculate the original block RU it is upper and lower, left,
Right four borderline 4N-1 gradient values, so that one 4 × (4N-1) a gradient values are obtained on four boundaries, by all ladders
Angle value is all added, and obtains gradient value and Sum, then the binary expression mode of the gradient value and Sum is moved to right the position (3+N),
Obtain gradient pre-filtering threshold value Thr；Judge on four boundaries with the presence or absence of the gradient less than the gradient pre-filtering threshold value Thr
Value, and if it exists, the gradient value whole reset of the gradient pre-filtering threshold value Thr will be less than on four boundaries；Again to i-th of side
4N-1 gradient value in boundary successively carries out label；Described i-th borderline 4N-1 gradient value is subjected to descending sort, from
And the corresponding sequence that gradient value corresponds to label is obtained, it is denoted as i-th of gradient value location information Posi, wherein gradient value is " 0 "
Label is also denoted as " 0 ", 1≤i≤4；With i-th of gradient value location information PosiWith j-th of gradient value location information PosjAs one
Group gradient value directional information, so that 6 groups of gradient value directional informations are obtained, wherein any one group of gradient value directional information is denoted as
Orit, 1≤t≤6；
The coarse search module is according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label respectively to institute
It states original block RU to be split, to obtain KtA coarse search divides block, wherein k-th of coarse search segmentation block is by the original
The two wedge blocks composition obtained after the segmentation of beginning block RU；Divide block according to k-th of coarse search, to the pixel in the original block RU
Value carries out mean value computation according to two wedge blocks respectively, obtains the mean value of two wedge blocks and is filled into corresponding wedge block,
To constitute k-th of coarse search prediction block；The coarse search rate distortion costs value for calculating k-th of coarse search prediction block, to obtain
KtThe coarse search rate distortion costs value of a coarse search prediction block；It is synchronous to calculate coarse search corresponding to 6 groups of gradient value directional informations
Rate distortion costs value, and therefrom select coarse search prediction block corresponding to the smallest coarse search rate distortion costs value as optimal thick
Search for prediction block, i ≠ j, 1≤j≤4；
Two gradient values of the essence search module according to optimal coarse search prediction block in division position, find adjacent respectively
Two gradient values, thus according to a gradient value and its two adjacent gradient values and another gradient value and its adjacent two
Gradient value is respectively split the optimal coarse search prediction block, obtains 8 essence search segmentation blocks；Wherein, s-th of essence search
Segmentation block is made of two wedge blocks obtained after original block RU segmentation；According to s-th of essence search segmentation block, to described
Pixel value in original block RU carries out mean value computation according to two wedge blocks respectively, obtains the mean value of two wedge blocks and is filled into
In corresponding wedge block, to constitute s-th of essence search prediction block；Calculate the smart searching rate distortion of s-th of essence search prediction block
Cost value, so that the essence search rate distortion costs value of 8 essence search prediction blocks is obtained, from this 8 essence search rate distortion costs values
With selected in minimum coarse search rate distortion costs value prediction block corresponding to the smallest rate distortion costs value as it is optimal essence search
Prediction block, finally according to it is described it is optimal essence search prediction block and the original block RU residual block is calculated, thus with it is described most
Excellent essence search prediction block and residual block realize that the data compression to the original block RU is transmitted；1≤s≤8.
2. the coding method of depth modelling mode in a kind of 3D coding and decoding video based on boundary gradient, it is characterized in that by following step
It is rapid to carry out:
Step 1, the region 4N × 4N in any depth image of note pixel value be original block RU, wherein N is positive integer, 1≤N≤
8；The borderline 4N-1 gradient value in four, upper and lower, left and right of the original block RU is calculated, thus on four boundaries altogether
Obtain 4 × (4N-1) a gradient values；
All gradient values are all added by step 2, obtain gradient value and Sum, then by the binary form of the gradient value and Sum
The position (3+N) is moved to right up to mode, obtains gradient pre-filtering threshold value Thr；
Judge on four boundaries with the presence or absence of the gradient value less than gradient pre-filtering threshold value Thr, and if it exists, then will be on four boundaries
Less than the gradient value whole reset of gradient pre-filtering threshold value Thr；
Step 3 successively carries out label to described i-th borderline 4N-1 gradient value, and a to i-th of borderline 4N-1
Gradient value carries out descending sort, obtains the corresponding sequence that gradient value corresponds to label, is denoted as i-th of gradient value location information Posi,
Wherein, gradient value is that the label of " 0 " is also denoted as " 0 ", 1≤i≤4；
Step 4, with i-th of gradient value location information PosiWith j-th of gradient value location information PosjAs one group of gradient value direction
Information, so that 6 groups of gradient value directional informations are obtained, wherein any one group of gradient value directional information is denoted as Orit, 1≤t≤6；i≠
J, 1≤j≤4；
Step 5, initialization t=1；
Step 6, according to t group gradient value directional information OritIn gradient value corresponding to non-" 0 " label respectively to the original block
RU is split, to obtain KtA coarse search divides block, wherein k-th of coarse search segmentation block is by the original block RU points
The two wedge blocks composition obtained after cutting；
Step 7 divides block according to k-th coarse search, to the pixel value in the original block RU according to two wedge blocks respectively into
Row mean value computation obtains the mean value of two wedge blocks and is filled into corresponding wedge block, to constitute k-th of coarse search prediction
Block；
Step 8, the coarse search rate distortion costs value for calculating k-th of coarse search prediction block, to obtain KtA coarse search prediction block
Coarse search rate distortion costs value；
T+1 is assigned to t by step 9, judges whether t > 6 is true, if so, then follow the steps 10；Otherwise return step 6；
Step 10 selects slightly to search corresponding to the smallest coarse search rate distortion costs value from all coarse search rate distortion costs values
Rope prediction block is as optimal coarse search prediction block；
Step 11, two gradient values according to optimal coarse search prediction block in division position, find two adjacent ladders respectively
Angle value；
Step 12, according to a gradient value and its two adjacent gradient values and another gradient value and its two adjacent gradients
Value is respectively split the optimal coarse search prediction block, obtains 8 essence search segmentation blocks；Wherein, s-th of essence search segmentation
Two wedge blocks that block obtains after being divided by the original block RU form；
Step 13 divides block according to s-th coarse search, to the pixel value in the original block RU according to two wedge blocks respectively into
Row mean value computation obtains the mean value of two wedge blocks and is filled into corresponding wedge block, to constitute s-th of essence search prediction
Block；
Step 14, the essence search rate distortion costs value for calculating s-th of essence search prediction block, to obtain 8 essence search prediction blocks
Essence search rate distortion costs value, selected from this 8 essence search rate distortion costs values and minimum coarse search rate distortion costs value
Prediction block corresponding to the smallest rate distortion costs value is as optimal essence search prediction block；
Step 15, according to it is described it is optimal essence search prediction block and the original block RU residual block is calculated, thus with it is described most
Excellent essence search prediction block and residual block realize that the data compression to the original block RU is transmitted；1≤s≤8.
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| CN105120290A (en) * | 2015-08-04 | 2015-12-02 | 宁波大学 | Fast coding method for depth video |
| CN105791862A (en) * | 2016-03-21 | 2016-07-20 | 杭州电子科技大学 | Three-dimensional video coding depth map internal mode selection method based on edge complexity |
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| CN105120290A (en) * | 2015-08-04 | 2015-12-02 | 宁波大学 | Fast coding method for depth video |
| CN105791862A (en) * | 2016-03-21 | 2016-07-20 | 杭州电子科技大学 | Three-dimensional video coding depth map internal mode selection method based on edge complexity |
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