CN107071479A - 3D video depth image predicting mode selecting methods based on dependency - Google Patents
3D video depth image predicting mode selecting methods based on dependency Download PDFInfo
- Publication number
- CN107071479A CN107071479A CN201710293900.3A CN201710293900A CN107071479A CN 107071479 A CN107071479 A CN 107071479A CN 201710293900 A CN201710293900 A CN 201710293900A CN 107071479 A CN107071479 A CN 107071479A
- Authority
- CN
- China
- Prior art keywords
- patterns
- dependency
- merge
- block
- depth image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000001419 dependent effect Effects 0.000 claims description 6
- 230000015572 biosynthetic process Effects 0.000 claims description 5
- 238000003786 synthesis reaction Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- VBRBNWWNRIMAII-WYMLVPIESA-N 3-[(e)-5-(4-ethylphenoxy)-3-methylpent-3-enyl]-2,2-dimethyloxirane Chemical compound C1=CC(CC)=CC=C1OC\C=C(/C)CCC1C(C)(C)O1 VBRBNWWNRIMAII-WYMLVPIESA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
- 235000013399 edible fruits Nutrition 0.000 description 2
- 241000251730 Chondrichthyes Species 0.000 description 1
- 241000388484 Mandragora caulescens Species 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- YYJNOYZRYGDPNH-MFKUBSTISA-N fenpyroximate Chemical compound C=1C=C(C(=O)OC(C)(C)C)C=CC=1CO/N=C/C=1C(C)=NN(C)C=1OC1=CC=CC=C1 YYJNOYZRYGDPNH-MFKUBSTISA-N 0.000 description 1
- 235000020044 madeira Nutrition 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
- H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/103—Selection of coding mode or of prediction mode
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
- H04N19/146—Data rate or code amount at the encoder output
- H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The invention discloses a kind of 3D video depth image predicting mode selecting methods based on dependency.Method is：Tentatively judge that present encoding block selects the possibility of Skip patterns first with dependency；Then for the high encoding block of possibility, compared by Skip patterns with the rate distortion RD cost of 2N × 2N Merge patterns and DIS patterns, determine whether current block selects Skip patterns, and terminate predictive mode selection course in advance.Present invention reduces the complexity of depth image predictive coding, the scramble time needed for prediction is reduced, and ensure that final decoding end synthesizes the video quality at visual angle.
Description
Technical field
The invention belongs to video coding and decoding technology field, specifically a kind of 3D video depth maps based on dependency are pre-
Survey mode quick selecting method.
Background technology
The video format of emerging multiple views plus depth figure is the topmost form of 3D video systems of future generation.It is used
The texture maps information of a small amount of viewpoint represents a 3D video scene with the depth map information of additional respective viewpoints, and more
View information can be synthesized by the 3D Renderings based on depth image.Due in current 3D video systems, depth
Figure is for providing parallax information and guiding building-up process to play critical effect, so the research of depth map encoding has its important
Practical significance.
Fig. 1 lists all possible predictive coding pattern of depth map.It is worth noting that, in 3D-HEVC each time
Predictive mode selection course can all travel through these patterns, and this make it that the complexity of whole predictive coding process is high.At present, it is academic
The research that boundary has much simplified for depth map predictive mode selection course.Document 1 (J.M.Kang, K.D.Chung, ' A
Fast Mode Decision using Texture Information for Intra Depth Coding in 3DVC,’
in 19th IEEE Symposium on Computers and Communications(ISCC).,Madeira,
Portugal, June.2014, pp.1-5.) one kind is proposed using correspondence texture view information to determine depth map frame in advance
The method of pattern.Document 2 (Z.Wang, L.Mo, D.Li, T.Xu, ' Fast depth map intra prediction
algorithm based on edge detection and CU partition,’in 11th International
Conference on Wireless Communications., Shanghai, China, Sep.2015, pp.1-5.) propose
A kind of edge detection method for depth coding block, and different candidate modes collection is used to smooth block and edge block.
And in document 3 (Miok, K., Nam, L., and Li, S.:‘Fast single depth intra mode decision for
depth map coding in 3D-HEVC’,Int.Conf.on ICMEW,Turin,Italian,June 2015,pp.1-
6) in, Mirk M et al. devise a kind of by considering that variance and estimation distortion determine the quick of quadtree coding structure in advance
Method.
However, existing depth map encoding technology does not fully take into account the correlation between multiple viewpoints, Er Qieshen
The characteristics of degree figure large area region is smooth is not also well used.On the other hand, in multiple views texture video coding field,
Correlation between viewpoint has been studied very deep.(N.Zhang, Y.W.Chen, S.W.Lei, the .et al. of document 4:
‘Fast encoder decsion for texture coding,’ITU-T SG 16 WP 3 and ISO/IEC JTC 1/
SC 29/WG11, JCT3V-E0173, July.2013) a kind of information of the adjacent block using 5 base viewpoints is proposed to predict
The method of current texture block coding mode.If 5 base viewpoint adjacent blocks all select Merge patterns, and Skip patterns are being worked as
Preceding piece of encoding rate distortion RD-cost is less than 2N × 2N Merge patterns, then current block selects Skip patterns and terminates mould in advance
Formula selection course.Wherein, 5 base viewpoint adjacent blocks of current block are as shown in Figure 2.Based on this method, texture graph code can be with
The scramble time of reduction 32%, while not damaging video quality.The Rule of judgment of termination in advance proposed in above-mentioned document is
C[10]。
In view of correlation is observed the above method being transplanted to depth in the fabulous performance of texture video coding field between viewpoint
The effect come in Video coding.Table 1, which gives, meets condition C[10]Depth coding block final choice predictive mode distribution.From
In table, it is easy to see that the average accounting of Skip patterns is up to 97.84%.This shows, in depth map, the prediction in different points of view
The correlation of pattern is also high.On the other hand, it is notable that in remaining 2.16% distribution in table 1 in addition to Merge,
The average accountings of DIS are more than 60%.This is due to DIS as a kind of prediction mould proposed for the smooth feature of depth map large area
Formula, can preferably presentation code block in some smooth regions.
Table 1 meets the predictive mode distribution of condition C [10] depth coding block final choice
Sum it up, existing depth map fast coding technology does not fully take into account the correlation between viewpoint, it is existing
The complexity of depth map mode selection algorithm still have to be reduced.
The content of the invention
It is an object of the invention to provide a kind of quickly 3D video depth image predictive modes based on dependency
System of selection, on the premise of video quality in ensureing synthesis visual angle, simplifies the calculating process of depth image predictive mode selection.
The technical solution for realizing the object of the invention is：A kind of 3D video depths image prediction based on dependency
Mode selecting method, comprises the following steps：
Step 1：For the dependent viewpoint encoding block of input, whether all 5 neighboring reference encoding blocks of base viewpoint are judged
Merge patterns be have selected as predictive coding pattern, if it is, carrying out step 2；If it is not, then skipping to step 5；
Step 2：Calculate the rate distortion RD- that present encoding block is based on Skip patterns, 2N × 2N Merge patterns and DIS patterns
cost；
Step 3：Judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge patterns and DIS patterns, such as
Fruit is then to carry out step 4；If it is not, then skipping to step 5；
Step 4：Skip patterns are set to the predictive mode of present encoding block, and predictive mode selection course is terminated, and is jumped
To step 6；
Step 5：3D-HEVC original predictive mode selection processes are carried out to present encoding block；
Step 6：Terminate the predictive coding mode selection processes of current block.
Further, 5 neighboring reference encoding blocks of base viewpoint described in step 1, including by depth parallax estimating searching
Obtained corresponding reference block, and with the reference block four direct neighbor reference blocks up and down.
Further, the calculation expression of the rate distortion RD-cost described in step 2 is as follows：
J (m)=DVSO(m)+λ·B(m) m∈C
Wherein, C is the set of all possible predictive mode, and J (m) refers to the rate distortion RD-cost of correspondence m patterns,
Dvso (m) is the distortion by the obtained present modes of View Synthesis optimisation technique VSO, and λ is Lagrange multiplier, and B (m) is to use
Mode m encodes the bit number of current block.
Further, judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge moulds described in step 3
Formula and DIS patterns, wherein：Merge patterns include the Skip patterns of not communicating predicted residual error and 2N × 2N of communicating predicted residual error
Merge patterns, DIS patterns are intra-frame encoding mode.
Compared with prior art, its remarkable advantage is the present invention：(1) using the correlation between viewpoint come predetermined depth figure
Coding mode, greatly reduce encoder complexity, and extend the research direction in depth map encoding field；(2) design
The characteristics of combining new depth coding pattern DIS, it is ensured that video quality is not damaged by.
Brief description of the drawings
Fig. 1 is possible to pattern diagram by depth map predictive coding in 3D-HEVC.
Fig. 2 is current dependent examination in the 3D video depth map predicting mode selecting methods of the invention based on dependency
The neighboring reference blocks schematic diagram of 5 base viewpoints of point encoding block.
Fig. 3 is the flow chart of the 3D video depth map predictive mode fast selecting methods of the invention based on dependency.
Embodiment
The present invention predicts the predictive mode of current dependent viewpoint depth coding block using the correlation between viewpoint.When 5
When the individual adjacent encoder block from base viewpoint all selects Merge patterns, present encoding block is just very possible also to select Merge moulds
Formula.And in order to further determine that Merge patterns if appropriate for present encoding block is represented, we are by the rate distortion RD- of Skip patterns
Cost and 2N × 2N Merge patterns and DIS patterns are compared, if can be sufficient as the motion vector obtained by Merge patterns
Current block is enough represented well, then the rate distortion RD-cost of the Skip patterns of not communicating predicted residual error is certainly less than 2N × 2N
Merge patterns and DIS patterns.
The detailed process provided with reference to Fig. 3, the above method specifically includes following steps：
Step 1：For the dependent viewpoint encoding block of input, whether all 5 neighboring reference encoding blocks of base viewpoint are judged
Merge patterns be have selected as predictive coding pattern, if it is, carrying out step 2；If it is not, then skipping to step 5；
Step 2：Calculate the rate distortion RD- that present encoding block is based on Skip patterns, 2N × 2N Merge patterns and DIS patterns
cost；
Step 3：Judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge patterns and DIS patterns, such as
Fruit is then to carry out step 4；If it is not, then skipping to step 5；
Step 4：Skip patterns are set to the predictive mode of present encoding block, and predictive mode selection course is terminated, and is jumped
To step 6；
Step 5：3D-HEVC original predictive mode selection processes are carried out to present encoding block；
Step 6：Terminate the predictive coding mode selection processes of current block.
Further, 5 neighboring reference encoding blocks of base viewpoint described in step 1, including by depth parallax estimating searching
Obtained corresponding reference block, and with the reference block four direct neighbor reference blocks up and down.
Further, the calculation expression of the rate distortion RD-cost described in step 2 is as follows：
J (m)=DVSO(m)+λ·B(m) m∈C
Wherein, C is the set of all possible predictive mode, and J (m) refers to the rate distortion RD-cost of correspondence m patterns,
Dvso (m) is the distortion by the obtained present modes of View Synthesis optimisation technique VSO, and λ is Lagrange multiplier, and B (m) is to use
Mode m encodes the bit number of current block.
Further, judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge moulds described in step 3
Formula and DIS patterns, wherein：Merge patterns include the Skip patterns of not communicating predicted residual error and 2N × 2N of communicating predicted residual error
Merge patterns, DIS patterns are intra-frame encoding mode.
Below by embodiment, technical scheme is described in further detail.
Embodiment
The present embodiment shows the 3D video depth image predictive mode fast selecting methods based on dependency, its
Flow is as shown in figure 3, its step includes：
Step 1：For the dependent viewpoint encoding block of input, whether all 5 neighboring reference encoding blocks of base viewpoint are judged
Merge patterns be have selected as its predictive coding pattern, if it is, carrying out step 2；If it is not, then skipping to step 5；
Step 2：Calculate the rate distortion RD- that present encoding block is based on Skip patterns, 2N × 2N Merge patterns and DIS patterns
Cost, its calculation formula is：
J (m)=DVSO(m)+λ·B(m) m∈C
Step 3：Judge whether the rate distortion of Skip patterns is less than 2N × 2N Merge patterns and DIS patterns, if it is,
Carry out step 4；If it is not, then skipping to step 5；
Step 4：Skip patterns are set to the predictive mode of present encoding block, and predictive mode selection course shifts to an earlier date end
Only, step 6 is skipped to；
Step 5：3D-HEVC original predictive mode selection processes are carried out to present encoding block；
Step 6：Terminate the predictive coding mode selection processes of current block.
Above-mentioned depth map encoding fast method is integrated into 3D-HEVC test models (HTM 16.1) encoder, and with
The original models of HTM 16.1 carry out performance comparision.Cycle tests and parameter are all referring to document " Mller, K., and Vetro,
A.:‘Common test conditions of 3DV core experiments’,ITU-T SG 16 WP 3 and ISO/
The standard proposed in IEC JTC 1/SC 29/WG 11 JCT3V-G1100, January 2014 ".Document " N.Zhang,
Y.W.Chen,S.W.Lei,.et al.:‘Fast encoder decsion for texture coding,’ITU-T SG
The and ISO/IEC JTC 1/SC 29/WG 11, JCT3V-E0173 of 16 WP 3, the texture video that July.2013 " is proposed is compiled
Code fast method will be transplanted in depth map encoding, and the comparison of performance is carried out with the inventive method on same platform.
Table 2 gives in view of scramble time and BDBR coding efficiency compare.Wherein, BDBR represents equal Objective Video matter
Under conditions of amount, the saving situation of original HTM methods bit rate is compared.BDBR calculating is based on total bit rate and synthesis viewpoint
Objective quality PSNR.What the scramble time compared is relative to the saving situation of original HTM16.1 methods scramble time.
As can be known from Table 2, coding method of the invention is compared to current state-of-the-art method (being integrated in HTM 16.1)
It can save for average 22.1% time, while only increasing 0.01% BDBR.This shows that coding method of the invention is dropped
Low encoder complexity, and ensure that the quality of encoded video.And corresponding Zhang coding method is not due to being for deep
Spend what graph code was proposed, while fast coding effect is obtained, the video quality that it can not keep relative stability.
The Comparative result of the inventive method of table 2 and Zhang algorithm
The ratio for being applicable encoding block of the inventive method of table 3 and the accuracy rate for being applicable block prediction mode selection
| Cycle tests | It is applicable the ratio of encoding block | The accuracy rate of model selection |
| Balloons | 67.90% | 99.30% |
| Kendo | 54.69% | 99.12% |
| Newspaper | 67.26% | 99.16% |
| GT_Fly | 75.53% | 99.85% |
| Poznan_Hall2 | 72.11% | 99.76% |
| Poznan_Street | 75.48% | 99.67% |
| Shark | 66.92% | 98.86% |
| Undo_Dancer | 67.80% | 99.09% |
| Average | 68.46% | 99.35% |
Table 3 is the accuracy rate that method of the invention is terminated in advance and is applicable the ratio of the encoding block of this method.Can from table 3
To find out, average 68.46% encoding block meets the condition for shifting to an earlier date termination pattern selection, wherein, up to 99.35% terminates in advance
The encoding block of predictive mode selection course correctly have selected predictive mode.This shows that the method applicability of the present invention is wide, accurately
Degree is high, practical.
Claims (4)
1. a kind of 3D video depth image predicting mode selecting methods based on dependency, it is characterised in that including as follows
Step：
Step 1：For the dependent viewpoint encoding block of input, judge whether 5 neighboring reference encoding blocks of base viewpoint all select
Merge patterns are as predictive coding pattern, if it is, carrying out step 2；If it is not, then skipping to step 5；
Step 2：Calculate the rate distortion RD- that present encoding block is based on Skip patterns, 2N × 2N Merge patterns and DIS patterns
cost；
Step 3：Judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge patterns and DIS patterns, if
It is then to carry out step 4；If it is not, then skipping to step 5；
Step 4：Skip patterns are set to the predictive mode of present encoding block, and predictive mode selection course is terminated, and skips to step
Rapid 6；
Step 5：3D-HEVC original predictive mode selection processes are carried out to present encoding block；
Step 6：Terminate the predictive coding mode selection processes of current block.
2. the 3D video depth image predicting mode selecting methods according to claim 1 based on dependency, it is special
Levy and be, 5 neighboring reference encoding blocks of base viewpoint described in step 1, including the correspondence obtained by depth parallax estimating searching
Reference block, and with the reference block four direct neighbor reference blocks up and down.
3. the 3D video depth image predicting mode selecting methods according to claim 1 based on dependency, it is special
Levy and be, the calculation expression of the rate distortion RD-cost described in step 2 is as follows：
J (m)=DVSO(m)+λ·B(m) m∈C
Wherein, C is the set of all possible predictive mode, and J (m) refers to rate the distortion RD-cost, Dvso (m) of correspondence m patterns
It is the distortion by the obtained present modes of View Synthesis optimisation technique VSO, λ is Lagrange multiplier, and B (m) is compiled with mode m
The bit number of code current block.
4. the 3D video depth image predicting mode selecting methods according to claim 1 based on dependency, it is special
Levy and be, judge whether the rate distortion RD-cost of Skip patterns is less than 2N × 2N Merge patterns and DIS moulds described in step 3
Formula, wherein：Merge patterns include the Skip patterns of not communicating predicted residual error and 2N × 2N Merge moulds of communicating predicted residual error
Formula, DIS patterns are intra-frame encoding mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710293900.3A CN107071479B (en) | 2017-04-28 | 2017-04-28 | 3D video depth image prediction mode selection method based on viewpoint correlation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710293900.3A CN107071479B (en) | 2017-04-28 | 2017-04-28 | 3D video depth image prediction mode selection method based on viewpoint correlation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107071479A true CN107071479A (en) | 2017-08-18 |
| CN107071479B CN107071479B (en) | 2020-06-05 |
Family
ID=59604316
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710293900.3A Expired - Fee Related CN107071479B (en) | 2017-04-28 | 2017-04-28 | 3D video depth image prediction mode selection method based on viewpoint correlation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107071479B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110300302A (en) * | 2019-06-05 | 2019-10-01 | 锐捷网络股份有限公司 | A kind of method for video coding, device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104301739A (en) * | 2013-07-18 | 2015-01-21 | 联发科技（新加坡）私人有限公司 | Multi-view video coding method |
| CN104539970A (en) * | 2014-12-21 | 2015-04-22 | 北京工业大学 | 3D-HEVC interframe coding merge mode fast decision making method |
| CN104837019A (en) * | 2015-04-30 | 2015-08-12 | 上海交通大学 | AVS-to-HEVC optimal video transcoding method based on support vector machine |
| CN106210741A (en) * | 2016-09-10 | 2016-12-07 | 天津大学 | A kind of based on the deep video encryption algorithm of dependency between viewpoint |
-
2017
- 2017-04-28 CN CN201710293900.3A patent/CN107071479B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104301739A (en) * | 2013-07-18 | 2015-01-21 | 联发科技（新加坡）私人有限公司 | Multi-view video coding method |
| CN104539970A (en) * | 2014-12-21 | 2015-04-22 | 北京工业大学 | 3D-HEVC interframe coding merge mode fast decision making method |
| CN104837019A (en) * | 2015-04-30 | 2015-08-12 | 上海交通大学 | AVS-to-HEVC optimal video transcoding method based on support vector machine |
| CN106210741A (en) * | 2016-09-10 | 2016-12-07 | 天津大学 | A kind of based on the deep video encryption algorithm of dependency between viewpoint |
Non-Patent Citations (4)
| Title |
|---|
| ZHANG NA ET AL: "Fast encoder decision for texture coding", 《JCT3V-E0173》 * |
| 周作成: "基于3D HEVC质量优化和高效视频编码方法研究", 《中国优秀博士学位论文全文数据库》 * |
| 李焕青: "3D HEVC视频编码技术研究", 《中国优秀硕士学位论文全文数据库》 * |
| 陈亚芬: "3D HEVC虚拟视点合成优化及预测技术研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110300302A (en) * | 2019-06-05 | 2019-10-01 | 锐捷网络股份有限公司 | A kind of method for video coding, device and storage medium |
| CN110300302B (en) * | 2019-06-05 | 2021-11-12 | 锐捷网络股份有限公司 | Video coding method, device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107071479B (en) | 2020-06-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110087087A (en) | VVC interframe encode unit prediction mode shifts to an earlier date decision and block divides and shifts to an earlier date terminating method | |
| CN104539970B (en) | A kind of 3D HEVC interframe encodes merging patterns high-speed decision method | |
| CN104038760B (en) | A kind of wedge shape Fractionation regimen system of selection of 3D video depths image frame in and system | |
| CN107277506B (en) | Motion vector accuracy selection method and device based on adaptive motion vector precision | |
| CN108124154A (en) | Fast selecting method, device and the electronic equipment of inter-frame forecast mode | |
| CN104378643A (en) | Intra-frame prediction mode selection method and system of 3D (3-dimension) video plus depth image | |
| CN108347605B (en) | Quick decision-making method for 3D video depth image quad-tree coding structure division | |
| CN103491369B (en) | A kind of interframe prediction encoding method and encoder | |
| CN103813178B (en) | Rapid high efficiency video coding (HEVC) method based on depth and space-time relevancy of coding units | |
| CN108712648A (en) | A kind of quick inner frame coding method of deep video | |
| CN104811729B (en) | A kind of video multi-reference frame coding method | |
| CN104125473A (en) | 3D (three dimensional) video depth image intra-frame predicting mode selecting method and system | |
| CN102801976A (en) | Inter-frame module selecting method based on three-dimensional wavelet video code | |
| CN106888379B (en) | Applied to the interframe fast video code-transferring method for H.264 arriving HEVC | |
| CN107318016A (en) | A kind of HEVC inter-frame forecast mode method for rapidly judging based on zero piece of distribution | |
| CN103327327A (en) | Selection method of inter-frame predictive coding units for HEVC | |
| CN105898332A (en) | Rapid depth image frame internal mode type judgment method aiming at 3D-HEVC (Three Dimensional- High Efficiency Video Coding) standard | |
| Chen et al. | Sum-of-gradient based fast intra coding in 3D-HEVC for depth map sequence (SOG-FDIC) | |
| CN107295336B (en) | Adaptive fast coding dividing elements method and device based on image correlation | |
| CN101959067B (en) | Decision method and system in rapid coding mode based on epipolar constraint | |
| CN106803962B (en) | 3D video depth map method for choosing frame inner forecast mode based on bayesian criterion | |
| CN101895761B (en) | Quick intraframe prediction algorithm | |
| KR100947447B1 (en) | Method and its apparatus for fast mode decision in multi-view video coding | |
| CN100592797C (en) | Fast motion estimating method | |
| CN107071479A (en) | 3D video depth image predicting mode selecting methods based on dependency |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200605 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |