CN104065975B - Based on the frame per second method for improving that adaptive motion is estimated - Google Patents
Based on the frame per second method for improving that adaptive motion is estimated Download PDFInfo
- Publication number
- CN104065975B CN104065975B CN201410305458.8A CN201410305458A CN104065975B CN 104065975 B CN104065975 B CN 104065975B CN 201410305458 A CN201410305458 A CN 201410305458A CN 104065975 B CN104065975 B CN 104065975B
- Authority
- CN
- China
- Prior art keywords
- motion vector
- block
- motion
- frame
- texture
- 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.)
- Expired - Fee Related
Links
Landscapes
- Image Analysis (AREA)
Abstract
The present invention provides a kind of method that frame per second is lifted.The method is broadly divided into four steps, respectively carries out adaptive motion estimation using the texture classifying method based on rim detection, obtains motion vector；Motion vector is post-processed, using the inaccurate motion vector of multi-direction extension blocks motion vector restorative procedure amendment；Estimate the motion vector in hole region and overlapping block region；Motion compensation synthesis insertion frame is carried out according to the motion vector for obtaining.The problems such as method that frame per second that the present invention puts forward is lifted can effectively solve the motion blur that traditional frame per second method for improving brings, blocking effect, cavity, overlapping block, substantially increases the quality of synthetic video.
Description
Technical field
The present invention relates to a kind of method that frame-rate video is lifted, belongs to video data process field.
Background technology
It is a kind of by improving video frame rate lifting the video post-process method of video quality that frame per second is lifted.Due to liquid crystal
The restriction of display own hardware condition, occurs motion blur and motion jitter phenomenon when the video of motion intense is played,
It is referred to as ghost effect, the viewing effect of terminal use can be had a strong impact on.Frame per second lift technique is by lifting original video frame per second
Ghost effect can be effectively reduced, video quality is improved；Under conditions of channel width is restricted, must drop in coding side
Low transmission data volume, only transmits the video content of a part, then can recover by frame per second lift technique in decoding end
Whole video content, had both improved the utilization rate of channel width, in turn ensure that video quality can meet user's viewing demand.By
Diversified application is lifted in video frame rate, frame per second lift technique is more and more important in consumer electronics field.HDTV and multimedia
PC systems can play the video higher than broadcast video stream frame per second, and video frame rate lift technique just may apply to lift original regarding
Frequency frame per second is improving the viewing effect of terminal use.
The current method for mainly having two class frame per second to be lifted, a kind of method do not account for the movable information between frame, only
It is to obtain inserting frame by correlated pixel value linear combination between consecutive frame, typical method includes：Frame repeats and frame is average；It is another
Class method is based drive method, and most of frame per second method for improving is all based on the motion estimation and compensation of block.Base
The quality of video can effectively be improved in the frame per second method for improving of motion, but while also generate such as motion blur, block
The problems such as effect, cavity, overlapping block.
The content of the invention
The empty problem occurred in being lifted for solution frame per second, this application provides a kind of move what is repaired based on hole region
Method, i.e., go out the motion of hole region according to the motion vector estimation of the empty block around the correspondence position of reference frame in insertion frame
Vector, carries out motion compensation filling, relatively adds the cavity of movable information to repair only with the method for reference frame pixel value filling before
Multiple effect is more preferable；To obtain accurate motion vector, to solve the problems, such as the motion blur occurred in frame per second lifting, blocking effect, this
Application divides different macro blocks according to the textural characteristics of reference frame, to texture there is provided a kind of adaptive movement estimation method
Not abundant block adopts overlapped block motion algorithm for estimating, by the size for expanding block carry out estimation reduce it is inaccurate move to
The probability that amount occurs；First motion algorithm for estimating is adopted to the block of texture-rich；Further to obtain correct motion vector,
Motion vector post-processing stages, introduce motion vector angle absolute difference and absolute error and (SAD, Sum of Absolute
Differences) motion vector is classified, inaccurate motion vector is repaired using multi-direction extension blocks motion vector
Method, obtains optimal motion vectors.
In the present invention, rim detection is carried out to present frame first, includes that according to each block the number of edge pixel point is drawn
Divide the type of macro block, the block that the block and texture for being divided into texture-rich does not enrich；Then, the block not enriched to texture carries out overlapping block
Estimation, carries out first motion estimation to the block of texture-rich；Afterwards, drawn using motion vector angle absolute difference and sad value
Partite transport moving vector species, inaccurate motion vector carry out motion reparation and obtain accurate motion vector；Finally, to hole area
Domain and overlapping region carry out estimation, i.e., using motion of the hole region in former frame around same position block in insertion frame
Vector estimates the motion vector of hole region, and first motion compensation is carried out to which obtains inserting frame.
The technical solution of the present invention is as follows：
A kind of frame per second method for improving estimated based on adaptive motion, it is characterised in that the method is comprised the following steps：
Step 1：Original video is processed, frame is processed as；
Step 2：Frame is divided into into texture-rich block according to edge detection results and texture does not enrich block, to different types of piece
Adaptive motion estimation is carried out, motion vector is obtained；
Step 3：Using multi-direction extension movement vector restorative procedure, inaccurate motion vector is corrected；
Step 4：Estimate the motion vector of hole region；
Step 5：Motion compensation obtains inserting frame；
Step 6：Insertion frame and primitive frame are synthesized into the video of high frame per second.
Preferably, in step 2, rim detection is carried out using Sobel Operator (sobel operator) and obtains edge letter
Breath, in counting each macro block, the number comprising edge pixel point simultaneously calculates mean value, when edge pixel point is included in each macro block
When number is more than mean value, it is believed that be the block of texture-rich, otherwise it is that texture does not enrich block；For the block that texture does not enrich, adopt
Estimated with overlapped block motion, for the block of texture-rich is estimated using first motion.
Preferably, in step 3, according to each piece of motion vector angle absolute difference and absolute error and (SAD, Sum
Of Absolute Differences) motion vector classification is carried out, for inaccurate motion vector, respectively by level and perpendicular
Nogata to motion vector value increase and decrease 1 form nine candidate motion vectors, calculate the corresponding match block of nine motion vectors respectively
With the sad value of current block, it is optimal motion vectors to choose the minimum motion vector of correspondence sad value, correct it is inaccurate move to
Amount.
Preferably, in step 4, for hole region, according to empty block position correspondence position in former frame in insertion frame
The motion vector estimation for putting block around goes out the motion vector of empty block；It is for overlapping region, corresponding using smallest match criterion
Motion vector is used as optimal motion vectors.
Description of the drawings
Fig. 1 is disposed of in its entirety block diagram of the present invention.
Fig. 2 is rim detection Sobel warp factor schematic diagram, wherein (a) is the horizontal detection factor, it is (b) vertical detection
The factor.
Fig. 3 is the result displaying figure of rim detection, wherein first frame of (a) for YUV normal video foreman sequences, (b)
For the edge detection results of (a).
Fig. 4 is overlapped block motion method of estimation schematic diagram, wherein (a) represents an overlapping block on present frame, (b) is represented
Overlapped block motion is estimated.
Fig. 5 is multi-direction extension blocks motion vector restorative procedure schematic diagram, wherein (a) represents the extension blocks on present frame,
B () represents the candidate motion vector of nine different directions.
Fig. 6 is that schematic diagram is repaired in hole region motion.
Fig. 7 is Bus video simulation comparative result figures.
Fig. 8 is Foreman video simulation comparative result figures.
Specific embodiment
The present invention adopts adaptive motion method of estimation according to the abundant degree of image texture, then is transported by multi-direction extension blocks
Moving vector restorative procedure, obtains optimal motion vectors, then carries out estimation to cavity and overlapping block region, is mended by moving
Repay and complete to insert frame, effectively solve the problems, such as cavity, overlapping block, serve reduction ghost effect, the effect of blocking effect, reach
The target of the high frame-rate video of reconstruct high-quality.
The present invention is further detailed with reference to specific embodiment (but not limited to this example) and accompanying drawing.
(1) process to original digital image：
(1) read in video；
(2) counter t=1 is set, and t frames is preserved successively as present frame, is denoted as ft；T+2 frames are used as next frame, note
Make ft+2；Reserved t+1 frames are denoted as f as frame is inserted intot+1；
(2) estimation stages of motion vector：
(1) rim detection based on Sobel Operator (sobel operator)：
Sobel operators are a discreteness difference operators, for calculating the approximation of the gradient of brightness of image function.In image
Any point use this operator, it will produce corresponding gradient vector or its law vector, so as to carry out rim detection.Such as accompanying drawing
Shown in 2, it is Sobel warp factors, matrix of the operator comprising two groups of 3x3, respectively transverse direction and longitudinal direction, such as accompanying drawing 2 (a) are shown
For the horizontal detection factor, 2 (b) is vertical detecting factor, it is made planar convolution with image, you can draw transverse direction and longitudinal direction respectively
Brightness difference approximation.If with ftRepresent present frame, GxAnd GyThe image ash of Jing transverse direction and longitudinal direction rim detections is represented respectively
Angle value, its formula are as follows：
(formula 1)
Then ftIn the value of each pixel calculate by formula 2：
| G |=| Gx|+|Gy| (formula 2)
If gradient G is more than a certain threshold values, then it is assumed that the point (x, y) is marginal point, and accompanying drawing 3 is the result of rim detection,
The first frame artwork of wherein 3 (a) for standard yuv video foreman sequences；3 (b) is the result figure of the first frame border detection.
(2) textural characteristics classification：
The edge of image refers to the edge in two homogeneous image regions with different gray scales, i.e. surrounding gray-scale intensity has instead
The set of those pixels of difference change；And image texture is the change of gray scale and color in image in general sense, one
A connected pixel set for repeating to meet in individual image-region given gamma characteristic constitutes a texture region, so
On definite meaning, the texture of edge phenogram picture can be used.According to the edge detection graph obtained in step (1), A (x, y) is denoted as；
A (x, y) is divided into into several 8 × 8 blocks, the number that edge pixel point occurs in counting each piece is denoted as Nm×n(x), wherein m,
N represents each piece of subscript；The all pieces of mean values comprising edge pixel point number are calculated, ave (x) is denoted as；If Nm×n(x) ＞
Ave (x), then it is assumed that block of the block for texture-rich, otherwise it is assumed that being the block that texture does not enrich.
(3) adaptive motion estimation：
Asymmetric cross multi-level hexagonal point search (Unsymmetrieal-CrossMuti- is adopted in the application
Hexagon Search) block matching motion estimation method, the method belongs to a kind of rapid motion estimating method, operand only less than
The 10% of full-search algorithm, while and higher estimation accuracy can be kept；In the application using absolute error and (SAD,
Sum of Absolute Differences) used as matching criterior, computational methods are shown in formula 3, formula 4；
(formula 3)
(formula 4)
Wherein, (x, y) is ftPixel to be matched in frame；(dx, dy) is ft+2The pixel of candidate in frame；Bx, ByPoint
The long wide scope of macro block is not represented, v represents the optimal motion vectors for estimating, i.e. optimal motion vectors for minimum sad value correspondence
Motion vector.
According to the Texture classification in step (2), adaptive movement estimation method is adopted for different types of piece.For ft
The block that texture does not enrich in frame, easily in ft+2Frame finds more match block, but these match blocks are not necessarily best match
Block, so adopting overlapped block motion method of estimation, that is, the block for expanding to 16 × 16 calculates optimal motion vectors as match block, this
Some important texture recognition information are may included in a little overlapping blocks, the accuracy of estimation is improve.Such as 4 institute of accompanying drawing
Show, region representation f shown in left slash in 4 (a)tThe block that texture does not enrich in frame, region shown in right slash are the overlapping block of extension,
4 (b) represents the process that unidirectional overlapped block motion is estimated；For the block of texture-rich, belong to mostly edge block, using the little of 8*8
Match block can refine edge object, and it is the different objects still again minimum error probability of sad value to reduce due to two；
(3) motion vector post-processing stages：
(1) bilateral filtering of motion vector：
A. the judgement of motion vector reliability：
Step1：The mean value of the motion vector of eight blocks of decision block (being designated as B blocks) and its surrounding is wanted in calculating：
(formula 5)
V in formulamFor mean value, viThe motion vector of eight blocks around representing respectively.
Step2:Calculating difference：
(formula 6)
V in formulamFor mean value, v1Represent the motion vector of B blocks.
Step3:Calculate mean difference：
Dc=| vm-v1| (formula 7)
Step4:Judge, if Dc＞ Dn, then v1For unreliable motion vector, bilateral filtering is needed.
B. bilateral filtering is carried out to unreliable motion vector：
v1smooth=bfilter2 [v1,v2,v3,...,v9] (formula 8)
(2) based on motion vector differential seat angle and the motion vector classification of sad value：
If the angle of a block motion vector is little with the angle difference of surrounding block motion vector, then think the block
Motion vector is relatively accurate；If the matching criterior sad value of the motion vector of a block is relatively small, then think the block
Motion vector be also relatively accurate, therefore the absolute difference and sad value of motion vector angle can be utilized transport judging the block
The dynamic accuracy estimated；Using the absolute difference of 9 calculation of motion vectors angle of formula, normalized absolute mistake is calculated using formula 10
Difference and SAD (Sum of Absolute Differences)：
(formula 9)
Wherein, Anglemean(F, N) represents ftThe absolute difference of the motion vector angle of current block and four blocks of surrounding in frame,
Angle (T) represents ftThe motion vector angle of current block, Angle (N in framen) represent ftCurrent block upper and lower, left and right four in frame
The motion vector angle of individual block.
(formula 10)
Wherein SADmean(F, N) represents ftThe normalization SAD of current block in frame, T (i, j) represent ftThe institute of current block in frame
There is pixel value, R (i, j) represents ft+2The all pixels value of match block in frame.By calculating normalized SAD, can be 0-1's
In the range of divide motion vector, make division more accurate.
The criteria for classification of motion vector：
If a. Angelmean(F, N) ＜ A1, judge that this motion vector is accurate；
If b. A1≤Anglemean(F,N)≤A2, then judge SADmean(F,R)；
If c. SADmean(F, R) ＞ B, judge that this motion vector is inaccurate.
Wherein A1、A2, B be threshold value, be all the histogram of the absolute difference and normalized SAD according to motion vector angle, really
Fixed threshold value.In this application, A1=0.45, A2=1.8, B=0.06.
(3) reparation of inaccurate motion vector：
According to the motion vector classification result that step (2) draws, inaccurate motion vector adopts multi-direction extension blocks
Motion vector restorative procedure, corrects inaccurate motion vector.May bag in the block corresponding to inaccurate motion vector
Containing multiple moving objects, can just make motion vector absolute difference and sad value all than larger, so being divided into fritter by inaccurate piece
Just can be with refined motion object, so as to improve the accuracy of estimation.Meanwhile, in order to ensure moving object edge integrity, little
Extension blocks are increased on the basis of block, the mistake estimation that local optimum is brought is it also avoid.As shown in accompanying drawing 5 (a), first, first
By current block, 8 × 8 pieces are divided into four 4 × 4 pieces, and each fritter being divided into is expanded to 6 × 6 block；Then by motion vector
Horizontally and vertically vector increase and decrease 1 respectively, form the motion vector of nine groups of candidates, shown in such as Fig. 5 (b), wherein (a) is represented will repair
Positive vector, (b) --- (i) represent respectively upper left, upper, upper right, left, lower-left, under, bottom right, in the right side eight groups of Candidate Motions to
This nine groups of candidate motion vectors are respectively adopted 6 × 6 pieces of calculating sad values by amount, are selected the corresponding motion vector of minimum sad value and are made
For the optimal motion vectors of amendment.
(4) the motion reparation of hole region：
Using empty block position in insertion frame, in former frame, around correspondence position, the motion vector estimation of block goes out empty block
Motion vector；For overlapping region, using the corresponding motion vector of smallest match criterion as optimal motion vectors.
(1) hole region estimation：As shown in Figure 6,6 (a) represents ftFrame, 6 (b) represent ft+1Frame, 6 (c) are represented
ft+2Frame, the block of left brace filling represent normal motion estimation block, in ft+1There is cavity in the block of stain filling in frame, the block exists
ftIn frame corresponding piece be stain filling block, then just take the motion vector of the block of the right slash filling of eight around stain block
Intermediate value as ft+1The motion vector of empty block in frame.
(2) hole region motion compensation：As shown in Equation 11, complete to insert frame using the method for similar bi directional motion compensation
Cavity filling:
(formula 11)
Wherein ft+1To treat the block that is inserted in interleave, ftAnd ft+2Match block respectively in former frame and a later frame.
(5) motion compensation stage：As shown in Equation 12, carry out first motion compensation.
(formula 12)
This patent obtains simulation result using standard yuv video cycle tests Bus sequences and Foreman sequences, with tradition
Overlapped block motion compensation frame per second method for improving, based on three sides filter frame per second method for improving, based on full search frame per second lifted
Method is compared.As shown in Figure 7, be Bus sequences subjective comparison diagram, figure (a)-(d) represent artwork, full search respectively
Methods and resultses figure, three side filtering method result figures, the present processes result figure, figure (e)-(g) is the thin of three kinds of methods respectively
Section enlarged drawing；As shown in Figure 8, be Foreman sequences subjective comparison diagram, figure (a)-(d) represent artwork, full search side respectively
Method result figure, three side filtering method result figures, the present processes result figure, scheme the details that (e)-(g) is three kinds of methods respectively
Enlarged drawing；As shown in table 1, it is that the objective evaluation of three kinds of methods compares, the standard of evaluation is Y-PSNR PSNR (Peak
Signal to Noise Ratio) and structural similarity SSIM (structural similarity index
Measurement) can be seen that the method for this patent all has in subjective effect or objective effect to be obviously improved, effectively
The problems such as solving cavity, overlapping block, blocking effect.
Table 1：Objective simulation result comparison diagram
Claims (3)
1. it is a kind of based on adaptive motion estimate frame per second method for improving, it is characterised in that the method is comprised the following steps：
Step 1：Original video is processed, frame is processed as；
Step 2：Frame is divided into into texture-rich block according to edge detection results and texture does not enrich block, different types of piece is carried out
Adaptive motion estimation, obtains motion vector；
Step 3：Using multi-direction extension movement vector restorative procedure, inaccurate motion vector is corrected；
Step 4：Estimate the motion vector of hole region；
Step 5：Motion compensation obtains inserting frame；
Step 6：Insertion frame and primitive frame are synthesized into the video of high frame per second；
Also, rim detection is carried out using Sobel Operator in step 2, and obtains marginal information, included in counting each macro block
The number of edge pixel point simultaneously calculates mean value, when mean value is more than comprising edge pixel point number in each macro block, it is believed that
It is the block of texture-rich, is otherwise that texture does not enrich block；For the block that texture does not enrich, estimated using overlapped block motion, for
The block of texture-rich is estimated using first motion.
2. it is according to claim 1 based on adaptive motion estimate frame per second method for improving, it is characterised in that：In step 3
In, motion vector classification is carried out according to each piece of motion vector angle absolute difference and absolute error and sad value, for inaccurate
Motion vector, the motion vector value in horizontally and vertically direction increase and decrease 1 is formed into nine candidate motion vectors respectively, is calculated respectively
The sad value of the corresponding match block of nine motion vectors and current block, it is optimal movement to choose the minimum motion vector of correspondence sad value
Vector, corrects inaccurate motion vector.
3. it is according to claim 1 based on adaptive motion estimate frame per second method for improving, it is characterised in that：In step 4
In, for hole region, according to the motion vector estimation of empty block position block around correspondence position in former frame in insertion frame
Go out the motion vector of empty block；For overlapping region, using the corresponding motion vector of smallest match criterion as optimal movement to
Amount.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410305458.8A CN104065975B (en) | 2014-06-30 | 2014-06-30 | Based on the frame per second method for improving that adaptive motion is estimated |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410305458.8A CN104065975B (en) | 2014-06-30 | 2014-06-30 | Based on the frame per second method for improving that adaptive motion is estimated |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104065975A CN104065975A (en) | 2014-09-24 |
| CN104065975B true CN104065975B (en) | 2017-03-29 |
Family
ID=51553471
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410305458.8A Expired - Fee Related CN104065975B (en) | 2014-06-30 | 2014-06-30 | Based on the frame per second method for improving that adaptive motion is estimated |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104065975B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160182866A1 (en) * | 2014-12-19 | 2016-06-23 | Sony Corporation | Selective high frame rate video capturing in imaging sensor subarea |
| CN105100807B (en) * | 2015-08-28 | 2018-11-13 | 山东大学 | A kind of frame per second method for improving based on motion vector post-processing |
| CN105933714B (en) * | 2016-04-20 | 2017-06-09 | 济南大学 | A kind of 3 D video frame per second method for improving based on depth guiding extension Block- matching |
| CN106303546B (en) * | 2016-08-31 | 2019-05-14 | 四川长虹通信科技有限公司 | Conversion method and system in a kind of frame rate |
| CN113496504B (en) * | 2020-03-20 | 2022-11-29 | 展讯通信（上海）有限公司 | Image alignment method and device, storage medium and terminal |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102857728A (en) * | 2012-10-12 | 2013-01-02 | 重庆大学 | Video data frame rate up conversion method based on system analysis and design (SAD) and video on demand (VOD) matching rules |
| CN102868879A (en) * | 2011-07-05 | 2013-01-09 | 北京大学 | Method and system for converting video frame rate |
| CN103888767A (en) * | 2014-03-31 | 2014-06-25 | 山东大学 | Frame rate improving method with UMH block matching motion estimation and optical flow field motion estimation combined |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110255596A1 (en) * | 2010-04-15 | 2011-10-20 | Himax Technologies Limited | Frame rate up conversion system and method |
-
2014
- 2014-06-30 CN CN201410305458.8A patent/CN104065975B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102868879A (en) * | 2011-07-05 | 2013-01-09 | 北京大学 | Method and system for converting video frame rate |
| CN102857728A (en) * | 2012-10-12 | 2013-01-02 | 重庆大学 | Video data frame rate up conversion method based on system analysis and design (SAD) and video on demand (VOD) matching rules |
| CN103888767A (en) * | 2014-03-31 | 2014-06-25 | 山东大学 | Frame rate improving method with UMH block matching motion estimation and optical flow field motion estimation combined |
Non-Patent Citations (4)
| Title |
|---|
| 《Enhanced hexagonal-based search using Direction-Oriented Inner Search for Motion Estimation》;Bei-Ji Zou et al.;《IEEE Transactions on Circuits and Systems for Video Technology》;20090901;第20卷(第1期);156-160 * |
| 《Motion-Compensated Frame Interpolation Using Bilateral Motion Estimation and Adaptive Overlapped Block Motion Compensation》;Byeong-Doo Choi et al.;《IEEE Transactions on Circuits and Systems for Video Technology》;20070430;第17卷(第4期);407-416 * |
| 《New frame rate up-conversion using bi-directional motion estimation》;Byung-Tae Choi et al.;《IEEE Transactions on Consumer Electronics》;20000806;第46卷(第3期);603-609 * |
| 《帧率提升中的三维递归搜索块匹配改进算法》;侯正信 等;《计算机工程与应用》;20120319;第47卷(第33期);169-171 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104065975A (en) | 2014-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104065975B (en) | Based on the frame per second method for improving that adaptive motion is estimated | |
| CN102693531B (en) | Adaptive double-platform based infrared image enhancement method | |
| CN102883175B (en) | Methods for extracting depth map, judging video scene change and optimizing edge of depth map | |
| CN107968946B (en) | Video frame rate improving method and device | |
| US8699818B2 (en) | Method, system, and program for determining image quality based on pixel changes between image frames | |
| CN103888767B (en) | A kind of frame per second method for improving that UMH block-based motion estimations are combined with optical flow field estimation | |
| CN102131058B (en) | Speed conversion processing module and method of high definition digital video frame | |
| US8244054B2 (en) | Method, apparatus and integrated circuit capable of reducing image ringing noise | |
| CN104574337B (en) | Based on the image enchancing method that bilateral gamma correction and multi-scale image merge | |
| KR101761928B1 (en) | Blur measurement in a block-based compressed image | |
| Jin et al. | Quaternion-based impulse noise removal from color video sequences | |
| JPH0799660A (en) | Motion compensation predicting device | |
| CN103037217B (en) | The image detected in interpolated image damages | |
| CN106464865B (en) | Block-based static region detection method, device and system for video processing | |
| TW201327474A (en) | Method for generating depth by disparity estimation | |
| CN106210448B (en) | Video image jitter elimination processing method | |
| CN102202224A (en) | Caption flutter-free method and apparatus used for plane video stereo transition | |
| WO2010073177A1 (en) | Image processing | |
| CN103400367A (en) | No-reference blurred image quality evaluation method | |
| JP2011509617A (en) | Sparse geometry for super-resolution video processing | |
| CN103369208A (en) | Self-adaptive de-interlacing method and device | |
| CN107333027A (en) | A kind of method and apparatus of video image enhancement | |
| CN103514610B (en) | A kind of moving Object Segmentation method of stationary background | |
| CN102447870A (en) | Detection method for static objects and motion compensation device | |
| CN108270945A (en) | A kind of motion compensation denoising method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into 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: 20170329 Termination date: 20200630 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |