CN104135597A - Automatic detection method of jitter of video - Google Patents
Automatic detection method of jitter of video Download PDFInfo
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- CN104135597A CN104135597A CN201410318324.XA CN201410318324A CN104135597A CN 104135597 A CN104135597 A CN 104135597A CN 201410318324 A CN201410318324 A CN 201410318324A CN 104135597 A CN104135597 A CN 104135597A
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Abstract
The invention discloses an automatic detection method of jitter of a video. The automatic detection method comprises the following steps of: a first step, selecting feature points from a video picture; a second step, tracing and matching the feature points in an adjacent frame; a third step, removing abnormal points of the feature points to obtain an inter-frame motion vector; a fourth step, extracting a video jitter frequency and video jitter amplitude features; a fifth step, extracting histogram features of an inter-frame light stream vector; and a sixth step, judging the video jitter degree through a classifier. According to the automatic detection method, any prior knowledge is not needed while judging whether the video has jitter and the automatic detection method has the advantage of a higher accuracy rate.
Description
Technical field
Whether the present invention can be used for mobile phone, Digital Video, the first-class movement of monitoring camera or the captured digital video of permanent plant, exist shake automatically to detect, specifically a kind of video jitter automatic testing method to digital video picture.
Background technology
The digital video that mobile terminal is taken is often with video jitter, and the existence of video jitter affects the visual quality of video and such as subsequent treatment effects such as target identifications, therefore, exists at present various video digital image stabilization method for reducing video jitter degree.But, for a digital video arbitrarily, need to first judge its video jitter degree, could correct Video Stabilization method and the method parameter of selecting properly.
On the one hand, video jitter detection method can be used as the pretreatment module of Video Stabilization method, for any video, first detects video jitter degree, according to degree of jitter, selects corresponding digital image stabilization method and parameter; On the other hand, video jitter detection method can be used as the subsequent treatment module of Video Stabilization method, and the video after digital image stabilization method is processed carries out shaking detection, and the effect of digital image stabilization method is evaluated.
At present, shaking detection and the evaluation for digital video also do not have complete detection method and standard, the present invention to be intended to propose a kind of effective video shake automatic testing method.
Summary of the invention
The present invention proposes a kind of video jitter automatic testing method, and without any input video priori in the situation that, it can be used for choosing digital image stabilization method kind and regulates digital image stabilization method parameter, and can be used for evaluating the steady picture effect of digital image stabilization method.
For achieving the above object, the technical solution used in the present invention is: first the present invention finds equally distributed characteristic point in video pictures, and tracking characteristics point obtains sparse optical flow vector, then through abnormal light stream vector, gets rid of and obtains interframe light stream vector.Interframe light stream vector is averaged and obtained the estimation of inter frame motion model, from motion model, count video jitter frequency, video jitter amplitude two class jitter features, obtain light stream vector histogram as a class jitter feature according to interframe light stream vector.Finally above-mentioned three class video jitter features are input to the degree of jitter that the video jitter degree grader training obtains input video.
A kind of video jitter automatic testing method of the present invention, comprises the steps:
The first step, chooses characteristic point in present frame picture;
Second step, tracking and matching characteristic point in consecutive frame, obtains interframe light stream vector;
The 3rd step, the interframe light stream vector that second step is obtained carries out abnormity point eliminating;
The 4th step, the interframe light stream vector obtaining according to the 3rd step is estimated inter frame motion model, then according to this model extraction, goes out video jitter frequency, two features of video jitter amplitude;
The 5th step, the interframe light stream vector obtaining according to the 3rd step counts light stream vector histogram as video jitter another feature;
The 6th step, video jitter frequency, video jitter amplitude and light stream vector histogram three class jitter features that the 4th step and the 5th step are extracted are input to the grader training and obtain this video jitter degree.
Compared with prior art, the present invention has following beneficial effect:
The thought that the present invention adopts sparse optical flow to calculate is estimated video inter frame motion model, without any need for video priori, all applicable to most of video scene, and has higher accuracy rate.According to the degree of jitter of the video parameter of selecting video digital image stabilization method and digital image stabilization method better, improve efficiency and the effect of steady picture step.
Accompanying drawing explanation
By reading the detailed description of non-limiting example being done with reference to the following drawings, it is more obvious that other features, objects and advantages of the present invention will become:
Fig. 1 is a kind of video jitter automatic testing method overview flow chart that the present invention proposes.
Fig. 2 is the design sketch of feature point extraction in the present invention, and wherein in figure, dot is selected characteristic point position.
Fig. 3 is characteristic pattern when light stream vector histogram divides X and Y both direction statistics in the present invention, wherein figure (a) is stable video directions X light stream vector histogram, figure (b) is stable video Y-direction light stream vector histogram, figure (c) is slight jitter video directions X light stream vector histogram, figure (d) is slight jitter video Y-direction light stream vector histogram, figure (e) is violent shake video directions X light stream vector histogram, and figure (f) is violent shake video Y-direction light stream vector histogram.
Fig. 4 is that in the present invention, video jitter detects grader recall ratio--precision ratio curve.
Fig. 5 is that application case of the present invention lists intention.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.Following examples will contribute to those skilled in the art further to understand the present invention, but not limit in any form the present invention.It should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, can also make some distortion and improvement.These all belong to protection scope of the present invention.
As shown in Figure 1, the overview flow chart for one embodiment of the invention, specifically comprises:
The first step, chooses characteristic point in current video frame;
Second step, tracking and matching characteristic point in consecutive frame, obtains interframe light stream vector;
The 3rd step, the interframe light stream vector that second step is obtained carries out abnormity point removal; During abnormal light stream vector is removed, the light stream vector that phase place and amplitude and most of light stream vector are differed greatly is considered as abnormal light stream vector, has to be removed.
The 4th step, the interframe light stream vector obtaining according to the 3rd step is estimated inter frame motion model, then according to this model extraction, goes out video jitter frequency, two features of video jitter amplitude; In the estimation of inter frame motion model, inter frame motion model is translation model, uses the mean value of interframe light stream vector as the estimation of motion model.
The 5th step, the interframe light stream vector obtaining according to the 3rd step counts light stream vector histogram as video jitter feature; The histogrammic statistics of light stream vector, statistics be the light stream vector after abnormity point is got rid of, light stream vector histogram is added up all normal interframe light stream vectors in parallel.
The 6th step, video jitter frequency, video jitter amplitude and light stream vector histogram three class jitter features that the 4th step and the 5th step are extracted are input to the grader training, and obtain this video jitter degree.In this step: stable video and the artificial shake video that synthesize different jitter level of jitter level grader training set for taking.The selected standard of label of jitter level grader test set video is video jitter subjective assessment.Adopt SVMs as video jitter grade separation device.
Step based on above-mentioned, a kind of concrete implementation detail of video jitter automatic testing method is as follows:
Fig. 2 is feature point extraction schematic diagram in the present invention, and the chosen position of characteristic point should be evenly distributed in whole video pictures as far as possible, and characteristic point position is marked by dot.
When feature point tracking, the match is successful obtains after interframe light stream vector, if formula (1) is set up, thinks that video pictures exists once shake on directions X, if formula (2) is set up, thinks that video pictures exists once in the Y direction to shake.
[P(x)|
k+1-P(x)|
k]·[P(x)|
k-P(x)|
k-1]＜0 (1)
[P(y)|
k+1-P(y)|
k]·[P(y)|
k-P(y)|
k-1]＜0 (2)
In its Chinese style (1), first, the left side represents that on directions X, k frame is estimated to the interframe translational motion model of k+1 frame, second represents that on directions X, k-1 frame is estimated to the interframe translational motion model of k frame, in formula (2), first, the left side represents that in Y-direction, k frame is estimated to the interframe translational motion model of k+1 frame, and second represents that in Y-direction, k-1 frame is estimated to the interframe translational motion model of k frame.After having traveled through video sequence, just can obtain the frequency of video jitter.
Video jitter amplitude represents that inter frame motion model is towards the ultimate range of a direction (X positive direction, X opposite direction, Y positive direction or Y are in the other direction) cumulative movement.Video jitter amplitude will be done normalization operation, to adapt to the video pictures of different sizes.
Fig. 3 is light stream vector histogram feature figure in the embodiment of the present invention, when statistics light stream vector histogram, each light stream vector is projected to X and Y both direction takes statistics again, first take directions X histogrammic foundation as example illustrates.While adding up the histogram of directions X, be subdivided into both direction, one is the positive direction (value for just) of X, one is the opposite direction (value is for negative) of X, histogrammic step-length is 1% of video width, because the displacement of interframe light stream vector is seldom a part of, can be greater than 20% of video width, so histogram is divided into 40 grades, the both forward and reverse directions of X respectively has 20 grades.When light stream vector is when the displacement of directions X is greater than 20%, counted in the middle of last rank, from length, be 1% to 19% each own rank that step-length is 1% of video width, be greater than 19% unified calculation in a rank, the histogrammic transverse axis scope of light stream vector is from left to right followed successively by: (∞ ,-19%], (and 19% ,-18%],, (1%, 0], (0,1%], (1%, 2%] ..., (18%, 19%], (19% ,+∞) totally 40 grades.In like manner, for the statistics with histogram of Y-direction, be also same step-length and statistical method, unique different be that its step-length is 1% of video height, rather than video width 1%.Finally light stream vector histogram is done to normalization operation, the light stream vector that histogrammic height represents to be positioned at this interval accounts for the ratio of total light stream vector number.
Fig. 3 has drawn the light stream vector histogram of three videos, wherein, figure (a) is stable video directions X light stream vector histogram, figure (b) is stable video Y-direction light stream vector histogram, figure (c) is slight jitter video directions X light stream vector histogram, figure (d) is slight jitter video Y-direction light stream vector histogram, and figure (e) is violent shake video directions X light stream vector histogram, and figure (f) is violent shake video Y-direction light stream vector histogram.
Fig. 4 is that in the present invention, video jitter detects grader recall ratio--precision ratio curve.
Fig. 5 is that application case of the present invention lists intention, embodied the contribution of the present invention in steady picture process, the more perfect step of whole Video Stabilization.
From above-described embodiment, can find out, the present invention is judging whether video exists when shake without any need for priori, and has higher accuracy rate.
Above specific embodiments of the invention are described.It will be appreciated that, the present invention is not limited to above-mentioned specific implementations, and those skilled in the art can make various distortion or modification within the scope of the claims, and this does not affect flesh and blood of the present invention.
Claims (7)
1. a video jitter automatic testing method, is characterized in that described method comprises the steps:
The first step, chooses characteristic point in current video frame;
Second step, tracking and matching characteristic point in consecutive frame, obtains interframe light stream vector;
The 3rd step, the interframe light stream vector that second step is obtained carries out abnormity point removal;
The 4th step, the interframe light stream vector obtaining according to the 3rd step is estimated inter frame motion model, then according to this model extraction, goes out video jitter frequency, two features of video jitter amplitude;
The 5th step, the interframe light stream vector obtaining according to the 3rd step counts light stream vector histogram as video jitter feature;
The 6th step, video jitter frequency, video jitter amplitude and light stream vector histogram three class jitter features that the 4th step and the 5th step are extracted are input to the grader training, and obtain this video jitter degree.
2. a kind of video jitter automatic testing method according to claim 1, is characterized in that, in described the 3rd step: the light stream vector that phase place and amplitude and most of light stream vector are differed larger is considered as abnormal light stream vector, has to be removed.
3. a kind of video jitter automatic testing method according to claim 1, is characterized in that, in described the 4th step: inter frame motion model is translation model, using the mean value of interframe light stream vector as estimation.
4. a kind of video jitter automatic testing method according to claim 1, is characterized in that, in described the 5th step: light stream vector histogram is added up all normal interframe light stream vectors in parallel.
5. according to a kind of video jitter automatic testing method described in claim 1-4 any one, it is characterized in that, in described the 6th step: adopt the stable video of taking and the shake video that manually synthesizes different jitter level as grader training set.
6. a kind of video jitter automatic testing method according to claim 5, is characterized in that, the selected standard of label of the test set video of described grader is video jitter subjective assessment.
7. a kind of video jitter automatic testing method according to claim 6, is characterized in that, described the 6th step adopts SVMs as grader.
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| CN106210448A (en) * | 2016-07-22 | 2016-12-07 | 恒业智能信息技术（深圳）有限公司 | A kind of video image dithering Processing for removing method |
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| CN111372000A (en) * | 2020-03-17 | 2020-07-03 | Oppo广东移动通信有限公司 | Video anti-shake method and apparatus, electronic device, and computer-readable storage medium |
| CN111372000B (en) * | 2020-03-17 | 2021-08-17 | Oppo广东移动通信有限公司 | Video anti-shake method and apparatus, electronic device, and computer-readable storage medium |
| CN113691733A (en) * | 2021-09-18 | 2021-11-23 | 北京百度网讯科技有限公司 | Video jitter detection method and device, electronic equipment and storage medium |
| JP2022160646A (en) * | 2021-09-18 | 2022-10-19 | ベイジン バイドゥ ネットコム サイエンス テクノロジー カンパニー リミテッド | Method of detecting video jitter, device, electronic apparatus, and storage medium |
| CN113887547A (en) * | 2021-12-08 | 2022-01-04 | 北京世纪好未来教育科技有限公司 | Key point detection method and device and electronic equipment |
| CN113887547B (en) * | 2021-12-08 | 2022-03-08 | 北京世纪好未来教育科技有限公司 | Key point detection method and device and electronic equipment |
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