CN102760296A - Movement analyzing method for objects in multiple pictures - Google Patents
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Abstract
The invention discloses a movement analyzing method for objects in multiple pictures. The movement analyzing method is applicable to an image acquiring device and comprises the following steps of first, obtaining the sum of a plurality of groups of first absolute difference values according to noise of the image acquiring device under a plurality of light sources; obtaining two pictures under the condition that a shooting light source of the light sources is arranged; then calculating the sum of a plurality of second absolute difference values between the two pictures; finding out a plurality of object blocks with object grains; calculating a regional movement vector of each object block according to the sum of the second absolute difference values; calculating a first reliability of each object block according to the sum of the second absolute difference values and the sum of the group of first absolute difference values corresponding to the shooting light source; and estimating the regional movement vectors according to the first reliabilities so as to obtain a universal movement vector. The movement analyzing method can be used for estimating the accurate universal movement vector.
Description
Technical field
The present invention relates to a kind of image processing method, relate in particular to the mobile analytical approach of object in a kind of many pictures.
Background technology
It is a lot of that the image noise generates reason; For example via the signal amplifier effect, used heat, camera lens and photo-sensitive cell (sensor) reciprocation; Reasons such as signal transduction process interference all can cause noise, and each photo-sensitive cell more has influence in various degree with different camera lenses cooperations.In Flame Image Process is now used, many pictures (multi-frame) synthetic with the technology of analyzing by extensive discussions and use, but running into a very big difficult problem is how correct analysis goes out the kinematic relation between different pictures.(3D noise reduction) is example with three-dimensional noise reduction, moves analysis result between wrong different pictures in case use, the situation of (ghost) that the image after synthetic will find easily that image retention is arranged.The mode of analyzing movement of objects between the picture is a lot; But take into account that the analysis speed under the high picture update rate (high frame rate) must reach soon; After hardware cost was not hoped high factor; Simpler efficient analytic approach is absolute difference and (Sum of Absolute Difference, mode SAD).
Yet, under high noise environment, only lean on very easily profiling error of words that absolute difference and computing analyze.Moreover the logic that absolute difference of calculating and minimum block is regarded as place, optimal approximation block place also has very big problem.Because, under high noise environment, in fact absolute difference and numerical value not little.For example, if maximum absolute difference and with the minimum absolute difference value with when numerically almost not having difference in the Search Area (search region) assert it is that the coordinate points risk that corresponds to is very big to the minimum position of numerical value actually, be easy to make the mistake.In addition, serve as that the influence property that the movement of objects analysis can reduce noise is really done on the basis with likelihood function (like lihood function).But; The needed calculation resources of these class methods is too high; Under high picture update rate, be that the Related product on basis needs very high arithmetic speed and usefulness especially with complementary metal oxide semiconductor (CMOS) photo-sensitive cell (Complementary Metal-Oxide-Semiconductor sensor, CMOS sensor).As this complicacy algorithm consuming time again and be difficult in real image product, putting into practice.
Summary of the invention
The present invention provides the mobile analytical approach of object in a kind of many pictures, the influence that can avoid noise that movement of objects is analyzed.
The present invention proposes the mobile analytical approach of object in a kind of many pictures, is applicable to an image acquiring device, comprises the following steps.At first, set noise down at a plurality of light sources according to image acquiring device, obtain many group first absolute differences and.Each light source set respectively corresponding one group of first absolute difference with.Then, the photographic light sources in these light sources are set obtains one first picture and one second picture by image acquiring device under setting.Then, calculate between first picture and second picture a plurality of second absolute differences with.Afterwards, find out a plurality of object block of the texture process of object in first picture and second picture.Afterwards, according to these second absolute differences with, calculate these object block and divide other regional motion-vector.Then, according to these second absolute differences and with photographic light sources set pairing this organize first absolute difference with, calculate these object block and divide other one first fiduciary level.Then, according to these these regional motion-vectors of first fiduciary level assessment, to obtain a universe motion-vector.
In one embodiment of this invention, according to the noise of image acquiring device under these light sources are set, obtain these organize first absolute difference and step, comprise the following steps.At first, under these light sources are set, one lamp box is obtained many images respectively by image acquiring device.Then, write down these images first absolute difference of diverse location with.
In one embodiment of this invention, write down these images first absolute difference of diverse location and step, comprise the following steps.Write down first absolute difference of four corner pixels of each block in these images, with first absolute difference of inserting out picture number in each block by first absolute difference of four corner pixels and interior with.
In one embodiment of this invention, write down these images first absolute difference of diverse location and step, comprise the following steps.Write down pixel in each blocks of these images shared first absolute difference with.
In one embodiment of this invention, these light source conditions comprise the light sensitivity of brightness, color and the image acquiring device of lamp box, on every side in the dim light at least one of them.
In one embodiment of this invention, find out the step of these object block, comprise the following steps.At first, calculate each block between first picture and second picture maximum absolute difference and with the minimum absolute difference value with.Then, according to maximum absolute difference in each block and with minimal difference and difference, judge that each block is these object block one of them or a background block.
In one embodiment of this invention, according to these these regional motion-vectors of first fiduciary level assessment,, comprise the following steps to obtain the step of universe motion-vector.At first, according to maximum absolute difference in each block and with minimal difference and difference, calculate these object block and divide other one second fiduciary level.Then, according to these first fiduciary levels and these regional motion-vectors of these second fiduciary levels assessment, to obtain the universe motion-vector.
In one embodiment of this invention, according to these first fiduciary levels and these regional motion-vectors of these second fiduciary levels assessment,, comprise the following steps to obtain the step of universe motion-vector.At first, according to these first fiduciary levels and these second fiduciary levels, calculate a plurality of average coherences.Then, according to these regional motion-vectors of these average coherence weightings, to obtain the universe motion-vector.
In one embodiment of this invention, set the pairing step that this organizes first absolute difference and calculates these first fiduciary levels, comprise the following steps according to these second absolute differences with photographic light sources.At first, calculate this organize first absolute difference and a standard deviation tolerance.Then, calculate these second absolute differences and with this organize first absolute difference and a ratio.Come again, than correlative value and standard deviation tolerance, to obtain these first fiduciary levels.
Based on above-mentioned, the present invention by obtain in advance receive first absolute difference that noise influence produces with, cooperate diverse location calculated between two pictures second absolute difference again and come the assessment area motion-vector, and can assess out universe motion-vector comparatively accurately.
For letting the above-mentioned feature and advantage of the present invention can be more obviously understandable, hereinafter is special lifts embodiment, and conjunction with figs. elaborates as follows.
Description of drawings
Fig. 1 is the process flow diagram of the mobile analytical approach of object in many pictures of first embodiment of the invention.
Fig. 2 is the synoptic diagram of the picture that image acquiring device obtained.
Fig. 3 is the curve synoptic diagram of relative second, first absolute difference of first fiduciary level of Fig. 1 and ratio.
Fig. 4 is the process flow diagram of the mobile analytical approach of object in many pictures of first embodiment of the invention.
Fig. 5 A and Fig. 5 B are respectively the picture view in order to declare record first absolute difference and dual mode.
Fig. 6 in order to explain maximum absolute difference and with the minimum absolute difference value and picture view.
Fig. 7 is in order to the curve synoptic diagram of second fiduciary level and maximum, least absolute value difference and relation to be described.
Reference numeral:
B, B1: block
D: standard deviation tolerance
E, L: pixel
F: picture
F1 F2 F3: image
M: storer
O1-O7: object block
P1: position
S110-S170, S210-S274: step
SADmax: maximum absolute difference with
SADmin: the minimum absolute difference value with
Embodiment
Fig. 1 is the process flow diagram of the mobile analytical approach of object in many pictures of first embodiment of the invention.In the present embodiment, the flow process of Fig. 1 is applicable to image acquiring device (not shown)s such as digital camera, digital code camera.Please refer to Fig. 1, at first carry out step S110, set noise down at a plurality of light sources according to image acquiring device, obtain many group first absolute differences and.Each light source set respectively corresponding one group of first absolute difference with.For instance, can take the lamp box of different brightness in advance by image acquiring device.After the diverse location to the lamp box image calculates, can obtain first absolute difference that simple noise caused under different brightness with.
In detail, can photograph high brightness (LV14) from low-light level (LV2) a tunnel to lamp box, and corresponding various nitometer first absolute difference of calculating the lamp box image with.In order to save storer, first absolute difference under can a recording section progression brightness with.For example only write down first under LV2, LV5, the LV10 determine to difference and, and first absolute difference and the available interpolation method of LV3, LV4 etc. are calculated.Except brightness, the also situation of image recordable deriving means under different light sensitivity, for example light sensitivity 100 one tunnel photographed light sensitivity 3200.In another embodiment, can also take by image acquiring device contrast colors chart (color chart), with of the influence of prior measurement different color blocks color noise (color noise).
In addition, in another embodiment, also can take into account the camera lens influence of color dim light (lens color shading) on every side.The noise of color can compare seriously around the image in theory, and we make example with a situation.Under light sensitivity 800, lamp box brightness LV10, to lamp box take down different positions all calculate first absolute difference with, and note.Because the noise at dim light place, corner is comparatively serious usually, therefore first absolute difference and numerical value should be bigger.Along with light sensitivity is high more, first absolute difference and also should be bigger.Store for ease, can the lamp box image be divided into a plurality of blocks, and be recorded in first absolute difference that the block centermost calculates with.Since the noise influence can let first absolute difference with error range is arranged, so also can calculate its mean value and standard deviation through the result that experiment is repeatedly noted down, to obtain the data of calibration (calibration).For instance, compared to light sensitivity 800, first absolute difference under the light sensitivity 3200 and standard deviation can be higher.
What deserves to be mentioned is, can try to achieve earlier according to the above-mentioned practice image acquiring device photo-sensitive cell the noise characteristic and note in advance.In addition, though to same block carry out first absolute difference and calculating, each time to numerical value also have deviation, therefore in the information of this otherness of record, the otherness of first absolute difference and variation also can be noted.In addition, take and get by dull scene (VERIVIDE COLOR ASSESMENT CABINET) because of the image of analyzing again.Therefore, but the independent analysis influence property of noise (random noise) at random.
Then carry out step S120, the photographic light sources in these light sources are set obtains one first picture and one second picture by image acquiring device under setting.Carry out step S130 then, calculate between first picture and second picture a plurality of second absolute differences with.Fig. 2 is the synoptic diagram of the picture that image acquiring device obtained.Please refer to Fig. 2, after having obtained two continuous picture F, can picture F be divided into a plurality of block B.Then, to these blocks B calculate respectively second absolute difference with.
Carry out step S140 afterwards, find out a plurality of object block of the texture process of object in first picture and second picture.With Fig. 2 is example, can find out 7 object block O1-O7 among all the block B from picture F.Carry out step S150 again, according to these second absolute differences with, calculate these object block O1-O7 and divide other regional motion-vector.In the present embodiment, because the block B beyond the object block O1-O7 belongs to the flat region among the picture F, and these flat regions are vulnerable to the noise interference.Therefore, present embodiment can be ignored the flat region, disturbs to avoid the flat region.Also perhaps, the weight raising with object block O1-O7 also can reduce the interference of flat region.
Then carry out step S160, according to these second absolute differences and with photographic light sources set pairing this organize first absolute difference with, calculate these object block O1-O7 and divide other one first fiduciary level.For instance, can be according to user's shooting setup parameter and Luminance Analysis result (for example obtaining) and the setting of hue analysis result (for example trying to achieve) taking-up photographic light sources by AWB (AWB) algorithm by camera automatic exposure (AE) algorithm.Suppose that the scene brightness analysis result that the user is taking is LV10, light sensitivity is set at 800, and the form and aspect of taking block are muted color, then gets into system storage and sets down first absolute difference and the taking-up of calibration in advance to these.Take out the first corresponding absolute difference and afterwards, calculate again second absolute difference and with first absolute difference and ratio, to obtain first fiduciary level.
Fig. 3 is the curve synoptic diagram of relative second, first absolute difference of first fiduciary level of Fig. 1 and ratio.Please refer to Fig. 3, second, first absolute difference and ratio very near 1 o'clock, represent second absolute difference and first absolute difference of operation result and calibration in advance and very approximate.Just, ratio only receives the influence of noise at random near this block of 1.In addition, because noise has certain variability (variance), so a standard deviation tolerance of definable d.Being positioned at the interval ratio of 1-d-1+d, all is may be the result that influences of pure noise at random.Further and since in the stochastic distribution theory sampling of the overwhelming majority still can compare near at ratio be 1 near, the fiduciary level here is still the highest.Therefore, standard deviation tolerance d can rely on first absolute difference that step S110 notes and otherness set.Under low speed, the d value is less; Under ISO, the d value is bigger.In addition, in practical application, the curve of Fig. 3 can be earlier through the processing of smoothing, not as limit.
Carry out step S170 then, according to these these regional motion-vectors of first fiduciary level assessment, to obtain a universe motion-vector.For instance, the weight of the regional motion-vector that first fiduciary level is high must improve, and the regional motion-vector that first fiduciary level is low is excessively then ignored, and therefore can obtain a universe motion-vector result comparatively accurately.
In the present embodiment, we propose to increase the method for the reliability that absolute difference and object analysis move, and analyze fiduciary level and make it take under the situation adjustment with difference.With the example that is applied as of three-dimensional noise reduction, high when analyzing fiduciary level, the weight (weighting) of frame filter (temporal filtering) is just heightened, otherwise turns down the weight of frame filter, makes the result be approximately pure spatial filtering (spatial filtering).Not only can avoid on the one hand analyzing the generation that causes ghost, improve the image quality after handling simultaneously because of mistake moves.
Fig. 4 is the process flow diagram of the mobile analytical approach of object in many pictures of first embodiment of the invention.Please refer to Fig. 1 and Fig. 4, present embodiment and last embodiment are similar, and its resemblance will repeat no more, and also can quote mutually at its difference place, not as limit.At first carry out step S210, set noise down at a plurality of light sources according to image acquiring device, obtain many group first absolute differences and.In the present embodiment, step S210 can comprise two sub-steps such as S212 and S214.At first carry out step S212, under these light sources are set, one lamp box is obtained many images respectively by image acquiring device.In the present embodiment, these light source conditions can comprise the light sensitivity of brightness, color and the image acquiring device of lamp box, on every side in the dim light at least one of them.
Carry out step S214 afterwards, calculate and write down these images first absolute difference of diverse location with.Fig. 5 A and Fig. 5 B are respectively the picture view in order to declare record first absolute difference and dual mode.Please refer to Fig. 5 A and Fig. 5 B, for letter economizes system storage, first absolute difference and available following the dual mode simplification of record:
Please be earlier with reference to figure 5A, the data of supposing storer M only put 64x64 pen first absolute difference and numeral, but true picture F1 size is 4096x4096.At this moment, the size of the corresponding block B1 in the true picture F1 is for long: (4096/64)=64; High: (4096/64)=64.That is to say, the pixel of the 64x64 among the block B1 with regard to first absolute difference of shared this opposite position P1 in storer M with.
Please again with reference to figure 5B, we also can use bilinear interpolation method (bilinear interpolation) obtain each first absolute difference and numerical value.For instance, for whole image F2, can write down in advance each block four corner pixels E first absolute difference with.First absolute difference that each pixel L is corresponding in wanting calculation block and the time, can reach with the bilinear interpolation algorithm.For example say that the image size is 4096x4096, can take out the corner pixels E of 65x65 end points, then each block size is 64x64.Thus, first absolute difference that can insert out picture number L in each block by first absolute difference of four corner pixels E and interior with.
Then carry out step S220, the photographic light sources in these light sources are set obtains one first picture and one second picture by image acquiring device under setting.Carry out step S230 then, calculate between first picture and second picture a plurality of second absolute differences with.
Carry out step S240 afterwards, find out a plurality of object block of the texture process of object in first picture and second picture.In the present embodiment, step S240 can comprise substep S242 and step S244.At first carry out step S242, calculate each block between first picture and second picture maximum absolute difference and with the minimum absolute difference value with.In detail, Fig. 6 for in order to explain maximum absolute difference and with the minimum absolute difference value and picture view.Please refer to Fig. 6, supposing has N block among the image F3, and then each block can calculate corresponding a maximum absolute difference and SADmax and a minimum absolute difference value and a SADmin.
Carry out step S244 then, according to maximum absolute difference in each block and with minimal difference and difference, judge that each block is these object block one of them or a background block.In general, there are contour of object or edge process in this zone of [SADmax-SADmin] big more expression, and this regional change of [SADmax-SADmin] more little expression is too small might to be flat region or the zone that has no the variation of moving.Therefore, present embodiment can preestablish a threshold value.As [SADmax-SADmin] of a block during, judge that then this block is an object block greater than threshold value; As [SADmax-SADmin] of a block during, judge that then this block is the background block less than threshold value.
Carry out step S250 again, according to these second absolute differences with, calculate these object block and divide other regional motion-vector.Then carry out step S260, according to these second absolute differences and with photographic light sources set pairing this organize first absolute difference with, calculate these object block and divide other one first fiduciary level.In the present embodiment, step S260 can comprise substep S262-S266.At first carry out step S262, calculate this organize first absolute difference and a standard deviation tolerance.Then carry out step S264, calculate this organize second absolute difference and with these first absolute differences and a ratio.Carry out step S266 again, than correlative value and standard deviation tolerance, to obtain these first fiduciary levels.
Carry out step S270 then, according to these these regional motion-vectors of first fiduciary level assessment, to obtain a universe motion-vector.In the present embodiment, step S270 can comprise substep S272 and S274.At first carry out step S272,, calculate these object block and divide other one second fiduciary level according to the difference of maximum absolute difference and SADmax and minimal difference and SADmin in each block of image F3.Fig. 7 is in order to the curve synoptic diagram of second fiduciary level and maximum, least absolute value difference and relation to be described.Please refer to Fig. 7,, move with this block analysis so and will have information comparatively reliably because [SADmax-SADmin] big more this block of expression has contour of object or edge process.Therefore, can second fiduciary level of this block be improved; It might be flat region or the zone that has no the variation of moving that this block of [otherwise SADmax-SADmin] more little expression changes too small.Therefore,, can't obtain reliable information, so second fiduciary level in this district is reduced if move with this block analysis.That is to say that [SADmax-SADmin] improves greater than second fiduciary level of threshold value (turning point among Fig. 7), and [SADmax-SADmin] is less than second fiduciary level reduction of threshold value.In addition, in practical application, the curve of Fig. 7 can be earlier through the processing of smoothing, not as limit.
Then carry out step S274, according to these first fiduciary levels and these regional motion-vectors of these second fiduciary levels assessment, to obtain the universe motion-vector.In detail, can calculate a plurality of average coherences earlier according to these first fiduciary levels and these second fiduciary levels.Then according to these regional motion-vectors of these average coherence weightings, to obtain the universe motion-vector.What deserves to be mentioned is that the weight of the regional motion-vector that average coherence is high must improve, the regional motion-vector that average coherence is low is excessively then ignored.Therefore, can obtain a result of universe motion-vector comparatively accurately.
In sum, the present invention by obtain in advance receive first absolute difference that noise influence produces with, cooperate diverse location calculated between two pictures second absolute difference again and come the assessment area motion-vector, and can assess out universe motion-vector comparatively accurately.In addition, because absolute difference and lower for the cost and the demand of hardware.Therefore, the present invention can save the required cost of hardware effectively.In addition, the present invention has the elasticity of height, can change the analytical approach of block property in the algorithm (block-wise) or pixel property (pixel-wise) because of size, calculated amount, the operation time of storer.Moreover, under the colour mixture (blending) of many pictures, also can avoid the generation of image retention (ghost).Therefore, the defective that can avoid general user to be difficult to accept.In addition, make the accuracy of colour mixture improve, can obtain best noise reduction quality in the flat region, and locate on the edge of also can keep the most correct details and contour of object because move to analyze.
Though the present invention discloses as above with embodiment, so it is not in order to limiting the present invention, any under those of ordinary skill in the technical field, when can doing a little change and retouching, and do not break away from the spirit and scope of the present invention.
Claims (9)
1. the mobile analytical approach of object in the picture more than a kind is applicable to an image acquiring device, comprising:
Set noise down according to this image acquiring device at a plurality of light sources, obtain many group first absolute differences with, wherein each light source set respectively corresponding one group of first absolute difference and;
Photographic light sources in those light sources are set obtains one first picture and one second picture by this image acquiring device under setting;
Calculate between this first picture and this second picture a plurality of second absolute differences with;
Find out a plurality of object block of the texture process of this object in this first picture and this second picture;
According to those second absolute differences with, calculate those object block and divide other regional motion-vector;
According to those second absolute differences and with this photographic light sources set pairing should group first absolute difference with, calculate those object block and divide other one first fiduciary level; And
According to those those regional motion-vectors of first fiduciary level assessment, to obtain a universe motion-vector.
2. the mobile analytical approach of object in many pictures according to claim 1, wherein according to the noise of this image acquiring device under those light sources are set, obtain those organize first absolute difference and step, comprising:
Under those light sources are set, one lamp box is obtained many images respectively by this image acquiring device; And
Calculate and write down those images first absolute difference of diverse location with.
3. the mobile analytical approach of object in many pictures according to claim 2, wherein write down those images first absolute difference of diverse location and step, comprising:
Write down first absolute difference of four corner pixels of each block in those images, with first absolute difference of inserting out picture number in each block by first absolute difference of these four corner pixels and interior with.
4. the mobile analytical approach of object in many pictures according to claim 2, wherein write down those images first absolute difference of diverse location and step, comprising:
Write down pixel in each blocks of those images shared first absolute difference with.
5. the mobile analytical approach of object in many pictures according to claim 2, those light source conditions wherein comprise:
The light sensitivity of the brightness of this lamp box, color and this image acquiring device, on every side in the dim light at least one of them.
6. the mobile analytical approach of object in many pictures according to claim 1 is wherein found out the step of those object block, comprising:
Calculate each block between this first picture and this second picture maximum absolute difference and with the minimum absolute difference value with; And
According to maximum absolute difference in each block and with minimal difference and difference, judge that each block is those object block one of them or a background block.
7. the mobile analytical approach of object in many pictures according to claim 6 wherein according to those those regional motion-vectors of first fiduciary level assessment, to obtain the step of this universe motion-vector, comprising:
According to maximum absolute difference in each block and with minimal difference and difference, calculate those object block and divide other one second fiduciary level; And
According to those first fiduciary levels and those regional motion-vectors of those second fiduciary levels assessment, to obtain this universe motion-vector.
8. the mobile analytical approach of object in many pictures according to claim 7 wherein according to those first fiduciary levels and those regional motion-vectors of those second fiduciary levels assessment, to obtain the step of this universe motion-vector, comprising:
According to those first fiduciary levels and those second fiduciary levels, calculate a plurality of average coherences; And
According to those regional motion-vectors of those average coherence weightings, to obtain this universe motion-vector.
9. the mobile analytical approach of object in many pictures according to claim 1 is wherein set the pairing step that should organize first absolute difference and those first fiduciary levels of calculating according to those second absolute differences with this photographic light sources, comprising:
Calculate this organize first absolute difference and a standard deviation tolerance;
Calculate those second absolute differences and with this organize first absolute difference and a ratio; And
Compare this ratio and this standard deviation tolerance, to obtain those first fiduciary levels.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104811584A (en) * | 2014-01-29 | 2015-07-29 | 晨星半导体股份有限公司 | Image processing circuit and method |
| CN109741363A (en) * | 2019-01-11 | 2019-05-10 | 湖南国科微电子股份有限公司 | Motion determination method, apparatus and electronic equipment based on block difference |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1187988C (en) * | 1997-12-25 | 2005-02-02 | 三菱电机株式会社 | Motion compensating apparatus, moving image coding apparatus and method |
| US6985172B1 (en) * | 1995-12-01 | 2006-01-10 | Southwest Research Institute | Model-based incident detection system with motion classification |
| CN1806444A (en) * | 2004-05-10 | 2006-07-19 | 三星电子株式会社 | Adaptive-weighted motion estimation method and frame rate converting apparatus employing the method |
| CN101026761A (en) * | 2006-02-17 | 2007-08-29 | 中国科学院自动化研究所 | Motion estimation method of rapid variable-size-block matching with minimal error |
| US20090116557A1 (en) * | 2003-12-23 | 2009-05-07 | Nair Hari N | Temporal motion vector filtering |
| TW201101841A (en) * | 2009-06-30 | 2011-01-01 | Pixel Technologies Inc U | Device and method for adaptive blending motion compensation interpolation in frame rate up-conversion |
-
2011
- 2011-04-29 CN CN201110110598.6A patent/CN102760296B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6985172B1 (en) * | 1995-12-01 | 2006-01-10 | Southwest Research Institute | Model-based incident detection system with motion classification |
| CN1187988C (en) * | 1997-12-25 | 2005-02-02 | 三菱电机株式会社 | Motion compensating apparatus, moving image coding apparatus and method |
| US20090116557A1 (en) * | 2003-12-23 | 2009-05-07 | Nair Hari N | Temporal motion vector filtering |
| CN1806444A (en) * | 2004-05-10 | 2006-07-19 | 三星电子株式会社 | Adaptive-weighted motion estimation method and frame rate converting apparatus employing the method |
| CN101026761A (en) * | 2006-02-17 | 2007-08-29 | 中国科学院自动化研究所 | Motion estimation method of rapid variable-size-block matching with minimal error |
| TW201101841A (en) * | 2009-06-30 | 2011-01-01 | Pixel Technologies Inc U | Device and method for adaptive blending motion compensation interpolation in frame rate up-conversion |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104811584A (en) * | 2014-01-29 | 2015-07-29 | 晨星半导体股份有限公司 | Image processing circuit and method |
| CN104811584B (en) * | 2014-01-29 | 2018-03-27 | 晨星半导体股份有限公司 | Image-processing circuit and method |
| CN109741363A (en) * | 2019-01-11 | 2019-05-10 | 湖南国科微电子股份有限公司 | Motion determination method, apparatus and electronic equipment based on block difference |
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