CN101355647B - System and method for estimating video noise - Google Patents
System and method for estimating video noise Download PDFInfo
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- CN101355647B CN101355647B CN2007101390531A CN200710139053A CN101355647B CN 101355647 B CN101355647 B CN 101355647B CN 2007101390531 A CN2007101390531 A CN 2007101390531A CN 200710139053 A CN200710139053 A CN 200710139053A CN 101355647 B CN101355647 B CN 101355647B
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Abstract
The invention relates to a system and a method for video noise estimation, used for executing the noise estimation on a video. A storing device stores the former video of the video. A noise estimating device executes the noise estimation on the video and a video subarea of the former video, and generates a noise estimating index corresponding to the video subarea. A distribution calculating device calculates the distribution of plus and minus signs of the pixel difference value in the vide subarea of the video and the former video and outputs the number of plus signs and the number of minus signs. A confidence value generating device generates a confidence value according to the number of plus signs and the number of minus signs. A recursion filter performs the recursion filter calculation on the noise estimating index according to the confidence value so as to generate the noise estimated value of the video.
Description
Technical field
The present invention relates to the video technique field, particularly a kind of estimating video noise system and method.
Background technology
The TV signal generation noise that is interfered easily in transmission course, in order to reduce interference of noise, display apparatus side generally can provide noise reduction process.Yet,, all may produce different processing side effects no matter be the noise reduction mode of spatial domain or the noise reduction mode of time-domain.Generally speaking, preferable noise reduction mode is to analyze the noise level of input video earlier, carries out the noise reduction process of varying strength again according to different noise levels.
U.S. Pat 5,844,627 bulletins disclose a space noise reduction (spatial noisereduction) methods, and the composition of its first analysis space frequency is again to may being that the frequency band of noise contribution suppresses.Yet the method for space noise reduction can't be distinguished noise contribution and the vision signal composition in the space fully, can produce the fuzzy side effect of vision signal easily.U.S. Pat 6,259,489 bulletins disclose a time noise reduction (temporal noise reduction) method, it utilizes when tableaux, suppose that noise belongs to dereferenced (uncorrelated) on time shaft and mean value is zero, then can be the video point of different time the same space position, along with time shaft averages, can reach the variation value (variance) that reduces noise, produce vision signal than low noise intensity.Yet, though the mode of temporal noise reduction can reach on tableaux and not lose spatial resolution and carry out noise reduction, but the method must cooperate the part that has moving object to take place in the detection vision signal, avoid improperly that the sampling point of different spatial is average, produce the motion blur or the generation of ghost.
Generally speaking, when noise level was big, the audience was relatively also bigger for the permission because of side effect that noise reduction causes, when noise level was little, the audience diminished relatively for the permission because of side effect that noise reduction causes.For fear of using strong noise reduction filtering method and produced unacceptable flaw in the little vision signal of noise, or in the big vision signal of noise, use too small noise reduction filtering method and cause noise reduction degree deficiency, so measure the noise level in the incoming video signal accurately, use suitable noise reduction filtering intensity by this, being that good noise reduction process is required possesses.
Be the noise level in the measurement incoming video signal, U.S. Pat 5,657,401 bulletins utilize the absolute value of temporal difference and (sum of temporal absolute difference) and one group of critical value to compare.When this absolute value and falling within the critical value up and down of this group critical value, then an accumulator is added one, and statistics whether drop on this interval number of pixels in a predefined interval identical with a desired value, if difference then adjusts this group critical value, and by this critical value to react the noise level size in the vision signal.Yet, have the zone of motion in the picture, because those regional motion ratios are inequality, make and must utilize predefined desired value of counting to be difficult for decision, the measurement of noise level also is vulnerable to the influence that motion is counted in the picture.
At the problems referred to above, U.S. Pat 6,307,888 bulletin utilizations had been carried out the information of moltion estimation (motion estimation), signal is divided into static block and dynamic block, respectively and the block (dynamically) of correspondence position (static state) or corresponding motion compensation do computing (as the absolute value of difference and), and obtain the noise estimated value of static blocks and the dynamic noise estimated value of block respectively, the noise estimated value that mixes both to the end again.Such mode moltion estimation technology accurately of must arranging in pairs or groups could be done correct estimation to the noise level of dynamic block.Yet, do not comprise the action of moltion estimation and compensation in the general television display system.
During U.S. US2006/0221252 is open then the size distribution of the absolute difference on analysis time convert a characteristic value to, with characteristic value of itself and ideal distribution conversion relatively, be this reservation or abandon with the noise level that determines this picture to obtain.Different movement degrees generally can influence the situation that difference distributes, yet, the motion that may occur varying number in the vision signal is counted and is produced the run duration differences of different sizes, the distribution that causes absolute difference may be gradually along with the difference of motion change, this can increase the degree of difficulty of the critical value setting that final decision keeps or give up.Hence one can see that, and known video noise estimating system and method still have the space of improvement.
Summary of the invention
The purpose of this invention is to provide a kind of estimating video noise system and method, getting rid of the bigger noise estimated value of difference, and the interference that the noise level of avoiding estimating is moved, to obtain a reliable noise estimated value.
Another object of the present invention provides a kind of estimating video noise system and method, can avoid the setting of critical value and the noise level of adding up in reflection a period of time.
According to an aspect of of the present present invention, the present invention proposes a kind of estimating video noise system, it is in order to carry out the noise estimation to a video, this system comprises a storage device, a noise estimating apparatus, a distribution calculation element, a trust value generation device and a recursion filter.Last video of this this video of storage device stores; This noise estimating apparatus is coupled to this storage device, estimates so that a video subregion of this video and this last video is carried out noise, and produces the noise estimation index corresponding with this video subregion; This distribution calculation element is coupled to this noise estimating apparatus, calculating the distribution of this video and this last video sign of pixel value difference in the video subregion that this noise estimating apparatus is contained, and exports a positive sign number and a negative sign number; This trust value generation device is connected to this distribution calculation element, to produce a trust value according to this positive sign number and this negative sign number; This recursion filter is connected to this noise estimating apparatus and this trust value generation device, carries out the recursive filtering computing according to this trust value so that this noise is estimated index, to produce the noise estimated value of this video.
According to another aspect of the present invention, the present invention proposes a kind of estimating video noise method, and it is in order to carry out the noise estimation to a video, and this method comprises the following step: a storing step, and it stores last video of this video; One noise estimation step is carried out the noise estimation to a video subregion of this video and this last video, and produces the noise estimation index corresponding with this video subregion; One distribution calculation procedure is calculated the distribution of this video and this last video sign of pixel value difference in this video subregion, and exports a positive sign number and a negative sign number; One trust value produces step, produces a trust value according to this positive sign number and this negative sign number; One recursive filtering step is carried out the recursive filtering computing according to this trust value so that this noise is estimated index, to produce the noise estimated value of this video.
Description of drawings
Fig. 1 is the calcspar of estimating video noise of the present invention system.
Fig. 2 is video F[n of the present invention] and last video F[n-1] the schematic diagram of corresponding region.
Fig. 3 is the distribute calcspar of calculation element of the present invention.
Fig. 4 is the calcspar of recursion filter of the present invention.
The primary clustering symbol description
First comparator, 310 first counters 320
Second comparator, 330 second counters 340
First multiplier, 410 adders 420
Embodiment
Fig. 1 is the calcspar of estimating video noise of the present invention system.This estimating video noise system is to a video F[n] carry out the noise estimation, to produce this video F[n] the noise estimated value.This system comprises a storage device 110, a noise estimating apparatus 120, a distribution calculation element 130, a trust value generation device 140 and a recursion filter 150.
This storage device 110 stores video F[n] last video F[n-1].This noise estimating apparatus 120 is coupled to this storage device 110, with to this video F[n] and this last video F[n-1] a video subregion carry out the noise estimation, and produce a noise corresponding and estimate index noise_index with this video subregion.
This distribution calculation element 130 is coupled to this noise estimating apparatus 120, to calculate this video F[n] and this last video F[n-1] distribution of the sign of pixel value difference in the video subregion that this noise estimating apparatus 120 is contained, and export a positive sign number N o (+) and a negative sign number N o (-).
This trust value generation device 140 is connected to this distribution calculation element 130, to produce a trust value K and a complementary trust value 1-K according to this positive sign number N o (+) and this negative sign number N o (-).
This recursion filter 150 is connected to this noise estimating apparatus 120 and this trust value generation device 140, so that being estimated index noise_index, this noise carries out recursive filtering (recursive filtering) computing according to this trust value K, to produce the noise estimated value noise_measurement of this video.
Fig. 2 is video F[n of the present invention] and this last video F[n-1] the schematic diagram of corresponding region.120 pairs of these videos of this noise estimating apparatus F[n] a video subregion 210 and this last video F[n-1] a video subregion 220 carry out the noises estimation, and produce a noise corresponding and estimate index noise_index with this video subregion 210.In the present embodiment, this video subregion 210 is this video F[n] partial video.At other embodiment, this video subregion 210 is this video F[n] all videos, it is only given an example for convenience of description, and and interest field that unrestricted the present invention advocated.And this noise estimation index that is produced is noise_index:
The video subregion 210,220 contained for this noise estimating apparatus 120 of i, j wherein, P
N(i is j) for this video F[n] pixel of the video subregion 210 contained at this noise estimating apparatus 120, P
N-1(i is j) for this last video F[n-1] pixel of the video subregion 220 contained at this noise estimating apparatus 120.
Fig. 3 is the distribute calcspar of calculation element 130 of the present invention, and this distribution calculation element comprises one first comparator 310, one first counter 320, one second comparator 330 and one second counter 340.
One first input end of this first comparator 310 receives a pixel value P
N(i, j), one second input receives a pixel value P
N-1(i, j), as this pixel value P
N(i is j) greater than pixel value P
N-1(i in the time of j), produces one first triggering signal trigger1.This first counter 320 is connected to this first comparator 310, counts to produce this positive sign number N o (+) according to this first triggering signal trigger1.
These second comparator, 330 one first input ends receive this pixel value P
N(i, j), one second input receives this pixel value P
N-1(i, j), as this pixel value P
N(i is j) less than pixel value P
N-1(i in the time of j), produces one second triggering signal trigger2.This second counter 340 is connected to this second comparator 330, counts to produce this negative sign number N o (-) according to this second triggering signal trigger2.
This trust value K that this trust value generation device 140 is produced is:
1-{|No(+)-No(-)|/total_no}，
Wherein No (+) is this positive sign number, and No (-) is this negative sign number, all number of pixels of the video subregion 210 that total_no is contained for this noise estimating apparatus 120.This trust value generation device more produces a complementary trust value 1-K, and this complementation trust value is:
|No(+)-No(-)|/total_no。
Fig. 4 is the calcspar of recursion filter 150 of the present invention, and this recursion filter 150 comprises one first multiplier 410, an adder 420, a buffer 430 and one second multiplier 440.
This first multiplier 410 is connected to this trust value generation device 140 and this noise estimating apparatus 120, the one first input end receives this noise estimation index noise_index, one second input receives this trust value K, so that this noise estimation index noise_index is taken advantage of this trust value K, adjust noise estimation index adj_noise_index and produce one.
This adder 420 is connected to this first multiplier 410, and the one first input end receives this adjustment noise estimation index adj_noise_index, and one second input receives a feedback adjusting noise estimated value fbk_adj_noise_index.
This buffer 430 is connected to this adder 420, with the output of temporary this adder, and produces this noise estimated value noise_measurement.
This second multiplier 440 is connected to this trust value generation device 140 and this buffer 430, the one first input end receives this complementation trust value 1-K, one second input receives this noise estimated value noise_measurement, so that this noise estimated value noise_measurement is taken advantage of this complementation trust value 1-K, and produce this feedback adjusting noise estimated value fbk_adj_noise_index.
By above stated specification as can be known, the present invention is at video F[n] and last video F[n-1] in a predefined video subregion 210,220 in (for example 32x32 pixel), take absolute value after the difference of calculating pixel, with the absolute value addition of the difference calculated in this scope, obtain this noise estimation index noise_index again.Simultaneously, analyze in the scope of video subregion 210,220, pixel value difference is that positive number No (+) and pixel value difference is negative number No (-).In general situation, noise profile is that normal distribution and mean value are zero, and difference is that positive number No (+) and difference can be quite approaching for negative number No (-).When video moved influence the time, can cause difference is that positive number No (+) and difference become big for negative number No (-) difference, after analyzing sign and distributing, produces a trust value K (confident level index) value.Response by trust value K control recursion filter 150.When trust value K is high, improve this video F[n] ratio that accounts for of the initial noise estimation index noise_index that obtains, otherwise, then reduce this video F[n] ratio that accounts for of the initial noise estimation index noise_index that obtains, the interference of being moved with the noise level of avoiding estimating, and obtain a reliable noise estimated value noise_measurement.
Known technology often needs to determine a critical value to distinguish the difference that difference that noise causes or motion cause, difference distributes and generation trust value K and the present invention analyzes, dynamically adjust the parameter of recursion filter again, can avoid the setting of critical value and the noise level of adding up in reflection a period of time.
The generation of noise of the present invention estimation index noise_index be not limited to different time absolute difference and, also can be the result behind the space filtering.The analysis of adapted space noise profile level of confidence (the possible degree of noise or signal) is dynamically adjusted recursion filter again to obtain the noise level estimation of a spatial dimension (spaTIal domain).Scope of statistics also is not limited to video continuous in the space, can be divided into plurality of blocks and add up respectively.
The foregoing description is only given an example for convenience of description, and the interest field that the present invention advocated should be as the criterion so that claim is described, but not only limits to the foregoing description.
Claims (6)
1. estimating video noise system, it is in order to carry out the noise estimation to a video, and this system comprises:
One storage device, it stores last video of this video;
One noise estimating apparatus is coupled to this storage device, estimates so that a video subregion of this video and this last video is carried out noise, and produces the noise estimation index corresponding with this video subregion, and wherein, this noise estimation index is:
The video subregion contained for this noise estimating apparatus of i, j wherein, P
N(i, the j) pixel value of the video subregion of being contained at this noise estimating apparatus for this video, P
N-1(i, j) pixel value of the video subregion of being contained at this noise estimating apparatus for this last video;
One distribution calculation element, be coupled to this noise estimating apparatus, to calculate the distribution of this video and this last video sign of pixel value difference in the video subregion that this noise estimating apparatus is contained, and export a positive sign number and a negative sign number, wherein, this distribution calculation element receives this pixel value P
N(i, j) and this pixel value P
N-1(i, j), according to this pixel value P
N(i, j) and this pixel value P
N-1(i, size j) produce this positive sign number and this negative sign number respectively, as this pixel value P
N(i is j) greater than pixel value P
N-1(i produces this positive sign number in the time of j), as this pixel value P
N(i is j) less than pixel value P
N-1(i produces this negative sign number in the time of j);
One trust value generation device is connected to this distribution calculation element, and to produce a trust value according to this positive sign number and this negative sign number, this trust value is:
1-{|No(+)-No(-)|/total_no}，
This trust value generation device also produces a complementary trust value, and this complementation trust value is:
|No(+)-No(-)|/total_no，
Wherein, No (+) is this positive sign number, and No (-) is this negative sign number, all number of pixels of the video subregion that total_no is contained for this noise estimating apparatus; And
One recursion filter is connected to this noise estimating apparatus and this trust value generation device, according to this trust value this noise estimation index is carried out the recursive filtering computing, and to produce a noise estimated value of this video, this recursion filter comprises:
One first multiplier, be connected to this trust value generation device and this noise estimating apparatus, the one first input end receives this noise estimation index, and one second input receives this trust value, so that this noise estimation index is taken advantage of this trust value, adjust noise estimation index and produce one;
One adder is connected to this first multiplier, and the one first input end receives this adjustment noise estimation index, and one second input receives a feedback adjusting noise estimated value;
One buffer is connected to this adder, with the output of temporary this adder, and produces this noise estimated value; And
One second multiplier, be connected to this trust value generation device and this buffer, the one first input end receives this complementation trust value, and one second input receives this noise estimated value, with with the estimation of this noise is on duty should the complementation trust value, and produce this feedback adjusting noise estimated value.
2. estimating video noise as claimed in claim 1 system, wherein, this distribution calculation element comprises:
One first comparator, one first input end receive this pixel value P
N(i, j), one second input receives this pixel value P
N-1(i, j), as this pixel value P
N(i is j) greater than pixel value P
N-1(i in the time of j), produces one first triggering signal; And
One first counter is connected to this first comparator, counts to produce this positive sign number according to this first triggering signal.
3. estimating video noise as claimed in claim 2 system, wherein, this distribution calculation element comprises:
One second comparator, one first input end receive this pixel value P
N(i, j), one second input receives this pixel value P
N-1(i, j), as this pixel value P
N(i is j) less than pixel value P
N-1(i in the time of j), produces one second triggering signal; And
One second counter is connected to this second comparator, counts to produce this negative sign number according to this second triggering signal.
4. estimating video noise method, it is in order to carry out the noise estimation to a video, and this method comprises the following step:
One storing step, it stores last video of this video;
One noise estimation step is carried out the noise estimation to a video subregion of this video and this last video, and produces the noise estimation index corresponding with this video subregion, and wherein, this noise estimation index is:
Wherein i, j are the video subregion that the noise estimating apparatus is contained, P
N(i, the j) pixel value of the video subregion of being contained at this noise estimating apparatus for this video, P
N-1(i, j) pixel value of the video subregion of being contained at this noise estimating apparatus for this last video;
One distribution calculation procedure is calculated the distribution of this video and this last video sign of pixel value difference in the video subregion that this noise estimating apparatus is contained, and exports a positive sign number and a negative sign number, and wherein, this distribution calculation procedure is according to this pixel value P
N(i, j) and this pixel value P
N-1(i, size j) produce this positive sign number and this negative sign number respectively, as this pixel value P
N(i is j) greater than pixel value P
N-1(i produces this positive sign number in the time of j), as this pixel value P
N(i is j) less than pixel value P
N-1(i produces this negative sign number in the time of j);
One trust value produces step, produces a trust value according to this positive sign number and this negative sign number, and this trust value is:
1-{|No(+)-No(-)|/total_no}，
This trust value produces step and also produces a complementary trust value, and this complementation trust value is:
|No(+)-No(-)|/total_no，
Wherein, No (+) is this positive sign number, and No (-) is this negative sign number, all number of pixels of the video subregion that total_no is contained for this noise estimating apparatus; And
One recursive filtering step is carried out the recursive filtering computing according to this trust value to this noise estimation index, and to produce the noise estimated value of this video, this recursive filtering step comprises:
One first multiplication step, it receives this noise estimation index and this trust value, so that this noise estimation index is taken advantage of this trust value, adjusts noise estimation index and produce one;
One addition step, it receives this adjustment noise estimation index and receives a feedback adjusting noise estimated value;
One temporary step in order to the output of temporary this addition step, and produces this noise estimated value; And
One second multiplication step, it receives this complementation trust value and receives this noise estimated value, with the estimation of this noise is on duty should the complementation trust value, and produce this feedback adjusting noise estimated value.
5. estimating video noise method as claimed in claim 4, wherein, this distribution calculation procedure comprises:
One first comparison step is as this pixel value P
N(i is j) greater than pixel value P
N-1(i in the time of j), produces one first triggering signal; And
One first counting step is counted to produce this positive sign number according to this first triggering signal.
6. estimating video noise method as claimed in claim 5, wherein, this distribution calculation procedure also comprises:
One second comparison step is as this pixel value P
N(i is j) less than pixel value P
N-1(i in the time of j), produces one second triggering signal; And
One second counting step is counted to produce this negative sign number according to this second triggering signal.
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| CN1419680A (en) * | 2001-01-26 | 2003-05-21 | 皇家菲利浦电子有限公司 | Spatio-temporal filter unit and image display apparatus comprising such a spatio-temporal filter unit |
| CN1781459A (en) * | 2004-12-01 | 2006-06-07 | Ge医疗***环球技术有限公司 | Dose evaluating method and X-ray CT apparatus |
| CN1867040A (en) * | 2005-05-19 | 2006-11-22 | 晨星半导体股份有限公司 | Noise reduction method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1419680A (en) * | 2001-01-26 | 2003-05-21 | 皇家菲利浦电子有限公司 | Spatio-temporal filter unit and image display apparatus comprising such a spatio-temporal filter unit |
| CN1781459A (en) * | 2004-12-01 | 2006-06-07 | Ge医疗***环球技术有限公司 | Dose evaluating method and X-ray CT apparatus |
| CN1867040A (en) * | 2005-05-19 | 2006-11-22 | 晨星半导体股份有限公司 | Noise reduction method |
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| JP特开2001-45336A 2001.02.16 |
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