Pulse or digital communications – Bandwidth reduction or expansion – Television or motion video signal
Reexamination Certificate
1999-03-11
2001-10-23
Le, Vu (Department: 2713)
Pulse or digital communications
Bandwidth reduction or expansion
Television or motion video signal
C375S240160, C375S240270, C348S607000, C348S620000, C382S275000
Reexamination Certificate
active
06307888
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to a method for estimating the noise level in a video sequence.
BACKGROUND OF THE INVENTION
EP-A-0735747 discloses a method of noise measurement in conjunction with a block-matching motion estimation algorithm, the principle of which is to derive a noise level from the minimum of accumulated absolute pixel difference values, leading to a displaced field or frame differences (DFD) value, the accumulation taking place over predetermined pixel blocks.
A paper by Q. Zhang and R. Ward, entitled “Automatic assessment of signal-to-thermal noise ratio of television images”, Vol. 41, No. 1, IEEE Transactions on Consumer Electronics (February 1995), discloses a method for measuring the noise level from the TV pictures as such. This method is based on the application of a two-dimensional highpass filter on the images in order to remove the majority of the (non-noisy) image content. Thereafter the smoothest regions of the picture, i.e. those having minimum energy with respect to brightness variations, are selected and the noise power is estimated from their remaining average power.
That paper says that in digital image processing the customary procedure to estimate the level of thermal noise in the image is to analyse “smooth regions, i.e. regions containing constant luminance (grey levels)”.
SUMMARY OF THE INVENTION
The method described in EP-A-0735747 lacks robustness because it is based solely on the minimum of the distribution of estimates for each block of the picture, and therefore depends on the shape and deviation of this distribution. The method described by Zhang et al. suffers from the same shortcoming, as the computation of the noise level is eventually based on the low-end tail of the distribution of noise energies over subimages of a picture. Thus, for pictures with few areas of high spatial frequencies, there is a risk of under-estimating the noise level.
The proposed method alleviates this problem by biasing the estimation largely on averages, rather than minima, of noise energy measurements. The measurement performed over static areas, in particular, is independent of the spatial frequency contents of the picture.
It is one object of the invention to disclose a method for more reliable noise estimation.
In the invention, additional motion information provided by e.g. a motion-compensated interpolation is used in order to compute a more robust and accurate estimation of the noise level in a video sequence. Ideally, if motion estimation is error-free, the remaining differences between the grey levels of input pixels from the two source picture blocks put in correspondence by an estimated motion vector must be the result of noise.
The additional motion information may also be derived from motion vector information of an MPEG bitstream.
Modifying the field or frame rate of a video sequence by interpolating pictures located temporally between the source pictures is required for picture rate upconversion or standards conversion. The best conversion quality is achieved if the motion of objects in the source sequence is estimated and used to interpolate each pixel along the direction of its associated motion vector. Another application of this technique is noise reduction by means of a temporal filter, with the goal of improving either the picture quality or the coding efficiency, e.g. of MPEG2 encoders. Motion estimation can be performed by finding the vectors that provide the best match between pixels or blocks of pixels mapped from a previous or current picture to a next picture. The mathematical criterion used for the selection of a motion vector is usually the minimisation of the sum of the absolute values of the displaced field difference or displaced frame difference of a pixel block, as described in FIG.
1
. An intermediate field or frame IF to be interpolated is located temporally between a previous field or frame PF and a next field or frame NF.
The temporal distance between PF and NF is T, between PF and IF &agr;*T, and between IF and NF (1—&agr;)*T. The zero vector 0=(0,0) passes through points I
p
(x,y) in PF, I(x,y) in IF, and I
n
(x,y) in NF. A current candidate motion vector v=(v
x
, v
y
) passes through points I
p
(x−&agr;*v
x
, y−&agr;*v
y
) in PF, I(x,y) in IF, and I
n
(x+(1−&agr;)*v
x
, y+(1−&agr;)*v
y
) in NF.
The frame difference (for vector 0) is FD=I
n
(x,y)−I
p
(x,y).
The displaced frame difference for vector v is DFD(v)=I
n
(x+(1−&agr;)*v
x
, y+(1−&agr;)*v
y
)−I
p
(x−&agr;*v
x
, y−&agr;*v
y).
The interpolation of the output pictures is carried out along the direction of the estimated motion vectors. The quality of the interpolation is limited by the accuracy of the motion vectors, except in static parts of the pictures where the motion is known to be exactly zero. It is therefore advantageous to detect static areas in the source images and to implement a specific interpolation mode for moving pixels, thereby optimising the interpolation output resolution. A specific solution for detecting such static areas is disclosed in another application of the applicant, internal reference PF980013, filed at the same date.
The inventive noise level estimation, however, is based on source pictures only. Therefore, if
FIG. 1
is applied to the noise level estimation, intermediate field or frame IF is that current source picture for which the noise level is to be estimated.
According to the invention, the results of two different noise level computing methods can be combined in order to improve the reliability of the noise level estimation. One computation relies on the analysis of DFDs, the other is based on the values of the field or frame differences (FD) over static areas.
The availability of an accurate estimate of the noise level potentially improves the performance of many image processing algorithms in the presence of noise because it allows to adapt the algorithm parameters and thresholds to that noise level. Applications include: motion estimation, noise reduction, detection of static areas, film mode and film phase detection, detection of cuts, and many others.
In principle, the inventive method is suited for estimating the noise level for a current source field or frame of a video sequence, based on the differences between pixel values of blocks in a previous field or frame and corresponding pixel values of corresponding blocks in a future field or frame, wherein either said previous or said future field or frame can be said current field or frame itself, and wherein at least one block of each corresponding couple of blocks is a motion-compensated pixel block or is mapped to the other block by an associated motion vector estimate. In addition, static picture areas can be determined and the differences between pixel values of blocks in a static picture area of a previous field or frame and corresponding pixel values of corresponding blocks in a future field or frame can be used to estimate a further noise level estimate which is then combined with said noise level estimate in order to form a final noise level estimate, wherein said previous and/or said future field or frame used for the evaluation of said differences between pixel values of a block in a static picture area can be different from said previous and/or said future field or frame used for the evaluation of the differences concerning said motion-compensated pixel blocks or said mapped blocks.
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patent: 5442407 (1995-08-01), Iu
patent: 5500685 (1996-03-01), Kokaram
patent: 5548659 (1996-08-01), Okamoto
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patent: 5610729 (1997-03-01), Nakajima
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patent: 562407A1 (1993-09-01), None
patent: 735747A1 (1996-10-01), None
patent: 735 747 A1 (1996-10-01), None
Boyce, J.M., “Noise reducing of image sequences using adaptive motion compensated frame averaging”, IEEE ICASSP-92, vol. 3,
Eriksen Guy H.
Kurdyla Ronald H.
Le Vu
Thomson Licensing S.A
Tripoli Joseph S.
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