Method and system of noise-adaptive motion detection in an interlaced video sequence

A motion decision value provides a dependable estimate whether motion occurs in a given region of a video image in an interlaced video sequence. The motion detection is particularly applicable in the conversion from interlaced video to progressive video. An input first is fed to an absolute value former which computes a frame difference signal from a difference between the first field and the second field in one frame. A point-wise motion detection signal is computed based on the frame difference signal and noise in the video sequence, wherein the point-wise motion detection signal is noise-adaptive. The point-wise motion detection signal is then followed by a region-wise motion detection that combines the point-wise motion detection signal with an adjacent point-wise motion detection signal delayed by one field. The motion decision value is then computed from the region-wise motion detection signal and output for further processing in the video signal processing system, such as for choosing whether the spatially interpolated video signal value or the temporally interpolated video signal value should be used for the output.

FIELD OF THE INVENTION

The present invention relates generally to motion detection in video sequences, and in particular to noise-adaptive motion detection in interlaced video sequences.

BACKGROUND OF THE INVENTION

In the development of current Digital TV (DTV) systems, it is essential to employ video format conversion units because of the variety of the video formats adopted in many different DTV standards worldwide. For example, the ATSC DTV standard system of North America adopted 1080×1920 interlaced video, 720×1280 progressive video, 720×480 interlaced and progressive video, etc. as its standard video formats for digital TV broadcasting.

Video format conversion operation is to convert an incoming video format to a specified output video format to properly present the video signal on a display device (e.g., monitor, FLCD, Plasma display) which has a fixed resolution. A proper video format conversion system is important as it can directly affect the visual quality of the video of a DTV Receiver. Fundamentally, video format conversion operation requires advanced algorithms for multi-rate system design, poly-phase filter design, and interlaced to progressive scanning rate conversion or simply deinterlacing, where deinterlacing represents an operation that doubles the vertical scanning rate of the interlaced video signal.

Historically, deinterlacing algorithms were developed to enhance the video quality of NTSC TV receivers by reducing the intrinsic annoying artifacts of the interlaced video signal such as a serrate line observed when there is motion between fields, line flickering, raster line visibility, and field flickering. This also applies to the DTV Receiver.

Elaborate deinterlacing algorithms utilizing motion detection or motion compensation allow doubling the vertical scanning rate of the interlaced video signal especially for stationary (motionless) objects in the video signal. Motion detection based deinterlacing operation can be used for analog and digital TV receivers.

A number of deinterlacing algorithms exist. Such deinterlacing algorithms can be categorized into two classes: 2-D (spatial) deinterlacing algorithms and 3-D (spatio-termporal) deinterlacing algorithms depending on the use of motion information embedded in consecutive interlaced video sequence. It is well-known that a 3-D deinterlacing algorithm based on motion detection provides more pleasing performance than a 2-D deinterlacing algorithm. The key point of a 3-D deinterlacing algorithm is precisely detecting motion in the interlaced video signals.

Existing methods disclose estimating a motion decision factor based on the frame difference signal and the sample correlation in vertical direction. These methods provide a way of reducing the visual artifacts that can arise from false motion detection by utilizing the sample correlation in vertical direction of the sampling point where the value is to be interpolated. However, such methods may not provide a true motion detection method when there are high frequency components in vertical direction. As a consequence, such methods do not increase the vertical resolution even when there is no real motion between fields. In other methods, the filtering result of the frame difference is compared with a constant value to determine the motion detection signal. However, in such methods, performance deteriorates when noise in the video sequence increases.

BRIEF SUMMARY OF THE INVENTION

The present invention addresses the above shortcomings. In one embodiment the present invention provides a method of computing a motion decision value for a video processing system, comprising the steps of: inputting a video signal with an interlaced video sequence of fields; computing a frame difference signal from a difference between a previous field and a next field in the video sequence; computing a point-wise motion detection signal based on the frame difference signal and noise in the video sequence, wherein the point-wise motion detection signal is noise-adaptive; and computing the motion decision value as a function of the point-wise motion detection signal.

In another aspect, the present invention provides a method of processing interlaced video signals, comprising the steps of: spatially interpolating a value of the video: signal at a given location from a video signal of at least one adjacent location in a given video field; temporally interpolating the value of the video signal at the given location from a video signal at the same location in temporally adjacent video fields; forming a motion decision value for the same location as discussed above; and mixing an output signal for the video signal at the given location from the spatially interpolated signal and the temporally interpolated signal and weighting the output signal in accordance with the motion decision value.

The present invention further provides systems to implement the above methods. Other embodiments, features and advantages of the present invention will be apparent from the following specification taken in conjunction with the following drawings.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment the present invention provides a robust method of estimating a noise-adaptive motion decision parameter in an interlaced video sequence. Further, the present invention provides a deinterlacing system utilizing the motion decision parameter estimation method.

In order to systematically describe the deinterlacing problem and the methods of the present invention, in the following description let xndenote the incoming interlaced video field at time instant t=n, and xn(v,h) denote the associated value of the video signal at the geometrical location (v,h) where v represents vertical location and h represents horizontal location.

Referring to the example inFIG. 1, an image at t=m represents a top field10and an image at t=m+1 represents a bottom field20of an interlaced video sequence. By the definition of a interlaced video signal, the signal values of xnare available only for the even lines, i.e., v=0, 2, 4, . . . , if xnis the top field10. Similarly, the signal values of Xnare available only for the odd lines of v (i.e., V=1, 3, 5, . . . ) if xnis the bottom field20. Conversely, the signal values of xnare not available for odd lines if xnis a top field10signal and the signal values of xnare not available for even lines if xnis a bottom field20.

Top and bottom fields10,20are typically available in turn in time. It is assumed that the input interlaced video is corrupted by independent, identically distributed additive and stationary zero-mean Gaussian noise with variance σ02, that is, each available signal value xn(v,h) can be denoted as xn(v,h)={circumflex over (x)}n(v,h)+δn(v,h), where {circumflex over (x)}n(v,h) is the true pixel value without noise corruption and δn(v,h) is the Gaussian distributed noise component. It is further assumed that the noise variance σ02is already known, manually set or pre-detected by a separated noise estimation unit. σ0represents noise standard deviation.

Based upon the above description of the interlaced video signal, a deinterlacing problem can be stated as a process to reconstruct or interpolate the unavailable signal values of in each field. That is, the deinterlacing problem is to reconstruct the signal values of xnat odd lines (v=1, 3, 5, . . . ) for top field xnand to reconstruct the signal values of xnat even lines (v=0, 2, 4, . . . ) for bottom field xn.

For clarity of description herein, the deinterlacing problem is simplified as a process which reconstructs or interpolates the unavailable signal value of xnat the ith line where the signal values of the lines at i±1, i±3, i±5, . . . are available. More simply, deinterlacing is to interpolate the value of xn(i,h), which is not originally available. Because xn−1and xn+1have different sampling phase from xn, the signal values of xn−1(i,h) and xn+1(i,h) are available, whereby motion detection can be incorporated with the deinterlacing problem. This relation is depicted by example inFIG. 2, where dotted lines (or, white circles) represent “no data available”0and the solid lines (or, black circles) represent “available data”.

Referring toFIG. 3, a method of estimating a motion decision parameter mn(i,h) according to the present invention is now described. Fundamentally, mn(i,h) is estimated from the incoming interlaced video sequence and associated with the point-to-point degree of motion in the interlaced video sequence. The importance of estimating mn(i,h) can be easily understood fromFIGS. 2 and 3. Assume precise motion detection information is available when interpolating xn(i,h), and no motion is detected at the spatial location (i,h), then the best interpolation for xn(i,h) is to use the value of xn−1(i,h). This is because no motion is introduced between t=n−1 and t=n+1 at the spatial location (i,h), implying that the value of xn(i,h) would be close to the value of xn−1(i,h). The motion decision parameter allows utilizing motion information for deinterlacing, and properly mixing the temporal information described below.

First, the frame difference signal Dnis computed as the difference between the fields in one frame interval as Dn=|xn+1−xn−1| which is associated with a scene change that occurred between the fields xn+1and xn−1. The frame difference signal is then low pass filtered as dn=LPF(Dn) where LPF(108) represents a low pass filtering process over the input video signal.

The M×N kernel (WM×N) of the low pass filter, LPF(108), can be expressed as

Based on the analysis in the commonly assigned patent application titled “Methods to estimate noise variance from a video sequence,” filed Nov. 17, 2004, Ser. No. 10/991,265(incorporated herein by reference), it can be seen that any value Dnin the non-motion region is a random variable with probability density function (p.d.f):

The filtered result dnin the non-motion region is also a random variable with a p.d.f. pd(z), satisfying:

In one example, if the noise standard deviation is σ0=3.0, and the kernel is

the p.d.f. pd(z) is as shown inFIG. 4.

It should be mentioned that LPF(•) can be an all-pass filter depending on the choice of the kernel WM×N. As such, if the kernel is set as M=N=1 and w11=1, the LPF(•) becomes the all-pass filter and, thus, dn=Dn.

Next, a point-wise motion detection signal is computed as:
i fn(i,h)=TK(dn(i,h))tm (1)

where TK(•) denotes a threshold function. An example implementation of TK(108) can be represented as:

in which K is a constant value. The above function TK(•) outputs hard-switching motion detection signals, illustrated by the example curve inFIG. 5A.

The threshold Kσ0is automatically adjusted according to the noise standard deviation of the video sequence. Robust performance can thus be obtained against noise. The value K can be determined by the error probability of detecting a non-motion pixel as a motion pixel:

Other noise-adaptive methods can also be used for computing soft-switching motion detection signals. From the stochastic characteristic of dn(v,h), a monotonically increasing curve can be used for implementing the function TK(•) as illustrated by examples inFIGS. 5B-F.

Then, the point-wise motion detection signal is filtered in spatial and temporal domains to obtain the motion decision parameter mn(i,h):
mn(i,h)=F(fn(i,h)).

An example implementation of the filter F(•) is shown inFIG. 6and described further below. In this example, hard-switching point-wise motion detection signal is used. First, the region-wise motion detection signal is computed by a non-linear method as
φn(i,h)=fn(i,h)∥fn−1(i−1,h)∥fn−1(i+1,h),

where fn−1(•) denotes the one field delayed motion detection signal in relation (1), where the notation ∥ denotes the logical OR operation. Other methods can be used if soft-switching point-wise motion detection signal is used, such as
φn(i,h) =max(fn(i,h), fn−1(i−1,h), fn−1(i+1,h)).

The region-wise motion detection signal is then low-pass filtered to form the motion decision parameter mn(i,h). The A×B kernel, ΘA×B, of the low pass-filter can be expressed as

∑p=1A⁢∑q=1B⁢θp,q=1⁢).
For example, the kernel θA×Bcan be

FIG. 6shows a function block diagram of a motion decision calculator100that computes the motion decision parameter mn(i,h) using the function F(•) as described above. The motion decision calculator includes field memories102that provide sequencing of the interlaced values xn+1, xnand xn−1. A summing junction104is used along with an absolute value calculator106to compute Dnas the difference between the fields in one frame interval as Dn=|xn+1−xn−1| which is associated with a scene change that occurred between the fields xn+1and xn−1. Then a spatial LPF filter108is used to low-pass filter Dnto obtain dn. Threshold value Kσ0is applied to dnin the noise-adaptive threshold function110implementing TKabove. The function F(•) is implanted in a filter112, in which memories114allow sequencing of the values fn−1(•), and an OR junction116provides the region-wise motion detection signal φn(i,h) =fn(i,h) ∥fn−1(i−1,h) ∥fn−1(i+1,h). The region-wise motion detection signal is low-pass filtered in the LPF filter118to generate the motion decision parameter mn(i,h).

The computed motion decision parameter mn(i,h) can then used to mix a spatially interpolated signal and a temporally interpolated signal.FIG. 7shows a block diagram of an embodiment of an interpolator200for interpolating the value of xn(i,h) for the interlaced video sequence. The interpolator200comprises filed memories202, a spatial interpolator204, a temporal interpolator206, a motion decision processor208, and a mixer210. Field memories202sequence the interlaced values xn+1, xn, and xn−1.

The spatial interpolator204spatially interpolates the value of xn(i,h) by using a predetermined algorithm. The temporal interpolator206temporally interpolates the value of xn(i,h) by using a predetermined algorithm. The motion decision processor208computes the motion decision value, mn(i,h), as described above (e.g.FIG. 6), representing the degree of the motion at the interpolation location (i,h).

Conceptually, the value of the motion decision parameter is bounded as 0≦mn(i,h) ≦1, wherein mn(i,h) =0implies “no motion” and mn(i,h) =1 implies “motion”. The mixer210mixes the output signal of the spatial interpolator204and the output signal of the temporal interpolator206in accordance with the motion decision value mn(i,h). Denoting xns(i,h) and xnt(i,h) as the output signals of the spatial interpolator204and the temporal interpolator206, respectively, then the output signal of the mixer210(i.e., the interpolated signal) is represented as
xn(i,h) =(1−mn(i,h)) ·xnt(i,h) +mn(i,h) ·xns(i,h).tm (3)

In the example ofFIG. 7, the spatial and temporal interpolating algorithms can be selected freely because the present invention is directed to estimating the motion decision value mn(i,h) based on the noise standard deviation, and mixing of a spatially interpolated signal and a temporally interpolated signal in accordance with the estimated motion decision value.

Examples of the spatially interpolated signal xns(v,h) are
xns(i,h) =(xn(i−1,h) +xn(i+1,h))/2,

which corresponds to a line average, and
xns(i,h) =xn(i−1,h)

which corresponds to a method known as line doubling.

The present invention has been described in considerable detail with reference to certain preferred versions thereof; however, other versions are possible. Therefore, the spirit and scope of the appended claims should not be limited to the description of the preferred versions contained herein.