Pulse or digital communications – Bandwidth reduction or expansion – Television or motion video signal
Reexamination Certificate
1998-06-08
2001-04-17
Britton, Howard (Department: 2713)
Pulse or digital communications
Bandwidth reduction or expansion
Television or motion video signal
C348S699000, C375S240190
Reexamination Certificate
active
06219383
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a motion estimation method and apparatus; and, more particularly, to a method and apparatus for selectively detecting motion vectors of a wavelet transformed video signal.
DESCRIPTION OF THE PRIOR ART
The discrete wavelet transform (DWT) technique has recently attracted a considerable amount of attention in the art of image processing due to its flexibility in representing nonstationary image signals and its ability to adapt to human visual characteristics. A wavelet representation provides a multi-resolution/multi-frequency expression of a signal localized in both time and frequency.
Such versatilities are desirable in image and video coding applications. Since natural image and video signals are nonstationary in nature and a wavelet transform decomposes a nonstationary signal into a set of multi-scaled wavelets where each component becomes relatively more stationary, such transform method makes it easier to encode such nonstationary signals. Also, coding schemes and parameters can be adapted to the statistical properties of each wavelet, and hence coding each stationary component is more efficient than coding the whole nonstationary signal. In addition, the wavelet representation matches well with the spatially-tuned, frequency modulated properties experienced in human vision as reported by the research in psychophysics and physiology.
In a typical wavelet decomposition technique (see, e.g., U.S. Pat. No. 5,477,272 issued to Ya-Qin Zhang on Dec. 19, 1995), a video frame is decomposed into a plurality of layers with different resolutions, each subimage being in a same layer corresponding to each of different frequency bands.
FIG. 1
illustrates a conventional wavelet decomposition process wherein a current frame S
1
is applied to a first wavelet decomposition block
110
and decomposed into subimages of layer
1
, i.e., S
2
, W
2
1
, W
2
2
and W
2
3
. Then, the subimage S
2
is applied to a second wavelet decomposition block
120
and decomposed into subimages of layer
2
, i.e., S
4
, W
4
1
, W
4
2
, W
4
3
. Thereafter, the subimage S
4
is applied to a third wavelet decomposition block
130
and decomposed into subimages of layer
3
, i.e., S
8
, W
8
1
, W
8
2
, W
8
3
.
These subimages can be organized into a pyramid structure to provide a pictorial representation as shown in FIG.
2
. The wavelet transformed current frame S
1
has a resolution depth of 3 and consists of 10 subimages, with 3 subimages at each layer and one lowpass subimage. The subimage S
4
is formed by combining the subimage S
8
with the subimages W
8
1
to W
8
3
in the layer
3
; the subimage S
2
is formed by combining the subimage S
4
with the subimages W
4
1
to W
4
3
in the layer
2
; and the current frame S
1
is formed by combining the subimage S
2
with the subimages W
2
1
to W
2
3
in the layer
1
.
Referring to
FIG. 3A
, there is depicted a conventional multi-resolution motion estimation (MRME) scheme. First, a current frame S
1
is decomposed to thereby generate subimages S
8
, W
8
1
, W
8
2
, W
8
3
, W
4
1
, W
4
2
, W
4
3
, W
2
1
, W
2
2
and W
2
3
, and a previous frame PS
1
is also decomposed to yield subimages PS
8
, PW
8
1
, PW
8
2
, PW
8
3
, PW
4
1
, PW
4
2
, PW
4
3
, PW
2
1
, PW
2
2
and PW
2
3
, wherein the previous frame PS
1
and its subimages PS
8
, PW
8
1
, PW
8
2
, PW
8
3
, PW
4
1
, PW
4
2
, PW
4
3
, PW
2
1
, and PW
2
3
are not shown, for the sake of simplicity.
Then, each of the subimages of the S
1
is divided into a plurality of search blocks, wherein the sizes of search blocks within subimages of a same layer are identical. If the size of a search block within a subimage of a highest layer M is p×p, the size of a search block within a subimage of a layer m is p·2
M−m
×p·2
M−m
, M, p and m being positive integers, respectively, wherein typical values of M and p are 3 and 2, respectively.
Thereafter, each search block in each subimage is motion estimated with reference to a corresponding subimage of the PS
1
. For example, assuming that a search block
302
in the S
8
of
FIG. 3A
is motion estimated by using a conventional block matching algorithm, a search region corresponding to the search block
302
in the S
8
is formed in the PS
8
and a plurality of candidate blocks are generated in the search region. Then, error values between the search block
302
in the S
8
and the candidate blocks are calculated, wherein an error value is, e.g., a mean absolute error between a pixel value of the search block
302
in the S
8
and a corresponding pixel value of a candidate block.
Among the calculated error values, a minimum error value is selected and a difference between the search block
302
in the S
8
and an optimum candidate block
304
which yields the minimum error value is detected as a motion vector MVS
8
of the search block
302
in the S
8
.
In motion estimating a search block
306
in the W
8
1
, a search region corresponding to the search block
306
in the W
8
1
is formed in the PW
8
1
based on the MVS
8
. Specifically, a location which is same as that of the search block
306
in the W
8
1
is detected and the detected location is displaced by as much as the MVS
8
. The search region is formed around the displaced location and an optimum candidate block
308
is detected in the search region by motion estimating the search block
306
in the W
8
1
in a same manner as that of the search block
302
in the S
8
. Search blocks in the W
8
2
and the W
8
3
are also motion estimated in a similar manner as that of the search block
306
in the W
8
1
.
In motion estimating a search block
310
in the W
4
1
, a search region corresponding to the search block
310
in the W
4
1
is formed in the PW
4
1
based on a scaled motion vector 2MVS
8
. That is, a location which is same as that of the search block
310
in the W
4
1
is detected and the detected location is displaced by as much as the 2MVS
8
. The search region is formed around the displaced location and an optimum candidate block
312
is detected in the search region by motion estimating the search block
310
in the W
4
1
in a same manner as that of the search block
306
in the W
8
1
. Search blocks in the W
4
2
and the W
4
3
are also motion estimated in a similar manner as that of the search block
310
in the W
4
1
.
In motion estimating a search block
314
in the W
2
1
, a search region corresponding to the search block
314
in the W
2
1
is formed in the PW
2
1
based on a scaled motion vector 4MVS
8
. Specifically, a location which is same as that of the search block
314
in the W
2
1
is detected and the detected location is displaced by as much as the 4MVS
8
. The search region is formed around the displaced location and an optimum candidate block
316
is detected in the search region by motion estimating the search block
314
in the W
2
1
in a same manner as that of the search block
310
in the W
4
1
. Search blocks in the W
2
2
and the W
2
3
are also motion estimated in a similar manner as that of the search block
314
in the W
2
1
.
Meanwhile, if an optimum candidate block
316
corresponding to the search block
314
in the W
2
1
is detected as is shown in
FIG. 3A
, a displacement from the search block
314
in the W
2
1
to an optimum candidate block
318
is MVW
2
1
. Then, a difference between the 4MVS
8
and the MVW
2
1
is calculated and provided as a motion vector difference MVDW
2
1
of the search block
314
of the W
2
1
.
In such a MRME scheme, motion vectors for all subimages are detected and transferred, thereby complicating the computational process. Thus, a motion estimation scheme only for subimages of low frequency bands is developed as shown in FIG.
3
B. The new scheme is based on the concept that the subimage S
8
contains a major portion of the total energy present in the current frame S
1
although its size is only {fraction (1/64)} of that of S
1
and human vision is more perceptible to errors in lower frequency bands than those occurring in higher bands.
According to the ne
Anderson Kill & Olick PC
Britton Howard
Daewoo Electronics Co. Ltd.
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