Information Technology Reference
In-Depth Information
by shifting object data along the optic flow axis. (d) With motion compensation a moving object can still correlate
from one picture to the next so that noise reduction is possible.
Figure 3.53
(c) shows that if a high-quality standards conversion is required between two different frame rates, the
output frames can be synthesized by moving image data, not through time, but along the optic flow axis. This
locates objects where they would have been if frames had been sensed at those times, and the result is a judder-
free conversion. This process can be extended to drive image displays at a frame rate higher than the input rate so
that flicker and background strobing are reduced. This technology is available in certain high-quality consumer
television sets. This approach may also be used with 24 Hz film to eliminate judder in telecine machines.
Figure 3.53
(d) shows that noise reduction relies on averaging two or more images so that the images add but the
noise cancels. Conventional noise reducers fail in the presence of motion, but if the averaging process takes place
along the optic flow axis, noise reduction can continue to operate.
The way in which eye tracking avoides aliasing is fundamental to the perceived quality of television pictures. Many
processes need to manipulate moving images in the same way in order to avoid the obvious difficulty of processing
with respect to a fixed frame of reference. Processes of this kind are referred to as
motion compensated
and rely
on a quite separate process which has measured the motion.
Motion compensation is also important where interlaced video needs to be processed as it allows the best possible
de-interlacing performance.
3.16 Motion-estimation techniques
There are three main methods of motion estimation which are to be found in various applications: block matching,
gradient matching and phase correlation. Each have their own characteristics which are quite different.
3.16.1 Block matching
This is the simplest technique to follow. In a given picture, a block of pixels is selected and stored as a reference. If
the selected block is part of a moving object, a similar block of pixels will exist in the next picture, but not in the
same place. As
Figure 3.54
shows, block matching simply moves the reference block around over the second
picture looking for matching pixel values. When a match is found, the displacement needed to obtain it is used as a
basis for a motion vector.
Figure 3.54:
In block matching the search block has to be positioned at all possible relative motions within the
search area and a correlation measured at each one.
Whilst simple in concept, block matching requires an enormous amount of computation because every possible
motion must be tested over the assumed range. Thus if the object is assumed to have moved over a sixteen-pixel
range, then it will be necessary to test sixteen different horizontal displacements in each of sixteen vertical
positions; in excess of 65 000 positions. At each position every pixel in the block must be compared with every
pixel in the second picture. In typical video, displacements of twice the figure quoted here may be found,
particularly in sporting events, and the computation then required becomes enormous.
