Image Processing Reference
In-Depth Information
Fig. 8.2 Different blocking effects illustrated in a zoomed picture in order to increase visibility
How many bits needed to represent a compressed image in the video sequence
depends on the content of the video. Sometimes we require more bits than are avail-
able in the bandwidth. This means that the compression method will have to delete
some additional information, for example by a harder compression of the colors or
by simply ignoring details of one or more blocks. The consequence of the latter can
be that one or more blocks in the decompressed image contain less detail and hence
appear blurry or do not contain any detail at all, i.e., will be black. Such phenomena
are known as blocking artifacts and a few are illustrated in Fig. 8.2 .
The point of all the above is that you as a designer need to look into these issues
before doing video processing. It might be better to spend some extra money on a
good camera (and transmission) producing good data compared to spending lots of
time (in vain?) trying to compensate for poor data with clever algorithms. This is
especially true if developing a system based on color processing. A compromise can
be to use a cheap camera with poor quality video and then try to detect if blocking
has occurred and if so delete such images from the video sequence.
No matter the type of camera and compression algorithm, the captured video se-
quence may contain motion blur due to motion in the scene, see Sect. 2.2. A similar
problem is that the depth-of-field may not cover the entire FOV and hence moving
objects may be blurred due to an incorrect focus. Processing video containing blur
will possibly affect the results and should therefore be avoided is possible. One ap-
proach for doing so is to analyze each image and try to measure the level of blur.
If too blurry the image should be deleted. The consequence of a blurred image is
that the magnitudes of the edges in the image are small. So the blurriness can be
measured by analyzing the edges in the image, see Sect. 9.3.1. Another approach is
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