Image Processing Reference
In-Depth Information
Fig. 8.1 Compression and decompression of video
is of course if the quality lose associated with video compression is a problem in a
particular application or not? To be able to answer this we first need to understand
what compression is, see Fig. 8.1 .
Overall there exist two different types of compression; lossy and lossless. In the
latter type the captured video in the camera and the decompressed video on the
computer is exactly the same. This is virtually never used and hence not described
further. In the former type of compression some information will be lost. Many
different lossy video compression algorithms exist, but they all have a similar core.
First of all they are developed with focus on the human mind in the sense that if
a human looks at the captured video and the decompressed video, the difference
should be as small as possible. That is, the information lost in video compression
is optimized with respect to the human visual perception capabilities, i.e., a human
will not notice the missing information. This may not be optimal from a computer's
point of view, in the sense that the information lost in the compression can affect the
image processing algorithms, but this is just how it is.
Humans are more sensitive to changes in the lighting than changes in the colors.
The YC b C r color representation is therefore used, see Sect. 3.3.3, and the C b and C r
components are compressed harder than the Y component. Another aspect of human
perception involved in compression of video is the fact that humans are better at
seeing gradual changes in an image as opposed to rapid changes. This fact is utilized
by transforming the image into a new representation where the level of change is
apparent. Rapid changes are then compressed harder than gradual changes.
Another main ingredient in video compression is to exploit that consecutive im-
ages usually do not change very much. To exploit this the image is first divided
into a number of blocks. Each block is then used as a template to search for a
matching block in the previous image. Template matching is used for this pur-
pose, see Sect. 5.2.1. The two blocks are now subtracted and their difference is
usually small and hence can be represented by fewer bits than the original block.
This is done for all blocks in the input. The last component in video compres-
sion is similar to what is used for image, sound and text compression, namely
entropy coding . This covers lossless methods that can compress based on the sta-
tistical nature of the data. For example, say we have the following pixel values:
2 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 67 , 12 , 12 , 12 , 12 , 10. Using entropy coding this can be written
as 2 , 3 , 255 , 6 , 67 , 12 , 255 , 3 , 10, where 255 indicates that the next value states the
number of repetitions, that is: 3 , 255 , 6
3 , 3 , 3 , 3 , 3 , 3 , 3. Originally we had 14
values and now we only have nine values, i.e., a compression factor of 14 / 9
1 . 56.
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