Databases Reference
In-Depth Information
4.8 Applications
Arithmetic coding is used in a variety of lossless and lossy compression applications. It is
a part of many international standards. In the area of multimedia there are a few principal
organizations that develop standards. The International Standards Organization (ISO) and the
International Electrotechnical Commission (IEC) are industry groups that work on multimedia
standards, while the International TelecommunicationsUnion (ITU), which is part of theUnited
Nations, works on multimedia standards on behalf of the member states of the United Nations.
Quite often these institutions work together to create international standards. In later chapters,
we will be looking at a number of these standards, and we will see how arithmetic coding is
used in image compression, audio compression, and video compression standards. For now,
let us look at the lossless compression example from the previous chapter.
In Tables
4.7
and
4.8
, we show the results of using adaptive arithmetic coding to encode the
same test images that were previously encoded using Huffman coding. We have included the
compression ratios obtained using Huffman code from the previous chapter for comparison.
Comparing these values to those obtained in the previous chapter, we can see very little change.
The reason is that because the alphabet size for the images is quite large, the value of
p
max
is
quite small, and the Huffman coder performs very close to the entropy.
As we mentioned before, a major advantage of arithmetic coding over Huffman coding
is the ability to separate the modeling and coding aspects of the compression approach. In
terms of image coding, this allows us to use a number of different models that take advantage
of local properties. For example, we could use different decorrelation strategies in regions of
the image that are quasi-constant and will, therefore, have differences that are small, and in
regions where there is a lot of activity, causing the presence of larger difference values.
T A B L E 4 . 7
Compression using adaptive arithmetic coding of pixel values.
Image
Bits/
Total Size
Compression Ratio
Compression Ratio
Name
Pixel
(bytes)
(arithmetic)
(Huffman)
Sena
6.52
53,431
1.23
1.16
Sensin
7.12
58,306
1.12
1.27
Earth
4.67
38,248
1.71
1.67
Omaha
6.84
56,061
1.17
1.14
T A B L E 4 . 8
Compression using adaptive arithmetic coding of pixel differences.
Image
Bits/
Total Size
Compression Ratio
Compression Ratio
Name
Pixel
(bytes)
(arithmetic)
(Huffman)
Sena
3.89
31,847
2.06
2.08
Sensin
4.56
37,387
1.75
1.73
Earth
3.92
32,137
2.04
2.04
Omaha
6.27
51,393
1.28
1.26
