Mahir Hassan

Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lossless Compression Algorithms
3- Entropy Coding Algorithms
(Content Dependent Coding)
1- Run-length Encoding (RLE)
Replaces sequence of the same consecutive bytes with number of
occurrences
Number of occurrences is indicated by a special flag (e.g., !)
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Example:
Source = abcccccccccdeffffggg (20 Bytes)
RLE Coded = abs! 9def! 4ggg (13 bytes)
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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2- Huffman Encoding
Statistical encoding
 To determine Huffman code, it is useful to construct a binary
tree
 Leaves are characters to be encoded
 Nodes carry occurrence probabilities of the characters
belonging to the sub tree
Example: How does a Huffman code look like for symbols with
statistical symbol occurrence probabilities?
P (A) = 3/20, P (B) = 11/20, P(C) = 1/20, P (D) = 3/20, P (E) =
2/20
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Huffman Encoding (Example) -2
Step 1: Sort all Symbols according to their probabilities (left to
right) from Smallest to largest these are the leaves of the Huffman
tree
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Compression ratio
If the compression and decompression processes induce no
information loss, then the compression scheme is lossless;
otherwise, it is lossy.
Compression ratio:
Compression ratio = B0 / B1
B0 – number of bits before compression
B1 – number of bits after compression
Symmetric compression
Requires same time for encoding and decoding used for live mode
applications (teleconference) performed once when enough time
is available decompression performed frequently, must be fast used
for retrieval mode applications (e.g., an interactive CD-ROM)
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Limitations
• Diverges from lower limit when probability of a particular
symbol becomes high
 always uses an integral number of bits
• Must send code book with the data
 Lowers overall efficiency
• Must determine frequency distribution (Histogram)
 Must remain stable over the data set
Lossless and lossy compression
Compressed Data
= compress (originalData)
DecompressedData
= decompress (compressed Data)
When originalData
= decompressedData, the Compression is lossless.
When originalData
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!= decompressedData, the compression is lossy
Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
……………………………………………………………………………………………………………..
Lossless and Lossy Compression
• Lossy compressors generally obtain much higher compression
ratios than do lossless compressors. Say 100 vs. 2.
• Lossless compression is essential in applications such as text file
compression.
• Lossy compression is acceptable in many imaging applications.
in video transmission, a slight loss in the transmitted video is not
noticed by the human eye.
1- Approaches of Differential Coding of Images:
Distributions for Original versus Derivative Images.
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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4- Source Coding Algorithms
1- Shift Coding (S-code)
• It is a variable encoding,
• it shows a good efficiency for coding the inputs set having
monastically decreasing probabilities
Procedure: The S-coding process is simply, based on:
1. partitioning the range of input set values into equal region,
each of them has a width (2n-1)
2. Then n-bits code words are used to index the first inputs
group (having the high frequency of occurrence)
3. And 2n-bits to encode the inputs located in the 2nd region and
so on.
 The mean length b of the code words for S-shift code could be
given by the following relation,
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Where; p (i) is the frequency of occurrence of the input i, & div is
the integer division operator.
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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LZW compression
LZW compression is the compression of a file into a smaller file
using a table-based lookup algorithm invented by Abraham
Lempel, Jacob Ziv, and Terry Welch.
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Example
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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Lecture 7: Lossless Compression Algorithms
Mahir Hassan
Multimedia 4th stage
kerabala University- Science College-Computer Science
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