lecture on data compression

Lecture 10
Data Compression
Dr Rajiv Srivastava
Director

Data Compression
• Data Compression is a reduction in the number of bits
needed to represent data. Compressing data can save
storage capacity, speed file transfer, and decrease costs
for storage hardware and network bandwidth.
• Data Compression refers to the reducing the number
of bits that need to be transmitted over
communication channel.
• Data Compression reduces the number of bits sent
• Data Compression becomes particularly important
when we send data with high size such as audio &
video
• Even with very fast transmission speed of data we
need to send data in short time. We need to
Compress data for this purpose.

• Virtually all form of data contain redundancy
i.e. it is the amount of wasted "space" used to
transmit certain data.
• By making use of more efficient data
representation methods, redundancy can be
reduced.
• Even 7 bit ASCII code has some redundancy in it.
• The goal of data compression is to represent an
information source (e.g. a data file, a speech signal,
an image, or a video signal) as accurately as
possible using the fewest number of bits.

Compression ratio
• Data compression ratio, also known as
compression power, is a term used to quantify the
reduction in data-representation size produced by a
data compression algorithm.
• The data compression ratio is analogous to the
physical compression ratio used to measure
physical compression of substances.

• Data compression ratio is defined as the ratio between
the uncompressed size and compressed size.
• Thus a representation that compresses a 10 MB file to
2MB has a compression ratio of 10/2 = 5, often notated
as an exploit ratio , 5:1 (read “five” to “one”) or as an
implicit ratio 5/1
• Note that this formulation applies equally for
compression, where the uncompressed size is that of the
original and for decompression, where the
uncompressed size is that of the reproduction.

• Sometimes the space saving is given instead,
which is defined as the reduction in size relative
to the uncompressed size.
•
• Thus a representation that compresses a 10 MB
file to 2 MB would yield a space saving of
1-2/10 = 0.8 often notated as a percentage,
80 %

• For signals of indefinite size , such as streaming
audio and video the compression ratio is
defined in terms of uncompressed and
compressed data rates instead of data sizes.
and instead of space savings , one speaks of
data-rate saving which is defined as the data-
rate reduction relative to the uncompressed
data rate.

• For example uncompressed songs in CD format
have a data rate of 16 bits /channel x 2 channel
x 44.1 kHz = 1.4 Mbits,
• AAC files on an iPod are typically compressed
to 128 kbits, yielding a compression ratio of
10.9 for data-rate saving of 0.91 or 91%.

Lossy Compression
• Lossy Compression refers to data compression techniques in
which some amount of data is lost. Lossy compression
technologies attempt to eliminate redundant or unnecessary
information.
• If the decompressed information need not be an exact replica
of the original information but something very close, we can
use a lossy data compression method.
• lossy compression reduces a file by permanently eliminating
certain information, especially redundant information. When
the file is uncompressed, only a part of the original
information is still there (although the user may not notice it).
Lossy compression is generally used for video and sound,
where a certain amount of information loss will not be
detected by most users.

• The JPEG image file, commonly used for
photographs and other complex still images
on the Web, is an image that has lossy
compression. Using JPEG compression, the
creator can decide how much loss to
introduce and make a trade-off between file
size and image quality. Most video
compression technologies, such as MPEG, use
a lossy technique.

Lossy Compression Contd..
The best example is a videoconference where there is an
acceptable amount of frame loss in order to deliver the
image in real time. People may appear jerky in their
movements, but you still have a grasp for what is happening
on the other end of the conference.
In the case of graphics files, some resolution may be lost in
order to create a smaller file. The loss may be in the form of
color depth or graphic detail. For example, high-resolution
details can be lost if a picture is going to be displayed on a
low-resolution device. Loss is also acceptable in voice and
audio compression, depending on the desired quality.

These methods are called lossy compression
methods because we will lose some of the
original data in the process.
Several methods have been developed using
lossy compression techniques. Joint
photographic experts group (JPEG) is used to
compress pictures and graphics. Motion picture
experts group (MPEG) is used to compress
video.

High Compression (Low Quality)
Low Compression (High Quality)

Lossless Compression
• The lossless compression refers to data compression
techniques in which no data is lost.
• For most types of data, lossless compression techniques
can reduce the space needed by only about 50%.
• For greater compression, one must use a lossy compression
technique. Note, however, that only certain types of data -
graphics, audio, and video -- can tolerate lossy
compression.
• You must use a lossless compression technique when
compressing data and programs.
• The PKZIP compression technology is an example of lossless
compression.

• With lossless compression, every single bit of
data that was originally in the file remains after
the file is uncompressed. All of the information
is completely restored.
• This is generally the technique of choice for text
or spreadsheet files, where losing words or
financial data could pose a problem.
• The Graphics Interchange File (GIF) is an image
format used on the Web that provides lossless
compression.

• In Lossless Data Compression the compressing &
decompressing algorithm are usually the inverse of
each other. In other words after decompressing we
will get exact data as they were before
compressing. Nothing is lost. The following are
some techniques used in lossless data compression
1. Null compression
2. Run Length Encoding

3. Statistical Compression
• Shannon Fano Encoding
• Huffman Encoding
– Adaptive Compression (Adaptive Huffman)
• Dictionary Based Compression
» LZ77, LZ78 Algorithms
» Lempel Ziv Welch (LZW) encoding

1. Null compression Replaces a series of blank
spaces with a compression code, followed by a
value that represents the number of spaces.
Example : hello friend how is your life?

2. Run Length Encoding(RLE)
When data contain strings of repeated symbols (such as
bits or characters), the strings can be replaced by a
special marker, followed by the repeated symbol,
followed by the number of occurrences. For
example, in given Figure, the symbol # is the
marker. The symbol being repeated (the run
symbol) follows the marker.
After the run symbol, the number of occurrences
(length) is shown by a two-digit number.
Example : This run-length encoding method can be
used in audio (silence is a run of 0s) and video (run
of a picture element having the same brightness
and color).

3. Statistical Compression
Another method of lossless compression is statistical
compression. This method uses short codes for frequent
symbols and long codes for infrequent symbols. In this way,
the length of the total data is reduced tremendously. The
following methods are very popular lossless compression
methods :
• Shannon Fano Encoding
• Huffman Encoding
– Adaptive Compression (Adaptive Huffman)
• Dictionary Based Compression
» LZ77, LZ78 Algorithms
» Lempel Ziv Welch (LZW) encoding

3. Relative Compression Another way of
reducing the number of bits is a method called
relative compression or differential encoding.
This is extremely useful if we are sending, for
example, video, Commercial TVs send 30
frames of 0s and 1s every second. However,
usually there is little difference between
consecutive frames. So, instead of sending an
entire frame, we send only the difference
between consecutive frames. The small
differences can be encoded into small streams
of bits.

Next Slides are for M Tech Course

Image Compression
• Grey code
• Error metrics
• Intuitive methods
• Image transforms
• Jpeg

• Dictionary based Compression Techniques

• Adaptive Compression
1. Adaptive Huffman coding : Its a variation of static
compression technique. The Adaptive Huffman
coding is the best example of this technique.
2. MNP5 :
• very popular and were used by many modems in the 1980s
and 1990s.
• The MNP5 method is a two-stage process that starts with
run-length encoding,
• followed by adaptive frequency encoding.
3. MNP7
• More complex and sophisticated than MNP5, MNP7 combines run-
length encoding with
• a two-dimensional variant of adaptive Huffman coding.

lecture on data compression

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