Image Compression: It is the Art & Science of reducing the amount of data required to represent an image
The number of images compressed and decompressed daily is innumerable
2. Overview
– Introduction
– Why do we need compression?
– Benefits of Data Compression
– Fundamentals
– Types of image compression
– Fidelity criteria
– Data Compression Methods
– Coding redundancy
– Huffman coding
3. Introduction
– Image Compression: It is the Art & Science of reducing the amount
of data required to represent an image.
– From a mathematical viewpoint: transforming a 2-D pixel array
into a statistically uncorrelated data set
– It is the most useful and commercially successful technologies in
the field of Digital Image Processing
– The number of images compressed and decompressed daily is
innumerable
4. Why do we need compression?
– To understand the need for compact image representation, consider the amount of
data required to represent a 2 hour Standard Definition (SD) using 720 x 480 x 24 bit
pixel arrays.
– A video is a sequence of video frames where each frame is a full color still image.
– Because video player must display the frames sequentially at rates near 30fps, SD
video data must be accessed at
30fps x (720x480)ppf x 3bpp = 31,104,000bps
– Fps : frames per second
– ppf : pixels per frame
– bpp :bytes per pixel & bps
– Bps: bytes per second
5. – Thus a 2 hour movie consists of 31,104,000 bps x (60^2) sph x 2 hrs
≈ 2.24 x 1011 bytes. OR 224GB of data
• sph = second per hour
– The compression must be even higher for HD, where image resolution
reach 1920 x 1080 x 24 bits/image.
– The objective of image compression is to reduce irrelevant and
redundant image data in order to be able to store or transmit data in an
efficient form
– Compression can reduce the transmission time by a factor of around 2 to
10 or more
Follow
6. Benefits of Data Compression
– Make optimal use of limited storage space
– Save time and help to optimize resources
• If compression and decompression are done in I/O processor,
less time is required to move data to or from storage subsystem,
freeing I/O bus for other work
• In sending data over communication line: less time to transmit
and less storage to host
7. Fundamentals
– Data Compression: It refers to the process of reducing the amount
of data required to represent a given quantity of information.
– Data Vs Information
– Data and Information are not the same thing; data are the means
by which information is conveyed.
– Because various amount of data can be used to represent the
same amount of information, representations that contain
irrelevant or repeated information are said to contain redundant
data
8. Fundamentals
– Let b & b’ denote the number of bits in two representations of the
same information, the relative data redundancy R of the
representation with b bits is
– R = 1 – (1/C); where, C commonly called the compression ratio, is
defined as C = b / b’
– If C = 10 (or 10:1), for larger representation has 10 bits of data for
every 1 bit of data in smaller representation.
– So, R = 0.9, indicating that 90% of its data is redundant.
9. Types of image compression
• Lossless image compression : is a compression algorithm that
allows the original image to be perfectly reconstructed from the
original data.
• Lossy image compression : is a type of compression where a
certain amount of information is discarded which means that some
data are lost and hence the image cannot be decompressed with
100% originality
– Used for compressing images and video files (our eyes cannot
distinguish subtle changes, so lossy data is acceptable).
10. Data compression
Original compressed decompressed
– Lossless compression : x = x’
– Lossy compression : x != x’
– Redundant data is removed in compression and
added during decompression
encoderdecoderx y X’
13. Data Compression Methods
• Data
compression is
about storing
and sending a
smaller number
of bits.
• There’re two
major categories
for methods to
compress data:
lossless and
lossy methods
compress pictures and graphics compress video compress audio
14. Coding redundancy
– A code is a system of symbols used to
represent a body of information or sets of
events.
– Each piece of event is assigned a code
word (code symbol). The number of
symbols in each code word is its length
16. Huffman coding
– is an entropy encoding algorithm
used for lossless data compression.
– The term refers to the use of a
variable length code table for
encoding a source symbol (such as
a character in a file) where the
variable-length code table has
been derived in a particular way
based on the estimated probability
of occurrence for each possible
value of the of the source symbol
19. Quiz
– The objective of image compression is to reduce
irrelevant and redundant image data ( )
– Compression can increase the transmission time ( )
– Huffman coding used for lossless data
compression( )
– Lossy compression Used for compressing images
and video files ( )
