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Image Compression: Techniques and Application
By- Nidhi Baranwal University of Allahabad
What and Why?
• Image compression is the technique of reducing the amount of data required to represent an image
• It involves: - reducing the storage required to save an image -reducing the bandwidth required to transmit it• Why? - to handle large amount of information such as multimedia - to fulfill the goal of representing an image with minimum number of bits of an acceptable image quality - for focusing on removal or reduction of several types of redundancy in data or information
Compression Algorithm
• The role of compression algorithm is to reduce the source data to a compressed form and decompress it to get the original data
• Any compression algorithm has two major components: - modeler: its purpose is to condition the image data for compression using the knowledge of data - coder: encoder codes the symbols using the model while decoder decodes the message from the compressed data
Compression Techniques
• Lossess: 1.Runlength 2.Huffma 3.Shannon Fano 4.Arithmetic 5.Dictionary based• Lossy:
1.Lossy Predictive 2.VectorQuantization 3.Block Transform 4.JPEG 5.MPEG
Redundancy and its Types
• Redundancy means repetitive data that may be present implicitly or explicitly
• Types: - coding redundancy : caused due to poor selection of coding technique - inter-pixel redundancy : called spacial/geometrical redundancy.It may be inter frame or intra frame - psychovisual redundancy : images that convey little or no information to the human observer are said to be psychovisually redundant - chromatic redundancy: it refers to the presence of unnecessary colors in an image
Arithmetic Coding
Algorithm/Pseudocode
Input symbol is lPreviouslow is the lower bound for the old intervalPrevioushigh is the upper bound for the old intervalRange is Previoushigh - Previouslow
Let Previouslow= 0, Previoushigh = 1, Range = Previoushigh – Previouslow =1WHILE (input symbol != EOF) get input symbol l Range = Previoushigh - Previouslow
New Previouslow = Previouslow + Range* intervallow of l New Previoushigh = Previouslow + Range* intervalhigh of lEND
Example
5 symbol message, a1a2a3a3a4 from 4 symbol source is coded.
Source Symbol Probability Initial Subinterval
a1 0.2 [0.0, 0.2)
a2 0.2 [0.2, 0.4)
a3 0.4 [0.4, 0.8)
a4 0.2 [0.8, 1.0)
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
Contd..
a1 a2 a3 a3 a4
1 0.2 0.08 0.072 0.0688 a4 a4 a4 a4 a4
a3 a3 a3 a3 a3
a2 a2 a2 a2 a2
a1 a1 a1 a1 a10 0 0.04 0.056 0.0624
.06752
.0688
Dictionary based Techniques_1.LZ77
Algorithm/Pseudocode
Example
Dictionary based Techniques_2.LZ78
Algorithm/Pseudocode
Example
Dictionary based Techniques_3.LZW
Algorithm/Pseudocode
Example
•
Applications of Image Compression
• Broadcast Television• Remote sensing via satellite• Military communication via radar, sonar• Tele conferencing• Computer communications• Facsimile transmission• Medical images : in computer tomography• Magnetic Resonance Imaging(MRI)• Satellite images, geological surveys, weather maps
