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Image compression: Techniques and Application
The document discusses image compression, highlighting its necessity for reducing data storage and transmission bandwidth while maintaining acceptable image quality. It details various compression techniques, including lossless and lossy methods, and outlines the roles of compression algorithms, particularly focusing on redundancy types. Additionally, it explores application areas such as military communication, medical imaging, and remote sensing.
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Image compression: Techniques and Application
- 1. Image Compression: Techniquesand Application By- Nidhi Baranwal University of Allahabad
- 2. 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
- 3. Compression Algorithm • Therole 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
- 4. Compression Techniques • Lossess: 1.Runlength 2.Huffma 3.ShannonFano 4.Arithmetic 5.Dictionary based • Lossy: 1.Lossy Predictive 2.VectorQuantization 3.Block Transform 4.JPEG 5.MPEG
- 5. Redundancy and itsTypes • 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
- 6. Arithmetic Coding Algorithm/Pseudocode Input symbolis l Previouslow is the lower bound for the old interval Previoushigh is the upper bound for the old interval Range is Previoushigh - Previouslow Let Previouslow= 0, Previoushigh = 1, Range = Previoushigh – Previouslow =1 WHILE (input symbol != EOF) get input symbol l Range = Previoushigh - Previouslow New Previouslow = Previouslow + Range* intervallow of l New Previoushigh = Previouslow + Range* intervalhigh of l END
- 7. 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)
- 8. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624
- 9. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624
- 10. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624
- 11. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624
- 12. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624
- 13. Contd.. a1 a2 a3a3 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 a1 0 0 0.04 0.056 0.0624 .06752 .0688
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- 20. Applications of ImageCompression • 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