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Basics of Image Compression

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The basics of why Image compression? is presented

The basics of why Image compression? is presented


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  • 1. K. Punnam Chandar Asst. Professor Dept. of Electronics and Comm. Eng. University College of Engineering Kakatiya University 84th Orientation Course Academic Staff College University of Hyderabad K. Punnam Chandar
  • 2. IMAGE 10 12 35 54 34 23 201 2 10 12 4 5 6 7 8 9 9 9 0 0 87 6 8 0 7 68 8 9 09 6 5 87 88 7 9 9 8 8 8 8 8 8 8 8 8 89 9 90 0 0 00 5 54 4 55 6 76 7 4 3 65 7 7 89 7 6 6 8 99 7 6 6 6 78 9 166 6 77 6 4 44 4 5 55 43 2 54 87 5 45 6 54 67 45 7 3 7 98 54 An image may be defined as a two-dimensional function f(x, y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. A small region in the digital image is shown in matrix. K. Punnam Chandar
  • 3. Images are every where Medical Images Photography First Picture of Moon Size: 1024x1024 Size: 512x512 K. Punnam Chandar
  • 4. Storage & Transmission • To store 100 images of size 1024x1024 the amount of memory required: One Image 1024x1024 = 1MB 100 Images 100x1MB= 100MB • To transmit 10 images of size 1024x1024 the amount of time required on a communication link of speed 10kbs is 1Hour . Solution: Compression K. Punnam Chandar
  • 5. Compression • To reduce the volume of data to be transmitted • To reduce the storage requirements • How is compression possible? – Redundancy in image data – Properties of human perception Compression Information Data: N1 Information Data: N2 K. Punnam Chandar
  • 6. If N1 and N2 denote the number of information –carrying Units in two data sets that represent the same information. The relative data redundancy RD of the first data set (the one characterized by N1) can be defined as Quantifying Redundancy Mathematically: 1 2 1 1 2 2 1 2 1 1 1 1 1 2 1 1 D D D D D R N N R N N N R N N N R N R N N R C           Where CR , commonly called the compression ratio K. Punnam Chandar
  • 7. Case i. N2=N1 Indicating that the first representation of the information contains no redundant data. Case ii. N2<<N1 Implying significant compression and highly redundant data. Case iii. N2>>N1 indicating that the second data set contains much more data than the original representation. Compression Information Data: N1 Information Data: N2 K. Punnam Chandar
  • 8. Redundancy in Images • In digital images, neighboring samples on a scanning line are normally similar (spatial redundancy) K. Punnam Chandar
  • 9. Coding Redundancy Coding Redundancy: assigning fixed code words to all the symbols results in Coding Redundancy Symbol Fixed Code Variable code a 00 0 b 01 01 c 10 10 d 11 001 Information: aaaaad Data N1: 000000000011 Data N2: 00000001 N1=12 N2= 8 Cr = 12/8=1.5 1.5:1 K. Punnam Chandar
  • 10. Summary • Data (Images) contains redundancy. • The type of redundancy present, need to be identified for processing . • Processed (compressed) data is suitable for transmission and storage. • The type of compression depends on application. • Compression is a viable technique to utilize the communication and storage resources optimally. K. Punnam Chandar
  • 11. Reference • The Images are taken from Digital Image Processing, Gonzalez 2nd Edition. K. Punnam Chandar