1. Image Compression (Chapter 8) CSC 446 Lecturer: Nada ALZaben

2. Outline:  Introduction.  Image Compression Model.  Compression Types.  Data Redundancy.  Redundancy Types.  Coding redundancy  Lossless compression

3. Introduction  Most data nowadays are available on line and for the limited storage space and communications requirement, methods of compressing data prior to storage and/or transmission are being interesting study field.  Image compression address the problem of reducing the amount of data required to represent a digital image.  Image compression is done prior to storage and/or transmission and then decompressed to reconstruct the original image.

4. Image Compression Model  Source encoder: removes input redundancy.  Channel encoder: increase the noise immunity of source encoder output.  Channel: if it is noise free the channel encoder and channel decoder is omitted.

5. Compression Types 1) Lossy image compression: is useful in applications such as broadcasting television and video conferencing in which certain amount of error is acceptable trade off for increased compression performance. 2) Lossless image compression: useful in image archiving such as medical records where the image will be compressed and decompressed without losing any information.

6. Data redundancy [1]

7. Data redundancy [2]

8. Redundancy Types 1) Coding redundancy. 2) Interpixel redundancy. 3) Psychvisual redundancy. Data compression is done if one or more of these types are achived.

9. Coding Redundancy

10. Coding Redundancy [example]

12. Lossless Compression  Assigning fewer bits to the more probabilty gray level than the least probable ones achive data compression which is called “variable- length coding”  Lossless compression  “Huffman code” is a kind of the variable length coding.

13. Lossless Compression  “Huffman code example”.  The letters A,B,C,D, and E are to be encoded and have relative probability of occurrence as follows:  p(A)=0.16, p(B)=0.51, p(C)=0.09, p(D)=0.13, p(E)=0.11  the two characters with the lowest probability are combined in the first binary tree which has the characters as leaves. p(CE)=0.20  Each right branch 1 and each left branch 0

14. “Huffman code example”.

15. Lossless Compression  “Run- length encoding”  Lossless compression.  The Idea of run-length encoding is replaceing long sequences (runs) of identical samples with a special code that indicates the value to be repeated and the number of times repeated. 1110010000 RLE (3,1)(2,0)(1,1)(4,0)