Image Compression

1. Image Compression อ.รัชดาพร คณาวงษ์ วิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยศิลปากรวิทยาเขตพระราชวังสนามจันทร์

2. Image Compression • Reducing the size of image data files • While retaining necessary information Original Image Compressed Image file extracted Image file compress decompress

3. Terminology • refer relation between original image and the compressed file • Compression Ratio • Bits per Pixel A larger number implies a better compression A smaller number implies a better compression

4. Compression Ratio (1) Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte. Original Image Size = 256X256(pixels) X 1(byte/pixel) = 65536 bytes

5. (2) Bits per Pixel Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte. Original Image Size = 256X256(pixels) X 1(byte/pixel) = 65536 bytes Compressed file = 6554(bytes)X8(bits/pixel) = 52432 bits

6. Why we want to compress? To transmit an RGB 512X512, 24 bit image via modem 28.2 kbaud(kilobits/second)

7. Key of compression • Reducing Data but Retaining Information DATA are used to convey information. Various amounts of data can be used to represent the same amount of information. It’s “Data redundancy” Relative data redundancy

8. Entropy • Average information in an image. • Average number of bits per pixel

9. Redundancy • Coding Redundancy • Interpixel Redundancy • Psychovisual Redundancy

10. Coding Redundancy • Occurred when data used to represent image are not utilized in an optimal manner

11. Coding Redundancy(cont) • An 8 gray-level image distribution shown in Table

12. Coding Redundancy(cont) • Original Image 8 possible gray level = 23

13. Interpixel Redundancy • Adjacent pixel values tend to be highly correlated

14. Psychovisual Redundancy • Some information is more important to the human visual system than other types of information

15. Compressed File Preprocessing Encoding Input Compressed File Decoding Postprocessing Output Compression System Model • Compression • Decompression

16. Types of Compression There are 2 types of Compression • Loseless Compression • Lossy Compression

17. Loseless Compression • No data are lost • Can recreated exactly original image • Often the achievable compression is mush less

18. Huffman Coding • Using Histogram probability • 5 Steps • Find the histogram probabilities • Order the input probabilities(smalllarge) • Addition the 2 smallest • Repeat step 2&3, until 2 probability are left • Backward along the tree assign 0 and 1

19. 40 30 20 10 0 1 2 3 Huffman Coding(cont) • Step 1 Histogram Probability p0 = 20/100 = 0.2 p1 = 30/100 = 0.3 p2 = 10/100 = 0.1 p3 = 40/100 = 0.4 • Step 2 Order p3 0.4 p1 0.3 p0 0.2 p2 0.1

20. Huffman Coding(cont) • Step 3 Add 2 smallest

21. Huffman Coding(cont) • The original Image :average 2 bits/pixel • The Huffman Code:average

22. Run-Length Coding • Counting the number of adjacent pixels with the same gray-level value • Used primarily for binary image • Mostly use horizontal RLC

23. Run-Length Coding(cont) Binary Image 8X8 horizontal

24. 1 0 1 Run-Length Coding(cont) • Extending basic RLC to gray-level image by using bit-plane coding • It will better if change the natural code into gray code 00 01 10 11 00 01 11 10 Natural Gray Code

25. Lempel-Ziv-Weich Coding(LZW) • Assign fixed-length code words to variable • GIF,TIFF,PDF

26. Lossy Compression • Allow a loss in the actual image data • Can not recreated exactly original image • Commonly the achievable compression is mush more • JPEG

27. Fidelity Criteria • Objective fidelity criteria • RMS Error • RMS Signal-To-Noise Ratio • Subjective fidelity criteria

28. JPEG