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Digital Image Processing 0909.452.01/0909.552.01 Fall 2001

Digital Image Processing 0909.452.01/0909.552.01 Fall 2001. Lecture 10 November 19, 2001. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall01/dip/. Grad Students: Final Project Presentations. Dec 10 th Dec 17 th. Plan. Digital Image Compression

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Digital Image Processing 0909.452.01/0909.552.01 Fall 2001

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  1. Digital Image Processing0909.452.01/0909.552.01Fall 2001 Lecture 10November 19, 2001 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall01/dip/

  2. Grad Students:Final Project Presentations Dec 10th Dec 17th

  3. Plan • Digital Image Compression • Fundamental principles • Image Compression Model • Recall: Information Theory • Image Compression Standards • CCITT Group 3 (FAX): Lossless • LZW (GIF, TIFF, ZIP): Lossless • DCT (JPEG): Lossy • Lab 4: Digital Image Compression

  4. DIP: Details

  5. Fundamentals • Justification • Applications • Principle • Redundancy • Types • Lossy • Lossless • demos/demo6dithering/

  6. Transform Quantize • Encode • Source • Channel f(x,y) Compression Model

  7. Recall: Measures of Information • Definitions • Probability • Information • Entropy • Source Rate • Recall: Shannon’s Theorem • If R < C = B log2(1 + S/N), then we can have error-free transmission in the presence of noise MATLAB DEMO: http://engineering.rowan.edu/~shreek/spring01/ecomms/ entropy.m

  8. Analog Message A/D Converter Source Encoder Digital Source Recall: Source Encoding • Why are we doing this? Source Symbols (0/1) Source Entropy Encoded Symbols (0/1) Source-Coded Symbol Entropy

  9. Source Encoding Requirements • Decrease Lav • Unique decoding • Instantaneous decoding

  10. Recall: Huffman Coding 2-Step Process • Reduction • List symbols in descending order of probability • Reduce the two least probable symbols into one symbol equal to their combined probability • Reorder in descending order of probability at each stage • Repeat until only two symbols remain • Splitting • Assign 0 and 1 to the final two symbols remaining and work backwards • Expand code at each split by appending a 0 or 1 to each code word • Example m(j) A B C D E F G H P(j) 0.1 0.18 0.4 0.05 0.06 0.1 0.07 0.04

  11. CCITT Group 3 1-D Standard for Bilevel Image Compression • Determine “run lengths” of black and white pixels on each line • Code these using a Huffman code • Conventions: • Each line begins with a zero-run length white code word • Each line ends with a special EOL code black run length white run length http://www.itu.int/publibase/itu-t/

  12. LZW Algorithm Initialize string table with single character strings Read first input character = w Read next input character = k y No more k’s? Stop Output = code(w) n y wk in string table? w = wk n Output = code(w) Put wk in string table w = k United States Patent No. 4,558,302, Patented by Unisys Corp.

  13. Information Concentration Data Compaction Feature Extraction Discrete Cosine Transform Discrete Cosine Transform >>dctdemo

  14. Laser Based Ultrasound* *Karta Technologies Inc., San Antonio, TX

  15. Example: Photothermal Shearography Images Before Deformation - After Deformation = Fringe Pattern Sample 10 0.254 mm depth -605.36 MPa stress

  16. 1 2 3 4 5 1 2 3 4 5 Preprocessing Fringe Pattern DCT Coefficients Zonal Mask DCT (1,1) (1,2) (2,1) (2,2) . . . Artificial Neural Network Feature Vector

  17. JPEG Compression Standard Compute DCT F(u,v) Reorder to form 1-D Sequence Level Shift f(x,y) Normalize Compute DC Coefficient Compute AC Coefficients http://www.jpeg.org/

  18. Karhunen-Loeve (Hotelling) Transform Hotelling transform of x • demos/demo7klt/

  19. Lab 4: Digital Image Compression http://engineering.rowan.edu/~shreek/fall01/dip/lab4.html

  20. Summary

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