Presentation given in the Seminar of B.Tech 6th Semester during session 2009-10 By Paramjeet Singh Jamwal, Poonam Kanyal, Rittitka Mittal and Surabhi Tyagi.

1. B.Tech (EEE) 2007-2011
Image
Compression
B.Tech (6th Semester)
Electrical & Electronics Engineering
College Of Engineering Roorkee

2. Contents ...
Image Compression
Lossy Compression
Lossless Compression
JPEG Compression Algorithm
DCT v/s DWT
Applications of Image Processing
Advantages /Disadvantages of Image Processing

3. What is Image Compression ?
 Application of data compression on digital images
 Reduce redundancy of the image data
Benefits of Image Compression
 Store data efficiently
 Transmit data efficiently

4. Lossy Compression
 Decompression retrieves data different
from the original
 Used to compress multimedia data
 Streaming media and internet telephony
Methods
JPEG
TIFF
MNG
PGF

5. Original Image Before Compression

6. Decompressed Image After Compression

7. Before Compression
186 KB
57053
26 KB
31760
After Compression
Image
Size
Colour Used

8. Lossless Compression
 Exact reconstruction of original data
 Executable programs and source codes
 Data loss cant be tolerated
Methods
JPEG
2000
GIF
PNG
TIFF

9. Original Image Before Compression

10. Decompressed Image After Compression

11. Before Compression
186 KB
57053
136 KB
57053
After Compression
Image
Size
Colour Used

12. What is JPEG ?
 Stands for Joint Photographics Experts
Group
 Lossy compression method
 Mostly used by digital cameras & web
usage
 Not suited for drawing , textual and iconic
graphics

13. Basics of JPEG Compression
 Human vision is insensitive to high spatial frequencies
 JPEG Takes advantage of this by compressing high frequencies more
coarsely and storing image as frequency data
Losslessly compressed
image,
150KB
JPEG compressed,
14KB

14. The JPEG Compression Algorithm
 Divide image into 8x8 pixel blocks
 Apply 2D Fourier Discrete Cosine Transform
(FDCT) Transform
 Apply coarse quantization to high spatial
frequency components
 Compress resulting data losslessly and store
8x8
pixel
blocks
FDCT
Frequency
Dependent
quantization
Quantization
Table
Zig-zag
scan
RLE
Huffman
Encoding
output

15. The JPEG File Structure
Short name
SOI
Bytes
0xFFD8
Size
none
Name
Start Of Image
SOF0
0xFFC0
variable size
Start Of Frame (Baseline
DCT)
SOF2
0xFFC2
variable size
Start Of Frame
(Progressive DCT)
DHT
0xFFC4
variable size
DQT
0xFFDB
variable size
DRI
0xFFDD
2 bytes
SOS
0xFFDA
variable size
RSTn
0xFFD0 … 0xFFD7
none
APPn
0xFFEn
variable size
Application-specific
COM
EOI
0xFFFE
0xFFD9
variable size
none
Comment
End Of Image
Define Huffman Table(s)
Define Quantization
Table(s)
Define Restart Interval
Start Of Scan
Restart

16. 1/7 : Divided into 8x8 blocks

17. 1/7 : Divided into 8x8 blocks

18. 2/7 : Convert RGB to YCbCr
 Simple color space model: [R,G,B] per pixel
 JPEG uses [Y, Cb, Cr] Model
 Y (Brightness) = 0.299R + 0.587G + 0.114B
Cb (Color blueness) = -0.1687R - 0.3313G + 0.5B + 128
Cr (Color redness) = 0.5R - 0.4187G - 0.0813B + 128

19. 2/7 : Convert RGB to YCbCr

20. 3/7 : Downsampling ( optional )
 Y is taken every pixel , and Cb,Cr are taken for a block of 2x2 pixels
 MCU(minimu
m coded unit) :
The smallest
group of data
units that is
coded.
 Data size
reduces to a
half
immediately

21. 4/7 : Apply DCT [ Discrete Cosine Transformation ]
2D DCT:
1D DCT:

22. 4/7 : Apply DCT [ Discrete Cosine Transformation ]
Shift operations
From [0, 255]
To [-128, 127]
DCT
Result

23. 5/7 : Quantization
Luminance Quantization Matrix
Chrominance Quantization Matrix
Each DCT coefficient F(u, v) is divided by the corresponding quantizer
step-size parameter Q(u, v) in the quantization matrix and rounded
to the nearest integer as

24. 5/7 : Quantization [ Quality Factor ]
Quality of the reconstructed image and the achieved
compression can be controlled by a user by selecting a
quality factor [ Q_JPEG ] :
 Q_JPEG ranges between 1 to 100
 When Q_JPEG is used, the entries in tables in previous slide is
scaled by the factor alpha (α), defined as :
 Q_JPEG is 100 for best reproduction

25. 5/7 : Quantization
DCT result
Quantization Matrix
Quantization
result

26. 6/7 : Zigzag reordering & RLE
Quantization
result

27. 7/7 : Huffman encoding
Values
G
0
0
-1, 1
1
-3, -2, 2, 3
2
-7,-6,-5,-4,5,6,7 3
.
4
.
5
.
.
.
.
.
.
.
.
.
.
.
.
.
.
-32767..32767 15
Real saved values
.
0,1
00, 01, 10, 11
000,001,010,011,100,101,110,111
.
.
.
.
.
.
.
.
.
 RLC result:
[0, -3] [0, 12] [0,
3]......EOB
 After group number added:
[0,2,00b] [0,4,1100b]
[0,2,00b]
...... EOB
 First Huffman coding (i.e. for
[0,2,00b] ):
[0, 2, 00b] => [100b,
00b]
Input : 512 bits
Output : 113 bits
% Red : 22.07 %

28. JPEG Compression Ratio
500KB image,
minimum
compression
40KB image,
half
compression
11KB
image, max
compression

29. Effects of varying JPEG Compression Ratio
Uncompressed image
Half compression,
Blurring around sharp
edges
Max compression,
8-pixel blocks
apparent, large distortion
in high-frequency areas

30. DWT v/s DCT
 Images containing sharp
edges/continuous curves
 Uses more optimal set
of functions to represent
sharp edges
 Wavelets are finite in
extent
Different families of wavelets

31. DWT v/s DCT
Wavelet compression
file size: 1861 bytes
compression ratio - 105.6
JPEG compression
file size: 1895 bytes
compression ratio - 103.8
Source: http://www.barrt.ru/parshukov/about.htm.

32. Applications of Image Processing
Computer Vision
Optical Sorting
Face Detection
Feature Detection
Augmented Reality
Remote Sensing
Medical Image Processing

33. Advantages/Disadvantages of Image Processing
Post-processing
High cost
Easy Sharing
Easy Retrieval
Environment
Friendly
Multiple Use
Disadvantages
Advantages
Easy Storage
Extra Knowledge
High Maintenance
Standardization
Shape/Size of
detectors

34. Any

35. info4eee
An initiative for B.Tech (EEE) Student