Project Presentation presented in the final semester of B.Tech (EEE) during the session of 2010-11 By Paras Prateek Bhatnagar, Paramjeet Singh Jamwal, Nisha Rajani & Preeti Kumari.

1. COMPRESSION
PREPARED BY:
NISHA RAJANI (01)
PARAMJEET SINGH JAMWAL (07)
PARAS PRATEEK BHATNAGAR
(08)
PREETI KUMARI (12)
UNDER
GUIDANCE
OF :
Mr. A. S. YADAV
Mr. A. SHUKLA

2. Contents …
Why We Opted For This Project & Our Aim ?
What is IMAGE COMPRESSION & Its Benefits ?
MEDICAL IMAGE COMPRESSION & Differences .
What is JPEG Compression?
Our Results of IMAGE COMPRESSION Using DCT .
Tasks Left In Our Project .
Applications of MEDICAL IMAGE COMPRESSION .

3. Shortcomings of present IMAGE COMPRESSION methods :
 A large bandwidth is required for transmission .
 A large time is required for compression .
Lack of perfect lossless method which could preserve data
Reduce the size & compression time of the image with least
effect on its quality .
Why we Opted For This Project ?
Our Aim .

4. Application of data compression on
digital images
Reduce redundancy in the image data
Store data efficiently
Transmit data efficiently
What is IMAGE COMPRESSION ?
Benefits of IMAGE COMPRESSION .

5. MEDICAL IMAGE COMPRESSION .
 Production of visual representations of body
parts, tissues or organs
 Used in clinical diagnosis
 Encompasses X-ray methods , MRI & CT Scan
Differences From Normal IMAGE .
Significant information in a small area
 Monochromatic background colour .

6.  Stands for Joint Picture Experts Group
 Most popular LOSSY compression method
 Extensively used by digital cameras , mobiles &
web usage
 Not suited for drawing , textual and iconic
graphics
 Uses extensions – JPG or JPEG
What is JPEG Compression ?

7.  Human vision insensitive to
high spatial frequencies
 High frequencies more
coarsely compressed &
filtered
 Application of HPF
 Image stored as frequency
data
Original
image
[ 150KB ]
JPEG
image
[ 14KB ]
Basics of JPEG Compression
Frequency
Domain
image

8. 8x8
pixel
blocks
RGB
To
YCbCr
DCT Zigzag
scan
RLE
Huffman Encoding
Quantization
The JPEG Compression Algorithm
100100010001000100100001111100010100
Original image
[ 150KB ]
JPEG Compressed
Data [ 14 KB ]

9. 1/6 : 8x8 Block splitting

10. 2/6 : Convert RGB to YCbCr

11. DCT
Result
3/6 : Apply 2D Discrete Cosine Transformation [ DCT ]

12. DCT result Quantization Matrix
Quantization
result
4/6 : Quantization

13. Zigzag scan
5/6 : Zigzag Scan + RLE

14.  RLE 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]
Values G Real saved values
0
-1, 1
-3, -2, 2, 3
-7,-6,-5,-4,5,6,7
.
.
.
.
.
.
.
.
.
-32767..32767
0
1
2
3
4
5
.
.
.
.
.
.
.
15
.
0,1
00, 01, 10, 11
000,001,010,011,100,101,110,111
.
.
.
.
.
.
.
.
.
6/6 : Huffman Encoding

15. Huffman
Decoding
RLE
Zigzag
To
8x8
Matrix
IDCT
YCbCr
To
RGB
Image
Reconstruction
Quantization
Multiplication
The JPEG Decompression Algorithm
100100010001000100100001111100010100
Decoded image
[ 150KB ]
JPEG Compressed
Data [ 14 KB ]

16. Basic TERMS used :
PSNR : Ratio between the maximum possible power of a
signal and the power of its corrupting noise .
MSE : The difference between an estimator and the true
value of the quantity being estimated.

17. Basic TERMS used :
CR : The reduction in data-representation size produced by a
data compression algorithm.
BITRATE : Number of bits that are used for representing a
pixel .

18. PEPPERS IMAGE
90 % 60 %
40 % 20 %
0
20
40
60
80
100
120
140
160
180
200
0 0.2 0.4 0.6 0.8 1
BITRATE
Variation of Image Parameters with
BITRATE
CR
MSE
PSNR
Our Results of Normal Image using DCT

19. Our Results of Medical Image using DCT
CAT SCAN IMAGE
0
5
10
15
20
25
30
35
40
45
50
0 0.2 0.4 0.6 0.8 1
BITRATE
Variation of Image Parameters with
BITRATE
CR
MSE
PSNR
90 % 60 %
40 % 20 %

20. X-RAY IMAGE
0
10
20
30
40
50
60
0 0.2 0.4 0.6 0.8 1
BITRATE
Variation of Image Parameters with
BITRATE
CR
MSE
PSNR
90 % 60 %
40 % 20 %
Our Results of Medical Image using DCT

21.  Image Compression using DWT , JPEG
2000 & SPIHT
 RLE Coding using Arithmetic coding
technique
 Comparison & conclusion about the
best method
Tasks left in our project

22. Magnetic resonance imaging (MRI)
Breast Thermography
Tomography
Ultrasound
X-Rays
Scientigraphy
Applications of Medical Image Compression

23. Any