image compression
DESCRIPTION
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.TRANSCRIPT
ImageCompression
Paramjeet Singh JamwalPoonam Kanyal
Rittika MittalSurbhi Tyagi
B.Tech (EEE) 2007-2011
B.Tech (6th Semester)Electrical & Electronics Engineering
College Of Engineering Roorkee
Contents ...
Advantages /Disadvantages of Image Processing
Applications of Image Processing
DCT v/s DWT
JPEG Compression Algorithm
Lossless Compression
Lossy Compression
Image Compression
What is Image Compression ?
Application of data compression on digital images Reduce redundancy of the image data
Store data efficiently
Transmit data efficiently
Benefits of Image Compression
Lossy Compression
Decompression retrieves data different from the original
Used to compress multimedia data
Streaming media and internet telephony
Methods
JPEG TIFF MNG PGF
Original Image Before Compression
Decompressed Image After Compression
Before Compression
Size Colour UsedImage
After Compression
186 KB
26 KB
57053
31760
Lossless Compression
Exact reconstruction of original data
Executable programs and source codes
Data loss cant be tolerated
Methods
JPEG2000
GIF PNG TIFF
Original Image Before Compression
Decompressed Image After Compression
Before Compression
Size Colour UsedImage
After Compression
186 KB
136 KB
57053
57053
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
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
The JPEG Compression Algorithm
8x8 pixelblocks
FDCT
Frequency Dependent quantization
Zig-zag scan
RLE HuffmanEncoding
Quantization Table
output
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
The JPEG File StructureShort name Bytes Size Name
SOI 0xFFD8 none 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 Define Huffman Table(s)
DQT 0xFFDB variable size Define Quantization Table(s)
DRI 0xFFDD 2 bytes Define Restart Interval
SOS 0xFFDA variable size Start Of Scan
RSTn 0xFFD0 … 0xFFD7 none Restart
APPn 0xFFEn variable size Application-specific
COM 0xFFFE variable size CommentEOI 0xFFD9 none End Of Image
1/7 : Divided into 8x8 blocks
1/7 : Divided into 8x8 blocks
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
2/7 : Convert RGB to YCbCr
3/7 : Downsampling ( optional ) Y is taken every pixel , and Cb,Cr are taken for a block of 2x2 pixels
MCU(minimum coded unit) : The smallest group of data units that is coded.
Data size reduces to a half immediately
4/7 : Apply DCT [ Discrete Cosine Transformation ]
2D DCT:
1D DCT:
4/7 : Apply DCT [ Discrete Cosine Transformation ]
Shift operations
From [0, 255]
To [-128, 127]
DCT Result
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
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
5/7 : Quantization
DCT result Quantization Matrix
Quantization result
6/7 : Zigzag reordering & RLE
Quantization result
7/7 : Huffman encoding
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 %
Values G Real saved values
0-1, 1
-3, -2, 2, 3-7,-6,-5,-4,5,6,7
.
.
.
.
.
.
.
.
.-32767..32767
012345.......
15
.0,1
00, 01, 10, 11000,001,010,011,100,101,110,111
.
.
.
.
.
.
.
.
.
JPEG Compression Ratio
500KB image, minimum
compression
40KB image, half
compression
11KB image, max
compression
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
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
DWT v/s DCT
Wavelet compressionfile size: 1861 bytescompression ratio - 105.6
JPEG compression file size: 1895 bytescompression ratio - 103.8
Source: http://www.barrt.ru/parshukov/about.htm.
Applications of Image Processing
Computer Vision
Optical Sorting
Face Detection
Feature Detection
Augmented Reality
Remote Sensing
Medical Image Processing
Advantages/Disadvantages of Image ProcessingAdvantages
Post-processing
Easy Storage
Easy Sharing
Easy Retrieval
Environment Friendly
Multiple Use
DisadvantagesHigh cost
Extra Knowledge
High Maintenance
Standardization
Shape/Size of detectors
Any
QUESTIONS ?
THE END !!!
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