Digital Image Processing 0909.452.01/0909.552.01 Fall 2001
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Transcript of Digital Image Processing 0909.452.01/0909.552.01 Fall 2001
S. Mandayam/ DIP/ECE Dept./Rowan University
Digital Image ProcessingDigital Image Processing
0909.452.01/0909.552.010909.452.01/0909.552.01 Fall 2001Fall 2001
Shreekanth MandayamECE Department
Rowan University
http://engineering.rowan.edu/~shreek/fall01/dip/
Lecture 10Lecture 10November 19, 2001November 19, 2001
S. Mandayam/ DIP/ECE Dept./Rowan University
Grad Students:Grad Students:Final Project PresentationsFinal Project Presentations
Dec 10th
Dec 17th
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PlanPlan
• 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
S. Mandayam/ DIP/ECE Dept./Rowan University
DIP: DetailsDIP: Details
G ray-level Histogram
Spatial
DF T DC T
Spectral
Digital Image Characteristics
Point Processing M asking Filtering
Enhancem ent
Degradation M odels Inverse Filtering W iener Filtering
Restoration
Pre-Processing
Inform ation Theory
LZW (gif)
Lossless
Transform -based (jpeg)
Lossy
Com pression
Edge Detection
Segm entation
Shape Descriptors Texture M orphology
Description
Digital Im age Processing
S. Mandayam/ DIP/ECE Dept./Rowan University
FundamentalsFundamentals
• Justification
• Applications
• Principle• Redundancy
• Types• Lossy• Lossless
demos/demo6dithering/
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Compression ModelCompression Model
f(x,y) Transform QuantizeEncode• Source• Channel
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Recall: Measures of Recall: Measures of InformationInformation
• 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
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Recall: Source EncodingRecall: Source Encoding
• Why are we doing this?
AnalogMessage
A/DConverter
DigitalSource
SourceEncoderSource
Symbols (0/1)
Source Entropy
EncodedSymbols
(0/1)
Source-CodedSymbol Entropy
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Source Encoding Source Encoding RequirementsRequirements
• Decrease Lav
• Unique decoding
• Instantaneous decoding
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Recall: Huffman CodingRecall: Huffman Coding2-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
• Examplem(j) A B C D E F G HP(j) 0.1 0.18 0.4 0.05 0.06 0.1 0.07 0.04
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CCITT Group 3 1-D Standard CCITT Group 3 1-D Standard for Bilevel Image Compressionfor 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/
S. Mandayam/ DIP/ECE Dept./Rowan University
LZW AlgorithmLZW AlgorithmInitialize string table with single character strings
Read first input character = w
Read next input character = k
No more k’s? Output = code(w) Stop
wk in string table?
Output = code(w)
w = wk
w = k Put wk in string table
y
n
n
y
United States Patent No. 4,558,302, Patented by Unisys Corp.
S. Mandayam/ DIP/ECE Dept./Rowan University
Discrete Cosine TransformDiscrete Cosine Transform
• Information Concentration
• Data Compaction
• Feature Extraction
Discrete Cosine Transform
1-....N 1,2 u for
N2
0 u for N1
(u)
2N1)(2x
cos f(x) (u) C(u)1-N
0x
u
>>dctdemo
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Laser Based Ultrasound*Laser Based Ultrasound*
*Karta Technologies Inc., San Antonio, TX
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Example: Photothermal Example: Photothermal Shearography ImagesShearography Images
Before Deformation - After Deformation = Fringe Pattern
Sample 100.254 mm depth-605.36 MPa stress
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PreprocessingPreprocessing
Fringe Pattern
DCT
DCT Coefficients
Zonal Mask
1 2
3
4
5
1 2 3 4 5
(1,1)(1,2)(2,1)(2,2)
.
.
.
FeatureVector
ArtificialNeuralNetwork
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JPEG Compression JPEG Compression StandardStandard
f(x,y) LevelShift
ComputeDCTF(u,v)
Normalize Reorder to form 1-D Sequence
ComputeDC Coefficient
ComputeAC Coefficients
http://www.jpeg.org/
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Karhunen-Loeve (Hotelling) Karhunen-Loeve (Hotelling) TransformTransform
i
ii
i
Txxx
x
eA
λe
Nixλ
mxmxEC
xEm
Nx
of rowsh Matrix wit :
toingcorrespond rsEigenvecto :
,...,2,1 ,of sEigenvalue :
matrix Covariance :))((
rmean vecto :
vector1:
)( xmxAy
Hotelling transform of x
demos/demo7klt/
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Lab 4: Digital Image Lab 4: Digital Image CompressionCompression
http://engineering.rowan.edu/~shreek/fall01/dip/lab4.html
S. Mandayam/ DIP/ECE Dept./Rowan University
SummarySummary