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# Digital Image Processing ECE.09.452 - PowerPoint PPT Presentation

Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007. Lecture 8 November 12, 2007. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall07/dip/. Plan. Digital Image Compression Fundamental principles Image Compression Model

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### Digital Image ProcessingECE.09.452/ECE.09.552Fall 2007

Lecture 8November 12, 2007

Shreekanth Mandayam

ECE Department

Rowan University

http://engineering.rowan.edu/~shreek/fall07/dip/

• Digital Image Compression

• Fundamental principles

• Image Compression Model

• Recall: Information Theory

• Image Compression Standards

• DCT (JPEG): Lossy

• LZW (GIF, TIFF, ZIP): Lossless

• Lab 3: Digital Image Restoration

• Lab 4: Digital Image Compression

• Discussion: Final Project

• Justification

• Applications

• Principle

• Redundancy

• Types

• Lossy

• Lossless

• demos/demo6dithering/

Quantize

• Encode

• Source

• Channel

f(x,y)

Compression Model

• 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/spring07/ecomms/entropy.m

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

• Decrease Lav

• Unique decoding

• Instantaneous decoding

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

Data Compaction

Feature Extraction

Discrete Cosine Transform

Discrete Cosine Transform

>>dctdemo

*Karta Technologies Inc., San Antonio, TX

Before Deformation - After Deformation = Fringe Pattern

Sample 10

0.254 mm depth

-605.36 MPa stress

1

2

3

4

5

Preprocessing

Fringe Pattern

DCT Coefficients

DCT

(1,1)

(1,2)

(2,1)

(2,2)

.

.

.

Artificial

Neural

Network

Feature

Vector

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/

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.

Hotelling transform of x

• demos/demo7klt/

http://engineering.rowan.edu/~shreek/fall07/dip/lab3.html

http://engineering.rowan.edu/~shreek/fall07/dip/lab4.html