Digital image processing ece 09 452 ece 09 552 fall 2007
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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 processing ece 09 452 ece 09 552 fall 2007 l.jpg

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/


Slide2 l.jpg
Plan

  • 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



  • Fundamentals l.jpg
    Fundamentals

    • Justification

    • Applications

    • Principle

      • Redundancy

  • Types

    • Lossy

    • Lossless

    • demos/demo6dithering/


    Compression model l.jpg

    Transform

    Quantize

    • Encode

    • Source

    • Channel

    f(x,y)

    Compression Model


    Recall measures of information l.jpg
    Recall: Measures of Information

    • 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


    Recall source encoding l.jpg

    Analog

    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


    Source encoding requirements l.jpg
    Source Encoding Requirements

    • Decrease Lav

    • Unique decoding

    • Instantaneous decoding


    Recall huffman coding l.jpg
    Recall: Huffman Coding

    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


    Discrete cosine transform l.jpg

    Information Concentration

    Data Compaction

    Feature Extraction

    Discrete Cosine Transform

    Discrete Cosine Transform

    >>dctdemo


    Laser based ultrasound l.jpg
    Laser Based Ultrasound*

    *Karta Technologies Inc., San Antonio, TX


    Example photothermal shearography images l.jpg
    Example: Photothermal Shearography Images

    Before Deformation - After Deformation = Fringe Pattern

    Sample 10

    0.254 mm depth

    -605.36 MPa stress


    Preprocessing l.jpg

    1 2 3 4 5

    1

    2

    3

    4

    5

    Preprocessing

    Fringe Pattern

    DCT Coefficients

    Zonal Mask

    DCT

    (1,1)

    (1,2)

    (2,1)

    (2,2)

    .

    .

    .

    Artificial

    Neural

    Network

    Feature

    Vector


    Jpeg compression standard l.jpg
    JPEG Compression Standard

    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/


    Lzw algorithm l.jpg
    LZW Algorithm

    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.


    Karhunen loeve hotelling transform l.jpg
    Karhunen-Loeve (Hotelling) Transform

    Hotelling transform of x

    • demos/demo7klt/


    Lab 3 digital image restoration l.jpg
    Lab 3: Digital Image Restoration

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


    Lab 4 digital image compression l.jpg
    Lab 4: Digital Image Compression

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




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