Tour Guide

Image Compression

Image Manipulation

Image Analysis

Image Acquisition

Image Perception

Image Display

Image Generation

D.I.P.ThemePark

Why D.I.P.?Why D.I.P.?

Reasons for compressionReasons for compression– Image data need to be accessed at a different time or locatioImage data need to be accessed at a different time or locatio

nn– Limited storage space and transmission bandwidthLimited storage space and transmission bandwidth

Reasons for manipulationReasons for manipulation– Image data might experience nonideal acquisition, transmissiImage data might experience nonideal acquisition, transmissi

on or display (e.g., restoration, enhancement and interpolation or display (e.g., restoration, enhancement and interpolation) on)

– Image data might contain sensitive content (e.g., fight against Image data might contain sensitive content (e.g., fight against piracy, conterfeit and forgery) piracy, conterfeit and forgery)

– To produce images with artistic effect (e.g., pointellism)To produce images with artistic effect (e.g., pointellism)Reasons for analysisReasons for analysis– Image data need to be analyzed automatically in order to redImage data need to be analyzed automatically in order to red

uce the burden of human operators uce the burden of human operators – To teach a computer to “see” in A.I. tasksTo teach a computer to “see” in A.I. tasks

Shannon’s Picture on Shannon’s Picture on Communication (1948)Communication (1948)

sourceencoder

channel

sourcedecoder

source destination

Examples of source: Human speeches, photos, text messages, computer programs …

Examples of channel: storage media, telephone lines, wireless transmission …

super-channel

channelencoder

channeldecoder

The goal of communication is to move informationfrom here to there and from now to then

Source Coding in Image Communication: Image Compression Why do we need image compression?

-Example: digital camera (4Mpixel)

Raw data – 24bits, 5.3M pixels 16M bytes

256M memory card ($30-50) 16 pictures

JPEGencoder

raw image (16M bytes)

compressed JPEG file (1M bytes)

compression ratio=16 256 pictures

Lossless Image Compression

Definition

- Decompressed image will be mathematically identical to the original one (zero error)

- highly depends on the image type and content

-Storage and transmission of medical images

synthetic images >10

photographic images 1~3

Compression ratio

Applications

Popular Lossless Image Compression Techniques

WinZip

- Based on the celebrated Lempel-Ziv algorithminvented nearly 30 years ago

-Based on an enhanced version of LZ algorithmby Welch in 1983-Was introduced by CompuServe in 1987 and madepopular until it was not royalty-free in 1994

GIF (Graphic Interchange Format)

PNG (Portable Network Graphics)

GIF Liberation Day: June 20, 2003

Lossy Image Compression

JPEGdecoder

original raw image (262,144 bytes)

compressed JPEG file (20,407 bytes)

decompressed image

high compression ratio

low compression ratio

low quality

high quality

QQ100

0

From JPEG to JPEG2000

JPEG (CR=64) JPEG2000 (CR=64)

discrete cosine transform based wavelet transform based

Tour Guide

Image Compression

Image Manipulation

Image Analysis

Image Acquisition

Image Perception

Image Display

Image Generation

D.I.P.ThemePark

salt and pepper (impulse) noise

Image Manipulation (I): Noise Removal

Noise contamination is often inevitable during the acquisition

additive white Gaussian noise

License plate is barely legible due to motion blurring

Image Manipulation (II): Deblurring

overly-exposed image

Image Manipulation (III): Contrast Enhancement

under-exposed image

Example: aliasing artifacts in MRI image acquisition

Tradeoff between scanning time and image quality

Ideal quality, slow scanning

nonideal quality,fast scanning

Image Manipulation (IV): Aliasing Reduction

small

large

digital zooming

1M pixels

4M pixels

Resolution enhancement can be obtained by common imageprocessing software such as Photoshop or Paint Shop Pro

Image Manipulation (V): Image Interpolation

F.Y.I.: search “Gigapixel images” by Google

http://triton.tpd.tno.nl/gigazoom/Delft2.htm

=+

Merge multiple images of the same scene into one with larger FOV

Image Manipulation (VI): Image Mosaicing

There exist several mosaicing software for automatic stitching

blocks contaminated by channel errors

Image Manipulation (VII): Error Concealment

Block artifacts

Image Manipulation (VIII): Deblocking/Deringing

Ringing artifacts

jittering noise

Image Manipulation (IX): Dejittering

Image Manipulation (X): Image Inpainting

Image Inpainting Application: Restore Old Photos

25,680 colors (24 bits) 256 colors (8 bits)

Applications: video cell-phone, gameboy, portable DVD

Image Manipulation (XI): Color Quantization

grayscale: 0-255 halftoned: 0/255

Image Manipulation (XII): Image Halftoning

original scrambled

Image Manipulation (XIII): Image Scrambling/Hashing

Original image Modified image

Image Manipulation (XIV): Image Watermarking

Image Manipulation (XV): Image Stylization

Abysscomputergenerated

Image Manipulation (XVI): Image Rendering

Image-based Rendering

Tour Guide

Image Compression

Image Manipulation

Image Analysis

Image Acquisition

Image Perception

Image Display

Image Generation

D.I.P.ThemePark

Image Analysis (I): Edge Detection

Image Analysis (II): Face Detection

Image Analysis (III): Change Detection

Change Detection in Medical Application

Image Analysis (IV): Image Matching

Antemortem dental X-ray record Postmortem dental X-ray record

Image Matching in Biometrics

Two deceivingly similar fingerprints of two different people

Image Analysis (V): Image Segmentation

License number can be automaticallyextracted from the image of license plate

Image Analysis (VI): Object Recognition

Object Recognition in Military Applications

Image-based monitoring system prevents drowning

Image Analysis (VII): Event Recognition

Only send out “important” motion pictures such as home-runs

Image Analysis (VIII): Video Summarization

retrieved building images

Image Analysis (IX): Content-based Image Retrieval