Tour Guide Image Compression Image Manipulation Image Analysis Image Acquisition Image Perception...
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Transcript of Tour Guide Image Compression Image Manipulation Image Analysis Image Acquisition Image Perception...
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