Tour Guide Image Compression Image Manipulation Image Analysis Image Acquisition Image Perception Image Display Image Generation D.I.P. Theme Park

Why D.I.P.? Reasons for compression –Image data need to be accessed at a different time or location –Limited storage space and transmission bandwidth Reasons for manipulation –Image data might experience nonideal acquisition, transmission or display (e.g., restoration, enhancement and interpolation) –Image data might contain sensitive content (e.g., fight against piracy, conterfeit and forgery) –To produce images with artistic effect (e.g., pointellism) Reasons for analysis –Image data need to be analyzed automatically in order to reduce the burden of human operators –To teach a computer to “see” in A.I. tasks

Shannon’s Picture on Communication (1948) source encoder channel source decoder sourcedestination Examples of source: Human speeches, photos, text messages, computer programs … Examples of channel: storage media, telephone lines, wireless transmission … super-channel channel encoder channel decoder The goal of communication is to move information from 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 JPEG encoder 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 algorithm invented nearly 30 years ago -Based on an enhanced version of LZ algorithm by Welch in Was introduced by CompuServe in 1987 and made popular 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 JPEG decoder original raw image (262,144 bytes) compressed JPEG file (20,407 bytes) decompressed image high compression ratio low compression ratio low quality high quality Q Q 100 0

From JPEG to JPEG2000 JPEG (CR=64)JPEG2000 (CR=64) discrete cosine transform basedwavelet transform based

Tour Guide Image Compression Image Manipulation Image Analysis Image Acquisition Image Perception Image Display Image Generation D.I.P. Theme Park

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 image processing software such as Photoshop or Paint Shop Pro Image Manipulation (V): Image Interpolation

F.Y.I.: search “Gigapixel images” by Google = + 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: 0-255halftoned: 0/255 Image Manipulation (XII): Image Halftoning

original scrambled Image Manipulation (XIII): Image Scrambling/Hashing

Original imageModified image Image Manipulation (XIV): Image Watermarking

Image Manipulation (XV): Image Stylization

Abyss computer generated 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. Theme Park

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 recordPostmortem 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 automatically extracted 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