Edward J. Delp Intel December 3, 1999 Slide 1

Video and Image Processing At Purdue

Edward J. DelpVideo and Image Processing Laboratory (VIPER)School of Electrical and Computer Engineering

Purdue University West Lafayette, Indiana, USA

email: ace@ecn.purdue.edu

http://www.ece.purdue.edu/~ace

Edward J. Delp Intel December 3, 1999 Slide 2

Acknowledgements• Students -

– Eduardo Asbun

– Dan Hintz

– Paul Salama

– Ke Shen

– Martha Saenz

– Eugene Lin

– Ray Wolfgang

– Greg Cook

– Sheng Liu

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Intel T4E Project

• Purdue awarded $6.2 million in August 1997 for equipment

– this is one of many strong relationships between Intel and Purdue

• Has had a very significant impact on how we do research!

THANKS!http://www.cs.purdue.edu/homes/jtk/intel/

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Image and Video Processing at Purdue

Purdue has a rich history 60 year history in image and video processing.

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VIPER Research Projects

• Scalable Video and Color Image Compression

– still image compression (CEZW)

– high and low bit rate video compression (SAMCoW)

– wireless video

• Error Concealment

• Content Addressable Video Databases (ViBE)

– Scene Change Detection and Identification

– Pseudo-Semantic Scene Labeling

• Multimedia Security: Digital Watermarking

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VIPER Research Projects

• Multicast Video

• Analysis of Mammograms

• Embedded Image and Video Processing

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Other Purdue Projects• Electronic Imaging - Jan Allebach and Charles Bouman

– half-tone printing

– compound document compression

– image databases

• Remote Sensing - David Langrebe

• Medical Imaging - Charles Bouman, Peter Doerschuk, Thomas Talavage, Edward Delp

– computed imagng

– functional MRI

– x-ray crystallography

– breast imaging

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Analysis of Mammograms

Density 1 Density 2 Density 3 Density 4

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Detection Results

Automatic Detection Ground Truth

A 12.4mm lesion detected at the second coarsest resolution

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Detection ResultsA 6.6mm lesion detected at the finest resolution

Automatic Detection Ground Truth

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ViBE: A New Paradigm for Video Database Browsing and Search

• ViBE has four components

– scene change detection and identification

– hierarchical shot representation

– pseudo-semantic shot labeling

– active browsing based on relevance feedback

• ViBE provides an extensible framework that will scale as the video data grows in size and applications increase in complexity

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Video Analysis: Overview

Audio data

Image data (DC frames)

MPEG-related data(MVs, AC coeffs, etc.)

Compressedvideo

sequence

Proc.

Proc.

Closed-caption information Proc.

Transitionlocations and types

ShottreesProc.

Captions

Shot attributes

DataExtraction

ShotTransitionDetection

and Identification

IntrashotClustering

ShotLabeling

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Navigation via the Similarity Pyramid

Zoom in

Zoom out

Zoom in

Zoom out

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Browser Interface

Relevance SetSimilarity Pyramid Control Panel

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Video Over IP: Unicast

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Video Over IP: Multicast

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Video Over IP

• Currently multicasting 3 streams

• Multicast experiments with Europe

• Multicast HDTV over Internet2

• Issues:

– what is the backward information?

– which video compression technique?

– how is network control connected to the server/encoder?

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• Scenario

– an owner places digital images on a network server and wants to detect the redistribution of altered versions

• Goals

– verify the owner of a digital image

– detect forgeries of an original image

– identify illegal copies of the image

– prevent unauthorized distribution

Why is Digital Watermarking Important?

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Why is Watermarking Important?

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Why is Watermarking Important?

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Why Watermarking is Important?

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Why is Watermarking Important?

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VW2D Watermarked Image

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Image Adaptive Watermarks (DCT)

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Scalable Image and Video Compression

• Problem: desire to have a compression technique that allows decompression to be linked to the application

– databases, wireless transmission, Internet imaging

– will support both high and low data rate modes

• Other desired properties:

– error concealment

– will support the protection of intellectual property rights (watermarking)

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Rate Scalable Image and Video Coding

• Applications– Internet streaming– Image and video database search - browsing

– Video servers

– Teleconferencing and Telemedicine

– Wireless Networks

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Scalability

• Picture Coding Symposium(April 1999) - panel on “The Future of Video Compression,” importance of scalability:

– rate scalability (Internet and wireless)

– temporal scalability (Internet and wireless)

– spatial scalability (databases - MPEG-7)

– content scalability (MPEG-4)

(Computational Scalability - implementation issues)

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Scalability

“Author and Compress once - decompress on any platform feed by any data pipe”

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Scalability: Compression Standards

• Scalability in JPEG

– progressive mode

– JPEG 2000

• Scalability in MPEG-2

– scalability is layered

• Scalability in MPEG-4

– layered

– “content”

– fine grain scalability (fgs)

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Color Embedded Zero-Tree Wavelet (CEZW)

• Developed new technique known as Color Embedded Zero-Tree Wavelet (CEZW)

• Modified EZW with trees connecting all color components

– can be extended to other color spaces

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Spatial Orientation Trees

EZW SPIHT

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New Spatial Orientation Tree (CEZW)

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Color Image Compression

Original CEZW

JPEG SPIHT

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Coding Artifacts

OriginalCEZW

JPEGSPIHT

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Comparison

JPEG 0.25 bits/pixel CEZW 0.25 bits/pixel

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Color Compression - Experiments

• Objectives:

– Evaluate scalable color image compression techniques

– Color Transformations

– Spatial Orientation Trees and Coding Schemes

– Embedded Coding • Embedded Zerotree Wavelet: Shapiro (Dec’93)

• Set Partitioning in Hierarchical Trees: Said & Pearlman (Jun’96)

• Color Embedded Zerotree Wavelets: Shen & Delp (Oct ‘97)

M. Saenz, P. Salama, K. Shen and E. J. Delp, "An Evaluation of Color Embedded Wavelet Image Compression Techniques," VCIP 1999

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Metrics

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SAMCoW

• New scalable video compression technique - Scalable Adaptive Motion COompensated Wavelet compression

• Features of SAMCoW:

– use wavelets on entire frame and for prediction error frames

– uses adaptive motion compensation to reduce error propagation

– CEZW is used for the wavelet coder on both the intra-coded frames and prediction error frames

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Generalized Hybrid Codec

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Adaptive Motion Compensation

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SAMCoW Enhancements

• B frames (ICIP98)

• unrestricted motion vectors (ICIP98)

• half-pixel motion searches (ICIP98)

• detailed study of PEF (ICIP99 and VLBW99)

– denoising techniques

• bit allocation and rate control (ICIP99)

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Error Concealment

• In data networks, channel errors or congestion can cause cell or packet loss

• When compressed video is transmitted, cell loss causes macroblocks and motion vectors to be removed from compressed data streams

• Goal of error concealment: Exploit redundant information in a sequence to recover missing data

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Error Concealment

Original frame Damaged frame

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Approaches for Error Concealment

• Two approaches for error concealment:

– Active concealment: Use of error control coding techniques and retransmission

• unequal error protection

– Passive concealment: The video stream is post-processed to reconstruct missing data

• Passive concealment is necessary:

– where active concealment cannot be used due to compliance with video transmission standards

– when active concealment fails

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Error Concealment

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Error Concealment

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Future Work

• Video Streaming (wired and wireless)

• Color Compression experiments (JPEG2000)

• Video databases ViBE

• Video watermarking

• Internet2 and multicasting scalable video

• Error concealment in embedded codecs

Edward J. Delp Intel December 3, 1999 Slide 48

How I Spent My Summer