Video and Image Processing At Purdue

1. Video and Image Processing At Purdue Edward J. Delp Video 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

2. Acknowledgements • Students - • Eduardo Asbun • Dan Hintz • Paul Salama • Ke Shen • Martha Saenz • Eugene Lin • Ray Wolfgang • Greg Cook • Sheng Liu

3. 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/

4. Image and Video Processing at Purdue Purdue has a rich history 60 year history in image and video processing.

5. 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

6. VIPER Research Projects • Multicast Video • Analysis of Mammograms • Embedded Image and Video Processing

7. 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

8. Density 1 Density 2 Density 3 Density 4 Analysis of Mammograms

9. Detection Results A 12.4mm lesion detected at the second coarsest resolution Automatic Detection Ground Truth

10. Detection Results A 6.6mm lesion detected at the finest resolution Automatic Detection Ground Truth

11. 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

12. Video Analysis: Overview Closed-caption information Proc. Audio data Proc. Shot Transition Detection and Identification Compressed video sequence Image data (DC frames) Data Extraction Proc. MPEG-related data (MVs, AC coeffs, etc.) Shot attributes Transition locations and types Shot Labeling Shot trees Intrashot Clustering Proc. Captions

13. Navigation via the Similarity Pyramid Zoom in Zoom out Zoom in Zoom out

14. Browser Interface Control Panel Similarity Pyramid Relevance Set

15. Video Over IP: Unicast

16. Video Over IP: Multicast

17. 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?

18. Why is Digital Watermarking Important? • 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

19. Why is Watermarking Important?

20. Why is Watermarking Important?

21. Why Watermarking is Important?

22. Why is Watermarking Important?

23. VW2D Watermarked Image

24. Image Adaptive Watermarks (DCT)

25. 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)

26. Rate Scalable Image and Video Coding • Applications • Internet streaming • Image and video database search - browsing • Video servers • Teleconferencing and Telemedicine • Wireless Networks

27. 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)

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

29. 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)

30. 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

31. Spatial Orientation Trees EZW SPIHT

32. New Spatial Orientation Tree (CEZW)

33. Color Image Compression Original CEZW JPEG SPIHT

34. Coding Artifacts CEZW Original JPEG SPIHT

35. Comparison JPEG 0.25 bits/pixel CEZW 0.25 bits/pixel

36. 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

37. Metrics

38. 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

39. Generalized Hybrid Codec

40. Adaptive Motion Compensation

41. 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)

42. 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

43. Error Concealment Original frame Damaged frame

44. 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

45. Error Concealment

46. Error Concealment

47. 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

48. How I Spent My Summer