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Image and Video Compression

Image and Video Compression. Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University. Overview. Contributors Wojciech Szpankowski Ananth Grama Edward Delp What are the demands on compression

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Image and Video Compression

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  1. Image and Video Compression Edward J. DelpVideo and Image Processing Laboratory (VIPER) School of Electrical and Computer EngineeringPurdue University

  2. Overview • Contributors • Wojciech Szpankowski • Ananth Grama • Edward Delp • What are the demands on compression • New approaches: scalable techniques and pattern matching approaches • Error robustness: concealment • Security

  3. Purdue University • Purdue has a rich 65 year history in video and imaging • Why do compression?

  4. The “Digital Image” Problem • A 1024x1024 image has 1,048,576 pixels at • 24 bits/pixel = 25,165,824 bits • A video (NTSC/CCIR 601) • 760x480 = 345,600 pixels • 30 frames/sec = 10,368,000 pixels/sec • 16 bits/pixel(4:2:2) = 165,888,000 bits/sec

  5. Digital Video Rates • CIF (4:1:1) with 12 bits/pixel 31,104,000 bits/sec • CCIR 601 (4:2:2) with 16 bits/pixel 165,888,000 bits/sec • HDTV (GA 1920x1080, 4:2:2, 60 frames/sec, Proscan) with 20 bits/pixel 2,488,320,00 bits/sec

  6. Scalable Scalable - “Author and compress ONCE  decompress on ANY platform feed by ANY data pipe”

  7. Scalability • Date rate scalability • SNR or quality scalability • Spatial scalability • Temporal scalability • Computational scalability • “Content” scalability

  8. Scalable Compression • Applications • Internet delivery (aid in QoS) • Image and video database search - browsing • Video servers • Teleconferencing and telemedicine • Wireless networks • Kodak’s Photo-CD • Distributed multimedia documents

  9. Scalability: Standards • Scalability in JPEG • Progressive mode • JPEG 2000 • Scalability in MPEG-2 • Scalability is layered • Scalability in MPEG-4 • Layered • “Content”

  10. Embedded Coding • Continuously scalable • All compressed data embedded in a single bit stream • Embed the important information at the beginning of the bit stream • Can truncate at any data rate or decoded quality

  11. Scalable Compression • Two new approaches • Color Embedded Zero Tree Compression (CEZW) • Scalable Adaptive Motion Compensation Wavelet Compression (SAMCoW)

  12. Scalable Color Compression CEZW Original SPIHT JPEG

  13. Coding Artifacts CEZW Original JPEG SPIHT

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

  15. 2D-Pattern Matching Compression • Where does this pattern match in image or video frame? • Central Theme is lossy extension to Lempel-Ziv algorithm • Strong theoretical underpinnings • Use for both images and video • Use for synthetic images and text - fits into MPEG-4

  16. Pattern Matching Compression Pattern Matching JPEG

  17. Error Concealment (1)

  18. Error Concealment (2)

  19. Security:Watermarking

  20. ViBE • 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

  21. Zoom in Zoom out Zoom in Zoom out

  22. Browser Interface Similarity Pyramid Control Panel Relevance Set

  23. Proposed Equipment • Encoders/Decoders • Used for populating databases with video and images using current standards • Networking Systems • Used to test new ideas in scalable compression and pattern matching techniques

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