Image and Video Compression by Osf3YB8

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									 Image and Video Compression
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                      Edward J. Delp
     Video and Image Processing Laboratory (VIPER)
      School of Electrical and Computer Engineering
                    Purdue University
 Overview
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        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                                2
 Purdue University
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        Purdue has a rich 65 year history in
         video and imaging

        Why do compression?




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 The “Digital Image” Problem
MS
I  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


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 Digital Video Rates
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      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

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 Scalable
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     Scalable -

     “Author and compress ONCE 
       decompress on ANY platform feed by
       ANY data pipe”




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 Scalability
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      Date rate scalability
      SNR or quality scalability
      Spatial scalability
      Temporal scalability
      Computational scalability
      “Content” scalability



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 Scalable Compression
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        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
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 Scalability: Standards
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        Scalability in JPEG
         • Progressive mode
         • JPEG 2000
        Scalability in MPEG-2
         • Scalability is layered
        Scalability in MPEG-4
         • Layered
         • “Content”
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 Embedded Coding
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      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

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




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 Scalable Color Compression
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                              CEZW



 Original




                              SPIHT
 JPEG

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 Coding Artifacts
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                    CEZW
 Original




                    JPEG
SPIHT


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 Comparison
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     JPEG 0.25 bits/pixel   CEZW 0.25 bits/pixel


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 2D-Pattern Matching Compression
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        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

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 Pattern Matching Compression
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            Pattern Matching




                 JPEG
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 Error Concealment (1)
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 Error Concealment (2)
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 Security:Watermarking
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 ViBE
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        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

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MS   Zoom in
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     Zoom out




     Zoom in
     Zoom out




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Browser Interface

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       Similarity Pyramid   Control Panel   Relevance Set

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 Proposed Equipment
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        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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