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MSI
Image and Video CompressionImage and Video Compression
Edward J. DelpEdward J. DelpVideo and Image Processing Laboratory (VIPER) Video and Image Processing Laboratory (VIPER)
School of Electrical and Computer EngineeringSchool of Electrical and Computer EngineeringPurdue UniversityPurdue University
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OverviewOverview
ContributorsContributors• Wojciech SzpankowskiWojciech Szpankowski• Ananth GramaAnanth Grama• Edward DelpEdward Delp
What are the demands on compressionWhat are the demands on compression• New approaches: scalable techniques and New approaches: scalable techniques and
pattern matching approachespattern matching approaches• Error robustness: concealmentError robustness: concealment• SecuritySecurity
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Purdue UniversityPurdue University
Purdue has a rich 65 year history in Purdue has a rich 65 year history in video and imagingvideo and imaging
Why do compression?Why do compression?
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The “Digital Image” ProblemThe “Digital Image” Problem
A 1024x1024 image has 1,048,576 A 1024x1024 image has 1,048,576 pixels at pixels at • 24 bits/pixel = 25,165,824 bits24 bits/pixel = 25,165,824 bits
A video (NTSC/CCIR 601) A video (NTSC/CCIR 601) • 760x480 = 345,600 pixels 760x480 = 345,600 pixels
• 30 frames/sec = 10,368,000 pixels/sec30 frames/sec = 10,368,000 pixels/sec
• 16 bits/pixel(4:2:2) = 165,888,000 bits/sec16 bits/pixel(4:2:2) = 165,888,000 bits/sec
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Digital Video RatesDigital Video Rates
CIF (4:1:1) with 12 bits/pixel CIF (4:1:1) with 12 bits/pixel
31,104,000 bits/sec31,104,000 bits/sec CCIR 601 (4:2:2) with 16 bits/pixel CCIR 601 (4:2:2) with 16 bits/pixel
165,888,000 bits/sec165,888,000 bits/sec HDTV (GA 1920x1080, 4:2:2, 60 HDTV (GA 1920x1080, 4:2:2, 60
frames/sec, Proscan) with 20 bits/pixel frames/sec, Proscan) with 20 bits/pixel
2,488,320,00 bits/sec2,488,320,00 bits/sec
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ScalableScalable
Scalable - Scalable -
““Author and compress ONCE Author and compress ONCE decompress on ANY platform feed by decompress on ANY platform feed by ANY data pipe”ANY data pipe”
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ScalabilityScalability
Date rate scalabilityDate rate scalability SNR or quality scalabilitySNR or quality scalability Spatial scalabilitySpatial scalability Temporal scalabilityTemporal scalability Computational scalabilityComputational scalability ““Content” scalabilityContent” scalability
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Scalable CompressionScalable Compression
ApplicationsApplications• Internet delivery (aid in QoS)Internet delivery (aid in QoS)• Image and video database search - browsingImage and video database search - browsing• Video serversVideo servers• Teleconferencing and telemedicineTeleconferencing and telemedicine• Wireless networksWireless networks• Kodak’s Photo-CDKodak’s Photo-CD• Distributed multimedia documentsDistributed multimedia documents
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MSI Scalability in JPEGScalability in JPEG• Progressive modeProgressive mode• JPEG 2000JPEG 2000
Scalability in MPEG-2Scalability in MPEG-2• Scalability is layeredScalability is layered
Scalability in MPEG-4Scalability in MPEG-4• LayeredLayered• ““Content”Content”
Scalability: StandardsScalability: Standards
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Embedded CodingEmbedded Coding
Continuously scalableContinuously scalable All compressed data embedded in a All compressed data embedded in a
single bit streamsingle bit stream Embed the important information at the Embed the important information at the
beginning of the bit streambeginning of the bit stream Can truncate at any data rate or Can truncate at any data rate or
decoded qualitydecoded quality
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Scalable CompressionScalable Compression
Two new approachesTwo new approaches• Color Embedded Zero Tree Compression Color Embedded Zero Tree Compression
(CEZW)(CEZW)• Scalable Adaptive Motion Compensation Scalable Adaptive Motion Compensation
Wavelet Compression (SAMCoW)Wavelet Compression (SAMCoW)
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Original
CEZW
JPEG
SPIHT
Scalable Color CompressionScalable Color Compression
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Coding ArtifactsCoding Artifacts
Original
CEZW
JPEGSPIHT
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ComparisonComparison
JPEG 0.25 bits/pixel CEZW 0.25 bits/pixel
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2D-Pattern Matching Compression2D-Pattern Matching Compression
Where does this pattern match in image Where does this pattern match in image or video frame?or video frame?• Central Theme is lossy extension to Central Theme is lossy extension to
Lempel-Ziv algorithmLempel-Ziv algorithm• Strong theoretical underpinningsStrong theoretical underpinnings• Use for both images and videoUse for both images and video• Use for synthetic images and text - fits into Use for synthetic images and text - fits into
MPEG-4MPEG-4
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Pattern Matching CompressionPattern Matching Compression
Pattern Matching
JPEG
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Error Concealment (1)Error Concealment (1)
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Error Concealment (2)Error Concealment (2)
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Security:WatermarkingSecurity:Watermarking
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ViBEViBE
ViBE has four componentsViBE has four components• Scene change detection and identificationScene change detection and identification• Hierarchical shot representationHierarchical shot representation• Pseudo-semantic shot labelingPseudo-semantic shot labeling• Active browsing based on relevance Active browsing based on relevance
feedbackfeedback ViBE provides an extensible framework ViBE provides an extensible framework
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MSIZoom in
Zoom out
Zoom in
Zoom out
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Browser InterfaceBrowser Interface
Relevance SetSimilarity Pyramid Control Panel
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Proposed EquipmentProposed Equipment
Encoders/Decoders Encoders/Decoders • Used for populating databases with video Used for populating databases with video
and images using current standardsand images using current standards Networking SystemsNetworking Systems• Used to test new ideas in scalable Used to test new ideas in scalable
compression and pattern matching compression and pattern matching techniquestechniques