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Scalable Video ConferencingUsing Subband Transform Coding
and Layered Multicast Transmission
Mathias JohansonSwedish Research Institute for Information Technology
Scalability in Videoconferencing
• Large number of video receivers (and senders)
• Multiple quality levels in a single multipoint conference session
• Differentiated host and network requirements
• Realizable over public internetworks
• CODEC operates at fixed bandwidth
• Multipoint operation involves gateways
• Differentiated quality levels in a multipoint session require transcoders that are expensive and introduce latency
• Often dependent on level 2 network protocols (e.g. ISDN systems)
Limitations of Traditional Videoconferencing Systems
Approach...
• Scalable codec based on subband transform coding
• Receiver-driven layered IP-multicast transmission
• Software implementation + DSP-based implementation
Layered Video Coding
• Temporal layering– Increased number of refinement layers correspond to
higher framerate
• Spatial layering– Increased number of refinement layers correspond to
higher image resolution
• Layered quantization– Increased number of refinement layers correspond to
finer quantization
Temporal Layering
Channel 1
Channel 2
Channel 3
Channel 4
Transmission channels that can be received independently
Images of a video sequence
Spatial Layering
Channel 1
Transform
Channel 3
Channel 2
Original imageBase signal + refinement signals
Layered image and video encoding/compression formats
• Hierarchical JPEG
• MPEG-2 scalable mode– temporal, spatial, SNR scalability
• H.263 scalable mode
• Wavelets
Block-based DCT
Subband transform
Base layer
Refinementlayer
Down-sample
x(t)Encode
Encode
Decode
Upsample
Spatial scalability in block based image and video encodings
Wavelet-based approach to spatial scalability
Glow
x(t)
(t)y0
(t)y1
2
2Ghigh
base layer
refinement layer
Quadrature mirror filters implementing the wavelet transform
Encode
Encode
Wavelet transform
Iterate….
horizontal transform vertical transform
Original image
Transformed image
Wavelet compression
• Colorspace conversion and subsampling– RGB -> YCrCb 4:2:2
• Wavelet transform (separately on Y, Cr, Cb)– Subband decomposition
• Quantization of each subband/component– Lossy compression step
• Huffman encoding– entropy coding
Communication Architecture
• Transmit the subbands of the transformed images on separate channels that can be received independently
• Multicasting
• Leaf-initiated JOIN-mechanism
RLMReceiver-driven Layered (IP) Multicast
224.3.4.5
224.3.4.6
224.3.4.7
224.3.4.8
Refinement layers
Base layer
RInternet
Sender Receiver (4 layers)
Receiver (1 layer)
High bandwidth
Low bandwidthMulticast router
Wavelet RTP header
FOLW
HQ1
Q2Q3
Fragmentation OffsetLayer NumberWidth
HeightY Quantization Factor
Cr Quantization FactorCb Quantization Factor
H1H2H3
L1L2
L3
Y Huffman Table SizeCr Huffman Table SizeCb Huffman Table Size
Y Data LengthCr Data Length
Cb Data Length
FO L W H Q1 Q2 Q3 H1 H2 H3 L1 L2 L3
0 32 64 96 128 160 192
Prototype implementation
• Based on Smile!
• Software wavelet codec
• Receiver-driven layered IP multicast network module
• RTP/RTCP
• Spatial and temporal scalability
• SGI O2, MIPS R5000 processor
Usage Scenario highly heterogeneous environment
RHigh-speed LAN
Internet
Dial-up access
Medium qualityLow quality
High quality
Leased Line
Leased Line
Transmitter