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Introduction
Compression Performance Conclusions
Large Camera Arrays• Capture multi-viewpoint images of a scene/object. • Potential applications abound:
• surveillance, special movie effects. • Image-based rendering [Levoy ’96]
• Joint encoding of multiple views cannot be used
Distributed Compression for Large Camera Arrays
• A distributed compression scheme for large camera arrays.
• Low-complexity Wyner-Ziv encoder
• Allows independent encoding of each camera view but centralized decoding to exploit inter-viewpoint image similarities.
• The existence of rendered side information and the use of shape adaptation techniques enhances compression efficiency.
• Experimental results show superior rate-PSNR performance over JPEG2000 and a JPEG-like SA-DCT coder, especially at low bit rates.
• Pixel domain coding and shape adaptation help to avoid blurry edges around the object (e.g., in JPEG2000) and blocky artifacts from block-based transform (e.g., in the SA-DCT coder).
Xiaoqing Zhu, Anne Aaron and Bernd GirodDepartment of Electrical Engineering, Stanford University
System Description
Rendering of Side Information
• The geometry model is reconstructed from silhouette information of the conventional camera views
• Side information of the Wyner-Ziv camera views are rendered based on pixel correspondences derived from the geometry.
Encoder Complexity
0.54
0.18
1.2
1.4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Buddha Garfield
Wyner-Ziv Coder JPEG2000
CPU Execution Timemilliseconds(ms) per picture
Basic Operations
• The Wyner-Ziv encoder needs:• 1 quantization step and 3 look-up-table procedures per pixel • shape extraction and coding
• The JPEG2000 compressor needs:• Multi-level 2-D DWT: ~ 5 multiplications per pixel • Content-based arithmetic coding
…
WZ-ENC
…
GeometryReconstruction
Rendering
Wyner-Ziv Cameras Conventional Cameras
DistributedEncoding
CentralizedDecoding
WZ-ENC
WZ-DEC WZ-DEC
Geometry Information
Side Information
Shape Adaptation
• Only encode pixels within the object shape
• Object shapes are obtained by chroma keying, compressed with JBIG, and then transmitted to the decoder.
Wyner-Ziv Decoder
ScalerQuantizer
Turbo Coder
Wyner-Ziv Encoder
TurboDecoder Reconstruction
X 'XQ
Buffer
Y
Parity BitsQ
Request Bits
Wyner-Ziv Codec
• The Wyner-Ziv coder in comparison with JPEG2000 and a SA-DCT coder, using the synthetic Buddha and the real-world Garfield data sets.
• Shape information is derived from perfect geometry for Buddha and coded at 0.0814 bpp for Garfield. The overhead of shape coding is counted in the Wyner-Ziv coder and the SA-DCT coder
[Aaron ’02]
Shape Architecture
Proposed Scheme• Apply Wyner-Ziv coding to multi-viewpoint images• Distributed encoding and joint decoding of the images, hence to benefit from the inter-viewpoint coherence.
Stanford Camera Array, Courtesy of Computer Graphics Lab, Stanford
0 0.05 0.1 0.15 0.2 0.25 0.3313233343536373839404142434445
bpp
PSN
R (d
B)
Buddha
WynerZivJPEG2000SA-DCT
0.05 0.1 0.15 0.2 0.2537
38
39
40
41
42
43
44
45
46
47
bpp
PSN
R (
dB)
Garfield
WynerZivJPEG2000SA-DCT
Rate-PSNR Curve
JPEG2000SA-DCT CoderWyner-Ziv Coder
Reconstructed Images
Rate = 0.11 bpp PSNR = 39.87 dB Rate = 0.12 bpp PSNR = 38.89 dB Rate = 0.11 bpp PSNR = 37.43 dB
Rate = 0.13 bpp PSNR = 42.68 dBRate = 0.15 bpp PSNR = 41.86 dBRate = 0.13 bpp PSNR = 44.08 dB
[Ramanathan ‘01]
Contact: [email protected]
