Introduction Compression Performance Conclusions Large Camera Arrays Capture multi-viewpoint images of a scene/object. Potential applications abound: surveillance,

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  • IntroductionCompression PerformanceConclusionsLarge 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 usedDistributed 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 UniversitySystem DescriptionRendering 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 CPU Execution Timemilliseconds(ms) per pictureBasic 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 codingShape 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 DecoderScalerQuantizerTurbo CoderWyner-Ziv EncoderTurboDecoderReconstruction

    BufferParity BitsRequest BitsWyner-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 ArchitectureProposed 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[Ramanathan 01]Contact: zhuxq@stanford.edu

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