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• W320170191 abstract "This thesis deals with the tremendous amounts of data produced by camera arrays used in emerging technologies like 3D-TV and free-viewpoint TV. Being highly redundant, the imagery is eligible for efficient compression. A commonly used data format is N-texture/N-depth where each camera image is accompanied by pixelwise scene depth values. For this format, we present a novel codec. Tree-structured depth images are proposed to encode the depth information. Their advantage is efficient self-sustained compression without side information. To process the texture, we extend the class of Motion-Compensated Orthogonal Transforms by a wavelet decomposition along the view axis using depth image based rendering (DIBR). To cope with prevalent visibility issues of DIBR, orthogonal shape-adaptive transforms have been developed. The combination of the proposals yields a rate-distortion optimal codec. This system exploits the specific properties of depth images as opposed to previous approaches which rather attach a depth coder to a texture coder. The performance is evaluated by encoding multiview testsets and measuring the MSE after decoding. In the process, a MEX-file implementation of the Embedded Block Coding with Optimized Truncation algorithm has been created. We believe its speed boost can prove valuable for other’s projects in the field." @default.
• W320170191 created "2016-06-24" @default.
• W320170191 creator A5078528804 @default.
• W320170191 date "2011-01-01" @default.
• W320170191 modified "2023-09-27" @default.
• W320170191 title "Compression and View Interpolation for Multiview Imagery" @default.
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• W320170191 hasPublicationYear "2011" @default.
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