This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-d motion estimation to create a homogenous preprocesseddata to be compressed by a 3-d image ...
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This paper proposes an efficient compression scheme for compressing time-varying medical volumetric data. The scheme uses 3-d motion estimation to create a homogenous preprocesseddata to be compressed by a 3-d image compression algorithm using hierarchical vector quantization. A new block distortion measure, called variance of residual (VOR), and three 3-d fast block matching algorithms are used to improve the motion estimation process in term of speed anddata fidelity. The 3-d image compression process involves the application of two different encoding techniques based on the homogeneity of input data. Our method can achieve a higher fidelity and faster decompression time compared to other lossy compression methods producing similar compression ratios. The combination of 3-d motion estimation using VOR and hierarchical vector quantization contributes to the good performance. (C) 2011 Elsevier Ltd. All rights reserved.
This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of fra...
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ISBN:
(纸本)9781479928392
This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step anddo not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-ddynamic surfaces. This representation is shown to achieve compression of 4-ddata and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standarddatasets using free-viewpoint video and 3-d tracking applications.
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