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Transfer Function-Guided Saliency-Aware Compression for Transmitting Volumetric Data

delete2020-09-01
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OA
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J
Ji Hwan Park *
A
Arie Kaufman
DOI:10.1109/TMM.2017.2757759delete
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Abstract

Abstract

En 中文
We introduce a transfer-function-guided three-dimensional (3-D) block-based saliency-aware compression scheme for volumetric data that is both content and spatially scalable. Salient 3-D volumetric blocks are identified and weighted with the help of a transfer function which is used to render the data. We describe our method in the form of a framework for processing, progressive transmission, and visualization of volumetric data on a target device, such as a mobile device with limited computational resources. In particular, we address the transmission bottleneck incurred when transferring 3-D volumetric data. Identified salient regions are progressively transmitted to the target device. The received data are rendered progressively in the respective order with a predefined or user-defined transfer function. Our method is developed with medical applications in mind, where preservation of all information is essential for clinical diagnosis. Because our method is integrated into a resolution scalable coding scheme with an integer wavelet transform of the image, it allows the rendering of each significant region at a different resolution up to fully lossless reconstruction. We perform a thorough qualitative and quantitative evaluation of the saliency detection method and the resulting saliency-aware compression schemes. Our results show reduced error in representation of the volumetric data with our method.
Keywords:
Three-dimensional displays
Image coding
Data visualization
Rendering (computer graphics)
Encoding
Discrete cosine transforms
Compression
saliency
volume visualization
wavelets
discrete cosine transform
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Journal

IEEE Transactions on Multimedia cover
IEEE Transactions on Multimedia
IF:
9.7
Papers:
4.5K
Citations:
2.4W

Organization

S
state university of new york (suny) system
Scholars:
6.5W
Papers: 5.8W
Citations: 65