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

  • Stony Brook University

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Article number8052504
Pages (from-to)2262-2277
Number of pages16
JournalIEEE Transactions on Multimedia
Volume22
Issue number9
DOIs
StatePublished - Sep 2020

Keywords

  • Compression
  • discrete cosine transform
  • saliency
  • volume visualization
  • wavelets

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