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Efficient Hierarchical Structure of Wavelet-Based Compression for Large Volume Data Sets

机译：大数据集的基于小波压缩的高效分层结构

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摘要

With volume size increasing, it is necessary to develop a highly efficient compression algorithm, which is suitable for progressive refinement between the data server and the browsing client. For three-dimensional large volume data, an efficient hierarchical algorithm based on wavelet compression was presented, using intra-band dependencies of wavelet coefficients. Firstly, after applying blockwise hierarchical wavelet decomposition to large volume data, the block significance map was obtained by using one bit to indicate significance or insignificance of the block. Secondly, the coefficient block was further subdivided into eight sub-blocks if any significant coefficient existed in it, and the process was repeated, resulting in an incomplete octree. One bit was used to indicate significance or insignificance, and only significant coefficients were stored in the data stream. Finally, the significant coefficients were quantified and compressed by arithmetic coding. The experimental results show that the proposed algorithm achieves good compression ratios and is suited for random access of data blocks. The results also show that the proposed algorithm can be applied to progressive transmission of 3D volume data.
机译：随着卷大小的增加，有必要开发一种高效的压缩算法，该算法适用于数据服务器和浏览客户端之间的逐步优化。针对三维大数据，提出了一种基于小波压缩的高效分层算法，利用小波系数的带内相关性。首先，将块状分层小波分解应用于大容量数据后，通过使用一位指示块的重要性或无意义获得块重要性图。其次，如果系数块中存在任何重要系数，则将其进一步细分为八个子块，并重复该过程，从而导致八叉树不完整。一位用于指示有效或无效，并且仅有效系数存储在数据流中。最后，通过算术编码对有效系数进行量化和压缩。实验结果表明，该算法具有良好的压缩率，适用于数据块的随机访问。结果还表明，所提出的算法可以应用于3D体数据的渐进传输。

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