Study on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize

We propose an improved HVQ approach by combining two detail levels to compress space environment volumes, based on the variation characteristics including smooth variation and significantly positive correlation between variations in the same direction at two different scales. First, the space environment data is divided into 43 blocks. Then blocks are decomposed into a two-level hierarchical representation and each block is represented by a mean value and a detail vector. Finally, the detail vectors are encoded by a vector quantizer. During the vector quantization process, PCA-split is applied to compute an initial codebook, and then LBG-algorithm is conducted for codebook refinement and quantization. We take advantage of the codebook-retraining method to speed up the quantization of time series with temporal coherence. Furthermore, we employ the progressive rendering based on GPU for realtime interactive visualization. The results of our experiments prove that the algorithm proposed in this paper can significantly improve the compression rate and decoding speed without sacrificing fidelity in space environment domain.

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Compression and Rendering of Space Environment Volume Data Based on Improved HVQ Algorithm

Bao

Cai

Cui

et al. 2020

J. Phys.: Conf. Ser.

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A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

Bao¹,

Cai²,

Lin³

et al. 2021

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Study on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize

Cited by 2 publications

References 13 publications

Compression and Rendering of Space Environment Volume Data Based on Improved HVQ Algorithm

Compression and Rendering of Space Environment Volume Data Based on Improved HVQ Algorithm

A Weight Based Hierarchical Vector Quantization Algorithm for Space Environment Volume Data

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