Abstract
Considering empty region in the volumetric data occupying a certain percentage, an efficient large-scale data compression algorithm based on VQ is presented. Firstly, the entire volume data are divided into many smaller regular blocks, and the blocks are classified into two groups according to their average gradient values: one consists of those blocks with zero average gradient value, and the other consists of those with non-zero average gradient values. Secondly, only those blocks with non-zero average gradient values are decomposed into a three hierarchical representation and vector quantized. Finally, block data in different groups are reconstructed with different ways. When applying this algorithm to the volume data, all experimental results demonstrate the proposed algorithm is more efficient than most existing large-scale volume data compression algorithms.
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Xiao, D., Zhao, L., Yang, L., Li, Z., Li, K. (2009). An Efficient Large-Scale Volume Data Compression Algorithm. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_62
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DOI: https://doi.org/10.1007/978-3-642-01513-7_62
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