Volume 6 Number 2 (Feb. 2011)
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JSW 2011 Vol.6(2): 322-330 ISSN: 1796-217X
doi: 10.4304/jsw.6.2.322-330

A Content-based Classified Hierarchical Vector Quantization Algorithm for Volume Compression

Li-Ping Zhao1, Guang-Xue Yue1, 2, 3, De-Gui Xiao2, Xu Zhou1, Xiang Yu1, Fei Yu4
1School of Mathematics and Information Engineering, Jiaxing University, Jiaxing, Zhejiang, China
2School of Computer and Communication Engineering, Hunan University, Changsha, Hunan, China
3Department of Computer Science and Technology, Huaihua University, Huaihua, China
4Jiangsu Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow, China


Abstract—An improved volumetric compression algorithm is presented in this paper. Histogram technique is used for analyzing the trait of volume data. The volume data is then partitioned into volume bricks which will be classified into two groups, the blocks with meaningless information as one group(also called empty blocks), and those with meaningful information as the other group(also called object blocks). An efficient hierarchical VQ is applied to compress object blocks while for empty blocks, nothing is saved. Compare with analogous Volume Compression algorithm, experimental results demonstrate the proposed algorithm not only can improve the compression rate significantly on the premise of the good quality of reconstruction image, but also can obtain fast decoding speed.

Index Terms—Vector quantization, volume Classify, Object blocks, Volume compression, GPU

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Cite: Li-Ping Zhao, Guang-Xue Yue, De-Gui Xiao, Xu Zhou, Xiang Yu, Fei Yu, "A Content-based Classified Hierarchical Vector Quantization Algorithm for Volume Compression," Journal of Software vol. 6, no. 2, pp. 322-330, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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