Special Issue: Artificial Intelligence and Pattern Recognition

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Fast adaptive wavelet for remote sensing image compression

Bo Li{1,2, Run-Hai Jiao{1, and Yuan-Cheng Li1   

  1. 1Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing 100083, China 2State Key Laboratory of Virtual Reality Technologies, Beihang University, Beijing 100083, China
  • Received:2007-01-15 Revised:2007-07-03 Online:2007-09-10 Published:2007-09-10

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm (FAWS). Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

Key words: inequality with radicals; rationalization; dichotomous search;



[1] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. -\it IEEE Transactions on Signal Processing}, 1993, 41(12): 3445$\sim$3462.

[2] Said A, Pearlman W A. A new, fast, and efficient image codec based on set partitioning in hierarchical trees. -\it IEEE Transactions on Circuits and Systems for Video Technology}, 1996, 6(3): 243$\sim$249.

[3] Taubman D. High performance scalable image compression with EBCOT. -\it IEEE Transactions on Image Processing}, 2000, 9(7): 1158$\sim$1170.

[4] Battle G. A block spin construction of OnDelettes, Part I: Lemarie functions. -\it Commun. Math. Phys}, 1987, 110: 601$\sim$615.

[5] Lemarie P G. OnDelettes a localization exponentielles. -\it J. Math. Pures. and Appl.,} 1988, 67: 227$\sim$236.

[6] Daubechies I. Orthonormal bases of compactly supported wavelets. -\it Comm. Pure Appl. Math.}, 1988, 41: 909$\sim$996.

[7] Cohen A, Daubechies I, Feauveau J C. Biorthogonal bases of compactly supported wavelets. -\it Comm. Pure Appl. Math.}, 1992, 45: 485$\sim$560.

[8] Daubechies I. Ten Lectures on Wavelets. Philadelphia: SIAM PA, 1992.

[9] Vetterli M, Herley C. Wavelets and filter banks: Theory and design. -\it IEEE Transactions on Signal Processing}, 1992, 40(9): 2207$\sim$2232.

[10] Phoong S M, Kim C W \it et al. \rm A new class of two-channel biorthogonal filter banks and wavelet bases. -\it IEEE Transactions Signal Processing}, 1995, 43(3): 649$\sim$665.

[11] Charles K Chui. An Introduction to Wavelets. New York: Academic Press Inc., 1992.

[12] Oraintara S, Tran T D, Nguyen T Q. A class of regular biorthogonal linear-phase filter banks: Theory, structure and application in image coding. -\it IEEE Transactions Signal Processing}, 2003, 51(12): 3220$\sim$3235.

[13] Kirac A, Vaidyanathan P P. Theory and design of optimum FIR compaction filter. -\it IEEE Transactions on Signal Processing}, 1998, 46(4): 903$\sim$919.

[14] Villasenor J D, Belzer B, Liao J. Wavelets filter evaluation for image compression. -\it IEEE Transactions on Image Processing}, 1995, 4(8): 1053$\sim$1060.

[15] Masud S, McCanny J V. Finding a suitable wavelet for image compression applications. In -\it Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing}, 1998, pp.2581$\sim$2584.

[16] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison-Wesley, 1989.

[17] Li B, Wang H. Bit plane predicting image compression algorithm based wavelet packet transform. -\it Chinese Journal of Computers}, 1999, 22(7): 686$\sim$691.
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[1] Zhang Bo; Zhang Ling;. Statistical Heuristic Search[J]. , 1987, 2(1): 1 -11 .
[2] Meng Liming; Xu Xiaofei; Chang Huiyou; Chen Guangxi; Hu Mingzeng; Li Sheng;. A Tree-Structured Database Machine for Large Relational Database Systems[J]. , 1987, 2(4): 265 -275 .
[3] Lin Qi; Xia Peisu;. The Design and Implementation of a Very Fast Experimental Pipelining Computer[J]. , 1988, 3(1): 1 -6 .
[4] Sun Chengzheng; Tzu Yungui;. A New Method for Describing the AND-OR-Parallel Execution of Logic Programs[J]. , 1988, 3(2): 102 -112 .
[5] Zhang Bo; Zhang Tian; Zhang Jianwei; Zhang Ling;. Motion Planning for Robots with Topological Dimension Reduction Method[J]. , 1990, 5(1): 1 -16 .
[6] Wang Dingxing; Zheng Weimin; Du Xiaoli; Guo Yike;. On the Execution Mechanisms of Parallel Graph Reduction[J]. , 1990, 5(4): 333 -346 .
[7] Zhou Quan; Wei Daozheng;. A Complete Critical Path Algorithm for Test Generation of Combinational Circuits[J]. , 1991, 6(1): 74 -82 .
[8] Zhao Jinghai; Liu Shenquan;. An Environment for Rapid Prototyping of Interactive Systems[J]. , 1991, 6(2): 135 -144 .
[9] Shang Lujun; Xu Lihui;. Notes on the Design of an Integrated Object-Oriented DBMS Family[J]. , 1991, 6(4): 389 -394 .
[10] Xu Jianguo; Gou Yuchai; Lin Zongkai;. HEPAPS:A PCB Automatic Placement System[J]. , 1992, 7(1): 39 -46 .

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CN 11-2296/TP

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