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Spatial correlation genetic algorithm for fractal image compression

Author

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  • Wu, Ming-Sheng
  • Teng, Wei-Chih
  • Jeng, Jyh-Horng
  • Hsieh, Jer-Guang

Abstract

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In this paper, a spatial correlation genetic algorithm (SC-GA) is proposed to speed up the encoder. There are two stages for the SC-GA method. The first stage makes use of spatial correlations in images for both the domain pool and the range pool to exploit local optima. The second stage is operated on the whole image to explore more adequate similarities if the local optima are not satisfied. With the aid of spatial correlation in images, the encoding time is 1.5 times faster than that of traditional genetic algorithm method, while the quality of the retrieved image is almost the same. Moreover, about half of the matched blocks come from the correlated space, so fewer bits are required to represent the fractal transform and therefore the compression ratio is also improved.

Suggested Citation

  • Wu, Ming-Sheng & Teng, Wei-Chih & Jeng, Jyh-Horng & Hsieh, Jer-Guang, 2006. "Spatial correlation genetic algorithm for fractal image compression," Chaos, Solitons & Fractals, Elsevier, vol. 28(2), pages 497-510.
    • Handle: RePEc:eee:chsofr:v:28:y:2006:i:2:p:497-510
    DOI: 10.1016/j.chaos.2005.07.004
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    Citations

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    Cited by:

    1. Chiou, Juing-Shian & Cheng, Chun-Ming, 2009. "Stabilization analysis of the switched discrete-time systems using Lyapunov stability theorem and genetic algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 751-759.
    2. Akemi Gálvez & Iztok Fister & Andrés Iglesias & Iztok Fister & Valentín Gómez-Jauregui & Cristina Manchado & César Otero, 2022. "IFS-Based Image Reconstruction of Binary Images with Functional Networks," Mathematics, MDPI, vol. 10(7), pages 1-26, March.
    3. Chen, Zuoping & Ye, Zhenglin & Wang, Shuxun & Peng, Guohua, 2009. "Image magnification based on similarity analogy," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2370-2375.
    4. Zhou, Yi-Ming & Zhang, Chao & Zhang, Zeng-Ke, 2009. "An efficient fractal image coding algorithm using unified feature and DCT," Chaos, Solitons & Fractals, Elsevier, vol. 39(4), pages 1823-1830.
    5. Zhou, Yi-Ming & Zhang, Chao & Zhang, Zeng-Ke, 2008. "Fast hybrid fractal image compression using an image feature and neural network," Chaos, Solitons & Fractals, Elsevier, vol. 37(2), pages 623-631.
    6. Lu, Jian & Ye, Zhongxing & Zou, Yuru & Ye, Ruisong, 2008. "An enhanced fractal image denoising algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 38(4), pages 1054-1064.
    7. Chauveau, Julien & Rousseau, David & Richard, Paul & Chapeau-Blondeau, François, 2010. "Multifractal analysis of three-dimensional histogram from color images," Chaos, Solitons & Fractals, Elsevier, vol. 43(1), pages 57-67.
    8. Cerruti, Umberto & Dutto, Simone & Murru, Nadir, 2020. "A symbiosis between cellular automata and genetic algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    9. Lien, Chang-Hua, 2007. "Delay-dependent and delay-independent guaranteed cost control for uncertain neutral systems with time-varying delays via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 33(3), pages 1017-1027.
    10. Chapeau-Blondeau, François & Chauveau, Julien & Rousseau, David & Richard, Paul, 2009. "Fractal structure in the color distribution of natural images," Chaos, Solitons & Fractals, Elsevier, vol. 42(1), pages 472-482.

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