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Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021 (AISI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 100))

Abstract

Vector quantization image compression algorithm is an effective data compression technique for image reconstruction by codebook and index, effectively reducing the amount of transmitted data. However, the initial codebook can affect the quality of reconstructed images. This paper proposes a modified bat algorithm based on the adaptive separation search mode to optimize the design of the codebook using the mean square error as the adaptation value. The bat algorithm uses the pulse emission rate to switch the search mode and the loudness to determine the search range, but it still suffers from a shortage of search capability. Therefore, adaptive separation rules are introduced to avoid early convergence and to improve the global exploration capability. The update formulas of some parameters are modified to improve the search performance. Finally, the experimental data show that the initial codebook based on the modified bat algorithm can effectively improve the image reconstruction performance.

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References

  1. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)

    Article  Google Scholar 

  2. Yang, Y.P., Tsai, J.T., Chou, J.H.: PCA-based fast search method using PCA-LBG-based VQ codebook for codebook search. IEEE Access 4, 1332–1344 (2016)

    Article  Google Scholar 

  3. Zhang, C., Cheng, J., Liu, J., Pang, J., Huang, Q., Tian, Q.: Beyond explicit codebook generation: visual representation using implicitly transferred codebooks. IEEE Trans. Image Process. 24(12), 5777–5788 (2015)

    Article  MathSciNet  Google Scholar 

  4. Janet, B., Reddy, A.V., Domnic, S.: Incremental codebook generation for vector quantization in large scale content based image retrieval. In: 2010 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–4 (2010)

    Google Scholar 

  5. Zouache, D., Moussaoui, A., Abdelazizc, F.B.: A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem. Eur. J. Oper. Res. 246(1), 74–88 (2021)

    Article  MathSciNet  Google Scholar 

  6. Ntakolia, C., Iakovidis, D.K.: A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning. Comput. Oper. Res. 133, 105358 (2021)

    Google Scholar 

  7. Chiranjeevi, K., Jena, U.R.: Image compression based on vector quantization using cuckoo search optimization technique. Ain Shams Eng. J. 9(4), 1417–1431 (2018)

    Article  Google Scholar 

  8. Fonseca, C.S., Ferreira, F.A.B.S., Madeiro, F.: Vector quantization codebook design based on Fish School Search algorithm. Appl. Soft Comput. 73, 958–968 (2018)

    Article  Google Scholar 

  9. Yang, X.S.: A new metaheuristic bat-inspired algorithm. Comput. Knowl. Technol. 284, 65–74 (2010)

    MATH  Google Scholar 

  10. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  11. Yang, X.S., Deb, S.: Eagle strategy using Levy walk and firefly algorithms for stochastic optimization. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 101−111. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_9

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  13. Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Eco. Inform. 1(4), 355–366 (2006)

    Article  Google Scholar 

Download references

Acknowledgment

The Minjiang University partially supported this work under Grant MJY192026, 2021J011017, 103952021126 and 103952021128.

Author information

Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, 350121, China

    J. R. Guo, C. Y. Wu, Z. L. Huang, F. J. Wang & M. T. Huang

  2. Industrial Design and Research Institute, Minjiang University, Fuzhou, China

    C. Y. Wu

Authors
  1. J. R. Guo
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  2. C. Y. Wu
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  3. Z. L. Huang
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  4. F. J. Wang
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  5. M. T. Huang
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Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Artificial Intelligence, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VÅ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. Fujian University of Technology, Shulin District, New Taipei, Taiwan

    Kuo-Chi Chang

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. University of Salford, Manchester, UK

    Tarek Gaber

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Guo, J.R., Wu, C.Y., Huang, Z.L., Wang, F.J., Huang, M.T. (2022). Vector Quantization Image Compression Algorithm Based on Bat Algorithm of Adaptive Separation Search. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_16

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