Abstract
Fractal-based image compression techniques are well known for its fast decoding process and resolution-independent decoded images. However, these types of techniques take more time to encode images. Domain classification strategy can greatly reduce encoding period. This paper proposed a new strategy of domain classification that groups domains in three-level hierarchical classes to speed up domain searching procedure. Then, the technique is further modified by sorting domains of each class based on frequency of matching. The results show that both the presented schemes significantly decrease the encoding duration of fractal coding and there are no effects on compression ratio and image quality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nelson, M.: The Data Compression Book, 2nd edn. BPB Publications, India (2008)
Fisher, Y.: Fractal Image Compression: Theory and Application. Springer, New York (1995)
Barnsley, M.F.: Fractal Everywhere. Academic Press, New York (1993)
Jacquin, A.E.: Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans. Image Process. 1, 18–30 (1992)
Jacquin, A.E.: Fractal image coding: a review. Proc. IEEE 81(10), 1451–14654 (1993)
Xing, C., Ren, Y., Li, X.: A hierarchical classification matching scheme for fractal image compression. In: IEEE Congress on Image and Signal Processing (CISP08), Sanya, vol. 1, pp. 283–286. Hainan, China (2008)
Bhattacharya, N., Roy, S. K., Nandi, U., Banerjee, S.: Fractal image compression using hierarchical classification of sub-images. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-15), pp. 46–53. Berlin, Germany (2015)
Jayamohan, M., Revathy, K.: Domain classification using B+ trees in fractal image compression. In: IEEE National Conference on Computing and Communication Systems (NCCCS), p. 15. Durgapur, India (2012)
Jayamohan, M., Revathy, K.: An improved domain classification scheme based on local fractal dimension. Indian J. Comput. Sci. Eng. (IJCSE) 3(1), 138–145 (2012)
Nandi, U., Mandal, J. K.: Fractal image compression with adaptive quad-tree partitioning and archetype classification. In: IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) 2015, pp. 56–60. Kolkata, West Bengal, India (2015)
Nandi, U., Mandal, J.K.: Efficiency of adaptive fractal image compression with archetype classification and its modifications. Int. J. Comput. Appl. (IJCA) 38(2–3), 156–163 (2016)
Nandi, U., Mandal, J.K., Santra, S., Nandi, S.: Fractal image compression with quadtree partitioning and a new fast classification strategy. In: 3rd International Conference on Computer Communication, Control and Information Technology (C3IT-2015), pp. 1–4. Hooghly, West Bengal, India (2015)
Nandi, U., Mandal, J. K.: A novel hierarchical classification scheme for adaptive quadtree partitioning based fractal image coding. In: 52nd Annual Convention of Computer Society of India (CSI 2017), pp. 19–21. Science City, Kolkata, West Bengal, India (2018)
Nandi, U.: An adaptive fractal-based image coding with hierarchical classification strategy and its modifications. Innov. Syst. Soft. Eng. 15(1), 35–42 (2019)
Acknowledgements
This work is carried out by using infrastructure of the Dept. of Computer Sc., Vidyasagar University, Paschim Medinipur, West Bengal, India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nandi, U., Laya, B., Ghorai, A., Singh, M.M. (2021). Three-Level Hierarchical Classification Scheme: Its Application to Fractal Image Compression Technique. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-5679-1_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)