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Hybrid optimal algorithm-based 2D discrete wavelet transform for image compression using fractional KCA

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Abstract

Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve transform (KLT) is used to remove the spatial redundancies. In the second stage, 2D DWT is used to eliminate the intraband spatial redundancies. In the third stage, Fractional KCA (FKCA) is applied to improve the post-transformation process. FKCA is connected to the band of all wavelet sub-bands to minimize the spatial redundancy between intra sub-bands. Finally, the Hybrid Optimal algorithm (HOA) based FKCA is used to eliminate the residual and information redundancy among the neighboring bands. The experimental analysis of proposed 2D-DWT based Fractional KCA shows that the model improves the performance of compression data in terms of PSNR, MSSI, and VIF. Also, the multispectral image dataset shows the proposed compression model outperforms the existing compression models such as FKLT + PCA, ADWT + OADL, and DWT + DCT

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References

  1. Martina, M., Masera, G., Roch, M.R., Piccinini, G.: Result-based distributed-arithmetic-based filter architectures for approximately computing the DWT. IEEE Trans. Circ. Syst. I Regul. Pap. 62(8), 2103–2113 (2015)

    Article  Google Scholar 

  2. Prakash, M.S., Shaik, R.A.: Low-area and high-throughput architecture for an adaptive filter using distributed arithmetic. IEEE Trans. Circ. Syst. II Express Briefs 60(11), 781–785 (2013)

    Google Scholar 

  3. Qureshi, M.A., Deriche, M.: A new wavelet based efficient image compression algorithm using compressive sensing. Multimed. Tools Appl. 75(12), 6737–6754 (2016)

    Article  Google Scholar 

  4. Lee, Y., Hirakawa, K., Nguyen, T.Q.: Camera-aware multi-resolution analysis for raw image sensor data compression. IEEE Trans. Image Process. 27(6), 2806–2817 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  5. Sundararaj, Vinu: Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel. Pers. Commun. 104(1), 173–197 (2019)

    Article  Google Scholar 

  6. Sundararaj, V., Muthukumar, Selvi, Kumar, R.S.: An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput. Secur. 77, 277–288 (2018)

    Article  Google Scholar 

  7. Vinu, S.: An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int. J. Intell. Eng. Syst. 9(3), 117–126 (2016)

    Google Scholar 

  8. Rejeesh, M.R.: Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed. Tools Appl. 78(16), 22691–22710 (2019)

    Article  Google Scholar 

  9. Sundararaj, V.: Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int. J. Biomed. Eng. Technol. 31(4), 325 (2019)

    Article  Google Scholar 

  10. Geetha, V., Anbumani, V., Sasikala, S., Murali, L.: Efficient hybrid multi-level matching with diverse set of features for image retrieval. Soft Computing 24(16), 12267–12288 (2020)

    Article  Google Scholar 

  11. Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., Rejeesh, M.R. and Sundararaj, R., CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 2020

  12. Kefalas, N., Theodoridis, G.: Low-memory and high-performance architectures for the CCSDS 122.0-B-1 compression standard. Integration 69, 85–97 (2019)

    Article  Google Scholar 

  13. Shihab, H.S., Shafie, S., Ramli, A.R., Ahmad, F.: Enhancement of satellite image compression using a hybrid (DWT–DCT) algorithm. Sens. Imaging 18(1), 30 (2017)

    Article  Google Scholar 

  14. Yalamarthy, K.P., Dhall, S., Khan, M.T., Shaik, R.A.: Low-complexity distributed-arithmetic-based pipelined architecture for an LSTM network. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. (2018)

  15. Darji, A., Arun, R., Merchant, S.N., Chandorkar, A.: Multiplier-less pipeline architecture for lifting-based two-dimensional discrete wavelet transform. IET Comput. Digit. Tech. 9(2), 113–123 (2014)

    Article  Google Scholar 

  16. Hegde, G., Reddy, K.S., Ramesh, T.K.S.: A new approach for 1-D and 2-D DWT architectures using LUT based lifting and flipping cell. AEU-Int. J. Electron. Commun. 97, 165–177 (2018)

    Article  Google Scholar 

  17. Naik, P., Guhilot, H., Tigadi, A., Ganesh, P.: Reconfigured VLSI architecture for discrete wavelet transform. In: Soft Computing and Signal Processing, pp. 709–720. Springer, Singapore (2019)

  18. Sivanandam, L., Periyasamy, S., Oorkavalan, U.M.: Power transition X filling based selective Huffman encoding technique for test-data compression and Scan Power Reduction for SOCs. Microprocess. Microsyst. 72, 102937 (2020)

    Article  Google Scholar 

  19. Mohanty, B.K., Meher, P.K., Singhal, S.K., Swamy, M.N.S.: A high-performance VLSI architecture for reconfigurable FIR using distributed arithmetic. Integration 54, 37–46 (2016)

    Article  Google Scholar 

  20. Lu, Y., Duan, S., Halak, B., Kazmierski, T.J.: A cost-efficient error-resilient approach to distributed arithmetic for signal processing. Microelectron. Reliab. 93, 16–21 (2019)

    Article  Google Scholar 

  21. Malathkar, N.V., Soni, S.K.: A near lossless and low complexity image compression algorithm based on fixed threshold DPCM for capsule endoscopy. Multimed. Tools Appl., 1–16 (2020)

  22. Joshi, N., Sarode, T.: Validation and optimization of image compression algorithms. In: Information and Communication Technology for Sustainable Development, pp. 521–529. Springer, Singapore (2020)

  23. Turcza, P., Duplaga, M.: Energy-efficient image compression algorithm for high-frame rate multi-view wireless capsule endoscopy. J. Real-Time Image Proc. 16(5), 1425–1437 (2019)

    Article  Google Scholar 

  24. Lanz, D., & Kaup, A.: Graph-based compensated wavelet lifting for scalable lossless coding of dynamic medical data. IEEE Trans. Image Process. (2019)

  25. Ilango, S.S., Seenivasagam, V., Madhumitha, R.: Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression. Clust. Comput. 22(6), 13473–13486 (2019)

    Article  Google Scholar 

  26. Nirmalraj, S., Nagarajan, G.: Biomedical image compression using fuzzy transform and deterministic binary compressive sensing matrix. J. Ambient Intell. Hum. Comput. (2020). https://doi.org/10.1007/s12652-020-02103-x

    Article  Google Scholar 

  27. Zikiou, N., Lahdir, M., & Helbert, D.: Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression. Vis. Comput., 1–18 (2019)

  28. Wei, L., Sun, Q., Gao, X.: July. Kernel Generalized Canonical Correlation and a New Feature Fusion Strategy. In: International Conference on Artificial Intelligence and Security, pp. 488–500. Springer, Cham (2019)

  29. Zhang, J., Fowler, J.E., Liu, G.: Lossy-to-lossless compression of hyperspectral imagery using three-dimensional TCE and an integer KLT. IEEE Geosci. Remote Sens. Lett. 5(4), 814–818 (2008)

    Article  Google Scholar 

  30. Saghri, J.A., Schroeder, S., Tescher, A.G.: An adaptive two-stage KLT scheme for spectral decorrelation in hyperspectral bandwidth compression. In: Applications of Digital Image Processing XXXII (Vol. 7443, p. 744313). International Society for Optics and Photonics (2009)

  31. Penna, B., Tillo, T., Magli, E., Olmo, G.: Transform coding techniques for lossy hyperspectral data compression. IEEE Trans. Geosci. Remote Sens. 45(5), 1408–1421 (2007)

    Article  Google Scholar 

  32. Wang, L., Wu, J., Jiao, L., Shi, G.: Lossy-to-lossless hyperspectral image compression based on multiplierless reversible integer TDLT/KLT. IEEE Geosci. Remote Sens. Lett. 6(3), 587–591 (2009)

    Article  Google Scholar 

  33. Li, J., Liu, Z., Tian, S.-F.: An efficient onboard compression method for multispectral images using distributed post-transform in the wavelet domain in conjunction with a fast spectral decorrelator. Optical Rev 26(2), 247–261 (2019)

    Article  Google Scholar 

  34. Chakraborty, A. and Banerjee, A., 2019. A memory and area-efficient distributed arithmetic based modular VLSI architecture of 1D/2D reconfigurable 9/7 and 5/3 DWT filters for real-time image decomposition. Journal of Real-Time Image Processing 1–26

  35. Egho, C., Vladimirova, T., Sweeting, M.N.: Acceleration of karhunen-loeve transform for system-on-chip platforms. In: 2012 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 272–279. IEEE (2012)

  36. Blanes, I., Serra-Sagristà, J.: Cost and scalability improvements to the Karhunen-Loêve transform for remote-sensing image coding. IEEE Trans. Geosci. Remote Sens. 48(7), 2854–2863 (2010)

    Article  Google Scholar 

  37. Bravo, I., Mazo, M., Lázaro, J.L., Jiménez, P., Gardel, A., Marrón, M.: Novel HW architecture based on FPGAs oriented to solve the eigen problem. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 16(12), 1722–1725 (2008)

    Article  Google Scholar 

  38. Hao, P., Shi, Q.: “Matrix factorizations for reversible integer mapping. IEEE Trans. Signal Process. 49(10), 2314–2324 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  39. Mei, S., Khan, M.B., Zhang, Y., Du, Q.: Low-Complexity Hyperspectral Image Compression Using Folded PCA and JPEG2000. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 4756-4759. IEEE, 2018

  40. Delaunay, X., Chabert, M., Charvillat, V., Morin, G., Ruiloba, R.: Satellite image compression by directional decorrelation of wavelet coefficients. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1193–1196. IEEE 2008

  41. Delaunay, X., Chabert, M., Charvillat, V., Morin, G.: Satellite image compression by post-transforms in the wavelet domain. Signal Process. 90(2), 599–610 (2010)

    Article  MATH  Google Scholar 

  42. Chander, S., Vijaya, P., Dhyani, P.: Fractional lion algorithm—an optimization algorithm for data clustering. JCS 12(7), 323–340 (2016)

    Google Scholar 

  43. Shi, Cuiping, Wang, Liguo: Remote sensing image compression based on adaptive directional wavelet transform with content-dependent binary tree codec. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 12(3), 934–949 (2017)

    Article  MathSciNet  Google Scholar 

  44. Shihab, H.S., Shafie, S., Ramli, A.R., Ahmad, F.: Enhancement of satellite image compression using a hybrid (DWT–DCT) algorithm. Sens. Imaging 18(1), 1–30 (2017)

    Article  Google Scholar 

  45. Li, J., Fei X., Zheng, Y.: Compression of multispectral images with comparatively few bands using posttransform Tucker decomposition. Math. Probl. Eng. (2014)

  46. Uchaev, DmV, Uchaev, D.V., Esipov, A.S., Filatova, E.G.: Fractal approach to the choice of the compression ratio of hyperspectral images in the 3D–SPIHT method under the condition of subsequent classification of the decompressed images by the support vector machine. Curr. Probl. Remote Sens. Earth Space 14(4), 9–23 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of ECE, Kongu Engineering College, Perundurai, Erode, India

    V. Geetha, V. Anbumani, G. Murugesan & S. Gomathi

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  1. V. Geetha
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  2. V. Anbumani
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  3. G. Murugesan
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Correspondence to V. Geetha.

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Communicated by Y. Zhang.

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Geetha, V., Anbumani, V., Murugesan, G. et al. Hybrid optimal algorithm-based 2D discrete wavelet transform for image compression using fractional KCA. Multimedia Systems 26, 687–702 (2020). https://doi.org/10.1007/s00530-020-00681-6

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