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Optimization of Hyperspectral Images and Performance Evaluation Using Effective Loss Algorithm

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Book cover Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 436))

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Abstract

An effective lossy algorithm for compressing hyperspectral images using singular value decomposition (SVD) and discrete cosine transform (DCT) has been proposed. A hyperspectral image consists of a number of bands where each band contains some specific information. This paper suggests compression algorithms that compress the hyperspectral images by considering image data, band by band and compress each band employing SVD and DCT. The compression performance of the resultant images is evaluated using various objective image quality metrics.

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References

  1. Cheng, K., Dill, J.: Hyper spectral images lossless compression using the 3D binary EZW algorithm. In: Proceedings of the SPIE 8655, Image Processing: Algorithms and Systems XI, 865515, Feb 19, 2013. doi:10.1117/12.2002820

  2. Alissou, S.A., Zhang, Y.: Hyper spectral data compression using lasso algorithm for spectral decorrelation. In: Proceedings of the SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240A, May 22, 2014. doi:10.1117/12.2053265

  3. Cheng, K., Dill, J.: An improved EZW Hyper spectral Image compression. J. Comput. Commun. 2, 31–36. doi:10.4236/jcc.2014.22006

    Google Scholar 

  4. Nian, Y., He, M., Wan, J.: Low-Complexity compression algorithm for hyper spectral images based on distributed source coding. Math. Prob. Eng. 2013, Article ID 825673, 7 pp. (2013)

    Google Scholar 

  5. Anbarjafari, G. et al.: Lossy image compression using singular value decomposition and wavelet difference reduction. Digital Signal Processing (Impact Factor: 1.92). Sep 2013. doi:10.1016/j.dsp.2013.09.008

    Google Scholar 

  6. Jayaraman, S., Sakirajan, S.E., Veera Kumar, T.: Digital image processing. Tata McGraw-Hill Education Private Ltd (2009)

    Google Scholar 

  7. Wu, Y-G., Tai, S-C.: Medical image compression by discrete cosine transform spectral similarity strategy. IEEE Trans. Inf. Technol. Biomed. 5(3), 236, 243 (2001)

    Google Scholar 

  8. Mohamed Zorkany, E.l.: A hybrid image compression technique using neural network and vector quantization with DCT. Adv. Intell. Syst. Comput. 233, 233–24 (2014)

    Google Scholar 

  9. Rawat, C.S., Meher, S.: A hybrid image compression scheme using DCT and fractal image compression. Int. Arab J. Inf. Technol. 10(6), 553–555 (2013)

    Google Scholar 

  10. Balaji, L., Thyagharajan, K.: H.264/SVC Mode decision based on mode correlation and desired mode list. Int. J. Autom. Comput. 11(5), 510–516 (2008). ISSN:1476–8186

    Google Scholar 

  11. Kahu, S., Rahate, R.: Image compression using singular value decomposition. Int. J. Advancements Res. Technol. 2(8), (2013)

    Google Scholar 

  12. Sadek, R.A.: SVD based image processing applications: State of the Art, contributions and research challenges. Int. J. Adv. Comput. Sci. Appl. 3(7), (2012)

    Google Scholar 

  13. Watson, A.B.: Image compression using the discrete cosine transform. Math. J. 4(1), 81–88 (1994)

    Google Scholar 

  14. Zhou, X.H.: Research on DCT-based image compression quality. Cross Strait Quad-Regional Radio Sci. Wireless Technol. Conf. (CSQRWC) 2, 1490–1494 (2011)

    Google Scholar 

  15. Cabeen, K., Gent, P.: Image compression and the discrete cosine trans form, Math 45, College of the Redwoods

    Google Scholar 

  16. Maruthi, R., Sankarasubramanian, K.: Assessing the blurred image quality using some uni-variate and bi-variate measures, IJCECA-SERC-DST `ISSN 0974-4983, Spring Edition 2010, pp. 32–38, vol. 02, Issue 03, Scientific Engineering Research Corporation

    Google Scholar 

  17. Naidu, V.P.S., Raol, J.R.: Pixel level image fusion using wavelets and PCA. Defence Sci. J. 58(3), 338–352 (2008)

    Google Scholar 

  18. Desai, D., Kulkarni, L.: A quantitative comparative study of analytical and iterative reconstruction technique. Int. J. Image Process. (IJIP) 4(4), (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University College of Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, India

    Srinivas Vadali & J. V. R. Murthy

  2. Department of Mathematics, University College of Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, India

    G. V S. R. Deekshitulu

Authors
  1. Srinivas Vadali
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  2. G. V S. R. Deekshitulu
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  3. J. V. R. Murthy
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Corresponding author

Correspondence to Srinivas Vadali .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Chankyapuri, Rm 327, South Asian Univ, Akbar Bhawan, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Srinivas Vadali, Deekshitulu, G.V.S.R., Murthy, J.V.R. (2016). Optimization of Hyperspectral Images and Performance Evaluation Using Effective Loss Algorithm. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_77

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_77

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0447-6

  • Online ISBN: 978-981-10-0448-3

  • eBook Packages: EngineeringEngineering (R0)

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