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Performance Analysis of Image Compression Using LPWCF

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2019)

Abstract

Image compression is the most important feature for acheiving an efficient and secure data transfer. One of the main challenges in compression is developing an effective decompression. The input images that is compressed may not be more effectively restored in the decompression process that is based on quantization using Cosine Transformations or Wavelet transformations where the pixel information will be lost. To overcome these challenges, encoding process were employed. In the encoding process the pixel information were well protected but the compression efficiency is not improved. In order to overcome this challenge Lossless Patch Wise Code Formation (LPWCF) is employed. In the patch wise code generation the compression process is based on the pixel grouping and removing the relevant and recurrent pixels. In the proposed method, the images were first reduced in size by combining the current pixel with the previous pixel. The resulting image size is nearly the half of the size of the input image. The resulting image is then divided into small patches. In the patch recurrent pixels and their locations were identified. The identified pixel locations were placed prior to the pixel value and then the process is repeated for the complete image. The result of each patch acts as a code. In the receiver side the same process is reversed inorder to obtain a decompressed image. The process is completely reversible and hence the process can be employed in the process of transmission of the images. The performance of the process is measured in terms of the compression ratio, the image quality analysis of the input and the decompressed image based on PSNR, MSE and SSIM.

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References

  1. Saxena, L., Armstrong, L.: A survey of image processing techniques for agriculture (2014)

    Google Scholar 

  2. Rehman, M., Sharif, M., Raza, M.: Image compression: A survey. Res. J. Appl. Sci. Eng. Technol. 7, 656–672 (2014)

    Article  Google Scholar 

  3. Ramesh, S., Bharat, P., Anand, J., Selvan, J.A.: Analysis of lossy hyperspectral image compression techniques. Int. J. Comput. Sci. Mob. Comput. 3, 302–307 (2014)

    Google Scholar 

  4. Babu, K.S., Ramachandran, V., Thyagharajan, K., Santhosh, G.: Hyperspectral image compression algorithms—a review. In: Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Springer, pp. 127–138 (2015)

    Google Scholar 

  5. Puri, A., Sharifahmadian, E., Latifi, S.: A comparison of hyperspectral image compression methods. Int. J. Comput. Electr. Eng. 6, 493 (2014)

    Article  Google Scholar 

  6. Wang, L., Bai, J., Wu, J., Jeon, G.: Hyperspectral image compression based on Lapped transform and Tucker decomposition. Sig. Process. Image Commun. 36, 63–69 (2015)

    Article  Google Scholar 

  7. Sujithra, D., Manickam, T., Sudheer, D.: Compression of hyperspectral image using discrete wavelet transform and Walsh Hadamard transform. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE) 2, 314–319 (2013)

    Google Scholar 

  8. Cheng, K.-J., Dill, J.: Lossless to lossy dual-tree BEZW compression for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 52, 5765–5770 (2014)

    Article  Google Scholar 

  9. Huber-Lerner, M., Hadar, O., Rotman, S.R., Huber-Shalem, R.: Compression of hyperspectral images containing a subpixel target. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2246–2255 (2014)

    Article  Google Scholar 

  10. Du, Q., Ly, N., Fowler, J.E.: An operational approach to PCA+JPEG2000 compression of hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 2237–2245 (2014)

    Article  Google Scholar 

  11. Amrani, N., Laparra, V., Camps-Valls, G., Serra-Sagristà, J., Malo, J.: Lossless coding of hyperspectral images with principal polynomial analysis. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4023-4026 (2014)

    Google Scholar 

  12. Narmadha, D., Gayathri, K., Thilagavathi, K., Basha, N.: An optimal HSI image compression using DWT and CP. Int. J. Electr. Comput. Eng. 4, 411 (2014)

    Google Scholar 

  13. Wu, J., Kong, W., Mielikainen, J., Huang, B.: Lossless compression of hyperspectral imagery via clustered differential pulse code modulation with removal of local spectral outliers. IEEE Sig. Process. Lett. 22, 2194–2198 (2015)

    Article  Google Scholar 

  14. Nahavandi, S.K., Ghamisi, P., Kumar, L., Couceiro, M.: A novel adaptive compression technique for dealing with corrupt bands and high levels of band correlations in hyperspectral images based on binary hybrid GA-PSO for big data compression. Int. J. Comput. Appl. 109, 18–25 (2015)

    Google Scholar 

  15. Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B.: Compression of hyperspectral remote sensing images by tensor approach. Neurocomputing 147, 358–363 (2015)

    Article  Google Scholar 

  16. Shahriyar, S., Paul, M., Murshed, M., Ali, M.: Lossless hyperspectral image compression using binary tree based decomposition. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1-8 (2016)

    Google Scholar 

  17. Amrani, N., Serra-Sagristà, J., Laparra, V., Marcellin, M.W., Malo, J.: Regression wavelet analysis for lossless coding of remote-sensing data. IEEE Trans. Geosci. Remote Sens. 54, 5616–5627 (2016)

    Article  Google Scholar 

  18. Zhang, L., Wei, W., Zhang, Y., Yan, H., Li, F., Tian, C.: Locally similar sparsity-based hyperspectral compressive sensing using unmixing. IEEE Trans. Comput. Imaging 2, 86–100 (2016)

    Article  MathSciNet  Google Scholar 

  19. Fu, W., Li, S., Fang, L., Benediktsson, J.A.: Adaptive spectral-spatial compression of hyperspectral image with sparse representation. IEEE Trans. Geosci. Remote Sens. 55, 671–682 (2017)

    Article  Google Scholar 

  20. Shen, H., Pan, W.D., Wu, D.: Predictive lossless compression of regions of interest in hyperspectral images with no-data regions. IEEE Trans. Geosci. Remote Sens. 55, 173–182 (2017)

    Article  Google Scholar 

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Correspondence to V. P. Kulalvaimozhi .

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Kulalvaimozhi, V.P., Germanus Alex, M., John Peter, S. (2020). Performance Analysis of Image Compression Using LPWCF. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-28364-3_3

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